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

DIY algorithms of our own

I return to that interesting interface of science and business, which I touched upon in my before-last update, titled ‘Investment, national security, and psychiatry’ and which means that I return to discussing two research projects I start being involved in, one in the domain of national security, another one in psychiatry, both connected by the idea of using artificial neural networks as analytical tools. What I intend to do now is to pass in review some literature, just to get the hang of what is the state of science, those last days.

On the top of that, I have been asked by my colleagues to crash take the leadership of a big, multi-thread research project in management science. The multitude of threads has emerged as a circumstantial by-product of partly the disruption caused by the pandemic, and partly as a result of excessive partition in the funding of research. As regards the funding of research, Polish universities have sort of two financial streams. One consists of big projects, usually team-based, financed by specialized agencies, such as the National Science Centre (https://www.ncn.gov.pl/?language=en ) or the National Centre for Research and Development (https://www.gov.pl/web/ncbr-en ). Another one is based on relatively small grants, applied for by and granted to individual scientists by their respective universities, which, in turn, receive bulk subventions from the Ministry of Education and Science. Personally, I think that last category, such as it is being allocated and used now, is a bit of a relic. It is some sort of pocket money for the most urgent and current expenses, relatively small in scale and importance, such as the costs of publishing books and articles, the costs of attending conferences etc. This is a financial paradox: we save and allocate money long in advance, in order to have money for essentially incidental expenses – which come at the very end of the scientific pipeline – and we have to make long-term plans for it. It is a case of fundamental mismatch between the intrinsic properties of a cash flow, on the one hand, and the instruments used for managing that cash flow, on the other hand.

Good. This is introduction to detailed thinking. Once I have those semantic niceties checked out, I cut into the flesh of thinking, and the first piece I intend to cut out is the state of science as regards Territorial Defence Forces and their role amidst the COVID-19 pandemic. I found an interesting article by Tiutiunyk et al. (2018[1]). It is interesting because it gives a detailed methodology for assessing operational readiness in any military unit, territorial defence or other. That corresponds nicely to Hypothesis #2 which I outlined for that project in national security, namely: ‘the actual role played by the TDF during the pandemic was determined by the TDF’s actual capacity of reaction, i.e. speed and diligence in the mobilisation of human and material resources’. That article by Tiutiunyk et al. (2018) allows entering into details as regards that claim. 

Those details start unfolding from the assumption that operational readiness is there when the entity studied possesses the required quantity of efficient technical and human resources. The underlying mathematical concept is quite simple. I the given situation, adequate response requires using m units of resources at k% of capacity during time te. The social entity studied can muster n units of the same resources at l% of capacity during the same time te. The most basic expression of operational readiness is, therefore, a coefficient OR = (n*l)/(m*k). I am trying to find out what specific resources are the key to that readiness. Tiutiunyk et al. (2018) offer a few interesting insights in that respect. They start by noticing the otherwise known fact that resources used in crisis situations are not exactly the same we use in everyday course of life and business, and therefore we tend to hold them for a time longer than their effective lifecycle. We don’t amortize them properly because we don’t really control for their physical and moral depreciation. One of the core concepts in territorial defence is to counter that negative phenomenon, and to maintain, through comprehensive training and internal control, a required level of capacity.

As I continue going through literature, I come by an interesting study by I. Bet-El (2020), titled: ‘COVID-19 and the future of security and defence’, published by the European Leadership Network (https://www.europeanleadershipnetwork.org/wp-content/uploads/2020/05/Covid-security-defence-1.pdf ). Bet-El introduces an important distinction between threats and risks, and, contiguously, the distinction between security and defence: ‘A threat is a patent, clear danger, while risk is the probability of a latent danger becoming patent; evaluating that probability requires judgement. Within this framework, defence is to be seen as the defeat or deterrence of a patent threat, primarily by military, while security involves taking measures to prevent latent threats from becoming patent and if the measures fail, to do so in such a way that there is time and space to mount an effective defence’. This is deep. I do a lot of research in risk management, especially as I invest in the stock market. When we face a risk factor, our basic behavioural response is hedging or insurance. We hedge by diversifying our exposures to risk, and we insure by sharing the risk with other people. Healthcare systems are a good example of insurance. We have a flow of capital that fuels a manned infrastructure (hospitals, ambulances etc.), and that infrastructure allows each single sick human to share his or her risks with other people. Social distancing is the epidemic equivalent of hedging. When cutting completely or significantly throttling social interactions between households, we have each household being sort of separated from the epidemic risk in other households. When one node in a network is shielded from some of the risk occurring in other nodes, this is hedging.

The military is made for responding to threats rather than risks. Military action is a contingency plan, implemented when insurance and hedging have gone to hell. The pandemic has shown that we need more of such buffers, i.e. more social entities able to mobilise quickly into deterring directly an actual threat. Territorial Defence Forces seem to fit the bill.  Another piece of literature, from my own, Polish turf, by Gąsiorek & Marek (2020[2]), state straightforwardly that Territorial Defence Forces have proven to be a key actor during the COVID-19 pandemic precisely because they maintain a high degree of actual readiness in their crisis-oriented resources, as compared to other entities in the Polish public sector.

Good. I have a thread, from literature, for the project devoted to national security. The issue of operational readiness seems to be somehow in the centre, and it translates into the apparently fluent frontier between security and national defence. Speed of mobilisation in the available resources, as well as the actual reliability of those resources, once mobilized, look like the key to understanding the surprisingly significant role of Territorial Defence Forces during the COVID-19 pandemic. Looks like my initial hypothesis #2, claiming that the actual role played by the TDF during the pandemic was determined by the TDF’s actual capacity of reaction, i.e. speed and diligence in the mobilisation of human and material resources, is some sort of theoretical core to that whole body of research.

In our team, we plan and have a provisional green light to run interviews with the soldiers of Territorial Defence Forces. That basic notion of actually mobilizable resources can help narrowing down the methodology to apply in those interviews, by asking specific questions pertinent to that issue. Which specific resources proved to be the most valuable in the actual intervention of TDF in pandemic? Which resources – if any – proved to be 100% mobilizable on the spot? Which of those resources proved to be much harder to mobilise than it had been initially assumed? Can we rate and rank all the human and technical resources of TDF as for their capacity to be mobilised?

Good. I gently close the door of that room in my head, filled with Territorial Defence Forces and the pandemic. I make sure I can open it whenever I want, and I open the door to that other room, where psychiatry dwells. Me and those psychiatrists I am working with can study a sample of medical records as regards patients with psychosis. Verbal elocutions of those patients are an important part of that material, and I make two hypotheses along that tangent:

>> Hypothesis #1: the probability of occurrence in specific grammatical structures A, B, C, in the general grammatical structure of a patient’s elocutions, both written and spoken, is informative about the patient’s mental state, including the likelihood of psychosis and its specific form.

>> Hypothesis #2: the action of written self-reporting, e.g. via email, from the part of a psychotic patient, allows post-clinical treatment of psychosis, with results observable as transition from mental state A to mental state B.

I start listening to what smarter people than me have to say on the matter. I start with Worthington et al. (2019[3]), and I learn there is a clinical category: clinical high risk for psychosis (CHR-P), thus a set of subtler (than psychotic) ‘changes in belief, perception, and thought that appear to represent attenuated forms of delusions, hallucinations, and formal thought disorder’. I like going backwards upstream, and I immediately ask myself whether that line of logic can be reverted. If there is clinical high risk for psychosis, the occurrence of those same symptoms in reverse order, from severe to light, could be a path of healing, couldn’t it?

Anyway, according to Worthington et al. (2019), some 25% of people with diagnosed CHR-P transition into fully scaled psychosis. Once again, from the perspective of risk management, 25% of actual occurrence in a risk category is a lot. It means that CHR-P is pretty solid as risk assessment comes. I further learn that CHR-P, when represented as a collection of variables (a vector for friends with a mathematical edge), entails an internal distinction into predictors and converters. Predictors are the earliest possible observables, something like a subtle smell of possible s**t, swirling here and there in the ambient air. Converters are information that bring progressive confirmation to predictors.

That paper by Worthington et al. (2019) is a review of literature in itself, and allows me to compare different approaches to CHR-P. The most solid ones, in terms of accurately predicting the onset of full-clip psychosis, always incorporate two components: assessment of the patient’s social role, and analysis of verbalized thought. Good. Looks promising. I think the initial hypotheses should be expanded into claims about socialization.

I continue with another paper, by Corcoran and Cecchi (2020[4]). Generally, patients with psychotic disorders display lower a semantic coherence than ordinary. The flow of meaning in their speech is impended: they can express less meaning in the same volume of words, as compared to a mentally healthy person. Reduced capacity to deliver meaning manifests as apparent tangentiality in verbal expression. Psychotic patients seem to err in their elocutions. Reduced complexity of speech, i.e. relatively low a capacity to swing between different levels of abstraction, with a tendency to exaggerate concreteness, is another observable which informs about psychosis. Two big families of diagnostic methods follow that twofold path. Latent Semantic Analysis (LSA) seems to be the name of the game as regards the study of semantic coherence. Its fundamental assumption is that words convey meaning by connecting to other words, which further unfolds into assuming that semantic similarity, or dissimilarity, with a more or less complex coefficient joint occurrence, as opposed to disjoint occurrence inside big corpuses of language.  

Corcoran and Cecchi (2020) name two main types of digital tools for Latent Semantic Analysis. One is Word2Vec (https://en.wikipedia.org/wiki/Word2vec), and I found a more technical and programmatic approach there to at: https://towardsdatascience.com/a-word2vec-implementation-using-numpy-and-python-d256cf0e5f28 . Another one is GloVe, which I found three interesting references to, at https://nlp.stanford.edu/projects/glove/ , https://github.com/maciejkula/glove-python , and at https://pypi.org/project/glove-py/ .

As regards semantic complexity, two types of analytical tools seem to run the show. One is the part-of-speech (POS) algorithm, where we tag words according to their grammatical function in the sentence: noun, verb, determiner etc. There are already existing digital platforms for implementing that approach, such as Natural Language Toolkit (http://www.nltk.org/ ). Another angle is that of speech graphs, where words are nodes in the network of discourse, and their connections (e.g. joint occurrence) to other words are edges in that network. Now, the intriguing thing about that last thread is that it seems to had been burgeoning in the late 1990ies, and then it sort of faded away. Anyway, I found two references for an algorithmic approach to speech graphs, at https://github.com/guillermodoghel/speechgraph , and at https://www.researchgate.net/publication/224741196_A_general_algorithm_for_word_graph_matrix_decomposition .

That quick review of literature, as regards natural language as predictor of psychosis, leads me to an interesting sidestep. Language is culture, right? Low coherence, and low complexity in natural language are informative about psychosis, right? Now, I put that argument upside down. What if we, homo (mostly) sapiens have a natural proclivity to psychosis, with that overblown cortex of ours? What if we had figured out, at some point of our evolutionary path, that language is a collectively intelligent tool which, with is unique coherence and complexity required for efficient communication, keeps us in a state of acceptable sanity, until we go on Twitter, of course.  

Returning to the intellectual discipline which I should demonstrate, as a respectable researcher, the above review of literature brings one piece of good news, as regards the project in psychiatry. Initially, in this specific team, we assumed that we necessarily need an external partner, most likely a digital business, with important digital resources in AI, in order to run research on natural language. Now, I realized that we can assume two scenarios: one with big, fat AI from that external partner, and another one, with DIY algorithms of our own. Gives some freedom of movement. Cool.


[1] Tiutiunyk, V. V., Ivanets, H. V., Tolkunov, І. A., & Stetsyuk, E. I. (2018). System approach for readiness assessment units of civil defense to actions at emergency situations. Науковий вісник Національного гірничого університету, (1), 99-105. DOI: 10.29202/nvngu/2018-1/7

[2] Gąsiorek, K., & Marek, A. (2020). Działania wojsk obrony terytorialnej podczas pandemii COVID–19 jako przykład wojskowego wsparcia władz cywilnych i społeczeństwa. Wiedza Obronna. DOI: https://doi.org/10.34752/vs7h-g945

[3] Worthington, M. A., Cao, H., & Cannon, T. D. (2019). Discovery and validation of prediction algorithms for psychosis in youths at clinical high risk. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. https://doi.org/10.1016/j.bpsc.2019.10.006

[4] Corcoran, C. M., & Cecchi, G. (2020). Using language processing and speech analysis for the identification of psychosis and other disorders. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging. https://doi.org/10.1016/j.bpsc.2020.06.004

Investment, national security, and psychiatry

I need to clear my mind a bit. For the last few weeks, I have been working a lot on revising an article of mine, and I feel I need a little bit of a shake-off. I know by experience that I need a structure to break free from another structure. Yes, I am one of those guys. I like structures. When I feel I lack one, I make one.

The structure which I want to dive into, in order to shake off the thinking about my article, is the thinking about my investment in the stock market. My general strategy in that department is to take the rent, which I collect from an apartment in town, every month, and to invest it in the stock market. Economically, it is a complex process of converting the residential utility of a real asset (apartment) into a flow of cash, thus into a financial asset with quite steady a market value (inflation is still quite low), and then I convert that low-risk financial asset into a differentiated portfolio of other financial assets endowed with higher a risk (stock). I progressively move capital from markets with low risk (residential real estate, money) into a high-risk-high-reward market.

I am playing a game. I make a move (monthly cash investment), and I wait for a change in the stock market. I am wrapping my mind around the observable change, and I make my next move the next month. With each move I make, I gather information. What is that information? Let’s have a look at my portfolio such as it is now. You can see it in the table below:

StockValue in EURReal return in €Rate of return I have as of April 6ht, 2021, in the morning
CASH & CASH FUND & FTX CASH (EUR) € 25,82 €                                    –   €                                     25,82
ALLEGRO.EU SA € 48,86 €                               (2,82)-5,78%
ALTIMMUNE INC. – COMM € 1 147,22 €                            179,6515,66%
APPLE INC. – COMMON ST € 1 065,87 €                                8,210,77%
BIONTECH SE € 1 712,88 €                           (149,36)-8,72%
CUREVAC N.V. € 711,00 €                             (98,05)-13,79%
DEEPMATTER GROUP PLC € 8,57 €                               (1,99)-23,26%
FEDEX CORPORATION COMM € 238,38 €                              33,4914,05%
FIRST SOLAR INC. – CO € 140,74 €                             (11,41)-8,11%
GRITSTONE ONCOLOGY INC € 513,55 €                           (158,43)-30,85%
INPOST € 90,74 €                             (17,56)-19,35%
MODERNA INC. – COMMON € 879,85 €                             (45,75)-5,20%
NOVAVAX INC. – COMMON STOCK € 1 200,75 €                            398,5333,19%
NVIDIA CORPORATION – C € 947,35 €                              42,254,46%
ONCOLYTICS BIOTCH CM € 243,50 €                             (14,63)-6,01%
SOLAREDGE TECHNOLOGIES € 683,13 €                             (83,96)-12,29%
SOLIGENIX INC. COMMON € 518,37 €                           (169,40)-32,68%
TESLA MOTORS INC. – C € 4 680,34 €                            902,3719,28%
VITALHUB CORP.. € 136,80 €                               (3,50)-2,56%
WHIRLPOOL CORPORATION € 197,69 €                              33,1116,75%
  €       15 191,41 €                            840,745,53%

A few words of explanation are due. Whilst I have been actively investing for 13 months, I made this portfolio in November 2020, when I did some major reshuffling. My overall return on the cash invested, over the entire period of 13 months, is 30,64% as for now (April 6th, 2021), which makes 30,64% * (12/13) = 28,3% on the annual basis.

The 5,53% of return which I have on this specific portfolio makes roughly 1/6th of the total return in have on all the portfolios I had over the past 13 months. It is the outcome of my latest experimental round, and this round is very illustrative of the mistake which I know I can make as an investor: panic.

In August and September 2020, I collected some information, I did some thinking, and I made a portfolio of biotech companies involved in the COVID-vaccine story: Pfizer, Biontech, Curevac, Moderna, Novavax, Soligenix. By mid-October 2020, I was literally swimming in extasy, as I had returns on these ones like +50%. Pure madness. Then, big financial sharks, commonly called ‘investment funds’, went hunting for those stocks, and they did what sharks do: they made their target bleed before eating it. They boxed and shorted those stocks in order to make their prices affordably low for long investment positions. At the time, I lost control of my emotions, and when I saw those prices plummet, I sold out everything I had. Almost as soon as I did it, I realized what an idiot I had been. Two weeks later, the same stocks started to rise again. Sharks had had their meal. In response, I did what I still wonder whether it was wise or stupid: I bought back into those positions, only at a price higher than what I sold them for.

Selling out was stupid, for sure. Was buying back in a wise move? I don’t know, like really. My intuition tells me that biotech companies in general have a bright future ahead, and not only in connection with vaccines. I am deeply convinced that the pandemic has already built up, and will keep building up an interest for biotechnology and medical technologies, especially in highly innovative forms. This is even more probable as we realized that modern biotechnology is very largely digital technology. This is what is called ‘platforms’ in the biotech lingo. These are digital clouds which combine empirical experimental data with artificial intelligence, and the latter is supposed to experiment virtually with that data. Modern biotechnology consists in creating as many alternative combinations of molecules and lifeforms as we possibly can make and study, and then pick those which offer the best combination of biological outcomes with the probability of achieving said outcomes.

My currently achieved rates of return, in the portfolio I have now, are very illustrative of an old principle in capital investment: I will fail most of the times. Most of my investment decisions will be failures, at least in the short and medium term, because I cannot possibly outsmart the incredibly intelligent collective structure of the stock market. My overall gain, those 5,53% in the case of this specific portfolio, is the outcome of 19 experiments, where I fail in 12 of them, for now, and I am more or less successful in the remaining 7.

The very concept of ‘beating the market’, which some wannabe investment gurus present, is ridiculous. The stock market is made of dozens of thousands of human brains, operating in correlated coupling, and leveraged with increasingly powerful artificial neural networks. When I expect to beat that networked collective intelligence with that individual mind of mine, I am pumping smoke up my ass. On the other hand, what I can do is to do as many different experiments as I can possibly spread my capital between.

It is important to understand that any investment strategy, where I assume that from now on, I will not make any mistakes, is delusional. I made mistakes in the past, and I am likely to make mistakes in the future. What I can do is to make myself more predictable to myself. I can narrow down the type of mistakes I tend to make, and to create the corresponding compensatory moves in my own strategy.

Differentiation of risk is a big principle in my investment philosophy, and yet it is not the only one. Generally, with the exception of maybe 2 or 3 days in a year, I don’t really like quick, daily trade in the stock market. I am more of a financial farmer: I sow, and I wait to see plants growing out of those seeds. I invest in industries rather than individual companies. I look for some kind of strong economic undertow for my investments, and the kind of undertow I specifically look for is high potential for deep technological change. Accessorily, I look for industries which sort of logically follow human needs, e.g. the industry of express deliveries in the times of pandemic. I focus on three main fields of technology: biotech, digital, and energy.

Good. I needed to shake off, and I am. Thinking and writing about real business decisions helped me to take some perspective. Now, I am gently returning into the realm of science, without completely leaving the realm of business: I am navigating the somehow troubled and feebly charted waters of money for science. I am currently involved in launching and fundraising for two scientific projects, in two very different fields of science: national security and psychiatry. Yes, I know, they can conjunct in more points than we commonly think they can. Still, in canonical scientific terms, these two diverge.

How come I am involved, as researcher, in both national security and psychiatry? Here is the thing: my method of using a simple artificial neural network to simulate social interactions seems to be catching on. Honestly, I think it is catching on because other researchers, when they hear me talking about ‘you know, simulating alternative realities and assessing which one is the closest to the actual reality’ sense in me that peculiar mental state, close to the edge of insanity, but not quite over that edge, just enough to give some nerve and some fun to science.

In the field of national security, I teamed up with a scientist strongly involved in it, and we take on studying the way our Polish forces of Territorial Defence have been acting in and coping with the pandemic of COVID-19. First, the context. So far, the pandemic has worked as a magnifying glass for all the f**kery in public governance. We could all see a minister saying ‘A,B and C will happen because we said so’, and right after there was just A happening, with a lot of delay, and then a completely unexpected phenomenal D appeared, with B and C bitching and moaning they haven’t the right conditions for happening decently, and therefore they will not happen at all.  This is the first piece of the context. The second is the official mission and the reputation of our Territorial Defence Forces AKA TDF. This is a branch of our Polish military, created in 2017 by our right-wing government. From the beginning, these guys had the reputation to be a right-wing militia dressed in uniforms and paid with taxpayers’ money. I honestly admit I used to share that view. TDF is something like the National Guard in US. These are units made of soldiers who serve in the military, and have basic military training, but they have normal civilian lives besides. They have civilian jobs, whilst training regularly and being at the ready should the nation call.

The initial idea of TDF emerged after the Russian invasion of the Crimea, when we became acutely aware that military troops in nondescript uniforms, apparently lost, and yet strangely connected to the Russian government, could massively start looking lost by our Eastern border. The initial idea behind TDF was to significantly increase the capacity of the Polish population for mobilising military resources. Switzerland and Finland largely served as models.

When the pandemic hit, our government could barely pretend they control the situation. Hospitals designated as COVID-specific had frequently no resources to carry out that mission. Our government had the idea of mobilising TDF to help with basic stuff: logistics, triage and support in hospitals etc. Once again, the initial reaction of the general public was to put the label of ‘militarisation’ on that decision, and, once again, I was initially thinking this way. Still, some friends of mine, strongly involved as social workers supporting healthcare professionals, started telling me that working with TDF, in local communities, was nothing short of amazing. TDF had the speed, the diligence, and the capacity to keep their s**t together which many public officials lacked. They were just doing their job and helping tremendously.

I started scratching the surface. I did some research, and I found out that TDF was of invaluable help for many local communities, especially outside of big cities. Recently, I accidentally had a conversation about it with M., the scientist whom I am working with on that project. He just confirmed my initial observations.

M. has strong connections with TDF, including their top command. Our common idea is to collect abundant, interview-based data from TDF soldiers mobilised during the pandemic, as regards the way they carried out their respective missions. The purely empirical edge we want to have here is oriented on defining successes and failures, as well as their context and contributing factors. The first layer of our study is supposed to provide the command of TDF with some sort of case-studies-based manual for future interventions. At the theoretical, more scientific level, we intend to check the following hypotheses:      

>> Hypothesis #1: during the pandemic, TDF has changed its role, under the pressure of external events, from the initially assumed, properly spoken territorial defence, to civil defence and assistance to the civilian sector.

>> Hypothesis #2: the actual role played by the TDF during the pandemic was determined by the TDF’s actual capacity of reaction, i.e. speed and diligence in the mobilisation of human and material resources.

>> Hypothesis #3: collectively intelligent human social structures form mechanisms of reaction to external stressors, and the chief orientation of those mechanisms is to assure proper behavioural coupling between the action of external stressors, and the coordinated social reaction. Note: I define behavioural coupling in terms of the games’ theory, i.e. as the objectively existing need for proper pacing in action and reaction.   

The basic method of verifying those hypotheses consists, in the first place, in translating the primary empirical material into a matrix of probabilities. There is a finite catalogue of operational procedures that TDF can perform. Some of those procedures are associated with territorial military defence as such, whilst other procedures belong to the realm of civil defence. It is supposed to go like: ‘At the moment T, in the location A, procedure of type Si had a P(T,A, Si) probability of happening’. In that general spirit, Hypothesis #1 can be translated straight into a matrix of probabilities, and phrased out as ‘during the pandemic, the probability of TDF units acting as civil defence was higher than seeing them operate as strict territorial defence’.

That general probability can be split into local ones, e.g. region-specific. On the other hand, I intuitively associate Hypotheses #2 and #3 with the method which I call ‘study of orientation’. I take the matrix of probabilities defined for the purposes of Hypothesis #1, and I put it back to back with a matrix of quantitative data relative to the speed and diligence in action, as regards TDF on the one hand, and other public services on the other hand. It is about the availability of vehicles, capacity of mobilisation in people etc. In general, it is about the so-called ‘operational readiness’, which you can read more in, for example, the publications of RAND Corporation (https://www.rand.org/topics/operational-readiness.html).  

Thus, I take the matrix of variables relative to operational readiness observable in the TDF, and I use that matrix as input for a simple neural network, where the aggregate neural activation based on those metrics, e.g. through a hyperbolic tangent, is supposed to approximate a specific probability relative to TDF people endorsing, in their operational procedures, the role of civil defence, against that of military territorial defence. I hypothesise that operational readiness in TDF manifests a collective intelligence at work and doing its best to endorse specific roles and applying specific operational procedures. I make as many such neural networks as there are operational procedures observed for the purposes of Hypothesis #1. Each of these networks is supposed to represent the collective intelligence of TDF attempting to optimize, through its operational readiness, the endorsement and fulfilment of a specific role. In other words, each network represents an orientation.

Each such network transforms the input data it works with. This is what neural networks do: they experiment with many alternative versions of themselves. Each experimental round, in this case, consists in a vector of metrics informative about the operational readiness TDF, and that vector locally tries to generate an aggregate outcome – its neural activation – as close as possible to the probability of effectively playing a specific role. This is always a failure: the neural activation of operational readiness always falls short of nailing down exactly the probability it attempts to optimize. There is always a local residual error to account for, and the way a neural network (well, my neural network) accounts for errors consists in measuring them and feeding them into the next experimental round. The point is that each such distinct neural network, oriented on optimizing the probability of Territorial Defence Forces endorsing and fulfilling a specific social role, is a transformation of the original, empirical dataset informative about the TDF’s operational readiness.

Thus, in this method, I create as many transformations (AKA alternative versions) of the actual operational readiness in TDF, as there are social roles to endorse and fulfil by TDF. In the next step, I estimate two mathematical attributes of each such transformation: its Euclidean distance from the original empirical dataset, and the distribution of its residual error. The former is informative about similarity between the actual reality of TDF’s operational readiness, on the one hand, and alternative realities, where TDF orient themselves on endorsing and fulfilling just one specific role. The latter shows the process of learning which happens in each such alternative reality.

I make a few methodological hypotheses at this point. Firstly, I expect a few, like 1 ÷ 3 transformations (alternative realities) to fall particularly close from the actual empirical reality, as compared to others. Particularly close means their Euclidean distances from the original dataset will be at least one order of magnitude smaller than those observable in the remaining transformations. Secondly, I expect those transformations to display a specific pattern of learning, where the residual error swings in a predictable cycle, over a relatively wide amplitude, yet inside that amplitude. This is a cycle where the collective intelligence of Territorial Defence Forces goes like: ‘We optimize, we optimize, it goes well, we narrow down the error, f**k!, we failed, our error increased, and yet we keep trying, we optimize, we optimize, we narrow down the error once again…’ etc. Thirdly, I expect the remaining transformations, namely those much less similar to the actual reality in Euclidean terms, to display different patterns of learning, either completely dishevelled, with the residual error bouncing haphazardly all over the place, or exaggeratedly tight, with error being narrowed down very quickly and small ever since.

That’s the outline of research which I am engaging into in the field of national security. My role in this project is that of a methodologist. I am supposed to design the system of interviews with TDF people, the way of formalizing the resulting data, binding it with other sources of information, and finally carrying out the quantitative analysis. I think I can use the experience I already have with using artificial neural networks as simulators of social reality, mostly in defining said reality as a vector of probabilities attached to specific events and behavioural patterns.     

As regards psychiatry, I have just started to work with a group of psychiatrists who have abundant professional experience in two specific applications of natural language in the diagnosing and treating psychoses. The first one consists in interpreting patients’ elocutions as informative about their likelihood of being psychotic, relapsing into psychosis after therapy, or getting durably better after such therapy. In psychiatry, the durability of therapeutic outcomes is a big thing, as I have already learnt when preparing for this project. The second application is the analysis of patients’ emails. Those psychiatrists I am starting to work with use a therapeutic method which engages the patient to maintain contact with the therapist by writing emails. Patients describe, quite freely and casually, their mental state together with their general existential context (job, family, relationships, hobbies etc.). They don’t necessarily discuss those emails in subsequent therapeutic sessions; sometimes they do, sometimes they don’t. The most important therapeutic outcome seems to be derived from the very fact of writing and emailing.

In terms of empirical research, the semantic material we are supposed to work with in that project are two big sets of written elocutions: patients’ emails, on the one hand, and transcripts of standardized 5-minute therapeutic interviews, on the other hand. Each elocution is a complex grammatical structure in itself. The semantic material is supposed to be cross-checked with neurological biomarkers in the same patients. The way I intend to use neural networks in this case is slightly different from that national security thing. I am thinking about defining categories, i.e. about networks which guess similarities and classification out of crude empirical data. For now, I make two working hypotheses:

>> Hypothesis #1: the probability of occurrence in specific grammatical structures A, B, C, in the general grammatical structure of a patient’s elocutions, both written and spoken, is informative about the patient’s mental state, including the likelihood of psychosis and its specific form.

>> Hypothesis #2: the action of written self-reporting, e.g. via email, from the part of a psychotic patient, allows post-clinical treatment of psychosis, with results observable as transition from mental state A to mental state B.

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.

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.  

I needed that

It’s been quite a few days without me writing and posting anything new on my blog. This is one of those strange moments, when many different strands of action emerge, none is truly preponderant over the others, and I feel like having to walk down many divergent paths all at once. As such an exercise can end up in serious injuries, the smart way to go is to make those divergent paths converge at some point.

As usually in such situations of slight chaos in my head, I use the method of questions to put some order in it. Let’s do it. What do I want? I want to develop my theoretical concept of collectively intelligent social structure into a workable, communicable, and reproducible methodology of research. I want to use that methodology as intellectual core for a big project of research and development. The development part would be some kind of digital tool which, using an otherwise very simple version of artificial neural network, can run the diagnosis of a society (e.g. a city), regarding: a) the collective outcomes pursued by the collective intelligence of that society b) the patterns of collective learning, and more specifically the phenomena which are likely to knock that society out of balance as opposed to those which make it stabilize.

As I am writing these words, I intuitively guess that my investment in the stock market, such as I consistently do it, is successfully based on the hypothesis of collective intelligence in the stock market, and in the industries which I invest in. As I consistently oscillate around 50% of annual return on the cash invested in the stock market, that hypothesis of collective intelligence seems to be workable. When I think about my recipe for success, it strangely resembles the findings of my scientific research. In a paper published with the journal ‘Energy’, titled ‘Energy efficiency as manifestation of collective intelligence in human societies’, I found out that the coefficient of fixed assets per one patentable invention is a key variable that societies optimize, and prioritize over energy efficiency. When I look at my investment portfolio, and what seems to work in it, it is precisely about some kind of balance between innovation and assets. When that sweet spot is there, the company’s stock brings me nice return.

I want to develop my concept of collectively intelligent social structure into a method of teaching social sciences, and to interweave that teaching into the canonical subjects I teach: microeconomics, macroeconomics, international trade etc. I wonder how I can use that concept e.g. in business planning or in the analysis of contracts and legal acts.

What am I afraid of? What can possibly go wrong with my plans? Good question. My fears are essentially those of publicly acknowledged failure on my part. I am shit scared of being labelled as a loser, but also of being seen as someone who fails to take any challenge at all. There is another deep fear in me, and this is a strange fear, as it is interwoven with hope: it is both the fear and the hope of deep change in my existence, like changing my professional occupation for a radically new one, or moving to live in another place, that kind of thing. It looks like I dread two types of suffering: that coming from socially recognized failure in building my position in social hierarchy, and that coming from existential change. Yet, my apprehension vis a vis those two types of suffering is different. Socially recognized failure is something I simply want to avoid. Existential change is that strange case of love and hate, a bit like my practice of the Wim Hof method. As I think of it, overcoming the fear of change can lead me to discovering new, wonderful things in my life, and this is what I want.

As I connect the dots I have just written down, turns out that what I really need to do is to utilise my research on collective intelligence as a platform for deep existential change. What specific kind of change would both scare me and thrill me in the best possible combination? What kinds of change can I take into account at all? Change of job inside the same occupation, i.e. inside the academia, for one. Further reaching a change of occupation, thus going outside academia, is the next level of professional change. The slightly fantasque move in that department would be to transform my investment in the stock market into a small investment fund for innovative projects, like a start-up fund. Moving to another place – a different city or a different country – is another option. Change of environment can be enormously stimulating, I know it by experience. Besides, my home country, Poland, is progressively turning into a mix of a catholic version of Iran, i.e. a religious state, with what I remember from the times of communism. A big part of the Polish population seems to be delighted with the process, and I am not delighted at all. I intuitively feel that compulsive thinking about how much ours is what we have means heading towards a disaster, and we just serve ourselves a lot of tranquilizing pills to kill the otherwise quite legitimate fear. It is all becoming both scary and suffocating, and I feel like getting out of the swamp before I sink too deep. Still, I know that geographical move has to be backed with realistic assumptions as for my social role: job, family etc. I am the kind of big, steady animal, like a moose, and it is both physical and existential. Jumping from one rooftop to another, parkour-style, is something I like watching but I completely suck at. I need a path and a structure to achieve change. 

I am exploring my deeply hidden drivers, and I am trying to be honest with myself and my readers. Which of those existential moves looks the most tempting to me? I think that a progressive transition, or, I should rather say: expansion, of out the academia is the most thrilling to me. I want it to be a progressive expansion, with a path of progress and learning. What do I need to learn in that process? In order to answer that question, I need to define my endgame, i.e. the target state I am working up to. In other words, how will I know I have what I want? I know I have a method when it has been intersubjectively validated, either by publication or by practical use in a collective research project.  How will other people know I have what I want? How will other people know I have a valid method? They need to buy into its logic, and acknowledge it as fit for publication or for application in a collective research project.

Here comes a fortunate coincidence, which has just knocked me out of philosophizing and closer to actual life. A scientific journal, Applied Energy, has just rejected positively my manuscript titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, and I am sort of happy about it. Why being happy about rejection? Well, in the world of science, there are two types of rejection: the ‘f**k you, man!’ type, and the maybe-if-you-improve-and-develop type. With that specific manuscript, I have already knocked at the doors of many scientific journals, and each time I received the former type of rejection letter. This time, with Applied Energy, it is the latter type. The editorial letter I have just received states ‘While your submission is of interest to Applied Energy, your manuscript does not meet the following criteria, we are returning the manuscript to you before the review:

*Lack of scientific originality/novelty:

The novelty/originality shall be justified by highlighting that the manuscript contains sufficient contributions to the new body of knowledge. The knowledge gap needs to be clearly addressed in Introduction.

*Literature survey is not sufficient to present the most updated R&D status for further justification of the originality of the manuscript. You should carry out a thorough literature survey of papers published in a range of top energy journals in the last three/four years so as to fully appreciate the latest findings and key challenges relating to the topic addressed in your manuscript and to allow you to more clearly present your contributions to the pool of existing knowledge. In the case the subject is really novel and few or no specific references are found, the novelty of the subject, the methodology used and the similarity to other older or newer subjects should be explicitly addressed.

At this time, your submission will be rejected from Applied Energy but please feel free to re-submit to the journal once the aforementioned comments have been addressed’.

The journal Applied Energy is top of the food chain in as journals about energy economics come. Such a nice and polite rejection from them is an invitation to dialogue. At last! I really needed that.

As I am preparing teaching material for the next semester, and I am interweaving that stream of work with my research on collective intelligence in human societies. I drop by some published science, just to chat with Berghout, S., & Verbitskiy, E. (2021). On regularity of functions of Markov chains. Stochastic Processes and their Applications. https://doi.org/10.1016/j.spa.2020.12.006 . There is a state of reality Xn = {x1, x2, …, xn}, which we cannot observe directly; {Xn} slips easily of our observational capacity. Thus, instead of chasing ghosts, we nail down a set of observables {Yn} such that Yn = π (Xn), the π being a coding map of Xn so as we can observe through the lens of Yn.

These are the basic assumptions expressed in the paper by Berghout & Verbitskiy, and this is an important building bloc in my research and in my teaching. If I want to teach my hypothesis of collective intelligence to undergraduate students, I need to make it simple, and to show immediate benefits of using an analytical method based on it. I want to focus, for a moment, on the latter component, thus on practical applications. The hypothesis of collective intelligence implies that human societies are intelligent structures, and they learn new stuff by experimenting with many alternative versions of themselves. That capacity of learning by experimenting with ourselves, whilst staying structurally coherent, is precisely the gain out of being collectively intelligent. Here, I go a bit far with my next claim: I think we can enhance our capacity of collective learning if we accurately grasp and communicate the exact way we learn collectively, i.e. the exact way we experiment with many alternative versions of us doing things together. That hypothesis comes from my observation about myself, and about some other people I know: when I narrate to myself the way I learn something, my learning speeds up. What if we, humans being together, can speed up the process of our collective learning by narrating to ourselves the exact way we learn?

Here, I stress the ‘exact way’ part. We have culture, which recently turns into outrage culture, with a lot of moralizing and little action. Here, I allow myself to quote one of my students. The guy comes from Rwanda, Africa, and in the class of management, when we were discussing different business concepts my students come up with, he gave the example of an actual business model which apparently grows like hell in Rwanda and in Africa as a whole. You buy a small fleet of electric cars, like 5 – 10, you rent them, and you assure full technical support to your clients, and you build a charging station for those cars, powered by a solar farm just next door. Investment goes into five types of assets: land, solar farm with full equipment (big batteries for storage included), electric cars, and equipment for their maintenance. You sell rental hours, additional maintenance services, and energy from the charging station. Simple, clean, workable, just the way I like it.

When I heard that story from my student, I had one of those ‘F**k!’ realizations. In Europe, and I think in North America as well, when we want to do something for the planet and the climate, we start by bashing each other about how bad we are at it and how necessary it is to turn vegan, then we burn thousands of tons of fuel to gather in one place and do a big march for the planet, then we do a strike for climate, and finally we claim that the government should do something about the climate, and, by the way, it would be a good thing if Jeff Bezos gave away some of his wealth. In Rwanda, when those people realize they should take care of the climate and the planet, they develop businesses which do. I think their way is somehow more promising.

I come back to the exact way we learn collectively. There is the Greta-Thunberg-way of caring about the planet, and there is the Rwandan way. Both exist, both are different experimental versions of ourselves, and both get reinforced by communication. One march for the planet, properly covered by the media, incites further marches for the planet, and, in the same way, disseminating that business model – involving a small fleet of electric vehicles, charging stations and solar farms – is likely to speed up its development. Narrating to ourselves the ways we develop new technologies can speed up their development.

The exact way we learn collectively is made, in the first place, of the specific, alternative versions of the social structure. When I want to know the exact way we learn collectively, I need to look at the alternative versions (of our collective) which we are experimenting with, thus at the actual degrees of freedom we have in that experimentation. Those alternative versions are described in terms of observables that Yn = π (Xn), which, in turn, are our best epistemological take on the otherwise unobservable reality {Xn}, through the coding map π.

I can see something promising here, I mean in that notion of actual experimental versions of ourselves. My scientific discipline, i.e. social sciences with a strong edge of economics and management, is plagued by claims that things ‘should be done’ in a given way just because it worked locally. Recently, I witnessed a heated debate between some acquaintances of mine, on Facebook, as for which economic model is better: the American one or the Scandinavian one. You know, the thing about education, healthcare, economic equality and stuff. As I was observing the ball of thoughts being played between those people, I had the impression of seeing an argument without common field. One camp argued that because something works in Sweden or Finland, it should be applied everywhere, whilst their opponents claimed exactly the same about the American economic model. In the middle of that, I was watching the protagonists flexing their respective intellects, and I couldn’t help thinking about my own research on economic models. I found empirical evidence that economic systems, across the board, aim for optimizing the average number of hours worked per person per year, and the amount of education one needs to get into the job market. All the rest is apparently instrumental.

F**k! I got distracted once again. I am supposed to show practical applications of my hypothesis regarding collective intelligence. Here comes an idea for a research project, with some potential for acquiring a research grant, which is as practical an application as there can be, in science. In my update titled ‘Out-of-the-lab monsters’, I hypothesised that economic recovery after the COVID-19 pandemic will be somehow slower than we expect, and certainly very different in terms of business models and institutions. The pandemic has triggered accelerated change as regards the use of digital technologies, the prevalence of biotechnology as business, and as regards social roles that people can endorse. Therefore, it would be a good thing to know which specific direction that change is going to take.

My idea is to take a large sample of business entities listed in public stock markets, which disclose their activity via the mechanism of investor relations, and to study their publicly disclosed information in order to discover the exact way they take in their business models. I am formulating the following hypothesis: in the economic conditions peculiar to the COVID-19 pandemic, business entities build up their reserves of cash and cash-equivalent securities in order to reinforce their strategic flexibility as regards technological change