Unintentional, and yet powerful a reductor

As usually, I work on many things at the same time. I mean, not exactly at the same time, just in a tight alternate sequence. I am doing my own science, and I am doing collective science with other people. Right now, I feel like restating and reframing the main lines of my own science, with the intention to both reframe my own research, and be a better scientific partner to other researchers.

Such as I see it now, my own science is mostly methodological, and consists in studying human social structures as collectively intelligent ones. I assume that collectively we have a different type of intelligence from the individual one, and most of what we experience as social life is constant learning through experimentation with alternative versions of our collective way of being together. I use artificial neural networks as simulators of collective intelligence, and my essential process of simulation consists in creating multiple artificial realities and comparing them.

I deliberately use very simple, if not simplistic neural networks, namely those oriented on optimizing just one attribute of theirs, among the many available. I take a dataset, representative for the social structure I study, I take just one variable in the dataset as the optimized output, and I consider the remaining variables as instrumental input. Such a neural network simulates an artificial reality where the social structure studied pursues just one, narrow orientation. I create as many such narrow-minded, artificial societies as I have variables in my dataset. I assess the Euclidean distance between the original empirical dataset, and each of those artificial societies. 

It is just now that I realize what kind of implicit assumptions I make when doing so. I assume the actual social reality, manifested in the empirical dataset I study, is a concurrence of different, single-variable-oriented collective pursuits, which remain in some sort of dynamic interaction with each other. The path of social change we take, at the end of the day, manifests the relative prevalence of some among those narrow-minded pursuits, with others being pushed to the second rank of importance.

As I am pondering those generalities, I reconsider the actual scientific writings that I should hatch. Publish or perish, as they say in my profession. With that general method of collective intelligence being assumed in human societies, I focus more specifically on two empirical topics: the market of energy and the transition away from fossil fuels make one stream of my research, whilst the civilisational role of cities, especially in the context of the COVID-19 pandemic, is another stream of me trying to sound smart in my writing.

For now, I focus on issues connected to energy, and I return to revising my manuscript ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, as a resubmission to Applied Energy . According to the guidelines of Applied Energy , I am supposed to structure my paper into the following parts: Introduction, Material and Methods, Theory, Calculations, Results, Discussion, and, as sort of a summary pitch, I need to prepare a cover letter where I shortly introduce the reasons why should the editor of Applied Energy bother about my paper at all. On the top of all these formally expressed requirements, there is something I noticed about the general style of articles published in Applied Energy : they all demonstrate and discuss strong, sharp-cutting hypotheses, with a pronounced theoretical edge in them. If I want my paper to be accepted by that journal, I need to give it that special style.  

That special style requires two things which, honestly, I am not really accustomed to doing. First of all, it requires, precisely, to phrase out very sharp claims. What I like the most is to show people material and methods which I work with and sort of provoke a discussion around it. When I have to formulate very sharp claims around that basic empirical stuff, I feel a bit awkward. Still, I understand that many people are willing to discuss only when they are truly pissed by the topic at hand, and sharply cut hypotheses serve to fuel that flame.

Second of all, making sharp claims of my own requires passing in thorough review the claims which other researchers phrase out. It requires doing my homework thoroughly in the review-of-literature. Once again, not really a fan of it, on my part, but well, life is brutal, as my parents used to teach me and as I have learnt in my own life. In other words, real life starts when I get out of my comfort zone.

The first body of literature I want to refer to in my revised article is the so-called MuSIASEM framework AKA Multi-scale Integrated Analysis of Societal and Ecosystem Metabolism’. Human societies are assumed to be giant organisms, and transformation of energy is a metabolic function of theirs (e.g. Andreoni 2020[1], Al-Tamimi & Al-Ghamdi 2020[2] or Velasco-Fernández et al. 2020[3]). The MuSIASEM framework is centred around an evolutionary assumption, which I used to find perfectly sound, and which I have come to consider as highly arguable, namely that the best possible state for both a living organism and a human society is that of the highest possible energy efficiency. As regards social structures, energy efficiency is the coefficient of real output per unit of energy consumption, or, in other words, the amount of real output we can produce with 1 kilogram of oil equivalent in energy. My theoretical departure from that assumption started with my own empirical research, published in my article ‘Energy efficiency as manifestation of collective intelligence in human societies’ (Energy, Volume 191, 15 January 2020, 116500, https://doi.org/10.1016/j.energy.2019.116500 ). As I applied my method of computation with a neural network as simulator of social change, I found out that human societies do not really seem to max out on energy efficiency. Maybe they should but they don’t. It was the first realization, on my part, that we, humans, orient our collective intelligence on optimizing the social structure as such, and whatever comes out of that in terms of energy efficiency, is an unintended by-product rather than a purpose. That general impression has been subsequently reinforced by other empirical findings of mine, precisely those which I introduce in that manuscript ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, which I am currently revising for resubmission with Applied Energy . According to the guidelines of Applied Energy.

In practical terms, it means that when a public policy states that ‘we should maximize our energy efficiency’, it is a declarative goal which human societies actually do not strive for. It is a little as if a public policy imposed the absolute necessity of being nice to each other and punished any deviation from that imperative. People are nice to each other to the extent of current needs in social coordination, period. The absolute imperative of being nice is frequently the correlate of intense rivalry, e.g. as it was the case with traditional aristocracy. The French have even an expression, which I find profoundly true, namely ‘trop gentil pour être honnête’, which means ‘too nice to be honest’. My personal experience makes me kick into an alert state when somebody is that sort of intensely nice to me.

Passing from metaphors to the actual subject matter of energy management, it is a known fact that highly innovative technologies are usually truly inefficient. Optimization of efficiency, would it be energy efficiency or any other aspect thereof, is actually a late stage in the lifecycle of a technology. Deep technological change is usually marked by a temporary slump in efficiency. Imposing energy efficiency as chief goal of technology-related policies means systematically privileging and promoting technologies with the highest energy efficiency, thus, by metaphorical comparison to humans, technologies in their 40ies, past and over the excesses of youth.

The MuSIASEM framework has two other traits which I find arguable, namely the concept of evolutionary purpose, and the imperative of equality between countries in terms of energy efficiency. Researchers who lean towards and into the MuSIASEM methodology claim that it is an evolutionary purpose of every living organism to maximize energy efficiency, and therefore human societies have the same evolutionary purpose. It further implies that species displaying marked evolutionary success, i.e. significant growth in headcount (sometimes in mandibulae-count, should the head be not really what we mean it to be), achieve that success by being particularly energy efficient. I even went into some reading in life sciences and that claim is not grounded in any science. It seems that energy efficiency, and any denomination of efficiency, as a matter of fact, are very crude proportions we apply to complex a balance of flows which we have to learn a lot about. Niebel et al. (2019[4]) phrase it out as follows: ‘The principles governing cellular metabolic operation are poorly understood. Because diverse organisms show similar metabolic flux patterns, we hypothesized that a fundamental thermodynamic constraint might shape cellular metabolism. Here, we develop a constraint-based model for Saccharomyces cerevisiae with a comprehensive description of biochemical thermodynamics including a Gibbs energy balance. Non-linear regression analyses of quantitative metabolome and physiology data reveal the existence of an upper rate limit for cellular Gibbs energy dissipation. By applying this limit in flux balance analyses with growth maximization as the objective function, our model correctly predicts the physiology and intracellular metabolic fluxes for different glucose uptake rates as well as the maximal growth rate. We find that cells arrange their intracellular metabolic fluxes in such a way that, with increasing glucose uptake rates, they can accomplish optimal growth rates but stay below the critical rate limit on Gibbs energy dissipation. Once all possibilities for intracellular flux redistribution are exhausted, cells reach their maximal growth rate. This principle also holds for Escherichia coli and different carbon sources. Our work proposes that metabolic reaction stoichiometry, a limit on the cellular Gibbs energy dissipation rate, and the objective of growth maximization shape metabolism across organisms and conditions’. 

I feel like restating the very concept of evolutionary purpose as such. Evolution is a mechanism of change through selection. Selection in itself is largely a random process, based on the principle that whatever works for now can keep working until something else works even better. There is hardly any purpose in that. My take on the thing is that living species strive to maximize their intake of energy from environment rather than their energy efficiency. I even hatched an article about it (Wasniewski 2017[5]).

Now, I pass to the second postulate of the MuSIASEM methodology, namely to the alleged necessity of closing gaps between countries as for their energy efficiency. Professor Andreoni expresses this view quite vigorously in a recent article (Andreoni 2020[6]). I think this postulate doesn’t hold both inside the MuSIASEM framework, and outside of it. As for the purely external perspective, I think I have just laid out the main reasons for discarding the assumption that our civilisation should prioritize energy efficiency above other orientations and values. From the internal perspective of MuSIASEM, i.e. if we assume that energy efficiency is a true priority, we need to give that energy efficiency a boost, right? Now, the last time I checked, the only way we, humans, can get better at whatever we want to get better at is to create positive outliers, i.e. situations when we like really nail it better than in other situations. With a bit of luck, those positive outliers become a workable pattern of doing things. In management science, it is known as the principle of best practices. The only way of having positive outliers is to have a hierarchy of outcomes according to the given criterion. When everybody is at the same level, nobody is an outlier, and there is no way we can give ourselves a boost forward.

Good. Those six paragraphs above, they pretty much summarize my theoretical stance as regards the MuSIASEM framework in research about energy economics. Please, note that I respect that stream of research and the scientists involved in it. I think that representing energy management in human social structures as a metabolism is a great idea: it is one of those metaphors which can be fruitfully turned into a quantitative model. Still, I have my reserves.

I go further. A little more review of literature. Here comes a paper by Halbrügge et al. (2021[7]), titled ‘How did the German and other European electricity systems react to the COVID-19 pandemic?’. It points at an interesting point as regards energy economics: the pandemic has induced a new type of risk, namely short-term fluctuations in local demand for electricity. That, in turn, leads to deeper troughs and higher peaks in both the quantity and the price of energy in the market. More risk requires more liquidity: this is a known principle in business. As regards energy, liquidity can be achieved both through inventories, i.e. by developing storage capacity for energy, and through financial instruments. Halbrügge et al. come to the conclusion that such circumstances in the German market have led to the reinforcement of RES (Renewable Energy Sources). RES installations are typically more dispersed, more local in their reach, and more flexible than large power plants. It is much easier to modulate the output of a windfarm or a solar farm, as compared to a large fossil-fuel-based installation. 

Keeping an eye on the impact of the pandemic upon the market of energy, I pass to the article titled ‘Revisiting oil-stock nexus during COVID-19 pandemic: Some preliminary results’, by Salisu, Ebuh & Usman (2020[8]). First of all, a few words of general explanation as for what the hell is the oil-stock nexus. This is a phenomenon, which I saw any research about in 2017, which consists in a diversification of financial investment portfolios from pure financial stock into various mixes of stock and oil. Somehow around 2015, people who used to hold their liquid investments just in financial stock (e.g. as I do currently) started to build investment positions in various types of contracts based on the floating inventory of oil: futures, options and whatnot. When I say ‘floating’, it is quite literal: that inventory of oil really actually floats, stored on board of super-tanker ships, sailing gently through international waters, with proper gravitas (i.e. not too fast).

Long story short, crude oil has been increasingly becoming a financial asset, something like a buffer to hedge against risks encountered in other assets. Whilst the paper by Salisu, Ebuh & Usman is quite technical, without much theoretical generalisation, an interesting observation comes out of it, namely that short-term shocks, during the pandemic in financial markets had adversely impacted the price of oil more than the prices of stock. That, in turn, could indicate that crude oil was good as hedging asset just for a certain range of risks, and in the presence of price shocks induced by the pandemic, the role of oil could diminish.     

Those two papers point at a factor which we almost forgot as regards the market of energy, namely the role of short-term shocks. Until recently, i.e. until COVID-19 hit us hard, the textbook business model in the sector of energy had been that of very predictable demand, nearly constant in the long-perspective and varying in a sinusoidal manner in the short-term. The very disputable concept of LCOE AKA Levelized Cost of Energy, where investment outlays are treated as if they were a current cost, is based on those assumptions. The pandemic has shown a different aspect of energy systems, namely the need for buffering capacity. That, in turn, leads to the issue of adaptability, which, gently but surely leads further into the realm of adaptive changes, and that, ladies and gentlemen, is my beloved landscape of evolutionary, collectively intelligent change.

Cool. I move forward, and, by the same occasion, I move back. Back to the concept of energy efficiency. Halvorsen & Larsen study the so-called rebound effect as regards energy efficiency (Halvorsen & Larsen 2021[9]). Their paper is interesting for three reasons, the general topic of energy efficiency being the first one. The second one is methodological focus on phenomena which we cannot observe directly, and therefore we observe them through mediating variables, which is theoretically close to my own method of research. Finally, the phenomenon of rebound effect, namely the fact that, in the presence of temporarily increased energy efficiency, the consumers of energy tend to use more of those locally more energy-efficient goods, is essentially a short-term disturbance being transformed into long-term habits. This is adaptive change.

The model construed by Halvorsen & Larsen is a theoretical delight, just something my internal happy bulldog can bite into. They introduce the general assumption that consumption of energy in households is a build-up of different technologies, which can substitute each other under some conditions, and complementary under different conditions. Households maximize something called ‘energy services’, i.e. everything they can purposefully derive from energy carriers. Halvorsen & Larsen build and test a model where they derive demand for energy services from a whole range of quite practical variables, which all sums up to the following: energy efficiency is indirectly derived from the way that social structures work, and it is highly doubtful whether we can purposefully optimize energy efficiency as such.       

Now, here comes the question: what are the practical implications of all those different theoretical stances, I mean mine and those by other scientists? What does it change, and does it change anything at all, if policy makers follow the theoretical line of the MuSIASEM framework, or, alternatively, my approach? I am guessing differences at the level of both the goals, and the real outcomes of energy-oriented policies, and I am trying to wrap my mind around that guessing. Such as I see it, the MuSIASEM approach advocates for putting energy-efficiency of the whole global economy at the top of any political agenda, as a strategic goal. On the path towards achieving that strategic goal, there seems to be an intermediate one, namely that to narrow down significantly two types of discrepancies:

>> firstly, it is about discrepancies between countries in terms of energy efficiency, with a special focus on helping the poorest developing countries in ramping up their efficiency in using energy

>> secondly, there should be a priority to privilege technologies with the highest possible energy efficiency, whilst kicking out those which perform the least efficiently in that respect.    

If I saw a real policy based on those assumptions, I would have a few critical points to make. Firstly, I firmly believe that large human societies just don’t have the institutions to enforce energy efficiency as chief collective purpose. On the other hand, we have institutions oriented on other goals, which are able to ramp up energy efficiency as instrumental change. One institution, highly informal and yet highly efficient, is there, right in front of our eyes: markets and value chains. Each product and each service contain an input of energy, which manifests as a cost. In the presence of reasonably competitive markets, that cost is under pressure from market prices. Yes, we, humans are greedy, and we like accumulating profits, and therefore we squeeze our costs. Whenever energy comes into play as significant a cost, we figure out ways of diminishing its consumption per unit of real output. Competitive markets, both domestic and international, thus including free trade, act as an unintentional, and yet powerful a reductor of energy consumption, and, under a different angle, they remind us to find cheap sources of energy.


[1] Andreoni, V. (2020). The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304

[2] Al-Tamimi and Al-Ghamdi (2020), ‘Multiscale integrated analysis of societal and ecosystem metabolism of Qatar’ Energy Reports, 6, 521-527, https://doi.org/10.1016/j.egyr.2019.09.019

[3] Velasco-Fernández, R., Pérez-Sánchez, L., Chen, L., & Giampietro, M. (2020), A becoming China and the assisted maturity of the EU: Assessing the factors determining their energy metabolic patterns. Energy Strategy Reviews, 32, 100562.  https://doi.org/10.1016/j.esr.2020.100562

[4] Niebel, B., Leupold, S. & Heinemann, M. An upper limit on Gibbs energy dissipation governs cellular metabolism. Nat Metab 1, 125–132 (2019). https://doi.org/10.1038/s42255-018-0006-7

[5] Waśniewski, K. (2017). Technological change as intelligent, energy-maximizing adaptation. Energy-Maximizing Adaptation (August 30, 2017). http://dx.doi.org/10.1453/jest.v4i3.1410

[6] Andreoni, V. (2020). The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304

[7] Halbrügge, S., Schott, P., Weibelzahl, M., Buhl, H. U., Fridgen, G., & Schöpf, M. (2021). How did the German and other European electricity systems react to the COVID-19 pandemic?. Applied Energy, 285, 116370. https://doi.org/10.1016/j.apenergy.2020.116370

[8] Salisu, A. A., Ebuh, G. U., & Usman, N. (2020). Revisiting oil-stock nexus during COVID-19 pandemic: Some preliminary results. International Review of Economics & Finance, 69, 280-294. https://doi.org/10.1016/j.iref.2020.06.023

[9] Halvorsen, B., & Larsen, B. M. (2021). Identifying drivers for the direct rebound when energy efficiency is unknown. The importance of substitution and scale effects. Energy, 222, 119879. https://doi.org/10.1016/j.energy.2021.119879

The inflatable dartboard made of fine paper

My views on environmentally friendly production and consumption of energy, and especially on public policies in that field, differ radically from what seems to be currently the mainstream of scientific research and writing. I even got kicked out of a scientific conference because of my views. After my paper was accepted, I received a questionnaire to fill, which was supposed to feed the discussion on the plenary session of that conference. I answered those questions in good faith and sincerely, and: boom! I receive an email which says that my views ‘are not in line with the ideas we want to develop in the scientific community’. You could rightly argue that my views might be so incongruous that kicking me out of that conference was an act of mercy rather than enmity. Good. Let’s pass my views in review.

There is that thing of energy efficiency and climate neutrality. Energy efficiency, i.e. the capacity to derive a maximum of real output out of each unit of energy consumed, can be approached from two different angles: as a stationary value, on the one hand, or an elasticity, on the other hand. We could say: let’s consume as little energy as we possibly can and be as productive as possible with that frugal base. That’s the stationary view. Yet, we can say: let’s rock it, like really. Let’s boost our energy consumption so as to get in control of our climate. Let’s pass from roughly 30% of energy generated on the surface of the Earth, which we consume now, to like 60% or 70%. Sheer laws of thermodynamics suggest that if we manage to do that, we can really run the show. These is the summary of what in my views is not in line with ‘the ideas we want to develop in the scientific community’.

Of course, I can put forth any kind of idiocy and claim this is a valid viewpoint. Politics are full of such episodes. I was born and raised in a communist country. I know something about stupid, suicidal ideas being used as axiology for running a nation. I also think that discarding completely other people’s ‘ideas we want to develop in the scientific community’ and considering those people as pathetically lost would be preposterous from my part. We are all essentially wrong about that complex stuff we call ‘reality’. It is just that some ways of being wrong are more functional than others. I think truly correct a way to review the current literature on energy-related policies is to take its authors’ empirical findings and discuss them

under a different interpretation, namely the one sketched in the preceding paragraph.

I like looking at things with precisely that underlying assumption that I don’t know s**t about anything, and I just make up cognitive stuff which somehow pays off. I like swinging around that Ockham’s razor and cut out all the strong assumptions, staying just with the weak ones, which do not require much assuming and are at the limit of stylized observations and theoretical claims.

My basic academic background is in law (my Master’s degree), and in economics (my PhD). I look at social reality around me through the double lens of those two disciplines, which, when put in stereoscopic view, boil down to having an eye on patterns in human behaviour.

I think I observe that we, humans, are social and want to stay social, and being social means a baseline mutual predictability in our actions. We are very much about maintaining a certain level of coherence in culture, which means a certain level of behavioural coupling. We would rather die than accept the complete dissolution of that coherence. We, humans, we make behavioural coherence: this is our survival strategy, and it allows us to be highly social. Our cultures always develop along the path of differentiation in social roles. We like specializing inside the social group we belong to.

Our proclivity to endorse specific skillsets, which turn into social roles, has the peculiar property of creating local surpluses, and we tend to trade those surpluses. This is how markets form. In economics, there is that old distinction between production and consumption. I believe that one of the first social thinkers who really meant business about it was Jean Baptiste Say, in his “Treatise of Political Economy”. Here >> https://discoversocialsciences.com/wp-content/uploads/2020/03/Say_treatise_political-economy.pdf  you have it in the English translation, whilst there >>

https://discoversocialsciences.com/wp-content/uploads/2018/04/traite-deconomie-politique-jean-baptiste-say.pdf it is in its elegant French original.

In my perspective, the distinction between production and consumption is instrumental, i.e. it is useful for solving some economic problems, but just some. Saying that I am a consumer is a gross simplification. I am a consumer in some of my actions, but in others I am a producer. As I write this blog, I produce written content. I prefer assuming that production and consumption are two manifestations of the same activity, namely of markets working around tradable surpluses created by homo sapiens as individual homo sapiens endorse specific social roles.

When some scientists bring forth empirically backed claims that our patterns of consumption have the capacity to impact climate (e.g. Bjelle et al. 2021[1]), I say ‘Yes, indeed, and at the end of that specific intellectual avenue we find out that creating some specific, tradable surpluses, ergo the fact of endorsing some specific social roles, has the capacity to impact climate’. Bjelle et al. find out something which from my point of view is gobsmacking: whilst relative prevalence of particular goods in the overall patterns of demand has little effect on the emission of Greenhouse Gases (GHG) at the planetary scale, there are regional discrepancies. In developing countries and in emerging markets, changes in the baskets of goods consumed seem to have strong impact GHG-wise. On the other hand, in developed economies, however the consumers shift their preferences between different goods, it seems to be very largely climate neutral. From there, Bjelle et al. conclude into such issues as environmental taxation. My own take on those results is different. What impacts climate is social change occurring in developing economies and emerging markets, and this is relatively quick demographic growth combined with quick creation of new social roles, and a big socio-economic difference between urban environments, and the rural ones.

In the broad theoretical perspective, states of society which we label as classes of socio-economic development are far more than just income brackets. They are truly different patterns of social interactions. I had a glimpse of that when I was comparing data on the consumption of energy per capita (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ) with the distribution of gross national product per capita (https://data.worldbank.org/indicator/NY.GDP.PCAP.CD ). It looks as if different levels of economic development were different levels of energy in the social system. Each 100 ÷ 300 kilograms of oil equivalent per capita per year seem to be associated with specific institutions in society.

Let’s imagine that climate change goes on. New s**t comes our way, which we need to deal with. We need to learn. We form new skillsets, and we define new social roles. New social roles mean new tradable surpluses, and new markets with new goods in it. We don’t really know what kind of skillsets, markets and goods that will be. Enhanced effort of collective adaptation leads to outcomes impossible to predict in themselves. The question is: can we predict the way those otherwise unpredictable outcomes will take shape?         

My fellow scientists seem not to like unpredictable outcomes. Shigetomi et al. (2020[2]) straightforwardly find out empirically that ‘only the very low, low, and very high-income households are likely to achieve a reduction in carbon footprint due to their high level of environmental consciousness. These income brackets include the majority of elderly households who are likely to have higher consciousness about environmental protection and addressing climate change’. In my fairy-tale, it means that only a fringe of society cares about environment and climate, and this is the fringe which does not really move a lot in terms of new social role. People with low income have low income because their social roles do not allow them to trade significant surpluses, and elderly people with high income do not really shape the labour market.

This is what I infer from those empirical results. Yet, Shigetomi et al. conclude that ‘The Input-Output Analysis Sustainability Evaluation Framework (IOSEF), as proposed in this study, demonstrates how disparity in household consumption causes societal distortion via the supply chain, in terms of consumption distribution, environmental burdens and household preferences. The IOSEF has the potential to be a useful tool to aid in measuring social inequity and burden distribution allocation across time and demographics’.

Guys, like really. Just sit and think for a moment. I even pass over the claim that inequality of income is a social distortion, although I am tempted to say that no know human society has ever been free of that alleged distortion, and therefore we’d better accommodate with it and stop calling it a distortion. What I want is logic. Guys, you have just proven empirically that only low-income people, and elderly high-income people care about climate and environment. The middle-incomes and the relatively young high-incomes, thus people who truly run the show of social and technological change, do not care as much as you would like them to. You claim that inequality of income is a distortion, and you want to eliminate it. When you kick inequality out of the social equation, you get rid of the low-income folks, and of the high-income ones. Stands to reason: with enforced equality, everybody is more or less middle-income. Therefore, the majority of society is in a social position where they don’t give a f**k about climate and environment. Besides, when you remove inequality, you remove vertical social mobility along hierarchies, and therefore you give a cold shoulder to a fundamental driver of social change. Still, you want social change, you have just said it.  

Guys, the conclusions you derive from your own findings are the political equivalent of an inflatable dartboard made of fine paper. Cheap to make, might look dashing, and doomed to be extremely short-lived as soon as used in practice.   


[1] Bjelle, E. L., Wiebe, K. S., Többen, J., Tisserant, A., Ivanova, D., Vita, G., & Wood, R. (2021). Future changes in consumption: The income effect on greenhouse gas emissions. Energy Economics, 95, 105114. https://doi.org/10.1016/j.eneco.2021.105114

[2] Shigetomi, Y., Chapman, A., Nansai, K., Matsumoto, K. I., & Tohno, S. (2020). Quantifying lifestyle based social equity implications for national sustainable development policy. Environmental Research Letters, 15(8), 084044. https://doi.org/10.1088/1748-9326/ab9142

The fine details of theory

I keep digging. I keep revising that manuscript of mine – ‘Climbing the right hill – an evolutionary approach to the European market of electricity’ – in order to resubmit it to Applied Energy. Some of my readers might become slightly fed up with that thread. C’mon, man! How long do you mean to work on that revision? It is just an article! Yes, it is just an article, and I have that thing in me, those three mental characters: the curious ape, the happy bulldog, and the austere monk. The ape is curious, and it almost instinctively reaches for interesting things. My internal bulldog just loves digging out tasty pieces and biting into bones. The austere monk in me observes the intellectual mess, which the ape and the bulldog make together, and then he takes that big Ockham’s razor, from the recesses of his robe, and starts cutting bullshit out. When the three of those start dancing around a topic, it is a long path to follow, believe me.

In this update, I intend to structure the theoretical background of my paper. First, I restate the essential point of my own research, which I need and want to position in relation to other people’s views and research. I claim that energy-related policies, including those with environmental edge, should assume that whatever we do with energy, as a civilisation, is a by-product of actions purposefully oriented on other types of outcomes. Metaphorically, when I claim that a society should take the shift towards renewable energies as its chief goal, and take everything else as instrumental, is like saying that the chief goal of an individual should be to keep their blood sugar firmly at 80,00, whatever happens. What’s the best way to achieving it? Putting yourself in a clinic, under permanent intravenous nutrition, and stop experimenting with that thing people call ‘food’, ‘activity’, ‘important things to do’. Anyone wants to do it? Hardly anyone, I guess. The right level of blood sugar can be approximately achieved as balanced outcome of a proper lifestyle, and can serve as a gauge of whether our actual lifestyle is healthy.

Coming back from my nutritional metaphor to energy-related policies, there is no historical evidence that any human society has ever achieved any important change regarding the production of energy or its consumption, by explicitly stating ‘From now on, we want better energy management’. The biggest known shifts in our energy base happened as by-products of changes oriented on something else. In Europe, my home continent, we had three big changes. First, way back in the day, like starting from the 13th century, we harnessed the power of wind and that of water in, respectively, windmills and watermills. That served to provide kinetic energy to grind cereals into flour, which, in turn, served to feed a growing urban population. Windmills and watermills brought with them a silent revolution, which we are still wrapping our minds around. By the end of the 19th century, we started a massive shift towards fossil fuels. Why? Because we expected to drive Ferraris around, one day in the future? Not really. We just went terribly short on wood. People who claim that Europe should recreate its ‘ancestral’ forests deliberately ignore the fact that hardly anyone today knows what those ancestral forests should look like. Virtually all the forests we have today come from massive replantation which took place starting from the beginning of the 20th century. Yes, we have a bunch of 400-year-old oaks across the continent, but I dare reminding that one oak is not exactly a forest.

The civilisational change which I think is going on now, in our human civilisation, is the readjustment of social roles, and of the ways we create new social roles, in the presence of a radical demographic change: unprecedently high headcount of population, accompanied by just as unprecedently low rate of demographic growth. For hundreds of years, our civilisation has been evolving as two concurrent factories: the factory of food in the countryside, and the factory of new social roles in cities. Food comes the best when the headcount of humans immediately around is a low constant, and new social roles burgeon the best when humans interact abundantly, and therefore when they are tightly packed together in a limited space. The basic idea of our civilisation is to put most of the absolute demographic growth into cities and let ourselves invent new ways of being useful to each other, whilst keeping rural land as productive as possible.

That thing had worked for centuries. It had worked for humanity that had been relatively small in relation to available space and had been growing quickly into that space. That idea of separating the production of food from the creation of social roles and institutions was adapted precisely to that demographic pattern, which you can still find vestiges of in some developing countries, as well as in emerging markets, with urban population several dozens of times denser than the rural one, and cities that look like something effervescent. These cities grow bubbles out of themselves, and those bubbles burst just as quickly. My own trip to China showed me how cities can be truly alive, with layers and bubbles inside them. One is tempted to say these cities are something abnormal, as compared to the orderly, demographically balanced urban entities in developed countries. Still, historically, this is what cities are supposed to look like.

Now, something is changing. There is more of us on the planet than it has ever been but, at the same time, we experience unprecedently low rate of demographic growth. Whilst we apparently still manage to keep total urban land on the planet at a constant level (https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2 ), we struggle with keeping the surface of agricultural land up to our needs (https://data.worldbank.org/indicator/AG.LND.AGRI.ZS ). As in any system tilted out of balance, weird local phenomena start occurring, and the basic metrics pertinent to the production and consumption of energy show an interesting pattern. When I look at the percentage participation of renewable sources in the total consumption of energy (https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS ), I see a bumpy cycle which looks like learning with experimentation. When I narrow down to the participation of renewables in the total consumption of electricity ( https://data.worldbank.org/indicator/EG.ELC.RNEW.ZS), what I see is a more pronounced trend upwards, with visible past experimentation. The use of nuclear power to generate electricity (https://data.worldbank.org/indicator/EG.ELC.NUCL.ZS) looks like a long-run experiment, which now is in its phase of winding down.

Now, two important trends come into my focus. Energy efficiency, defined as average real output per unit of energy use (https://data.worldbank.org/indicator/EG.GDP.PUSE.KO.PP.KD) quite unequivocal a trend upwards. Someone could say ‘Cool, we purposefully make ourselves energy efficient’. Still, when we care to have a look at the coefficient of energy consumed per person per year (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE), a strong trend upwards appears, with some deep bumps in the past. When I put those two trends back to back, I conclude that what we really max out on is the real output of goods and services in our civilisation, and energy efficiency is just a means to that end.

It is a good moment to puncture an intellectual balloon. I can frequently see and hear people argue that maximizing real output, in any social entity or context, is a manifestation of stupid, baseless greed and blindness to the truly important stuff. Still, please consider the following line of logic. We, humans, interact with the natural environment, and interact with each other.  When we interact with each other a lot, in highly dense networks of social relations, we reinforce each other’s learning, and start spinning the wheel of innovation and technological change. Abundant interaction with each other gives us new ideas for interacting with the natural environment.

Cities have peculiar properties. Firstly, by creating new social roles through intense social interaction, they create new products and services, and therefore new markets, connected in chains of value added. This is how the real output of goods and services in a society becomes a complex, multi-layered network of technologies, and this is how social structures become self-propelling businesses. The more complexity in social roles is created, the more products and services emerge, which brings the development in greater a number of markets. That, in turn, gives greater a real output, greater income per person, which incentivizes to create new social roles etc. This how social complexity creates the phenomenon called economic growth.

The phenomenon of economic growth, thus the quantitative growth in complex, networked technologies which emerge in relatively dense human settlements, has a few peculiar properties. You can’t see it, you can’t touch it, and yet you can immediately feel when its pace changes. Economic growth is among the most abstract concepts of social sciences, and yet living in a society with real economic growth at 5% per annum is like a different galaxy when compared to living in a place where real economic growth is actually a recession of -5%. The arithmetical difference is just 10 percentage points, around the top of something underlying which makes the base of 1. Still, lives in those two contexts are completely different. At +5% in real economic growth, starting a new business is generally a sensible idea, provided you have it nailed down with a business plan. At – 5% a year, i.e. in recession, the same business plan can be an elaborate way of committing economic and financial suicide. At +5%, political elections are usually won by people who just sell you the standard political bullshit, like ‘I will make your lives better’ claimed by a heavily indebted alcoholic with no real career of their own. At -5%, politics start being haunted by those sinister characters, who look and sound like evil spirits from our dreams and claim they ‘will restore order and social justice’.

The society which we consider today as normal is a society of positive real economic growth. All the institutions we are used to, such as healthcare systems, internal security, public administration, education – all that stuff works at least acceptably smoothly when complex, networked technologies of our society have demonstrable capacity to increase their real economic output. That ‘normal’ state of society is closely connected to the factories of social roles which we commonly call ‘cities’. Real economic growth happens when the amount of new social roles – fabricated through intense interactions between densely packed humans – is enough for the new humans coming around. Being professionally active means having a social role solid enough to participate in the redistribution of value added created in complex technological networks. It is both formal science and sort of accumulated wisdom in governance that we’d better have most of the adult, able bodied people in that state of professional activity. A small fringe of professionally inactive people is somehow healthy a margin of human energy free to be professionally activated, and when I say ‘small’, it is like no more than 5% of the adult population. Anything above becomes both a burden and a disruption to social cohesion. Too big a percentage of people with no clear, working social roles makes it increasingly difficult to make social interactions sufficiently abundant and complex to create enough new social roles for new people. This is why governments of this world attach keen importance to the accurate measurement of the phenomenon quantified as ‘unemployment’.  

Those complex networks of technologies in our societies, which have the capacity to create social roles and generate economic growth, work their work properly when we can transact about them, i.e. when we have working markets for the final economic goods produced with those technologies, and for intermediate economic goods produced for them. It is as if the whole thing worked when we can buy and sell things. I was born in 1968, in a communist country, namely Poland, and I can tell you that in the absence of markets the whole mechanism just jams, progressively to a halt. Yes, markets are messy and capricious, and transactional prices can easily get out of hand, creating inflation, and yet markets give those little local incentives needed to get the most of human social roles. In the communist Poland, I remember people doing really strange things, like hoarding massive inventories of refrigerators or women’s underwear, just to create some speculative spin in an ad hoc, semi-legal or completely illegal market. It looks as if people needed to market and transact for real, amidst the theoretically perfectly planned society.   

Anyway, economic growth is observable through big sets of transactions in product markets, and those transactions have two attributes: quantities and prices AKA Q an P. It is like Q*P = ∑qi*pi. When I have – well, when we have – that complex network of technologies functionally connected to a factory of social roles for new humans, that thing makes ∑qi*pi, thus a lot of local transactions with quantities qi, at prices pi. The economic growth I have been so vocal about in the last few paragraphs is the real growth, i.e. in quantity Q = ∑qi. On the long run, what I am interested in, and my government is interested in, is to reasonably max out on ∆ Q = ∆∑qi. Quantities change slowly and quite predictably, whilst prices tend to change quickly and, mostly on the short term, chaotically. Measuring accurately real economic growth involving kicking the ‘*pi’ component out of the equation and extracting just ∆ Q = ∆∑qi. Question: why bothering with the observation of Q*P = ∑qi*pi when the real thing we need is just ∆ Q = ∆∑qi? Answer: because there is no other way. Complex networks of technologies produce economic growth by creating increasing diversity in social roles in concurrence with increasing diversity in products and their respective markets. No genius has come up, so far, with a method to add up, directly, the volume of visits in hairdresser’s salons with the volume of electric vehicles made, and all that with the volume of energy consumed.

I have ventured myself far from the disciplined logic of revision in my paper, for resubmitting it. The logical flow required in this respect by Applied Energy is the following: introduction first, method and material next, theory follows, and calculations come after. The literature which I refer to in my writing needs to have two dimensions: longitudinal and lateral. Laterally, I divide other people’s publications into three basic groups: a) standpoints which I argue with b) methods and assumptions which I agree with and use to support my own reasoning, and c) viewpoints which sort of go elsewhere, and can be interesting openings into something even different from what I discuss. Longitudinally, the literature I cite needs, in the first place, to open up on the main points of my paper. This is ‘Introduction’. Publications which I cite here need to point at the utility of developing the line of research which I develop. They need to convey strong, general claims which sort of set my landmarks.

The section titled ‘Theory’ is supposed to provide the fine referencing of my method, so as to both support the logic thereof, and to open up on the detailed calculations I develop in the following section. Literature which I bring forth here should contain specific developments, both factual and methodological, something like a conceptual cobweb. In other words, ‘Introduction’ should be provocative, whilst ‘Theory’ transforms provocation into a structure.

Among the recent literature I am passing in review, three papers come forth as provocative enough for me to discuss them in the introduction of my article:  Andreoni 2020[1], Koponen & Le Net 2021[2]. The first of the three on that list, namely the paper by professor Valeria Andreoni, well in the mainstream of the MuSIASEM methodology (Multi-scale Integrated Analysis of Societal and Ecosystem Metabolism), sets an important line of theoretical debate, namely the arguable imperative to focus energy-related policies, and economic policies in general, on two outcomes, namely on maximizing energy efficiency (i.e. maximizing the amount of real output per unit of energy consumption), and on minimizing cross sectional differences between countries as regards energy efficiency. Both postulates are based on the assumption that energy efficiency of national economies corresponds to the metabolic efficiency of living organisms, and that maxing out on both is an objective evolutionary purpose in both cases. My method has the same general foundations as MuSIASEM. I claim that societies can be studied similarly to living organisms.

At that point, I diverge from the MuSIASEM framework: instead of focusing on the metabolism of such organically approached societies, I pay attention to their collective cognitive processes, their collective intelligence. I claim that human societies are collectively intelligent structures, which learn by experimenting with many alternative versions of themselves whilst staying structurally coherent. From that assumption, I derive two further claims. Firstly, if we reduce disparities between countries with respect to any important attribute of theirs, including energy efficiency, we kick out of the game a lot of opportunities for future learning: the ‘many alternative versions’ part of the process is no more there. Secondly, I claim there is no such thing as objective evolutionary purpose, would it be maximizing energy efficiency or anything else. Evolution has no purpose; it just has the mechanism of selection by replication. Replication of humans is proven to happen the most favourably when we collectively learn fast and make a civilisation out of that learning.

Therefore, whilst having no objective evolutionary purpose, human societies have objective orientations: we collectively attempt to optimize some specific outcomes, which have the attribute to organize our collective learning the most efficiently, in a predictable cycle of, respectively, episodes marked with large errors in adjustment, and those displaying much smaller errors in that respect.

From that theoretical cleavage between my method and the postulates of the MuSIASEM framework, I derive two practical claims as regards economic policies, especially as regards environmentally friendly energy systems. Looking for homogeneity between countries is a road to nowhere, for one. Expecting that human societies will purposefully strive to maximize their overall energy efficiency is unrealistic a goal, and therefore it is a harmful assumption in the presence of serious challenges connected to climate change, for two. Public policies should explicitly aim for disparity of outcomes in technological race, and the race should be oriented on outcomes which are being objectively pursued by human societies.

Whilst disagreeing with professor Valeria Andreoni on principles, I find her empirical findings highly interesting. Rapid economic change, especially the kind of change associated with crises, seems to correlate with deepening disparities between countries in terms of energy efficiency. In other words, when large economic systems need to adjust hard and fast, they sort of play their individual games separately as regards energy efficiency. Rapid economic adjustment under constraint is conducive to creating a large discrepancy of alternative states in what energy efficiency can possibly be, in the context of other socio-economic outcomes, and, therefore, more material is there for learning collectively by experimenting with many alternative versions of ourselves.

Against that theoretical sketch, I place the second paper which I judge worth to introduce with: Koponen, K., & Le Net, E. (2021): Towards robust renewable energy investment decisions at the territorial level. Applied Energy, 287, 116552.  https://doi.org/10.1016/j.apenergy.2021.116552 . I chose this one because it shows a method very similar to mine: the authors build a simulative model in Excel, where they create m = 5000 alternative futures for a networked energy system aiming at optimizing 5 performance metrics. The model was based on actual empirical data as for those variables, and the ‘alternative futures’ are, in other words, 5000 alternative states of the same system. Outcomes are gauged with the so-called regret analysis, where the relative performance in a specific outcome is measured as the residual difference between its local value, and, respectively, its general minimum or maximum, depending on whether the given metric is something we strive to maximize (e.g. capacity), or to minimize (e.g. GHG).

I can generalize on the method presented by Koponen and Le Net, and assume that any given state of society can be studied as one among many alternative states of said society, and the future depends very largely on how this society will navigate through the largely uncharted waters of itself being in many alternative states. Navigators need a star in the sky, to find their North, and so do societies. Koponen and Le Net simulate civilizational navigation under the constraint of four stars, namely the cost of CO2, the cost of electricity, the cost of natural gas, and the cost of biomass. I generalize and say that experimentation with alternative versions of us being collectively intelligent can be oriented on optimizing many alternative Norths, and the kind of North we will most likely pursue is the kind which allows us to learn efficiently how to go from one alternative future to another.

Good. This is my ‘Introduction’. It sets the tone for the method I present in the subsequent section, and the method opens up on the fine details of theory.


[1] Andreoni, V. (2020). The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304

[2] Koponen, K., & Le Net, E. (2021): Towards robust renewable energy investment decisions at the territorial level. Applied Energy, 287, 116552.  https://doi.org/10.1016/j.apenergy.2021.116552  .

The traps of evolutionary metaphysics

I think I have moved forward in the process of revising my manuscript ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, as a resubmission Applied Energy . A little digression: as I provide, each time, a link to the original form of that manuscript, my readers can compare the changes I develop on, in those updates, with the initial flow of logic.

I like discussing important things in reverse order. I like starting from what apparently is the end and the bottom line of thinking. From there, I go forward by going back, sort of. In an article, the end is the conclusion, possibly summarized in 5 ÷ 6 bullet points and optionally coming together with a graphical abstract. I conclude this specific piece of research by claiming that energy-oriented policies, e.g. those oriented on developing renewable sources, could gain in efficiency by being: a) national rather than continental or global b) explicitly oriented on optimizing the country’s terms of trade in global supply chains c) just as explicitly oriented on the development of some specific types of jobs whilst purposefully winding down other types thereof.

I give twofold a base for that claim. Firstly, I have that stylized general observation about energy-oriented policies: globally or continentally calibrated policies, such as, for example, the now famous Paris Climate Agreement, work so slow and with so much friction that they become ineffective for any practical purpose, whilst country-level policies are much more efficient in the sense that one can see a real transition from point A to point B. Secondly, my own research – which I present in this article under revision – brings evidence that national social structures orient themselves on optimizing their terms of trade and their job markets in priority, whilst treating the energy-related issues as instrumental. That specific collective orientation seems, in turn, to have its source in the capacity of human social structures to develop a strongly cyclical, predictable pattern of collective learning precisely in relation to the terms of trade, and to the job market, whilst collective learning oriented on other measurable variables, inclusive of those pertinent to energy management, is much less predictable.

That general conclusion is based on quantitative results of my empirical research, which brings forth 4 quantitative variables – price index in exports (PL_X), average hours worked per person per year (AVH), the share of labour compensation in Gross National Income (LABSH), and the coefficient of human capital (HC – average years of schooling per person) – out of a total scope of 49 observables, as somehow privileged collective outcomes marked with precisely that recurrent, predictable pattern of learning.

The privileged position of those specific variables, against the others, manifests theoretically as their capacity to produce simulated social realities much more similar to the empirically observable state thereof than simulated realities based on other variables, whilst producing a strongly cyclical series of local residual errors in approximating said empirically observable state.

The method which allowed to produce those results generates simulated social realities with the use of artificial neural networks. Two types of networks are used to generate two types of simulation. One is a neural network which optimizes a specific empirical variable as its output, whilst using the remaining empirical variables as instrumental input. I call that network ‘procedure of learning by orientation’. The other type of network uses the same empirical variable as its optimizable output and replaces the vector of other empirical variables with a vector of hypothetical probabilities, corresponding to just as hypothetical social roles, in the presence of a random disturbance factor. I label this network as ‘learning procedure by pacing’.

The procedure of learning by orientation produces as many alternative sets of numerical values as there are variables in the original empirical dataset X used in research. In this case, it was a set made of n = 49 variables, and thus 49 alternative sets Si are created. Each alternative set Si consists of values transformed by the corresponding neural network from the original empirical ones. Both the original dataset X and the n = 49 transformations Si thereof can be characterized, mathematically, with their respective vectors of mean expected values. Euclidean distances between those vectors are informative about the mathematical similarity between the corresponding sets.

Therefore, learning by orientation produces n = 49 simulations Si of the actual social reality represented in the set X, when each such simulation is biased towards optimizing one particular variable ‘i’ from among the n = 49 variables studied, and each such simulation displays a measurable Euclidean similarity to the actual social reality studied. My own experience in applying this specific procedure is that a few simulations Si, namely those pegged on optimizing four variables – price index in exports [Si(PL_X)], average hours worked per person per year [Si[AVH)], the share of labour compensation in Gross National Income [Si(LABSH)], and the coefficient of human capital [Si(HC) – average years of schooling per person] – display much closer Euclidean a distance to the actual reality X than any other simulation. Much closer means closer by orders of magnitude, by the way. The difference is visible.

The procedure of learning by pacing produces n = 49 simulations as well, yet these simulations are not exactly transformations of the original dataset X. In this case, simulated realities are strictly simulated, i.e. they are hypothetical states from the very beginning, and individual variables from the set X serve as the basis for setting a trajectory of transformation for those hypothetical states. Each such hypothetical state is a matrix of probabilities, associated with two sets of social roles: active and dormant. Active social roles are being endorsed by individuals in that hypothetical society and their baseline, initial probabilities are random, non-null values. Dormant social roles are something like a formalized prospect for immediate social change, and their initial probabilities are null.

This specific neural network produces new hypothetical states in two concurrent ways: typical neural activation, and random disturbance. In the logical structure of the network, random disturbance occurs before neural activation, and thus I am describing details of the former in the first place. Random disturbance is a hypothetical variable, separate from probabilities associated with social roles. It is a random value 0 < d < 1, associated with a threshold of significance d*. When d > d*, d becomes something like an additional error, fed forward into the network, i.e. impacting the next experimental round performed in the process of learning.

In the procedure of learning by pacing, neural activation is triggered by aggregating partial probabilities, associated with social roles, and possibly pre-modified by the random disturbance, through the operation of weighed average of the type ∑ fj(pi, X(i,j), dj, ej-1,), where fj is the function of neural activation in the j-th experimental round of learning, pi is the probability associated with the i-th social role, X(i,j) is the random weight of pi in the j-th experimental round, dj stands for random disturbance specific to that experimental round, and ej-1 is residual error fed forward from the previous experimental round j-1.

Now, just to be clear: there is a mathematical difference, in that logical structure, between random disturbance dj, and the random weight X(i,j). The former is specific to a given experimental round, but general across all the component probabilities in that round. If you want, di is like an earthquake, momentarily shaking the entire network, and is supposed to represent the fact that social reality just never behaves as planned. This is the grain of chaos in that mathematical order. On the other hand, X(i,j) is a strictly formal factor in the neural activation function, and its only job is to allow experimenting with data.

Wrapping it partially up, the whole method I use in this article revolves around the working hypothesis that a given set of empirical data, which I am working with, represents collectively intelligent learning, where human social structures collectively experiment with many alternative versions of themselves and select those versions which offer the most desirable states in a few specific variables. I call these variables ‘collective orientations’ and I further develop that hypothesis by claiming that collective orientations have that privileged position because they allow a specific type of collective learning, strongly cyclical, with large amplitude of residual error.

In both procedures of learning, i.e. in orientation, and in pacing, I introduce an additional component, namely that of self-observed internal coherence. The basic idea is that a social structure is a structure because the functional connections between categories of phenomena are partly independent from the exact local content of those categories. People remain in predictable functional connections to their Tesla cars, whatever exact person and exact car we are talking about. In my method, and, as a matter of fact, in any quantitative method, variables are phenomenological categories, whilst the local values of those variables inform about the local content to find in respective categories. My idea is that mathematical distance between values represents temporary coherence between the phenomenological categories behind the corresponding variables. I use the Euclidean distance of the type E = [(a – b)2]0,5 as the most elementary measure of mathematical distance. The exact calculation I do is the average Euclidean distance that each i-th variable in the set of n variables keeps from each l-th variable among the remaining k = n – 1 variables, in the same experimental round j. Mathematically, it goes like: avgE = { ∑[(xi – xl)2]0,5 }/k. When I use avgE as internally generated input in a neural network, I use the information about internal coherence as meta-data in the process of learning.

Of course, someone could ask what the point is of measuring local Euclidean distance between, for example, annual consumption of energy per capita and the average number of hours worked annually per capita, thus between kilograms of oil equivalent and hours. Isn’t it measuring the distance between apples and oranges? Well, yes, it is, and when you run a grocery store, knowing the coherence between your apples and your oranges can come handy, for one. In a neural network, variables are standardized, usually over their respective maximums, and therefore both apples and oranges are measured on the same scale, for two.       

The method needs to be rooted in theory, which has two levels: general and subject-specific. At the general level, I need acceptably solid theoretical basis for positing the working hypothesis, as phrased out in the preceding paragraph, to any given set of empirical, socio-economic data. Subject-specific theory is supposed to allow interpreting the results of empirical research as conducted according to the above-discussed method.

General theory revolves around four core concepts, namely those of: intelligent structure, chain of states, collective orientation, and social roles as mirroring phenomena for quantitative socio-economic variables. Subject-specific theory, on the other hand, is pertinent to the general issue of energy-related policies, and to their currently most tangible manifestation, i.e., to environmentally friendly sources of energy.

The theoretical concept of intelligent structure, such as I use it in my research, is mostly based on another concept, known from evolutionary biology, namely that of adaptive walk in rugged landscape, combined with the phenomenon of tacit coordination. We, humans, do things together without being fully aware we are doing them together or even whilst thinking we oppose each other (e.g. Kuroda & Kameda 2019[1]). capacity for social evolutionary tinkering (Jacob 1977[2]) through tacit coordination, such that the given society displays social change akin to an adaptive walk in rugged landscape (Kauffman & Levin 1987[3]; Kauffman 1993[4]; Nahum et al. 2015[5]).

Each distinct state of the given society (e.g. different countries in the same time or different moments in time as regards the same country) is interpreted as a vector of observable properties, and each empirical instance of that vector is a 1-mutation-neighbour to at least one other instance. All the instances form a space of social entities. In the presence of external stressor, each such mutation (each entity) displays a given fitness to achieve the optimal state, regarding the stressor in question, and therefore the whole set of social entities yields a complex vector of fitness to cope with the stressor.

The assumption of collective intelligence means that each social entity is able to observe itself as well as other entities, so as to produce social adaptation for achieving optimal fitness. Social change is an adaptive walk, i.e. a set of local experiments, observable to each other and able to learn from each other’s observed fitness. The resulting path of social change is by definition uneven, whence the expression ‘adaptive walk in rugged landscape’. There is a strong argument that such adaptive walks occur at a pace proportional to the complexity of social entities involved. The greater the number of characteristics involved, the greater the number of epistatic interactions between them, and the more experiments it takes to have everything more or less aligned for coping with a stressor.

Somehow concurrently to the evolutionary theory, another angle of approach seems interesting, for solidifying theoretical grounds to my method: the swarm theory (e.g. Wood & Thompson 2021[6]; Li et al. 2021[7]). Swarm learning consists in shifting between different levels of behavioural coupling between individuals. When we know for sure we have everything nicely figured out, we coordinate, between individuals, by fixed rituals or by strongly correlated mutual reaction. As we have more and more doubts whether the reality which we think we are so well adapted to is the reality actually out there, we start loosening the bonds of behavioural coupling, passing through weakening correlation, and all the way up to random concurrence. That unbundling of social coordination allows incorporating new behavioural patterns into individual social roles, and then learning how to coordinate as regards that new stuff.   

As the concept of intelligent structure seems to have a decent theoretical base, the next question is: how the hell can I represent it mathematically? I guess that a structure is a set of connections inside a complex state, where complexity is as a collection of different variables. I think that the best mathematical construct which fits that bill is that of imperfect Markov chains (e.g. Berghout & Verbitskiy 2021[8]): there is a state of reality Xn = {x1, x2, …, xn}, which we cannot observe directly, whilst there is a set of observables {Yn} such that Yn = π (Xn), the π being a coding map of Xn. We can observe through the lens of Yn. That quite contemporary theory by Berghout and Verbitskyi sends to an older one, namely to the theory of g-measures (e.g. Keane 1972[9]), and all that falls into an even broader category of ergodic theory, which is the theory of what happens to complex systems when they are allowed to run for a long time. Yes, when we wonder what kind of adult our kids will grow up into, this is ergodic theory.

The adaptive walk of a human society in the rugged landscape of whatever challenges they face can be represented as a mathematical chain of complex states, and each such state is essentially a matrix: numbers in a structure. In the context of intelligent structures and their adaptive walks, it can be hypothesized that ergodic changes in the long-going, complex stuff about what humans do together happen with a pattern and are far from being random. There is a currently ongoing, conceptual carryover from biology to social sciences, under the general concept of evolutionary trajectory (Turchin et al. 2018[10]; Shafique et al. 2020[11]). That concept of evolutionary trajectory can be combined with the idea that our human culture pays particular attention to phenomena which make valuable outcomes, such as presented, for example, in the Interface Theory of Perception (Hoffman et al. 2015[12], Fields et al. 2018[13]). Those two theories taken together allow hypothesising that, as we collectively learn by experimenting with many alternative versions of our societies, we systematically privilege those particular experiments where specific social outcomes are being optimized. In other words, we can have objectively existing, collective ethical values and collective praxeological goals, without even knowing we pursue them.

The last field of general theory I need to ground in literature is the idea of representing the state of a society as a vector of probabilities associated with social roles. This is probably the wobbliest theoretical boat among all those which I want to have some footing in. Yes, social sciences have developed that strong intuition that humans in society form and endorse social roles, which allows productive coordination. As Max Weber wrote in his book ‘Economy and Society’: “But for the subjective interpretation of action in sociological work these collectivities must be treated as solely the resultants and modes of organization of the particular acts of individual persons, since these alone can be treated as agents in a course of subjectively understandable action”. The same intuition is to find in Talcott Parsons’ ‘Social system’, e.g. in Chapter VI, titled ‘The Learning of Social Role-Expectations and the Mechanisms of 138 Socialization of Motivation’: “An established state of a social system is a process of complementary interaction of two or more individual actors in which each conforms with the expectations of the other(’s) in such a way that alter’s reactions to ego’s actions are positive sanctions which serve to reinforce his given need-dispositions and thus to fulfill his given expectations. This stabilized or equilibrated interaction process is the fundamental point of reference for all dynamic motivational analysis of social process. […] Every society then has the mechanisms which have been called situational specifications of role-orientations and which operate through secondary identifications and imitation. Through them are learned the specific role-values and symbol-systems of that particular society or sub-system of it, the level of expectations which are to be concretely implemented in action in the actual role”.

Those theoretical foundations laid, the further we go, the more emotions awaken as the concept of social role gets included in scientific research. I have encountered views, (e.g. Schneider & Bos 2019[14]) that social roles, whilst being real, are a mechanism of oppression rather than social development. On the other hand, it can be assumed that in the presence of demographic growth, when each consecutive generation brings greater a number of people than the previous one, we need new social roles. That, in turn, allows developing new technologies, instrumental to performing these roles (e.g. Gil-Hernández et al. 2017[15]).

Now, I pass to the subject-specific, theoretical background of my method. I think that the closest cousin to my method, which I can find in recently published literature, is the MuSIASEM framework, where the acronym, deliberately weird, I guess, stands for ‘Multi-scale Integrated Analysis of Societal and Ecosystem Metabolism’. This is a whole stream of research, where human societies are studied as giant organisms, and the ways we, humans, make and use energy, is studied as a metabolic function of those giant bodies. The central assumption of the MuSIASEM methodology is that metabolic systems survive and evolve by maxing out on energy efficiency. The best metabolism for an economic system is the most energy-efficient one, which means the greatest possible amount of real output per unit of energy consumption. In terms of practical metrics, we talk about GDP per kg of oil equivalent in energy, or, conversely, about the kilograms of oil equivalent needed to produce one unit (e.g. $1 bln) of GDP. You can consult Andreoni 2020[16], Al-Tamimi & Al-Ghamdi 2020[17] or Velasco-Fernández et al. 2020[18], as some of the most recent examples of MuSIASEM being applied in empirical research.

This approach is strongly evolutionary. It assumes that any given human society can be in many different, achievable states, each state displaying a different energy efficiency. The specific state which yields the most real output per unit of energy consumed is the most efficient metabolism available to that society at the moment, and, logically, should be the short-term evolutionary target. Here, I dare disagreeing fundamentally. In nature, there is no such thing as evolutionary targets. Evolution happens by successful replication. The catalogue of living organisms which we have around, today, are those which temporarily are the best at replicating themselves, and not necessarily those endowed with the greatest metabolic efficiency. There are many examples of species which, whilst being wonders of nature in terms of biologically termed efficiency, are either endemic or extinct. Feline predators, such as the jaguar or the mountain lion, are wonderfully efficient in biomechanical terms, which translates into their capacity to use energy efficiently. Yet, their capacity to take over available habitats is not really an evolutionary success.

In biological terms, metabolic processes are a balance of flows rather than intelligent strive for maximum efficiency. As Niebel et al. (2019[19]) explain it: ‘The principles governing cellular metabolic operation are poorly understood. Because diverse organisms show similar metabolic flux patterns, we hypothesized that a fundamental thermodynamic constraint might shape cellular metabolism. Here, we develop a constraint-based model for Saccharomyces cerevisiae with a comprehensive description of biochemical thermodynamics including a Gibbs energy balance. Non-linear regression analyses of quantitative metabolome and physiology data reveal the existence of an upper rate limit for cellular Gibbs energy dissipation. By applying this limit in flux balance analyses with growth maximization as the objective function, our model correctly predicts the physiology and intracellular metabolic fluxes for different glucose uptake rates as well as the maximal growth rate. We find that cells arrange their intracellular metabolic fluxes in such a way that, with increasing glucose uptake rates, they can accomplish optimal growth rates but stay below the critical rate limit on Gibbs energy dissipation. Once all possibilities for intracellular flux redistribution are exhausted, cells reach their maximal growth rate. This principle also holds for Escherichia coli and different carbon sources. Our work proposes that metabolic reaction stoichiometry, a limit on the cellular Gibbs energy dissipation rate, and the objective of growth maximization shape metabolism across organisms and conditions’.  

Therefore, if we translate the principles of biological metabolism into those of economics and energy management, the energy-efficiency of any given society is a temporary balance achieved under constraint. Whilst those states of society which clearly favour excessive dissipation of energy are not tolerable on the long run, energy efficiency is a by-product of the strive to survive and replicate, rather than an optimizable target state. Human societies are far from being optimally energy efficient for the simple reason that we have plenty of energy around, and, with the advent of renewable sources, we have even less constraint to optimize energy-efficiency.

We, humans, survive and thrive by doing things together. The kind of efficiency that allows maxing out on our own replication is efficiency in coordination. This is why we have all that stuff of social roles, markets, institutions, laws and whatnot. These are our evolutionary orientations, because we can see immediate results thereof in terms of new humans being around. A stable legal system, with a solid centre of political power in the middle of it, is a well-tested way of minimizing human losses due to haphazard violence. Once a society achieves that state, it can even move from place to place, as local resources get depleted.

I think I have just nailed down one of my core theoretical contentions. The originality of my method is that it allows studying social change as collectively intelligent learning, whilst remaining very open as for what this learning is exactly about. My method is essentially evolutionary, whilst avoiding the traps of evolutionary metaphysics, such as hypothetical evolutionary targets. I can present my method and my findings as a constructive theoretical polemic with the MuSIASEM framework.


[1] Kuroda, K., & Kameda, T. (2019). You watch my back, I’ll watch yours: Emergence of collective risk monitoring through tacit coordination in human social foraging. Evolution and Human Behavior, 40(5), 427-435. https://doi.org/10.1016/j.evolhumbehav.2019.05.004

[2] Jacob, F. (1977). Evolution and tinkering. Science, 196(4295), 1161-1166

[3] Kauffman, S., & Levin, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. Journal of theoretical Biology, 128(1), 11-45

[4] Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. Oxford University Press, USA

[5] Nahum, J. R., Godfrey-Smith, P., Harding, B. N., Marcus, J. H., Carlson-Stevermer, J., & Kerr, B. (2015). A tortoise–hare pattern seen in adapting structured and unstructured populations suggests a rugged fitness landscape in bacteria. Proceedings of the National Academy of Sciences, 112(24), 7530-7535, www.pnas.org/cgi/doi/10.1073/pnas.1410631112    

[6] Wood, M. A., & Thompson, C. (2021). Crime prevention, swarm intelligence and stigmergy: Understanding the mechanisms of social media-facilitated community crime prevention. The British Journal of Criminology, 61(2), 414-433.  https://doi.org/10.1093/bjc/azaa065

[7] Li, M., Porter, A. L., Suominen, A., Burmaoglu, S., & Carley, S. (2021). An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence. Technological Forecasting and Social Change, 166, 120621. https://doi.org/10.1016/j.techfore.2021.120621

[8] Berghout, S., & Verbitskiy, E. (2021). On regularity of functions of Markov chains. Stochastic Processes and their Applications, Volume 134, April 2021, Pages 29-54, https://doi.org/10.1016/j.spa.2020.12.006

[9] Keane, M. (1972). Strongly mixingg-measures. Inventiones mathematicae, 16(4), 309-324. DOI

[10] Turchin, P., Currie, T. E., Whitehouse, H., François, P., Feeney, K., Mullins, D., … & Spencer, C. (2018). Quantitative historical analysis uncovers a single dimension of complexity that structures global variation in human social organization. Proceedings of the National Academy of Sciences, 115(2), E144-E151. https://doi.org/10.1073/pnas.1708800115

[11] Shafique, L., Ihsan, A., & Liu, Q. (2020). Evolutionary trajectory for the emergence of novel coronavirus SARS-CoV-2. Pathogens, 9(3), 240. https://doi.org/10.3390/pathogens9030240

[12] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[13] Fields, C., Hoffman, D. D., Prakash, C., & Singh, M. (2018). Conscious agent networks: Formal analysis and application to cognition. Cognitive Systems Research, 47, 186-213. https://doi.org/10.1016/j.cogsys.2017.10.003

[14] Schneider, M. C., & Bos, A. L. (2019). The application of social role theory to the study of gender in politics. Political Psychology, 40, 173-213. https://doi.org/10.1111/pops.12573

[15] Gil-Hernández, C. J., Marqués-Perales, I., & Fachelli, S. (2017). Intergenerational social mobility in Spain between 1956 and 2011: The role of educational expansion and economic modernisation in a late industrialised country. Research in social stratification and mobility, 51, 14-27. http://dx.doi.org/10.1016/j.rssm.2017.06.002

[16] Andreoni, V. (2020). The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304

[17] Al-Tamimi and Al-Ghamdi (2020), ‘Multiscale integrated analysis of societal and ecosystem metabolism of Qatar’ Energy Reports, 6, 521-527, https://doi.org/10.1016/j.egyr.2019.09.019

[18] Velasco-Fernández, R., Pérez-Sánchez, L., Chen, L., & Giampietro, M. (2020), A becoming China and the assisted maturity of the EU: Assessing the factors determining their energy metabolic patterns. Energy Strategy Reviews, 32, 100562.  https://doi.org/10.1016/j.esr.2020.100562

[19] Niebel, B., Leupold, S. & Heinemann, M. An upper limit on Gibbs energy dissipation governs cellular metabolism. Nat Metab 1, 125–132 (2019). https://doi.org/10.1038/s42255-018-0006-7

The art of pulling the right lever

I dig into the idea of revising my manuscript ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, in order to resubmit it to the journal Applied Energy , by somehow fusing it with two other, unpublished pieces of my writing, namely: ‘Behavioural absorption of Black Swans: simulation with an artificial neural network’, and ‘The labour-oriented, collective intelligence of ours: Penn Tables 9.1 seen through the eyes of a neural network’.

I am focusing on one particular aspect of that revision by recombination, namely on comparing the empirical datasets which I used for each research in question. This is an empiricist approach to scientific writing: I assume that points of overlapping, as well as possible synergies, are based, at the end of the day, on overlapping and synergies between the respective empirical bases of my different papers.

 In ‘Climbing the right hill […]’, my basic dataset consisted in m = 300 ‘country-year’ observations, in the timeframe from 2008 through 2017, and covering the following countries: Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Norway, and Turkey. The scope of variables covered is essentially that of Penn Tables 9.1, plus some variables from other sources, pertinent to the market of electricity, to the energy sector in general, and to technological change, namely:

>> The price fork, in € between the retail price of electricity, paid by households and really small institutional entities, on the one hand, and the prices paid by big institutional consumers

>> The capital value of that price fork, in € mln, thus the difference in prices multiplied by the quantity of electricity consumed

>> Total consumption of energy in the country (thousands of tonnes of oil equivalent)

>> The percentage share of electricity in the total consumption of energy

>> The percentage share of renewable sources in the total output of electricity

>> The number of resident patent applications per country per year

>> The coefficient of fixed assets per 1 resident patent application

>> The coefficient of resident patent applications per 1 million people

The full set, in Excel format, is accessible via the following link: https://discoversocialsciences.com/wp-content/uploads/2019/11/Database-300-prices-of-electricity-in-context.xlsx . I also used a recombination of that database, made of m = 3000 randomly stacked records from the m = 300 set, just in order to check the influence of order in ‘country-year’ observations upon the results I obtained

In the two other manuscripts, namely in ‘The behavioural absorption of Black Swans […]’ and in ‘The labour-oriented, collective intelligence of ours […]’, I used one and the same empirical database, made of m = 3006 ‘country-year’ records, all selected from Penn Tables 9.1 , with the criteria of selection being the fullness of information. In other words, I kicked out of Penn Tables 9.1. all the rows with empty cells, and what remains is the m = 3006 set.

As I attempt to make some sort of cross analysis between my results from those three papers, one crossing is obvious. Variables pertinent to the market of labour, i.e. the average number of hours worked per person per year (AVH), the percentage of labour compensation in the gross national income (LABSH), and the indicator of human capital (HC), informative about the average length of educational path in the professionally active people, seem to play a special role as collectively pursued outcomes. The special role of those three – AVH, LABSH, and HC – seems to be impervious to, respectively, the presence or the absence of the variables I added from other sources in ‘Climbing the right hill […]’. It also seems impervious to the geographical scope and the temporal window of observation.

The most interesting direction for a further exploration seems to be in the crossing of ‘Black Swans […]’ with ‘Climbing the right hill […]. I take the structure from ‘Black Swans […]’ – namely the model where the optimization of an empirical variable impacts a range of social roles – and I put in that model the dataset from  ‘Climbing the right hill […]’. I observe the patterns of learning occurring in the perceptron, as I take different empirical variables.

Variables which are strong collective orientations – AVH, LABSH, and HC – display a special pattern of learning, different from other variables. Their local residual error (i.e. the arithmetical difference between the value of neural activation function and the local empirical value at hand), swings in a wide amplitude, yet in a predictable cycle. It is a pattern of learning in the lines of ‘we make a lot of mistakes, then we minimize them, and then we repeat: a lot of mistakes followed by a period of accuracy’. Other variables, run through the same model, display something different: a general tendency to minimal error, with occasional, pretty random bumps. Not much error, and not much of a visible cycle in learning.

The national societies which I study, seem to orient themselves on outcomes which associate with strong and predictably cyclical amplitude of error, this with abundant learning in a predictable cycle. There is one more thing. When optimizing variables relative to the market of labour – AVH, LABSH, and HC – the model from ‘Black Swans […]’ shows relatively the highest resilience in the incumbent social roles, i.e. those in place before social disruption starts.

Good. Something takes shape. I am reframing the method and the material I want to introduce in the revised version of ‘Climbing the right hill […]’, for the journal Applied Energy, and I add some results and provisional conclusions.

When I take the empirical material from Penn Tables 9.1, thus when I observe the otherwise bloody chaotic thing called ‘society’ through the lens of quantitative variables pertinent to the broadly spoken real of macroeconomics, that material shows some repetitive, robust properties. When I run in through a learning procedure, expressed in the form of a simple neural network, the learning centred on optimizing variables pertinent to the labour market (AVH, LABSH, HC), as well as on the index of prices in export (PL_X), – yields artificial datasets more similar to the original one, in terms of Euclidean similarity, than any other such artificial dataset, optimizing other variables. That phenomenological hierarchy seems to be robust both to the modifications of scope, and those of spatial-temporal range. When I add variables pertinent to technological change and to the market of electricity, they obediently take their place in the rank, and don’t step forward. When I extend the geographical scope of observation from Europe to the whole world, and when I extend the window of observation from the initial {2008 ÷ 2017} to the longer {1954 ÷ 2017}, the same still holds.

As I try to explain why is it so, and I try to find an empirical explanation, I make another neural network, where each empirical variable from the original dataset is the optimized output, and optimization takes place by experimenting with a vector of probabilities assigned to a set of social roles, and a random factor of disturbance. The pattern of learning is observed as the distribution of residual errors over the entire experimental sequence of phenomenal instances. In that different perspective, the same variables which seem to be privileged collective outcomes – PL_X, AVH, LABSH, and HC – display a specific pattern of learning: they swing broadly in their error, and yet they swing in a predictable cycle. When my experimental neural network learns on other variables, the pattern is different, with the curve of error being much calmer, less bumpy, and yet much less cyclical.

I return to my method and to my theoretical assumptions. I recapitulate. I start by assuming that social reality is essentially chaotic and unobservable directly, yet I can make epistemological approximations of that thing and see how they work. In this specific piece of research, I make two such types of approximation, based on different assumptions. On the one hand, I assume that quantitative, commonly measured, socio-economic variables, such as those in Penn Tables 9.1 are partial expressions of change in that otherwise chaotic social reality, and we collect those values because they represent change in the collective outcomes which we value. On the other hand, I assume that social reality can be represented as a collection of social roles, in two distinct categories: the already existing, active social roles, accompanied by temporarily dormant, ready-to-be triggered roles. Those social roles are observable as the relative frequency of occurrence, thus as the probability that any given individual endorses them.

I further assume that human societies are collectively intelligent structures, which, in turn, means that we collectively learn by experimenting with many alternative versions of ourselves. By the way, I have been wondering whether this is a hypothesis or an assumption, and I settled for assumption, because I do not really bring any direct proof thereof, and yet I make the claim. Anyway, with the assumption of collective intelligence, I can simulate two mutually correlated processes of learning through experimentation. On the one hand, among all the collective outcomes represented with quantitative socio-economic variables, we learn hierarchically, i.e. we optimize some of those outcomes in the first place, whilst treating the other ones as instrumental to that chief goal. On the other hand, we optimize each of those outcomes, represented with quantitative variables, by experimenting with the relative prevalence (i.e. probability of endorsement) in distinct social roles.

That general theoretical perspective is the foundation which I use to both make an empirical method of research, and to substantiate the claim that public policies and business strategies which stimulate technological race with clear prime for winners and clear penalty for losers are likely to bring better results, especially on the long run, than policies and strategies aiming at erasing local idiosyncrasies and at creating uniformly distributed outcomes. My point is that the latter, i.e. policies oriented on nullifying local idiosyncrasies, lead either to the absence of idiosyncrasies, and, consequently, to the absence of different versions in ourselves to experiment with and learn, or they simply prove inefficient, as they try to move the wrong lever in the machine.

Now, looking through another door inside my head, I am presenting below the structure of semestral projects I assign to my students, in the Summer semester 2021, in two different, and yet somehow concurrent courses: International Trade Policy in the major International Relations, and International Management in the major Management. You will see how I teach, and how I get a bit obsessive about digging into the same ideas, over and over again.

The complex project to graduate the International Management course, Summer semester 2021

Our common goal: develop your understanding of the transition from the domestically based business structure to an international one.

Your goal: prepare a developed, well-informed business plan, for the development of a business, from the level of one national market, to the international level. That business plan is your semestral project, which you graduate the course of International Management with.

You can see this course as an opportunity to put together and utilize the partial learning you have from all the individual subject courses you have had so far.

Your deadline is June 25th, 2021. 

Definition – international scale of a business means that it becomes an economically significant choice to branch the operations into or move them completely to foreign markets. In other words, the essential difference between domestic management and international management – at least the difference we will focus on in this course – is that in domestic management the initial place of incorporation determines the strategy, whilst in international management the geographical location of operations and incorporation(s) is determined by strategic choices. 

You work with a business concept of your own, or you take one of the pre-prepared business plans available at the digital platform. These are graduation business plans prepared by students from other groups, in the Winter semester 2020/2021. In other words, you develop either on your own idea, or on someone else’s idea. One of the things you will find out is that different business concepts have different potential, and follow very different paths for going to the international level.

Below, you will find the list of those pre-prepared business plans. They are coupled with links to the archives of my blog, where you can download them from. Still, you can find them as well in the ‘Files’ section of the group ‘International Management’, folder ‘Class materials’.

>> Pizzeria >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Pizzeria-Business-plan.docx

>> Pancake Café >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Pancake-Cafe-Business-Plan.pptx

>> Never Alone >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Never-Alone-business-plan.pdf

>> 3D Virtual Fitting Room >> https://discoversocialsciences.com/wp-content/uploads/2021/03/3D-Virtual-Fitting-Room-Business-Plan.docx

>> ToyBox >> https://discoversocialsciences.com/wp-content/uploads/2021/03/ToyBox-Business-Plan.pdf

>> Chess Manufacturing (semi-finished, interesting to develop from that form) >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Chess-Business-Plan-Semi-Done.docx

>> Second-hand market for luxury goods >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Business-Plan-second-hand-market-for-luxury-fashion.docx

We will abundantly use real-life cases of big, internationally branched businesses as our business models. Some of them are those which you already know from past semesters, whilst other might be new to you:

>> Netflix >> https://ir.netflix.net/ir-overview/profile/default.aspx

>> Tesla >> https://ir.tesla.com/

>> PayPal >> https://investor.pypl.com/home/default.aspx

>> Solar Edge >> https://investors.solaredge.com/investor-overview

>> Novavax >> https://ir.novavax.com/investor-relations

>> Pfizer >> https://investors.pfizer.com/investors-overview/default.aspx

>> Starbucks >> https://investor.starbucks.com/ir-home/default.aspx

>> Amazon >> https://ir.aboutamazon.com/overview/default.aspx

That orientation on real business cases means that the course of International Management is, from your point of view, a course of market research, business planning, and basic empirical science, more than a theoretical course. This is precisely what we are going to be doing in our classes: market research, business planning, and basic empirical science. 

You can benefit from running yourself through my online course of business planning, to be found at https://discoversocialsciences.com/the-course-of-business-planning/ .

The basic structure of the business plan which you will prepare is the following:

  • Section 1: Executive summary. This is a summary of the essentials, developed in further sections of the business plan. Particular focus on why and how going international with that business concept.
  • Section 2: Description of the business concept. How do we create, and capture value added in that thing? What kind of value added is that? What are the goods we market? Who are our target customers? What kind of really existing, operational business models, observable in actually operational companies, do we emulate in that business?
  • Section 3: Market research. We focus on collecting and presenting information on our customers, and our competitors.
  • Section 4: Organization. How are we going to structure human work in that business? How many people do we need, and what kind of organizational structure should we make them work in? What is the estimate, total payroll per month and per year, in that organization?
  • Section 5: The strategy for going international. Can we develop an original, proprietary technology, and apply it in different national markets? Can we benefit from the economies of scale, or those of scope, as we go international? Can we optimize and standardize our business concept into a franchise, attractive for smaller partners in foreign markets? << this is the ‘INTERNATIONAL MANAGEMENT’ part of that business plan. Now, you demonstrate your understanding of what international management is.
  • Section 6: The corporate business structure. Do you see that business as one compact business entity, which operates internationally via digital platforms and contracts with external partners, or, conversely, would you rather create a network of affiliated companies in separate national (regional?) markets, all tied to and controlled by one mother company? Develop on those options and justify your choice. 
  • Section 7: The financial plan. Plan of revenues, costs, and of the resulting profit/loss for 3 years ahead. The balance sheet we need to start with, and its prospective changes over the next 3 years. The prospective cash-flow.

Guidelines for the graduation project in International Trade Policy Summer semester 2021

You graduate the course of ‘International Trade Policy’ by preparing a project. Your project will be a business report, the kind you could have to prepare if you are assistant to the CEO of a big firm, or to a prime minister. You are supposed to prepare a report on the impact of trade on individual businesses and national economies, in a sort of controlled economic experiment, limited in scope and in space. Your goal in the preparation of that project is to develop active understanding of international trade.

You can access the files provided as additional materials for this assignment in two ways. Below in this document, I provide links to the archives of my blog, ‘Discover social sciences’. On the other hand, all those files are to find in the ‘Files’ section of the ‘International Trade Policy’ group, in the folder ‘Class Materials’.

Your report will have two sections. In Section A, you study the impact of international trade on a set of businesses. Your business cases encompass real companies, some of which you already know from the course of microeconomics – Tesla, Netflix, Amazon, H&M – as well as new business entities which can emerge as per the business plans introduced below (these are real business plans made by students in other groups in the Winter semester 2020/2021).  

In the Section B of your report, imagine that you are the government of, respectively, Poland, Ukraine, and France. Imagine that businesses from Part A grow in your country. Given the macroeconomic characteristics of your national economy, which types of those businesses are likely to grow the most, and which are not really fit? As a country, as those businesses grow, would you see your exports grow, or would it be rather an increase in your imports? How would it affect your overall balance on trade? What would you do as a government and why?

Additional guidelines and materials for the Section A of your report:

You can make a simplifying assumption that businesses can develop with and through trade along two different, although not exactly exclusive paths:

  • Case A: there is a technology with potential for growth, which can be developed through expanding its target market, with exports or with franchise
  • Case B: the gives business can develop significant economies of scale and scope, and trade, i.e. exports or/and imports, are a way to achieve that

You can benefit from studying the model contract of sales in international trade: https://discoversocialsciences.com/wp-content/uploads/2020/02/sale_of_perishables_model_contract.pdf

… as well as studying the so-called Incoterms >> https://discoversocialsciences.com/wp-content/uploads/2020/03/Incoterms.pdf , which are standard conditions of delivery in international trade.

The early business concepts developed by students from other groups, which you are supposed to assess as for their capacity to grow through trade, are:

The investor relations sites of the real, big companies, whose development with trade you are supposed to study as well:

Additional guidelines and materials for the Section B of your report:

The so-called trade profiles of countries, accessible with the World Trade Organization: https://www.wto.org/english/res_e/publications_e/trade_profiles20_e.htm

Example of an international trade agreement, namely than between South Korea and Australia: https://discoversocialsciences.com/wp-content/uploads/2021/03/korea-australia-free-trade-agreement.pdf

Macroeconomic profiles of Poland, Ukraine, and France >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Macroeconomic-Profiles.xlsx

Phases of abundant experimentation

I am working, in parallel, on revising my manuscript, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, on the one hand, and on preparing catchy, interesting paths of teaching for the summer semester, at the university, on the other hand. As for the former, you can read more in my last two updates, namely in ‘Still some juice in facts’, and in ‘As it is ripe, I can harvest’. In this update, I will develop on that path of work, but first, I am sharing a piece of educational structure I came up with for my workshops in Macroeconomics, with the students of 1st year, Bachelor, major International Relations, at my home university, namely the Andrzej Frycz-Modrzewski Krakow University, Krakow, Poland. Below, I am copying the description of training assignment such as it is being presented to my students. 

For graduating workshops in Macroeconomics, Summer semester 2021, you will prepare just one, structured assignment. You can consider it as a follow up on the business plan you prepared in the course of Microeconomics.

You can take your business plan from the course of Microeconomics, or you can choose one of the business plans specifically provided as case studies for this assignment, namely:

>> https://discoversocialsciences.com/wp-content/uploads/2021/03/Switch-Park-Business-Plan.docx

>> https://discoversocialsciences.com/wp-content/uploads/2021/03/Peerket-Business-Plan.docx

>> https://discoversocialsciences.com/wp-content/uploads/2021/03/Foodies-Business-Plan.docx

Pick ONE business plan, once again: your own or one of the three provided as library. Review the customers’ profile in that particular business concept. Who are the customers? Are they individuals (households) or are they institutional (firms, public institutions etc.)?

Now, imagine the whole market of businesses such as the one described.

Those customers have a budget to finance the purchase of goods named in that business plan.

What other goods do they finance with the same budget?

What stream of cash does that budget come from?  Do they pay for those goods with their current income, or do they pay out of their capital base (i.e. from their assets)?

Now, take the entire population of those customers. Their AGGREGATE budgets represent aggregate demand, and that demand is derived from a stream of income, or from a capital base. In your analysis, at this point, phrase it out explicitly: ‘The market for this business concept is based on aggregate demand coming from the group of customers ABCD, and the value of that aggregate demand depends on the aggregate stream of income Y, or on the aggregate amount of assets X.’

Place that business plan in the context of the national economies whose macroeconomic profiles are provided in the file attached to this assignment (https://discoversocialsciences.com/wp-content/uploads/2021/03/Data-for-work-with-business-plans.xlsx). Those national economies are: Bulgaria, Croatia, Poland, Russia, Turkey, Ukraine, France, Italy, Latvia.

Use exhaustively, in an informed, articulate manner, the data provided in the attached file, to develop an analysis and answer the following question: ‘Which of these countries makes the best macroeconomic environment for the implementation of this specific business plan? Which of the countries is the worst macroeconomic environment in that respect? Provide, using the data at hand, informed argumentation for your choice’.  

Provide your answer in the form of a business report, something like an extended, macroeconomic analysis for the business plan you took on studying the macroeconomic environment for. As you will be working with the data supplied to assists your answer, you will go through the following macroeconomic variables:

Subject DescriptorUnitsScale
Gross domestic product, constant pricesNational currencyBillions
Gross domestic product, constant pricesPercent change
Gross domestic product, current pricesNational currencyBillions
Gross domestic product, current pricesU.S. dollarsBillions
Gross domestic product, current pricesPurchasing power parity; international dollarsBillions
Gross domestic product, deflatorIndex
Gross domestic product per capita, constant pricesNational currencyUnits
Gross domestic product per capita, constant pricesPurchasing power parity; 2017 international dollarUnits
Gross domestic product per capita, current pricesNational currencyUnits
Gross domestic product per capita, current pricesU.S. dollarsUnits
Gross domestic product per capita, current pricesPurchasing power parity; international dollarsUnits
Gross domestic product based on purchasing-power-parity (PPP) share of world totalPercent
Implied PPP conversion rateNational currency per current international dollar
Total investmentPercent of GDP
Gross national savingsPercent of GDP
Inflation, average consumer pricesIndex
Inflation, average consumer pricesPercent change
Inflation, end of period consumer pricesIndex
Inflation, end of period consumer pricesPercent change
Volume of imports of goods and servicesPercent change
Volume of Imports of goodsPercent change
Volume of exports of goods and servicesPercent change
Volume of exports of goodsPercent change
Unemployment ratePercent of total labor force
PopulationPersonsMillions
Current account balanceU.S. dollarsBillions
Current account balancePercent of GDP

Workshops will largely consist in explaining those macroeconomic concepts, and I strongly encourage you to study their meaning in a textbook, and in online resources. The simplest way is to type each of these categories into a Google search and study the results of that search.

Your assignment largely consists in developing credible statements of the type: ‘Country A seems to make the best macroeconomic environment for this business, because its macroeconomic variables X, Y and Z take values x, y and z’.

Now, teaching content shared, I am returning to revising my manuscript. I think I pretty much nailed down, in  the last update (‘As it is ripe, I can harvest’), the core of the reproducible method of research which I want to present. As I am working on phrasing out the finer details of that reproducible method, and position it vis a vis the corresponding theory, whilst instrumenting it with a computational model, I feel like returning to questions, which the journal Applied Energy requires to address in my cover letter. I remind those questions below.

>> (1) what is the novelty of this work?

>> (2) is the paper appealing to a popular or scientific audience?

>> (3) why the author thinks the paper is important and why the journal should publish it?

I start with a tentative answer to the last one, about the importance of that research, as well as about the usefulness of publishing it. When my research gets published, two things happen. Firstly, it is being peer-reviewed, and is published only after a specific ritual is accomplished. The ritual starts with editor of the journal judging the paper ripe for asking other scientists to review it, usually 2 or 3 of them. That release from the editor to the reviewers results in the reviewers having a go at the paper, and assessing whether it is acceptable at all, and what kind of critical remarks they have. Generally, the reviewers are not expected to be indiscriminately enthusiastic about the paper. The type of answer to expect from them is the ‘yes, but…’ type. Once they provide their reviews of my manuscript in that form, I am expected to revise once again, whilst explicitly addressing the critical remarks from reviewers in a separate statement. At this stage, I revise in a ‘yes, but…’ style. I am like: ‘Yes, at this point, you are right, prof. YUTOONJJK, and thus I am changing my stance accordingly, but at this other point, with all the due respect, I am holding my ground and here is why I am doing so: …’. This phase of revision is tricky. Technically, I could change everything in response to critical remarks, but it wouldn’t be the same paper anymore. In order to remain in the same scientific territory, I need, first of all, to study the same facts. Thus, my empirical base remains the same. The essential points of my method should stay in place as well, I just might need to support it with more convincing an argumentation. What I can really change in response to reviewers’ criticism, are some details in my calculations, and the interpretation I give to the results of my empirical investigation.

The first aspect of having my paper published is precisely my readiness, and my ability, to go gracefully and convincingly through that ritual of peer-review, and my response thereto. If I think that my paper deserves publishing, I indirectly suggest that when it passes the ritualised dialogue of peer-review, everybody involved will be better off, i.e. the scientific community will benefit from other scientists criticising me, and me responding to their criticism through a polite, informed statement that I am holding my ground, with maybe some tiny concessions. Another aspect of publication is the capacity, for me, to cite that publication of mine in the future. Why would I do it? Mostly when I will be applying for funding, it is frequently welcome to prove that the research I will intend to conduct is relevant, important, and I am not (entirely) mad in my methods of running that research. In other words, when my paper gets published, it gives me scientific firepower to develop on the same stream of research. That, in turn, requires me to define an acceptably coherent stream of research, for one, and that stream should have potential for development.

All in all, when I claim that the journal which I am submitting to should publish my paper, I should convincingly prove that my research can enrich the scientific community, and it has strong potential for future development. Those general remarks phrased out, I can apply that line of thinking to my manuscript.

Policies pertinent to energy systems, especially in the environmental perspective, frequently assume that significant idiosyncrasies in individual agents or in political entities (countries, regions etc.) are bad for progress, and they should be equalized. In other words, public policies should be equalizers, or redistributors of gains from the technological race. I could notice that theoretical stance in one of the articles I have recently quoted, namely in ‘The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis’. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304 (2020),  byprofessor Valeria Andreoni. Still, from the management point of view, or from the perspective of the new institutional school in economics, this is not necessarily true. If we want quick, deeply transformative technological change, we need a true technological race, with true winners and true losers. Equality does not really serve efficient adaptation.

I think that public policies supposed to drive rapid technological change should stimulate technological race, and stimulate inequality of outcomes in that race. In order to adapt to serious s**t, we need to experiment with many alternative ways of action. The question is: how exactly can we do it? How can governments experiment? In order to address that question, there is another one to answer: how exactly does that experimentation occur? What exactly is happening when we collectively experiment with ourselves, as a society? I think that the methodology I present in my paper creates a small opening up and into that realm of research: simulating social and technological change as a process of learning by trial and error.

Summing partly up that intellectual meandering of mine, I think that my paper deserves publishing because my method of studying social and technological change – as a manifestation of learning in collectively intelligent social structures, which adapt to stressors by creating many alternative versions of themselves and assessing their fitness to cope with said stressors – allows conceptualizing public policies and business strategies, in the sector of energy, as a process of heuristic, adaptive experimentation rather than as a linear path towards a determined end-state.

As I have spat this one out, I think that I need to combine that manuscript, namely ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, such as it is now, with two others, unpublished as well: ‘Behavioural absorption of Black Swans: simulation with an artificial neural network’, for one, and ‘The labour-oriented, collective intelligence of ours: Penn Tables 9.1 seen through the eyes of a neural network’, for two. They all operate on overlapping datasets, and they show different aspects of the same essential method.

The next question to address in my cover letter is the target audience of my paper. Is my article made for the popular audience, or rather for the scientific one? I am tempted to say: ‘for both’. Yet, I know this is a tricky question. It really means asking ‘Is my article refined enough, in terms of scientific method, to impress and influence my fellow scientists, or is it rather an interesting piece, detached from the main body of science, and served to non-scientific people in a tasty sauce?’. At the end of the day, I want to write it both ways, but the latter one will go down better as a book, later on. The form it has now, i.e. that of an article, my idea is addressed to a scientific audience, as a slightly provocative opening on an interesting perspective. Precisely, the deep intuition that I am opening a path of research rather than closing one, makes me stay at the level of short scientific form.

As I have provisionally walked myself through the cover letter which I should address to the editor of the journal Applied Energy , I come back to the structure I should give to the revised paper: ‘Introduction’, ‘Material and Methods’, ‘Theory’, ‘Calculation’, ‘Results’, ‘Discussion’, ‘Conclusion’, ‘Data availability’, ‘Glossary’, ‘Appendices’, Highlights, and Graphical Abstract.

As I intend to combine three manuscripts into one, the combined highlights of those three would be:

>> Public policies and business strategies can be studied as adaptive change in a collectively intelligent structure.

>> Markov chains of states are the general mathematical foundation of such an approach.

>> A simple perceptron can be used as computational tool for simulating social and technological change in real world.

>> The method presented allows discovering distinct, collectively pursued orientations of whole societies, and distinct types of collective learning.

>> Empirical findings suggest collective orientation on optimizing the labour market, rather than direct orientation on transforming the energy base of societies.

>> That collective orientation seems being pursued through an almost perfectly cyclical process of learning, where phases of abundant experimentation are interspersed with periods of relative homeostasis.

As it is ripe, I can harvest

I keep revising my manuscript titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, in order to resubmit it to the journal Applied Energy. In my last update, titled ‘Still some juice in facts’, I used the technique of reverted reading to break the manuscript down into a chain of ideas. Now, I start reviewing the most recent literature associated with those ideas. I start with Rosales-Asensio et al. (2020)[1], i.e. with ‘Decision-making tools for sustainable planning and conceptual framework for the energy–water–food nexus’. The paper comes within a broader stream of literature, which I already mentioned in the first version of my manuscript, namely within the so-called MUSIASEM framework, where energy management in national economies is viewed as metabolic function, and socio-economic systems in general are equated to metabolic structures. Energy, water, food, and land are considered in this paper as sectors in the economic system, i.e. as chains of markets with economic goods being exchanged. We know that energy, water and food are interconnected, and all the three are connected to the way that our human social structures work. Yet, in the study of those connections we have been going into growing complexity of theoretical models, hardly workable at all when applied to actual policies. Rosales-Asensio et al. propose a method to simplify theoretical models in order to make them functional in decision-making. Water, land, and food can be included into economic planning as soon as we explicitly treat them as valuable assets. Here, the approach by Rosales-Asensio et al. goes interestingly against the current of something that can be labelled as ‘popular environmentalism’. Whilst the latter treats those natural (or semi-natural, in the case of food base) resources as invaluable and therefore impossible to put a price tag on, Rosales-Asensio et al. argue that it is much more workable, policy-wise to do exactly the opposite, i.e. to give explicit prices and book values to those resources. The connection between energy, water, food, and the economy is being done as transformation of matrices, thus as something akin a Markov chain of states. 

The next article I pass in review is that by Al-Tamimi and Al-Ghamdi (2020), titled ‘Multiscale integrated analysis of societal and ecosystem metabolism of Qatar’ (Energy Reports, 6, 521-527, https://doi.org/10.1016/j.egyr.2019.09.019 ). This paper presents interesting findings, namely that energy consumption in Quatar, between 2006 and 2015, grew at a faster rate than GDP within the same period, and energy consumption per capita and energy intensity grew approximately at the same rate. That could suggest some kind of trade-off between productivity and energy intensity of an economy. Interestingly, the fall of productivity was accompanied by increased economic activity of the Quatar’s population, i.e. the growth of professionally active population, and thence of the labour market, was faster than the overall demographic growth.

In still another paper, titled ‘The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis’. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304 (2020),  professor Valeria Andreoni develops a line of research, where rapid economic change, even when it is a crisis-like change, contributes to reducing energy intensity of national economies. Still, some kind of blueprint for energy-efficient technological change needs to be in place, at the level of national policies. Energy-efficient technological change might be easier than we think, and yet, apparently, it needs some sort of accompanying economic change as its trigger. Energy efficiency seems to be correlated with competitive technological development in national economies. Financial constraints can hamper those positive changes. Cross-sectional (i.e. inter-country) gaps in energy efficiency are essentially bad for sustainable development. Public policies should aim at equalizing those gaps, by integrating the market of energy within EU. 

Velasco-Fernández, R., Pérez-Sánchez, L., Chen, L., & Giampietro, M. (2020), in the article titled ‘A becoming China and the assisted maturity of the EU: Assessing the factors determining their energy metabolic patterns’. Energy Strategy Reviews, 32, 100562.  https://doi.org/10.1016/j.esr.2020.100562 , bring empirical results somehow similar to mine, although with a different method. The number of hours worked per person per year is mentioned in this paper as an important variable of the MuSIASEM framework for China. There is, for example, a comparison of energy metabolized in the sector of paid work, as compared to the household sector. It is found that the aggregate amount of human work used in a given sector of the economy is closely correlated with the aggregate energy metabolized by that sector. The economic development of China, and its pattern of societal metabolism in using energy, displays increase in the level of capitalization of all sectors, while reducing the human activity (paid work) in all of them except in the services. In the same time, the amount of human work per unit of real output seems to be negatively correlated with the capital-intensity (or capital-endowment) of particular sectors in the economy. Energy efficiency seems to be driven by decreasing work-intensity and increasing capital-intensity.

I found another similarity to my own research, although under a different angle, in the article by Koponen, K., & Le Net, E. (2021): Towards robust renewable energy investment decisions at the territorial level. Applied Energy, 287, 116552.  https://doi.org/10.1016/j.apenergy.2021.116552 . The authors build a simulative model in Excel, where they create m = 5000 alternative futures for a networked energy system aiming at optimizing 5 performance metrics, namely: the LCOE cost of electricity, the GHG metric (greenhouse gases emission) for the climate, the density of PM2.5 and PM10 particles in the ambient air as a metric of health, capacity of power generation as a technological benchmark, and the number of jobs as social outcome. That complex vector of outcomes has been simulated as dependent on a vector of uncertainty as regards costs, and more specifically: cost of CO2, cost of electricity, cost of natural gas, and the cost of biomass. The model was based on actual empirical data as for those variables, and the ‘alternative futures’ are, in other words, 5000 alternative states of the same system. Outcomes are gauged with the so-called regret analysis, where the relative performance in a specific outcome is measured as the residual difference between its local value, and, respectively, its general minimum or maximum, depending on whether the given metric is something we strive to maximize (e.g. capacity), or to minimize (e.g. GHG). The regret analysis is very similar to the estimation of residual local error.

That short review of literature has the merit of showing me that I am not completely off the picture with the method and he findings which I initially presented to the editor of Applied Energy in that manuscript: ‘Climbing the right hill – an evolutionary approach to the European market of electricity’. The idea of understanding the mechanism of change in social structures, including the market of energy, by studying many alternative versions of said structure, seems to be catching in literature. I am progressively wrapping my mind around the fact that in my manuscript, the method is more important than the findings. The real value for money of my article seems to reside in the extent to which I can demonstrate the reproducibility and robustness of that method.

Thus, probably for the umpteenth time, I am rephrasing the fundamentals of my approach, and I am trying to fit it into the structure which Applied Energy recommends for articles submitted to their attention. I should open up with an ‘Introduction’, where I sketch the purpose of the paper, as well as the main points of the theoretical background which my paper stems from, although without entering into detailed study thereof. Then, I should develop on ‘Material and Methods’, with the main focus on making my method as reproducible as possible, and now comes the time to develop on, respectively, ‘Theory’ and ‘Calculation’, thus elaborating on the theoretical foundations of my research as pitched against literature, and on the detailed computational procedures I used. I guess that I need to distinguish, at this specific point, between the literature pertinent to the substance of my research (Theory), and that oriented on the method of working with empirical data (Calculation).

Those four initial sections – Introduction, Material and Methods, Theory, Calculation – open the topic up and then comes the time to give it a closure, with, respectively: ‘Results’, ‘Discussion’, and, optionally, a separate ‘Conclusion’. Over the top of that logical flow, I need to decorate with sections pertinent to ‘Data availability’, ‘Glossary’, and ‘Appendices’. As I get further back from the core and substance of my manuscript, and deeper into peripheral information, I need to address three succinct ways of presenting my research: Highlights, Graphical Abstract, and a structured cover letter. Highlights are 5 – 6 bullet points, 1 – 2 lines each, sort of abstract translated into a corporate presentation on slides. The Graphical Abstract is a challenge – as I need to present complex ideas in a pictographic form – and it is an interesting challenge. The structured cover letter should address the following points:

>> what is the novelty of this work?

>> is the paper appealing to a popular or scientific audience?

>> why the author thinks the paper is important and why the journal should publish it?

>> has the article been checked by an expert native speaker?

>> is the author available as reviewer?

Now, I ask myself fundamental questions. Why should anyone bother about the substance and the method of the research I present in my article. I noticed, both in public policies and in business strategies, a tendency to formulate completely unrealistic plans, and then to complain about other people not being smart enough to carry those plans out and up to happy ending. It is very visible in everything related to environmental policies and environmentally friendly strategies in business. Environmental activism consumes itself, very largely, in bashing everyone around for not being diligent enough in saving the planet.

To me, it looks very similarly to what I did many times as a person: unrealistic plans, obvious failure which anyone sensible could have predicted, frustration, resentment, practical inefficiency. I did it many times, and, obviously, whole societies are perfectly able to do it collectively. Action is key to success. A good plan is the plan which utilizes and reinforces the skills and capacities I already have, makes those skills into recurrent patterns of action, something like one good thing done per day, whilst clearly defining the skills I need to learn in order to be even more complete and more efficient in what I do. A good public policy, just as a good business strategy, should work in the same way.

When we talk about energy efficiency, or about the transition towards renewable energies, what is our action? Like really, what is the most fundamental thing we do together? Do we purposefully increase energy efficiency, in the first place? Do we deliberately transition to renewables? Yes, and no. Yes, at the end of the day we get those outcomes, and no, what we do on a daily basis is something else. We work. We do business. We study in order to get a job, or to start a business. We live our lives, from day to day, and small outcomes of that daily activity pile up, producing big cumulative change.   

Instead of discussing what we do completely wrong, and thus need to change, it is a good direction to discover what we do well, consistently and with visible learning. That line of action can be reinforced and amplified, with good results. The so-far review of literature suggests that research concerning energy and energy transition is progressively changing direction, from the tendency to growing complexity and depth in study, dominant until recently, towards a translation of those complex, in-depth findings into relatively simple decision-making tools for policies and business strategies.

Here comes my method. I think it is important to create an analytical background for policies and business strategies, where we take commonly available empirical data at the macro scale, and use this data to discover the essential, recurrently pursued collective outcomes of a society, in the context of specific social goals. My point and purpose is to nail down a reproducible, relatively simple method of discovering what whole societies are really after. Once again, I think about something simple, which anyone can perform on their computer, with access to Internet. Nothing of that fancy stuff of social engineering, with personal data collected from unaware folks on Facebook. I want the equivalent of a screwdriver in positive, acceptably fair social engineering.

How do I think I can make a social screwdriver? I start with defining a collective goal we think we should pursue. In the specific case of my research on energy it is the transition to renewable sources. I nail down my observation of achievement, regarding that goal, with a simple metric, such as e.g. the percentage of renewables in total energy consumed (https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS ) or in total electricity produced (https://data.worldbank.org/indicator/EG.ELC.RNEW.ZS ). I place that metric in the context of other socio-economic variables, such as GDP per capita, average hours worked per person per year etc. At this point, I make an important assumption as regards the meaning of all the variables I use. I assume that if a lot of humans go to great lengths in measuring something and reporting those measurements, it must be important stuff. I know, sounds simplistic, yet it is fundamental. I assume that quantitative variables used in social sciences represent important aspects of social life, which we do our best to observe and understand. Importance translates as significant connection to the outcomes of our actions.

Quantitative variables which we use in social sciences represent collectively acknowledged outcomes of our collective action. They inform about something we consistently care about, as a society, and, at the same time, something we recurrently produce, as a society. An array of quantitative socio-economic variables represents an imperfect, and yet consistently construed representation of complex social reality.

We essentially care about change. Both individual human nervous systems, and whole cultures, are incredibly accommodative. When we stay in a really strange state long enough to develop adaptive habits, that strange state becomes normal. We pay attention to things that change, whence a further hypothesis of mine that quantitative socio-economic variables, even if arithmetically they are local stationary states, serve us to apprehend gradients of change, at the level of collective, communicable cognition.

If many different variables I study serve to represent, imperfectly but consistently, the process of change in social reality, they might zoom on the right thing with various degrees of accuracy. Some of them reflect better the kind of change that is really important for us, collectively, whilst some others are just sort of accurate in representing those collectively pursed outcomes. An important assumption pops its head from between the lines of my writing: the bridging between pursued outcomes and important change. We pay attention to change, and some types of change are more important to us than others. Those particularly important changes are, I think, the outcomes we are after. We pay the most attention, both individually and collectively, to phenomena which bring us payoffs, or, conversely, which seriously hamper such payoffs. This is, once again on my path of research, a salute to the Interface Theory of Perception (Hoffman et al. 2015[2]; Fields et al. 2018[3]).

Now, the question is: how to extract orientations, i.e. objectively pursued collective outcomes, from that array of apparently important, structured observations of what is happening to our society? One possible method consists in observing trends and variance over time, and this is what I had very largely done, up to a moment, and what I always do now, with a fresh dataset, as a way of data mining. In this approach, I generally assume that a combination of relatively strong variance with strong correlation to the remaining metrics, makes a particular variable likely to be the driving undertow of the whole social reality represented by the dataset at hand.

Still, there is another method, which I focus on in my research, and which consists in treating the empirical dataset as a complex and imperfect representation of the way that collectively intelligent social structures learn by experimenting with many alternative versions of themselves. That general hypothesis leads to building supervised, purposefully biased experiments with that data. Each experiment consists in running the dataset through a specifically skewed neural network – a perceptron – where one variable from the dataset is the output which the perceptron strives to optimize, and the remaining variables make the complex input instrumental to that end. Therefore, each such experiment simulates an artificial situation when one variable is the desired and collectively pursued outcome, with other variables representing gradients of change subservient to that chief value.

When I run such experiments with any dataset, I create as many transformed datasets as there are variables in the game. Both for the original dataset, and for the transformed ones, I can calculate the mean value of each variable, thus construing a vector of mean expected values, and, according to classical statistics, such a vector is representative for the expected state of the dataset in question. I end up with both the original dataset and the transformed ones being tied to the corresponding vectors of mean expected values. It is easy to estimate the Euclidean distance between those vectors, and thus to assess the relative mathematical resemblance between the underlying datasets. Here comes something I discovered more than assumed: those Euclidean distances are very disparate, and some of them are one or two orders of magnitude smaller than all the rest. In other words, some among all the supervised experiments done yield a simulated state of social reality much more similar to the original, empirical one than all the other experiments. This is the methodological discovery which underpins my whole research in this article, and which emerged as pure coincidence, when I was working on a revised version of another paper, titled ‘Energy efficiency as manifestation of collective intelligence in human societies’, which I published with the journal ‘Energy’(https://doi.org/10.1016/j.energy.2019.116500 ).

My guess from there was – and still is – that those supervised experiments have disparate capacity to represent the social reality I study with the given dataset. Experiments which yield mathematical transformations relatively the most similar to the original set of empirical numbers are probably the most representative. Once again, the mathematical structure of the perceptron used in all those experiments is rigorously the same, and what makes the difference is the focus on one particular variable as the output to optimize. In other words, some among the variables studied represent much more plausible collective outputs than others.

I feel a bit lost in my own thinking. Good. It means I have generated enough loose thoughts to put some order in them. It would be much worse if I didn’t have thoughts to put order in. Productive chaos is better than sterile emptiness. Anyway, the reproducible method I want to present and validate in my article ‘Climbing the right hill – an evolutionary approach to the European market of electricity’ aims at discovering the collectively pursued social outcomes, which, in turn, are assumed to be the key drivers of social change, and the path to that discovery leads through the hypothesis that such outcomes are equivalent to specific a gradient of change, which we collectively pay particular attention to in the complex social reality, imperfectly represented with an array of quantitative socio-economic variables. The methodological discovery which I bring forth in that reproducible method is that when any dataset of quantitative socio-economic variables is being transformed, with a perceptron, into as many single-variable-optimizing transformations as there are variables in the set, 1 ÷ 3 among those transformations are mathematically much more similar to the original set of observations that all the other thus transformed sets. Consequently, in this method, it is expected to find 1 ÷ 3 variables which represent – much more plausibly than others – the possible orientations, i.e. the collectively pursued outcomes of the society I study with the given empirical dataset.

Ouff! I have finally spat it out. It took some time. The idea needed to ripe, intellectually. As it is ripe, I can harvest.


[1] Rosales-Asensio, E., de la Puente-Gil, Á., García-Moya, F. J., Blanes-Peiró, J., & de Simón-Martín, M. (2020). Decision-making tools for sustainable planning and conceptual framework for the energy–water–food nexus. Energy Reports, 6, 4-15. https://doi.org/10.1016/j.egyr.2020.08.020

[2] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[3] Fields, C., Hoffman, D. D., Prakash, C., & Singh, M. (2018). Conscious agent networks: Formal analysis and application to cognition. Cognitive Systems Research, 47, 186-213. https://doi.org/10.1016/j.cogsys.2017.10.003

Still some juice in facts

I am working on improving my manuscript titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, after it received an amicable rejection from the journal Applied Energy, and, in the same time, I am working on other stuff. As usually. Some of that other staff is a completely new method of teaching in the summer semester, sort of a gentle revolution, with glorious prospects ahead, and without guillotines (well, not always).

As for the manuscript, I intend to work in three phases. I restate and reformulate the main lines of the article, and this is phase one. I pass in review the freshest literature in energy economics, as well as in the applications of artificial neural networks therein, and this is phase two. Finally, in phase three, I plan to position my method and my findings vis a vis that latest research.

I start phase one. When I want to understand what I wrote about 1 year ago, it is very nearly as if I was trying to understand what someone else wrote. Yes, I function like that. I have pretty good long-term memory, and it is because I learnt to detach emotions from old stuff. I sort of archive my old thoughts in order to make room for the always slightly disquieting waterfall of new thoughts. I need to dig and unearth my past meaning. I use the technique of reverse reading to achieve that. I read written content from its end back upstream to its beginning, and I go back upstream at two levels of structure: the whole piece of text, and individual sentences. In practical terms, when I work with that manuscript of mine, I take the last paragraph of the conclusion, and I actively write it backwards word-wise (I keep proper names unchanged). See by yourself.

This is the original last paragraph: ‘What if economic systems, inclusive of their technological change, optimized themselves so as to satisfy a certain workstyle? The thought seems incongruous, and yet Adam Smith noticed that division of labour, hence the way we work, shapes the way we structure our society. Can we hypothesise that technological change we are witnessing is, most of all, a collectively intelligent adaptation in the view of making a growing mass of humans work in ways they collectively like working? That would revert the Marxist logic, still, the report by World Bank, cited in the beginning of the article, allows such an intellectual adventure. On the path to clarify the concept, it is useful to define the meaning of collective intelligence’.

Now, I write it backwards: ‘Intelligence collective of meaning the define to useful is it concept the clarify to path the on adventure intellectual an such allows article the of beginning the in cited World Bank by report the still logic Marxist the revert that would that. Working like collectively they ways in work humans of mass growing a making view of the in adaptation intelligent collectively a all of most is witnessing are we change technological that hypothesise we can? Society our structure we way the shapes work we way the hence labour of division that noticed Adam Smith yet and incongruous seems thought the workstyle certain a satisfy to as so themselves optimized change technological their of inclusive systems economic if what?

Strange? Certainly, it is strange, as it is information with its pants on its head, and this is precisely why it is informative. The paper is about the market of energy, and my last paragraph of conclusions is about the market of labour, and its connection to the market of energy.

I go further upstream in my writing. The before-last paragraph of conclusions goes like: ‘Since David Ricardo, all the way through the works of Karl Marks, John Maynard Keynes, and those of Kuznets, economic sciences seem to be treating the labour market as easily transformable in response to an otherwise exogenous technological change. It is the assumption that technological change brings greater a productivity, and technology has the capacity to bend social structures. In this view, work means executing instructions coming from the management of business structures. In other words, human labour is supposed to be subservient and executive in relation to technological change. Still, the interaction between technology and society seems to be mutual, rather than unidirectional (Mumford 1964, McKenzie 1984, Kline and Pinch 1996; David 1990, Vincenti 1994). The relation between technological change and the labour market can be restated in the opposite direction. There is a body of literature, which perceives society as an organism, and social change is seen as complex metabolic adaptation of that organism. This channel of research is applied, for example, in order to apprehend energy efficiency of national economies. The so-called MuSIASEM model is an example of that approach, claiming that complex economic and technological change, including transformations in the labour market, can be seen as a collectively intelligent change towards optimal use of energy (see for example: Andreoni 2017 op. cit.; Velasco-Fernández et al 2018 op. cit.). Work can be seen as fundamental human activity, crucial for the management of energy in human societies. The amount of work we perform creates the need for a certain caloric intake, in the form of food, which, in turn, shapes the economic system around, so as to produce that food. This is a looped adaptation, as, on the long run, the system supposed to feed humans at work relies on this very work’.

Here is what comes from reverted writing of mine: ‘Work very this on relies work at humans feed to supposed system the run long the on as adaptation looped a is this food that produce to around system economic the shapes turn in which food of form the in intake caloric certain a for need the creates perform we work of amount the societies human in energy of management the for crucial activity human fundamental as seen be can work. Energy of use optimal towards change intelligent collectively a as seen be can market labour the in transformations including change technological and economic complex that claiming approach that of example an is model MuSIASEM called so the economies national of efficiency energy apprehend to order in example for applied is research of channel this. Organism that of adaptation metabolic complex as seen is change social and organism an as society perceives which literature of body a is there. Direction opposite the in restated be can market labour the and change technological between relation the. Unidirectional than rather mutual be to seems society and technology between interaction the still. Change technological to relation in executive and subservient be to supposed is labour human words other in. Structures social bend to capacity the has technology and productivity a greater brings change technological that assumption the is it. Change technological exogenous otherwise an to response in transformable easily as market labour the treating ne to seem sciences economic Kuznets of those and Keynes […], Marks […] of works the through way the all Ricardo […]’.

Good. I speed up. I am going back upstream through consecutive paragraphs of my manuscript. The chain of 35 ideas which I write here below corresponds to the reverted logical structure (i.e. from the end backstream to the beginning) of my manuscript. Here I go. Ideas listed below have numbers corresponding to their place in the manuscript. The higher the number, the later in the text the given idea is phrased out for the first time.

>> Idea 35: The market of labour, i.e. the way we organize for working, determines the way we use energy.

>> Idea 34: The way we work shapes technological change more than vice versa. Technologies and workstyles interact

>> Idea 33: The labour market offsets the loss of jobs in some sectors by the creation of jobs in other sectors, and thus the labour market accommodates the emergent technological change.

>> Idea 32: The basket of technologies we use determines the ways we use energy. work in itself is human effort, and that effort is functionally connected to the energy base of our society

>> Idea 31: Digital technologies seem to have a special function in mediating the connection between technological change and the labour market

>> Idea 30: the number of hours worked per person per year (AVH), the share of labour in the GNI (LABSH), and the indicator of human capital (HC) seem to make an axis of social change, both as input and as output of the collectively intelligent structure.

>> Idea 29: The price index in exports (PL_X) comes as the chief collective goal pursued, and the share of public expenditures in the Gross National Income (CSH_G) appears as the main epistatic driver in that pursuit.

>> Idea 28: The methodological novelty of the article consists in using the capacity of a neural network to produce many variations of itself, and thus to perform evolutionary adaptive walk in rugged landscape.

>> Idea 27: The here-presented methodology assumes: a) tacit coordination b) evolutionary adaptive walk in rugged landscape c) collective intelligence d) observable socio-economic variables are manifestations of the past, coordinated decisions.

>> Idea 26: Variance observable in the average Euclidean distances that each variable has with the remaining 48 ones reflects the capacity of each variable to enter into epistatic interactions with other variables, as the social system studied climbs different hills, i.e. pursues different outcomes to optimize.

>> Idea 25: Coherence: across 48 sets Si out of the 49 generated with the neural network, variances in Euclidean distances between variables are quite even. Only one set Si yields different variances, namely the one pegged on the coefficient of patent applications per 1 million people.

>> Idea 24: the order of phenomenal occurrences in the set X does not have a significant influence on the outcomes of learning.

>> Idea 23: results of multiple linear regression of natural logarithms in the variables observed is compared to the application of an artificial neural network with the same dataset – to pass in review and to rework – lots of meaning there.

>> Idea 22: the phenomena assumed to be a disturbance, i.e. the discrepancy in retail prices of electricity, as well as the resulting aggregate cash flow, are strongly correlated with many other variables in the dataset. Perhaps the most puzzling is their significant correlation with the absolute number of resident patent applications, and with its coefficient denominated per million of inhabitants. Apparently, the more patent applications in the system, the deeper is that market imperfection.

>> Idea 21: Another puzzling correlation of these variables is the negative one with the variable AVH, or the number of hours worked per person per year. The more an average person works per year, in the given country and year, the less likely this local market is to display harmful differences in the retail prices of electricity for households.

>> Idea 20: On the other hand, variables which we wish to see as systemic – the share of electricity in energy consumption and the share of renewables in the output of electricity – have surprisingly few significant correlations in the dataset studied, just as if they were exogenous stressors with little foothold in the market as for yet. 

>> Idea 19: None of the four key variables regarding the European market of energy: a) the price fork in the retail market of electricity (€) b) the capital value of cash flow resulting from that price fork (€ mln) c) the share of electricity in energy consumption (%) and d) the share of renewables in electricity output (%)seems having been generated by a ‘regular’ Gaussian process: they all produce definitely too much outliers for a Gaussian process to be the case.

>> Idea 18: other variables in the dataset, the ‘regulars’ such as GDP or price levels, seem to be distributed quite close to normal, and Gaussian processes can be assumed to work in the background. This is a typical context for evolutionary adaptive walk in rugged landscape. An otherwise stable socio-economic environment gets disturbed by changes in the energy base of the society living in the whereabouts. As new stressors (e.g. the need to switch to electricity, from the direct combustion of fossil fuels) come into the game, some ‘mutant’ social entities stick out of the lot and stimulate an adaptive walk uphill.

>> Idea 17: The formal test of Euclidean distances, according to equation (1), yields a hierarchy of alternative sets Si, as for their similarity to the source empirical set X of m= 300 observations. This hierarchy represents the relative importance of variables, which each corresponding set Si is pegged on.

>> Idea 16: The comparative set XR has been created as a sequence of 10 stacked, pseudo-random permutations of the original set X has been created as one database. Each permutation consists in sorting the records of the original set X according to a pseudo-random index variable. The resulting set covers m = 3000 phenomenal occurrences.

>> Idea 15: The underlying assumption as regards the collective intelligence of that set is that each country learns separately over the time frame of observation (2008 – 2017), and once one country develops some learning, that experience is being taken and reframed by the next country etc. 

>> Idea 14: we have a market of energy with goals to meet, regarding the local energy mix, and with a significant disturbance in the form of market imperfections

>> Idea 13: special focus on two variables, which the author perceives as crucial for tackling climate change: a) the share of renewable energy in the total output of electricity, and b) the share of electricity in the total consumption of energy.

>> Idea 12: A est for robustness, possible to apply together with this method, is based on a category of algorithms called ‘random forest’

>> Idea 11: The vector of variances in the xi-specific fitness function V[xi(pj)] across the n sets Si has another methodological role to play: it can serve to assess the interpretative robustness of the whole complex model. If, across neural networks oriented on different outcome variables, the given input variable xi displays a pretty uniform variance in its fitness function V[xi(pj)], the collective intelligence represented in equations (2) – (5) performs its adaptive walk in rugged landscape coherently across all the different hills considered to walk up. Conversely, should all or most variables xi, across different sets Si, display noticeably disparate variances in V[xi(pj)], the network represents a collective intelligence which adapts in a clearly different manner to each specific outcome (i.e. output variable).

>> Idea 10: the mathematical model for this research is composed of 5 main equations, which, in the same time, make the logical structure of the artificial neural network used for treating empirical data. That structure entails: a) a measure of mathematical similarity between numerical representations of collectively intelligent structure b) the expected state of intelligent structure reverse engineered from the behaviour of the neural network c) neural activation and the error of observation, the latter being material for learning by measurable failure, for the collectively intelligent structure d) transformation of multi-variate empirical data into one number fed into the neural activation function e) a measure of internal coherence in the collectively intelligent structure

>> Idea 9: the more complexity, the more is the hyperbolic tangent, based on the expression e2h, driven away from its constant root e2. Complexity in variables induces greater swings in the hyperbolic tangent, i.e. greater magnitudes of error, and, consequently, longer strides in the process of learning.

>> Idea 8: Each congruent set Si is produced with the same logical structure of the neural network, i.e. with the same procedure of estimating the value of output variable, valuing the error of estimation, and feeding the error forward into consecutive experimental rounds. This, in turn, represents a hypothetical state of nature, where the social system represented with the set X is oriented on optimizing the given variable xi, which the corresponding set Si is pegged on as its output.

>> Idea 7: complex entities can internalize an external stressor as they perform their adaptive walk. Therefore, observable variance in each variable xi in the set X can be considered as manifestation of such internalization. In other words, observable change in each separate variable can result from the adaptation of social entities observed to some kind of ‘survival imperative’.

>> Idea 6: hypothesis that collectively intelligent adaptation in human societies, regarding the ways of generating and using energy, is instrumental to the optimization of other social traits.    

>> Idea 5: Adaptive walks in rugged landscape consist in overcoming environmental challenges in a process comparable to climbing a hill: it is both an effort and a learning, where each step sets a finite range of possibilities for the next step.

>> Idea 4: the MuSIASEM methodological framework – aggregate use of energy in an economy can be studied as a metabolic process

>> Idea 3: human societies are collectively intelligent about the ways of generating and using energy: each social entity (country, city, region etc.) displays a set of characteristics in that respect

>> Idea 2: adaptive walk of a collective intelligence happens in a very rugged landscape, and the ruggedness of that landscape comes from the complexity of human societies

>> Idea 1: Collective intelligence occurs even in animals as simple neurologically as bees, or even as the Toxo parasite. Collective intelligence means shifting between different levels of coordination.

As I look at that thing, namely at what I wrote something like one year ago, I have a doubly recomforting feeling. The article seems to make sense from the end to the beginning, and from the beginning to the end. Both logical streams seem coherent and interesting, whilst being slightly different in their intellectual melody. This is the first comfortable feeling. The second is that I have still some meaning, and, therefore, some possible truth, to unearth out of my empirical findings, and this is always a good thing. In science, the view of empirical findings squeezed out of the last bit of meaning and yet still standing as something potentially significant is one of the saddest perspectives one can have. Here, there is still some juice in facts. Good.  

Second-hand observations

MY EDITORIAL ON YOU TUBE

I keep reviewing, upon the request of the International Journal of Energy Sector Management (ISSN1750-6220), a manuscript entitled ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’. I have already formulated my first observations on that paper in the last update: I followed my suspects home, where I mostly focused on studying the theoretical premises of the model used in the paper under review, or rather of a model used in another article, which the paper under review heavily refers to.

As I go through that initial attempt to review this manuscript, I see I was bitching a lot, and this is not nice. I deeply believe in the power of eristic dialogue, and I think that being artful in verbal dispute is different from being destructive. I want to step into the shoes of those authors, technically anonymous to me (although I can guess who they are by their bibliographical references), who wrote ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’. When I write a scientific paper, my conclusion is essentially what I wanted to say from the very beginning, I just didn’t know how to phrase that s**t out. All the rest, i.e. introduction, mathematical modelling, empirical research – it all serves as a set of strings (duct tape?), which help me attach my thinking to other people’s thinking.

I assume that people who wrote ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ are like me. Risky but sensible, as assumptions come. I start from the conclusion of their paper, and I am going to follow upstream. When I follow upstream, I mean business. It is not just about going upstream the chain of paragraphs: it is going upstream the flow of language itself. I am going to present you a technique I use frequently when I really want to extract meaning and inspiration from a piece of writing. I split that writing into short paragraphs, like no more than 10 lines each. I rewrite each such paragraph in inverted syntax, i.e. I rewrite from the last word back to the first one. It gives something like Master Yoda speaking: bullshit avoid shall you. I discovered by myself, and this is backed by the science of generative grammar, that good writing, when read backwards, uncovers something like a second layer of meaning, and that second layer is just as important as the superficial one.

I remember having applied this method to a piece of writing by Deepak Chopra. It was almost wonderful how completely meaningless that text was when read backwards. There was no second layer. Amazing.

Anyway, now I am going to demonstrate the application of that method to the conclusion of ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’. I present paragraphs of the original text in Times New Roman Italic. I rewrite the same paragraphs in inverted syntax with Times New Roman Bold. Under each such pair ‘original paragraph + inverted syntax’ I write my second-hand observations inspired by those two layers of meaning, and those observations of mine come in plain Times New Roman.

Let’s dance.

Original text: Although increasing the investment in energy reduction can effectively improve the environmental quality; in a relatively short period of time, the improvement of environmental quality is also very obvious; but in the long run, the the influence of the system (1). In this study, the energy, economic and environmental (3E) four-dimensional system model of energy conservation constraints was first established. The Bayesian estimation method is used to correct the environmental quality variables to obtain the environmental quality data needed for the research.

Inverted syntax: Research the for needed data quality environmental the obtain to variables quality environmental the correct to used is method estimation Bayesian the established first was constraints conservation energy of model system dimensional four 3E environmental and economic energy the study this in system the of influence run long the in but obvious very also is quality environmental of improvement the time of period short relatively a in quality environmental the improve effectively can reduction energy in investment the increasing although.

Second-hand observations: The essential logic of using Bayesian methodology is to reduce uncertainty in an otherwise weakly studied field, and to set essential points for orientation. A Bayesian function essentially divides reality into parts, which correspond to, respectively, success and failure.

It is interesting that traits of reality which we see as important – energy, economy and environmental quality – can be interpreted as dimensions of said reality. It corresponds to the Interface Theory of Perception (ITP): it pays off to build a representation of reality based on what we want rather than on what we perceive as absolute truth.    

Original text: In addition, based on the Chinese statistical yearbook data, the Levenberg-Marquardt BP neural network method optimized by genetic algorithm is used to energy, economy and environment under energy conservation constraints. The parameters in the four-dimensional system model are effectively identified. Finally, the system science analysis theory and environment is still deteriorating with the decline in the discount rate of energy reduction inputs.

Inverted syntax: Inputs reduction energy of rate discount the in decline the with deteriorating still is environment and theory analysis science system the finally identified effectively are model system dimensional four the in parameters the constraints conservation energy under environment and economy energy to used is algorithm genetic by optimized method network neural Levenberg-Marquardt Backpropagation the data yearbook statistical Chinese the on based addition in.

Second-hand observations: The strictly empirical part of the article is relatively the least meaningful. The Levenberg-Marquardt BP neural network is made for quick optimization. It is essentially the method of Ordinary Least Squares transformed into a heuristic algorithm, and it can optimize very nearly anything. When using the Levenberg-Marquardt BP neural network we risk overfitting (i.e. hasty conclusions based on a sequence of successes) rather than the inability to optimize. It is almost obvious that – when trained and optimized with a genetic algorithm – the network can set such values in the model which allow stability. It simply means that the model has a set of values that virtually eliminate the need for adjustment between particular arguments, i.e. that the model is mathematically sound. On the other hand, it would be intellectually risky to attach too much importance to the specific values generated by the network. Remark: under the concept of ‘argument’ in the model I mean mathematical expressions of the type: [coefficient]*[parameter]*[local value in variable].

The article conveys an important thesis, namely that the rate of return on investment in environmental improvement is important for sustaining long-term commitment to such improvement.  

Original text: It can be better explained that effective control of the peak arrival time of pollution emissions can be used as an important decision for pollution emission control and energy intensity reduction; Therefore, how to effectively and reasonably control the peak of pollution emissions is of great significance for controlling the stability of Energy, Economy and Environment system under the constraint of energy reduction, regulating energy intensity, improving environmental quality and sustainable development.

Inverted syntax: Development sustainable and quality environmental improving intensity energy regulating reduction energy of constraint the under system environment and economy energy of stability the controlling for significance great of is emissions pollution of peak the control reasonably and effectively to how therefore reduction intensity energy and control emission pollution for decision important an as used be can emissions pollution of time arrival peak the of control effective that explained better be can.

Second-hand observations: This is an interesting logic: we can control the stability of a system by controlling the occurrence of peak states. Incidentally, it is the same logic as that used during the COVID-19 pandemic. If we can control the way that s**t unfolds up to its climax, and if we can make that climax somewhat less steep, we have an overall better control over the whole system.

Original text: As the environmental capacity decreases, over time, the evolution of environmental quality shows an upward trend of fluctuations and fluctuations around a certain central value; when the capacity of the ecosystem falls to the limit, the system collapses. In order to improve the quality of the ecological environment and promote the rapid development of the economy, we need more measures to use more means and technologies to promote stable economic growth and management of the ecological environment.

Inverted syntax: Environment ecological the of management and growth economic stable promote to technologies and means more use to measures more need we economy the of development rapid the promote and environment ecological the of quality the improve to order in collapse system the limit the to falls ecosystem the of capacity the when value central a around fluctuations and fluctuations of trend upward an shows quality environmental of evolution the time over decreases capacity environmental the as.    

Second-hand observations: We can see more of the same logic: controlling a system means avoiding extreme states and staying in a zone of proximal development. As the system reaches the frontier of its capacity, fluctuations amplify and start drawing an upward drift. We don’t want such a drift. The system is the most steerable when it stays in a somewhat mean-reverted state.  

I am summing up that little exercise of style. The authors of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’ claim that relations between economy, energy and environment are a complex, self-regulating system, yet the capacity of that system to self-regulate is relatively the most pronounced in some sort of central expected states thereof, and fades as the system moves towards peak states. According to this logic, our relations with ecosystems are always somewhere between homeostasis and critical disaster, and those relations are the most manageable when closer to homeostasis. A predictable, and economically attractive rate of return in investments that contribute to energy savings seems to be an important component of that homeostasis.

The claim in itself is interesting and very largely common-sense, although it goes against some views, that e.g. in order to take care of the ecosystem we should forego economics. Rephrased in business terms, the main thesis of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’ is that we can manage that dynamic system as long as it involves project management much more than crisis-management. When the latter prevails, things get out of hand. The real intellectual rabbit hole starts when one considers the method of proving the veracity of that general thesis.  The authors build a model of non-linear connections between volume of pollution, real economic output, environmental quality, and constraint on energy reduction. Non-linear connections mean that output variables of the model –  on the left side of each equation – are rates of change over time in each of the four variables. Output variables in the model are strongly linked, via attractor-like mathematical arguments on the input side, i.e. arguments which combine coefficients strictly speaking with standardized distance from pre-defined peak values in pollution, real economic output, environmental quality, and constraint on energy reduction. In simpler words, the theoretical model built by the authors of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’ resembles a spiderweb. It has distant points of attachment, i.e. the peak values, and oscillates between them.

It is interesting how this model demonstrates the cognitive limitations of mathematics. If we are interested in controlling relations between energy, economy, and environment, our first, intuitive approach is to consider these desired outcomes as dimensions of our reality. Yet, those dimensions could be different. If I want to become a top-level basketball player, I does not necessarily mean that social reality is truly pegged on a vector of becoming-a-top-level-basketball-player. Social mechanics might be structured around completely different variables. Still, with strong commitment, this particular strategy might work. Truth is not the same as payoffs from our actions. A model of relations energy-economy-environment pegged on desired outcomes in these fields might be essentially false in ontological terms, yet workable as a tool for policy-making. This approach is validated by the Interface Theory of Perception (see, e.g. Hoffman et al. 2015[1] and Fields et al. 2018[2]).

From the formal-mathematical point of view, the model is construed as a square matrix of complex arguments, i.e. the number of arguments on the left, input side of each equation is the same as the number of equations, whence the possibility to make a Jacobian matrix thereof, and to calculate its eigenvalues. The authors preset the coefficients of the model, and the peak-values so as to test for stability. Testing the model with those preset values demonstrates an essential lack of stability in the such-represented system. Stability is further tested by studying the evolution trajectory of the system. The method of evolution trajectory, in this context, seems referring to life sciences and the concept of phenotypic trajectory (see e.g. Michael & Dean 2013[3]), and shows that the system, such as modelled, is unstable. Its evolution trajectory can change in an irregular and chaotic way.

In a next step, the authors test their model with empirical data regarding China between 2000 and 2017. They use a Levenberg–Marquardt Backpropagation Network in order to find the parameters of the system. With thus-computed parameters, and the initial state of the system set on data from 1980, evolution trajectory of the system proves stable, in a multi cycle mode.

Now, as I have passed in review the logic of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’, I start bitching again, i.e. I point at what I perceive as, respectively, strengths and weaknesses of the manuscript. After reading and rereading the paper, I come to the conclusion that the most valuable part thereof is precisely the use of evolution trajectory as theoretical vessel. The value added I can see here consists in representing something complex that we want – we want our ecosystem not to kill us (immediately) and we want our smartphones and our power plants working as well – in a mathematical form, which can be further processed along the lines of evolution trajectory.

That inventive, intellectually far-reaching approach is accompanied, however, by several weaknesses. Firstly, it is an elaborate path leading to common-sense conclusions, namely that managing our relations with the ecosystem is functional as long as it takes the form of economically sound project management, rather than crisis management. The manuscript seems to be more of a spectacular demonstration of method rather than discovery in substance.

Secondly, the model, such as is presented in the manuscript, is practically impossible to decipher without referring to the article Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019, https://doi.org/10.1155/2019/3941920 . When I say ‘impossible’, it means that the four equations of the model under review are completely cryptic, as the authors do not explain their mathematical notation at all, and one has to look into this Zhao, L., & Otoo, C. O. A. (2019) paper in order to make any sense of it.

After cross-referencing those two papers and the two models, I obtain quite a blurry picture. In this Zhao, L., & Otoo, C. O. A. (2019) we have  a complex, self-regulating system made of 3 variables: volume of pollution x(t), real economic output y(t), and environmental quality z(t). The system goes through an economic cycle of k periods, and inside the cycle those three variables reach their respective local maxima and combine into complex apex states. These states are: F = maxk[x(t)], E = maxk[y(t)], H = maxk(∆z/∆y) – or the peak value of the impact of economic growth 𝑦(𝑡) on environmental quality 𝑧(𝑡) –  and P stands for absolute maximum of pollution possible to absorb by the ecosystem, thus something like P = max(F). With those assumptions in mind, the original model by Zhao, L., & Otoo, C. O. A. (2019), which, for the sake of presentational convenience I will further designate as Model #1, goes like:  

d(x)/d(t) = a1*x*[1 – (x/F)] + a2*y*[1 – (y/E)] – a3*z

d(y)/d(t) = -b1*x – b2*y – b3*z

d(z)/d(t) = -c1*x + c2*y*[1 – (y/H)] + c3*z*[(x/P) – 1]

The authors of ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ present a different model, which they introduce as an extension of that by Zhao, L., & Otoo, C. O. A. (2019). They introduce a 4th variable, namely energy reduction constraints designated as w(t). There is no single word as for what does it exactly mean. The first moments over time of, respectively, x(t), y(t), z(t), and w(t) play out as in Model #2:

d(x)/d(t)= a1*x*[(y/M) – 1] – a2*y + a3*z + a4w

d(y)/d(t) = -b1*x + b2*y*[1 – (y/F)] + b3*z*[1 – (z/E)] – b4*w

d(z)/d(t) = c1*x*[(x/N) – 1] – c2*y – c3*z – c4*w

d(w)/d(t) = d1*x – d2*y + d3*z*[1 – (z/H)] + d4*w*[(y/P) – 1]

No, I have a problem. When the authors of ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ present their Model #2 as a simple extension of Model #1, this is simply not true. First of all, Model #2 contains two additional parameters, namely M and N, which are not explained at all in the paper. They are supposed to be normal numbers and that’s it. If I follow bona fide the logic of Model #1, M and N should be some kind of maxima, simple or compound. It is getting interesting. I have a model with two unknown maxima and 4 known ones, and I am supposed to understand the theory behind. Cool. I like puzzles.

The parameter N is used in the expression [(x/N) – 1]. We divide the local volume of pollution x by N, thus we do x/N, and this is supposed to mean something. If we keep any logic at all, N should be a maximum of pollution, yet we already have two maxima of pollution, namely F and P. As for M, it is used in the expression [(y/M) – 1] and therefore I assume M is a maximum state of real economic output ‘y’. Once again, we already have one maximum in that category, namely ‘E’. Apparently, the authors of Model #2 assume that the volume of pollution x(t) can have three different, meaningful maxima, whilst real economic output y(t) has two of them. I will go back to those maxima further below, when I discuss the so-called ‘attractor expressions’ contained in Model #2.

Second of all, Model #2 presents a very different logic than Model #1. Arbitrary signs of coefficients ai, bi, ci and di are different, i.e. minuses replace pluses and vice versa. Attractor expressions of the type [(a/b) – 1] or [1 – (a/b)] are different, too. I am going to stop by these ones a bit longer, as it is important regarding the methodology of science in general. When I dress a hypothesis like y = a*x1 + b*x2, coefficients ‘a’ and ‘b’ are neutral in the sense that if x1 > 0, then a*x1 > 0 as well etc. In other words, positive coefficients ‘a’ and ‘b’ do not imply anything about the relation between y, x1, and x2.

On the other hand, when I say y = -a*x1 + b*x2, it is different. Instead of having a coefficient ‘a’, I have a coefficient ‘-a’, thus opposite to ‘y’. If x1 > 0, then a*x1 < 0 and vice versa. By assigning a negative coefficient to phenomenon x, I assume it works as a contrary force to phenomenon y. A negative coefficient is already a strong assumption. As I go through all the arbitrarily negative coefficients in Model #2, I can see the following strong claims:

>>> Assumption 1: the rate of change in the volume of pollution d(x)/d(t) is inversely proportional to the real economic output y.

>>> Assumption 2: the rate of change in real economic output d(y)/d(t) is inversely proportional to the volume of pollution x

>>> Assumption 3: the rate of change in real economic output d(y)/d(t) is inversely proportional to energy reduction constraints w.

>>> Assumption 4: the rate of change in environmental quality d(z)/d(t) is inversely proportional to environmental quality z.

>>> Assumption 5: the rate of change in environmental quality d(z)/d(t) is inversely proportional to real economic output y.

>>> Assumption 6: the rate of change in environmental quality d(z)/d(t) is inversely proportional to the volume of pollution x.

>>> Assumption 7: the rate of change in energy reduction constraints d(w)/d(t) is inversely proportional to real economic output y.

These assumptions would greatly benefit from theoretical discussion, as some of them, e.g. Assumption 1 and 2, seem counterintuitive.

Empirical data presented in ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ is probably the true soft belly of the whole logical structure unfolding in the manuscript. Authors present that data in the standardized form, as constant-base indexes, where values from 1999 are equal to 1. In Table 1 below, I present those standardized values:

Table 1

YearX – volume of pollutionY – real economic outputZ – environmental qualityW – energy reduction constraints
20001,96261,04551,10850,9837
20013,67861,10661,22280,9595
20022,27911,20641,34820,9347
20031,26991,4021,52830,8747
20042,30331,63821,80620,7741
20051,93521,85942,08130,7242
20062,07782,0382,45090,6403
20073,94372,21563,03070,5455
20086,55832,28083,59760,4752
20093,2832,39123,89970,4061
20103,33072,56564,6020,3693
20113,68712,75345,42430,3565
20124,0132,86086,03260,321
20133,92742,96596,60680,2817
20144,21673,02927,21510,2575
20154,28933,05837,68130,2322
20164,59253,10048,28720,2159
20174,61213,19429,22970,2147

I found several problems with that data, and they sum up to one conclusion: it is practically impossible to check its veracity. The time series of real economic output seem to correspond to some kind of constant-price measurement of aggregate GDP of China, yet it does not fit the corresponding time series published by the World Bank (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD ). Metrics such as ‘environmental quality’ (x) or energy reduction constraints (w) are completely cryptic. Probably, they are some sort of compound indices, and their structure in itself requires explanation.

There seems to be a logical loop between the theoretical model presented in the beginning of the manuscript, and the way that data is presented. The model presents an important weakness as regards functional relations inside arguments based on peak values, such as ‘y/M’ or ‘y/P’. The authors very freely put metric tons of pollution in fractional relation with units of real output etc. This is theoretical freestyle, which might be justified, yet requires thorough explanation and references to literature. Given the form that data is presented under, a suspicion arises, namely that standardization, i.e. having driven all data to the same denomination, opened the door to those strange, cross-functional arguments. It is to remember that even standardized through common denomination, distinct phenomena remain distinct. A mathematical trick is not the same as ontological identity.

Validation of the model with a Levenberg–Marquardt Backpropagation Network raises doubts, as well. This specific network, prone to overfitting, is essentially a tool for quick optimization in a system which we otherwise thoroughly understand. This is the good old method of Ordinary Least Squares translated into a sequence of heuristic steps. The LM-BN network does not discover anything about the system at hand, it just optimizes it as quickly as possible.

In a larger perspective, using a neural network to validate a model implies an important assumption, namely that consecutive states of the system form a Markov chain, i.e. each consecutive state is exclusively the outcome of the preceding state. It is to remember that neural networks in general are artificial intelligence, and intelligence, in short, means that we figure out what to do when we have no clue as for what to do, and we do it without any providential, external guidelines. The model presented by the authors clearly pegs the system on hypothetical peak values. These are exogenous to all but one possible state of the system, whence a logical contradiction between the model and the method of its validation.

Good. After some praising and some bitching, I can assess the manuscript by answering standard questions asked by the editor of the International Journal of Energy Sector Management (ISSN1750-6220).

  1. Originality: Does the paper contain new and significant information adequate to justify publication?

The paper presents a methodological novelty, i.e. the use of evolution trajectory as a method to study complex social-environmental systems, and this novelty deserves being put in the spotlight even more than it is in the present version of the paper. Still, substantive conclusions of the paper do not seem original at all.  

  • Relationship to Literature: Does the paper demonstrate an adequate understanding of the relevant literature in the field and cite an appropriate range of literature sources? Is any significant work ignored?

The paper presents important weaknesses as for bibliographical referencing. First of all, there are clear theoretical gaps as regards the macroeconomic aspect of the model presented, and as regards the nature and proper interpretation of the empirical data used for validating the model. More abundant references in these two fields would be welcome, if not necessary.

Second of all, the model presented by the authors is practically impossible to understand formally without reading another paper, referenced is a case of exaggerate referencing. The paper should present its theory in a complete way.   

  • Methodology: Is the paper’s argument built on an appropriate base of theory, concepts, or other ideas? Has the research or equivalent intellectual work on which the paper is based been well designed? Are the methods employed appropriate?

The paper combines a very interesting methodological approach, i.e. the formulation of complex systems in a way that makes them treatable with the method of evolution trajectory, with clear methodological weaknesses. As for the latter, three main questions emerge. Firstly, it seems to be methodologically incorrect to construe the cross-functional attractor arguments, where distinct phenomena are denominated one over the other. Secondly, the use of LM-BN network as a tool for validating the model is highly dubious. This specific network is made for quick optimization of something we understand and not for discovery inside something we barely understand.

Thirdly, the use of a neural network of any kind implies assuming that consecutive states of the system form a Markov chain, which is logically impossible with exogenous peak-values preset in the model.

  • Results: Are results presented clearly and analysed appropriately? Do the conclusions adequately tie together the other elements of the paper?

The results are clear, yet their meaning seems not to be fully understood. Coefficients calculated via a neural network represent a plausibly possible state of the system. When the authors conclude that the results so-obtained, combined with the state of the system from the year 1980, it seems really stretched in terms of empirical inference.

  • Implications for research, practice and/or society: Does the paper identify clearly any implications for research, practice and/or society? Does the paper bridge the gap between theory and practice? How can the research be used in practice (economic and commercial impact), in teaching, to influence public policy, in research (contributing to the body of knowledge)? What is the impact upon society (influencing public attitudes, affecting quality of life)? Are these implications consistent with the findings and conclusions of the paper?

The paper presents more implications for research than for society. As stated before, substantive conclusions of the paper boil down to common-sense claims, i.e. that it is better to keep the system stable rather than unstable. On the other hand, some aspects of the method used, i.e. the application of evolutionary trajectory, seem being very promising for the future. The paper seems to create abundant, interesting openings for future research rather than practical applications for now.

  • Quality of Communication: Does the paper clearly express its case, measured against the technical language of the field and the expected knowledge of the journal’s readership? Has attention been paid to the clarity of expression and readability, such as sentence structure, jargon use, acronyms, etc.

The quality of communication is a serious weakness in this case. The above-mentioned exaggerate reference to Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019, https://doi.org/10.1155/2019/3941920 is one point. The flow of logic is another. When, for example, the authors suddenly claim (page 8, top): ‘In this study we set …’, there is no explanation why and on what theoretical grounds they do so.

Clarity and correct phrasing clearly lack as regards all the macroeconomic aspect of the paper. It is truly hard to understand what the authors mean by ‘economic growth’.

Finally, some sentences are clearly ungrammatical, e.g. (page 6, bottom): ‘By the system (1) can be launched energy intensity […].   

Good. Now, you can see what a scientific review looks like. I hope it was useful. Discover Social Sciences is a scientific blog, which I, Krzysztof Wasniewski, individually write and manage. If you enjoy the content I create, you can choose to support my work, with a symbolic $1, or whatever other amount you please, via MY PAYPAL ACCOUNT.  What you will contribute to will be almost exactly what you can read now. I have been blogging since 2017, and I think I have a pretty clearly rounded style.

In the bottom on the sidebar of the main page, you can access the archives of that blog, all the way back to August 2017. You can make yourself an idea how I work, what do I work on and how has my writing evolved. If you like social sciences served in this specific sauce, I will be grateful for your support to my research and writing.

‘Discover Social Sciences’ is a continuous endeavour and is mostly made of my personal energy and work. There are minor expenses, to cover the current costs of maintaining the website, or to collect data, yet I want to be honest: by supporting ‘Discover Social Sciences’, you will be mostly supporting my continuous stream of writing and online publishing. As you read through the stream of my updates on https://discoversocialsciences.com , you can see that I usually write 1 – 3 updates a week, and this is the pace of writing that you can expect from me.

Besides the continuous stream of writing which I provide to my readers, there are some more durable takeaways. One of them is an e-book which I published in 2017, ‘Capitalism And Political Power’. Normally, it is available with the publisher, the Scholar publishing house (https://scholar.com.pl/en/economics/1703-capitalism-and-political-power.html?search_query=Wasniewski&results=2 ). Via https://discoversocialsciences.com , you can download that e-book for free.

Another takeaway you can be interested in is ‘The Business Planning Calculator’, an Excel-based, simple tool for financial calculations needed when building a business plan.

Both the e-book and the calculator are available via links in the top right corner of the main page on https://discoversocialsciences.com .

You might be interested Virtual Summer Camps, as well. These are free, half-day summer camps will be a week-long, with enrichment-based classes in subjects like foreign languages, chess, theater, coding, Minecraft, how to be a detective, photography and more. These live, interactive classes will be taught by expert instructors vetted through Varsity Tutors’ platform. We already have 200 camps scheduled for the summer.   https://www.varsitytutors.com/virtual-summer-camps .


[1] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[2] Fields, C., Hoffman, D. D., Prakash, C., & Singh, M. (2018). Conscious agent networks: Formal analysis and application to cognition. Cognitive Systems Research, 47, 186-213. https://doi.org/10.1016/j.cogsys.2017.10.003

[3] Michael, L. C., & Dean, C. A. (2013). Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. https://doi.org/10.4404/hystrix-24.1-6298

Social roles and pathogens: our average civilisation

MY EDITORIAL ON YOU TUBE

I am starting this update with a bit of a winddown on my previous excitement, expressed in Demographic anomalies – the puzzle of urban density. I was excited about the apparently mind-blowing, negative correlation of ranks between the relative density of urban population, on the one hand, and the consumption of energy per capita, on the other hand. Apparently, the lower the rank of the {[DU/DG] [Density of urban population / General density of population]} coefficient, the greater the consumption of energy per capita. All in all, it is not as mysterious as I thought. It is visible, that the average value of the [DU/DG] coefficient decreases with the level of socio-economic development. In higher-middle income countries, and in the high-income ones, [DU/DG] stays consistently below 10, whilst in poor countries it can even flirt with values above 100. In other words, relatively greater a national wealth is associated with relatively smaller a social difference between cities and the countryside. Still, that shrinking difference seems to have a ceiling around [DU/DG] = 2,00. In the realm of [DU/DG] < 2,00, we do not really encounter wealthy countries. In this category we have tropical island states, or entities such as West Bank and Gaza, which are demographic anomalies even against the background of cities in general being demographic anomalies. Among really wealthy countries, the lowest values in the [DU/DG] coefficient are to find with Belgium (2,39) and Netherlands (2,30).

I am taking it from the beginning, ‘it’ being the issue of cities and urbanisation. The beginning was my bewilderment when the COVID-19-related lockdowns started in my country, i.e. in Poland. I remember cycling through the post-apocalyptically empty streets of my hometown, Krakow, Poland, I was turning in my mind the news, regarding the adverse economic outcomes of the lockdown, and strange questions were popping up in my consciousness. How many human footsteps per day does a city need to thrive? How many face-to-face interactions between people do we need, to keep that city working?

I had that sudden realization that city life is all about intensity of human interaction.  I reminded another realization, which I experienced in November 2017. I was on a plane that had just taken off from the giant Frankfurt airport. It was a short flight, to Lyon, France – almost like a ballistic curve – and this is probably why the plane was gathering altitude very gently. I could see the land beneath, and I marvelled at the slightly pulsating, intricate streaks of light, down there, on the ground. It took me a few minutes to realize that the lights I was admiring were those of vehicles trapped in the gargantuan traffic jams, typical for the whole region of Frankfurt. Massively recurrent, utterly unpleasant, individual happening – being stuck in a traffic jam – was producing outstanding beauty, when contemplated from far above. 

As I rummaged a bit through literature, cities seem to have been invented, back in the day, as social contrivances allowing, on the one hand, relatively peaceful coexistence of many different ethnic groups in fertile lowlands, and, on the other hand, a clear focusing of demographic growth in limited areas, whilst leaving the majority of arable land to the production of food. With time, the unusually high density of population in cities started generating secondary and tertiary effects. Greater a density of population favours accelerated emergence of new social roles, which, in turn, stimulates technological change and the development of markets. Thus, initially, cities tend to differentiate sharply from the surrounding countryside. By doing so, they create a powerful creative power regarding aggregate income of the social group. When this income-generating force concurs, hopefully, with acceptably favourable natural conditions and with political stability, the whole place (i.e. country or region) starts getting posh, and, as it does so, the relative disparity between cities and the countryside starts to diminish down to some kind of no-go-further threshold, where urban populations are a little bit twice as dense as the general average of the country. In other words, cities are a demographic anomaly which alleviates social tensions, and allows social change through personal individuation and technological change, and this anomaly starts dissolving itself as soon as those secondary and tertiary outcomes really kick in.

In the presence of that multi-layer cognitive dissonance, I am doing what I frequently do, i.e. in a squid-like manner I produce a cloud of ink. Well, metaphorically: it is more of a digital ink. As I start making myself comfortable inside that cloud, axes of coordinates emerged. One of them is human coordination in cities, and a relatively young, interesting avenue of research labelled ‘social neuroscience’. As digital imaging of neural processes has been making itself some space, as empirical method of investigation, interesting openings emerge. I am undertaking a short review of literature in the field of social neuroscience, in order to understand better the link between us, humans, being socially dense, and us doing other interesting things, e.g. inventing quantum physics or publishing the ‘Vogue’ magazine.

I am comparing literature from 2010 with the most recent one, like 2018 and 2019. I snatched almost the entire volume 65 of the ‘Neuron’ journal from March 25, 2010, and I passed in review articles pertinent to social neuroscience. Pascal Belin and Marie-Helene Grosbras (2010[1]) discuss the implications of research on voice cognition in infants. Neurologically, the capacity to recognize voice, i.e. to identify people by their voices, emerges long before the capacity to process verbal communication. Apparently, the period stretching from the 3rd month of life through the 7th month is critical for the development of voice cognition in infants. During that time, babies learn to be sharper observers of voices than other ambient sounds. Cerebral processing of voice seems to be largely subcortical and connected to our perception of time. In other words, when we use to say, jokingly, that city people cannot distinguish the voices of birds but can overhear gossip in a social situation, it is fundamentally true. From the standpoint of my research it means that dense social interaction in cities has a deep neurological impact on people already in their infancy. I assume that the denser a population is, the more different human voices a baby is likely to hear, and learn to discriminate, during that 3rd ÷ 7th month phase of learning voice cognition. The greater the density of population, the greater the data input for the development of this specific function in our brain. The greater the difference between the city and the countryside, social-density-wise, the greater the developmental difference between infant brains as regards voice cognition.

From specific I pass to the general, and to a review article by Ralph Adolphs (2010[2]). One of the most interesting takeaways from this article is a strongly corroborated thesis that social neurophysiology (i.e. the way that our brain works in different social contexts) goes two ways: our neuro-wiring predisposes us to some specific patterns of social behaviour, and yet specific social contexts can make us switch between neurophysiological patterns. That could mean that every mentally healthy human is neurologically wired for being both a city slicker and a rural being. Depending on the context we are in, the corresponding neurophysiological protocol kicks in. Long-lasting urbanization privileges social learning around ‘urban’ neurophysiological patterns, and therefore cities can have triggered a specific evolutionary direction in our species.

I found an interesting, slightly older paper on risk-taking behaviour in adolescents (Steinberg 2008[3]). It is interesting because it shows connections between developmental changes in the brain, and the appetite for risk. Risk-taking behaviour is like a fast lane of learning. We take risks when and to the extent that we can tolerate both high uncertainty and high emotional tension in a specific context. Adolescents take risks in order to boost their position in social hierarchy and that seems to be a truly adolescent behaviour from the neurophysiological point of view. Neurophysiological adults, thus, roughly speaking, people over the age of 25, seem to develop increasing preference for strategies of social advancement based on long-term, planned action with clearly delayed rewards. Apparently, there are two distinct, neurophysiological protocols – the adolescent one and the adult one – as regards the quest for individual social role, and the learning which that role requires.

Cities allow more interactions between adolescents than countryside does. More interactions between adolescents stronger a reinforcement for social-role-building strategies based on short-term reward acquired at the price of high risk. That might be the reason why in the modern society, which, fault of a better term, we call ‘consumer society’, there is such a push towards quick professional careers. The fascinating part is that in a social environment rich in adolescent social interaction, the adolescent pattern of social learning, based on risk taking for quick reward, finds itself prolongated deep into people’s 40ies or even 50ies.

We probably all know those situations, when we look for something valuable in a place where we can reasonably expect to find valuable things, yet the search is not really successful. Then, all of a sudden, just next door to that well-reputed location, we find true jewels of value. I experienced it with books, and with people as well. So is the case here, with social neuroscience. As long as I was typing ‘social neuroscience’ in the search interfaces of scientific repositories, more or less the same essential content kept coming to the surface. As my internal curious ape was getting bored, it started dropping side-keywords into the search, like ‘serotonin’ and ‘oxytocin’, thus the names of hormonal neurotransmitters in us, humans, which are reputed to be abundantly entangled with our social life. The keyword ‘Serotonin’ led me to a series of articles on the possibilities of treating and curing neurodevelopmental deficits in adults. Not obviously linked to cities and urban life? Look again, carefully. Cities allow the making of science. Science allows treating neurodevelopmental deficits in adults. Logically, developing the type of social structure called ‘cities’ allows our species to regulate our own neurophysiological development beyond the blueprint of our DNA, and the early engram of infant development (see, for example: Ehninger et al. 2008[4]; Bavelier at al. 2010[5]).

When I searched under ‘oxytocin’, I found a few papers focused on the fascinating subject of epigenetics. This is a novel trend in biology in general, based on the discovery that our DNA has many alternative ways of expressing itself, depending on environmental stimulation. In other words, the same genotype can produce many alternative phenotypes, through different expressions of coding genes, and the phenotype produced depends on environmental factors (see, e.g. Day & Sweatt 2011[6]; Sweatt 2013[7]). It is a fascinating question: to what extent urban environment can trigger a specific phenotypical expression of our human genotype?

A tentative synthesis regarding the social neuroscience of urban life leads me to develop on the following thread: we, humans, have a repertoire of alternative behavioural algorithms pre-programmed in our central nervous system, and, apparently, at some biologically very primal level, a repertoire of different phenotypical expressions to our genotype. Urban environments are likely to trigger some of those alternative patterns. Appetite for risk, combined with quick learning of social competences, in an adolescent-like mode, seems to be one of such orientations, socially reinforced in cities.   

All that neuroscience thing leads me to taking once again a behavioural an angle of approach to my hypothesis on the connection between the development of cities, and technological change, all that dipped in the sauce of ‘What is going to happen due to COVID-19?’. Reminder for those readers, who just start to follow this thread: I hypothesise that, as COVID-19 hits mostly in densely populated urban areas, we will probably change our way of life in cities. I want to understand how exactly it can possibly happen. When the pandemic became sort of official, I had a crazy idea: what if I represented all social change as a case of interacting epidemics? I noticed that SARS-Cov-2 gives a real boost to some technologies and behaviours, whilst others are being pushed aside. Certain types of medical equipment, ethylic alcohol (as disinfectant!), online communication, express delivery services – all that stuff just boomed. There were even local speculative bubbles in the stock market, around the stock of medical companies. In my own investment portfolio, I earnt 190% in two weeks, on the stock of a few Polish biotechs, and it could have been 400%, had I played it better.

Another pattern of collective behaviour that SARS-Cov-2 has clearly developed is acceptance of authoritarian governance. Well, yes, folks. Those special ‘epidemic’ regimes most of us live under, right now, are totalitarian governance by instalments, in the presence of a pathogen, which, statistically, is less dangerous than driving one’s own car. There is quite convincing scientific evidence that prevalence of pathogens makes people much more favourable to authoritarian policies in their governments (see for example: Cashdan & Steele 2013[8]; Murray, Schaller & Suedfeld 2013[9]).    

On the other hand, there are social activities and technologies, which SARS-Cov-2 is adverse to: restaurants, hotels, air travel, everything connected to mass events and live performative arts. The retail industry is largely taken down by this pandemic, too: see the reports by IDC, PwC, and Deloitte. As for behavioural patterns, the adolescent-like pattern of quick social learning with a lot of risk taking, which I described a few paragraphs earlier, is likely to be severely limited in a pandemic-threatened environment.

Anyway, I am taking that crazy intellectual stance where everything that makes our civilisation is the outcome of epidemic spread in technologies and behavioural patterns, which can be disrupted by the epidemic spread of some real s**t, such as a virus. I had a look at what people smarter than me have written on the topic (Méndez, Campos & Horsthemke 2012[10]; Otunuga 2019[11]), and a mathematical model starts emerging.

I define a set SR = {sr1, sr2, …, srm} of ‘m’ social roles, defined as combinations of technologies and behavioural patterns. On the other hand, there is a set of ‘k’ pathogens PT = {pt1, pt2, …, ptk}. Social roles are essentially idiosyncratic and individual, yet they are prone to imperfect imitation from person to person, consistently with what I wrote in ‘City slickers, or the illusion of standardized social roles’. Types of social roles spread epidemically through civilization just as a pathogen would. Now, an important methodological note is due: epidemic spread means diffusion by contact. Anything spreads epidemically when some form of contact from human to human is necessary for that thing to jump. We are talking about a broad spectrum of interactions. We can pass a virus by touching each other or by using the same enclosed space. We can contaminate another person with a social role by hanging out with them or by sharing the same online platform.

Any epidemic spread – would it be a social role sri in the set SR or a pathogen ptj – happens in a population composed of three subsets of individuals: subset I of infected people, the S subset of people susceptible to infection, and subset R of the immune ones. In the initial phase of epidemic spread, at the moment t0, everyone is potentially susceptible to catch whatever there is to catch, i.e. subset S is equal to the overall headcount of population N, whilst I and R are completely or virtually non-existent. I write it mathematically as I(t0) = 0, R(t0) = 0, S(t0) = N(t0).

The processes of infection, recovery, and acquisition of immune resistance are characterized by 5 essential parameters: a) the rate β of transmission from person to person b) the recruitment rate Λ from general population N to the susceptible subset S c) the rate μ of natural death, d) the rate γ of temporary recovery, and e) ψ the rate of manifestation in immune resistance. The rates γ and ψ can be correlated, although they don’t have to. Immune resistance can be the outcome of recovery or can be attributable to exogenous factors.

Over a timeline made of z temporal checkpoints (periods), some people get infected, i.e. they contract the new virus in fashion, or they buy into being an online influencer. This is the flow from S to I. Some people manifest immunity to infection: they pass from S to R. Both immune resistance and infection can have various outcomes. Infected people can heal and develop immunity, they can die, or they can return to being susceptible. Changes in S, I, and R over time – thus, respectively, dS/dt, dI/dt, and dR/dt, can be described with the following equations:  

Equation [I] [Development of susceptibility]dS/dt = Λ βSI – μS + γI

Equation [II] [Infection]dI/dt = βSI – (μ + γ)I

Equation [III] [Development of immune resistance] dR/dt = ψS(t0) = ψN

We remember that equations [I], [II], and [III] can apply both to pathogens and new social roles. Therefore, we can have a social role sri spreading at dS(sri)/dt, dI(sri)/dt, and dR(sri)/dt, whilst some micro-beast ptj is minding its own business at dS(ptj)/dt, dI(ptj)/dt, and dR(ptj)/dt.

Any given civilization – ours, for example – experiments with the prevalence of different social roles sri in the presence of known pathogens ptj. Experimentation occurs in the form of producing many alternative, local instances of civilization, each based on a general structure. The general structure assumes that a given pace of infection with social roles dI(sri)/dt coexists with a given pace of infection with pathogens dI(ptj)/dt.

I further assume that ε stands for the relative prevalence of anything (i.e. the empirically observed frequency of happening), social role or pathogen. A desired outcome O is being collectively pursued, and e represents the gap between that desired outcome and reality. Our average civilization can be represented as:

Equation [IV] [things that happen]h = {dI(sr1)/dt}* ε(sr1) + {dI(sr2)/dt}* ε(sr2) + … + {dI(srn)/dt}* ε(srn) + {dI(ptj)/dt}* ε(ptj)

Equation [V] [evaluation of the things that happen] e = O – [(e2h – 1)/(e2h + 1)]*{1 – [(e2h – 1)/(e2h + 1)]}2

In equation [V] I used a neural activation function, the hyperbolic tangent, which you can find discussed more in depth, in the context of collective intelligence, in my article on energy efficiency. Essentially, the more social roles are there in the game, in equation [IV], the broader will the amplitude of error in equation [V], when error is produced with hyperbolic tangent. In other words, the more complex is our civilization, the more it can freak out in the presence of a new risk factor, such as a pathogen. It is possible, at least in theory, to reach a level of complexity where the introduction of a new pathogen, such as SARS-Covid-19, makes the error explode into such high a register that social learning either takes a crash trajectory and aims at revolution, or slows down dramatically.

The basic idea of our civilization experimenting with itself is that each actual state of things according to equation [IV] produces some error in equation [V], and we can produce social change by utilizing this error and learning how to minimize it.

Discover Social Sciences is a scientific blog, which I, Krzysztof Wasniewski, individually write and manage. If you enjoy the content I create, you can choose to support my work, with a symbolic $1, or whatever other amount you please, via MY PAYPAL ACCOUNT.  What you will contribute to will be almost exactly what you can read now. I have been blogging since 2017, and I think I have a pretty clearly rounded style.

In the bottom on the sidebar of the main page, you can access the archives of that blog, all the way back to August 2017. You can make yourself an idea how I work, what do I work on and how has my writing evolved. If you like social sciences served in this specific sauce, I will be grateful for your support to my research and writing.

‘Discover Social Sciences’ is a continuous endeavour and is mostly made of my personal energy and work. There are minor expenses, to cover the current costs of maintaining the website, or to collect data, yet I want to be honest: by supporting ‘Discover Social Sciences’, you will be mostly supporting my continuous stream of writing and online publishing. As you read through the stream of my updates on https://discoversocialsciences.com , you can see that I usually write 1 – 3 updates a week, and this is the pace of writing that you can expect from me.

Besides the continuous stream of writing which I provide to my readers, there are some more durable takeaways. One of them is an e-book which I published in 2017, ‘Capitalism And Political Power’. Normally, it is available with the publisher, the Scholar publishing house (https://scholar.com.pl/en/economics/1703-capitalism-and-political-power.html?search_query=Wasniewski&results=2 ). Via https://discoversocialsciences.com , you can download that e-book for free.

Another takeaway you can be interested in is ‘The Business Planning Calculator’, an Excel-based, simple tool for financial calculations needed when building a business plan.

Both the e-book and the calculator are available via links in the top right corner of the main page on https://discoversocialsciences.com .


[1] Belin, P., & Grosbras, M. H. (2010). Before speech: cerebral voice processing in infants. Neuron, 65(6), 733-735. https://doi.org/10.1016/j.neuron.2010.03.018

[2] Adolphs, R. (2010). Conceptual challenges and directions for social neuroscience. Neuron, 65(6), 752-767. https://doi.org/10.1016/j.neuron.2010.03.006

[3] Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental review, 28(1), 78-106. https://dx.doi.org/10.1016%2Fj.dr.2007.08.002

[4] Ehninger, D., Li, W., Fox, K., Stryker, M. P., & Silva, A. J. (2008). Reversing neurodevelopmental disorders in adults. Neuron, 60(6), 950-960. https://doi.org/10.1016/j.neuron.2008.12.007

[5] Bavelier, D., Levi, D. M., Li, R. W., Dan, Y., & Hensch, T. K. (2010). Removing brakes on adult brain plasticity: from molecular to behavioral interventions. Journal of Neuroscience, 30(45), 14964-14971. https://www.jneurosci.org/content/jneuro/30/45/14964.full.pdf

[6] Day, J. J., & Sweatt, J. D. (2011). Epigenetic mechanisms in cognition. Neuron, 70(5), 813-829. https://doi.org/10.1016/j.neuron.2011.05.019

[7] Sweatt, J. D. (2013). The emerging field of neuroepigenetics. Neuron, 80(3), 624-632. https://doi.org/10.1016/j.neuron.2013.10.023

[8] Cashdan, E., & Steele, M. (2013). Pathogen prevalence, group bias, and collectivism in the standard cross-cultural sample. Human Nature, 24(1), 59-75. https://doi.org/10.1007/s12110-012-9159-3

[9] Murray DR, Schaller M, Suedfeld P (2013) Pathogens and Politics: Further Evidence That Parasite Prevalence Predicts Authoritarianism. PLoS ONE 8(5): e62275. https://doi.org/10.1371/journal.pone.0062275

[10] Méndez, V., Campos, D., & Horsthemke, W. (2012). Stochastic fluctuations of the transmission rate in the susceptible-infected-susceptible epidemic model. Physical Review E, 86(1), 011919. http://dx.doi.org/10.1103/PhysRevE.86.011919

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