The type of riddle I like

Once again, I had quite a break in blogging. I spend a lot of time putting together research projects, in a network of many organisations, which I am supposed to bring to working together. I give it a lot of time and personal energy. It drains me a bit, and I like that drain. I like the thrill of putting together a team, agreeing about goals and possible openings. Since 2005, when I stopped running my own business and I settled for a quite academic career, I haven’t experienced that special kind of personal drive. I sincerely believe that every teacher should apply his or her own teaching in the everyday life of theirs, just to see if their teaching still corresponds to reality.

This is one of the reasons why I have made it a regular activity of mine to invest in the stock market. I teach economics, and the stock market is very much like the pulse of economics, in all its grades and shades, ranging from hardcore macroeconomic cycles, passing through the microeconomics of specific industries I am currently focusing on with my investment portfolio, and all the way down the path of behavioural economics. I teach management, as well, and putting together new projects in research is the closest I can come, currently, to management science being applied in real life.

Still, besides trying to apply my teaching in real life, I still do science. I do research, and I write about the things I think I have found out, on that research path of mine. I do a lot of research as regards the economics of energy. Currently, I am still revising a paper of mine, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’. Around the topic of energy economics, I have built more general a method of studying quantitative socio-economic data, with the technical hypothesis that said data manifests collective intelligence in human social structures. It means that whenever I deal with a collection of quantitative socio-economic variables, I study the dataset at hand by assuming that each multivariate record line in the database is the local instance of an otherwise coherent social structure, which experimentins with many such specific instances of itself and selects those offering the best adaptation to the current external stressors. Yes, there is a distinct sound of evolutionary method in that approach.

Over the last three months, I have been slowly ruminating my theoretical foundations for the revision of that paper. Now, I am doing what I love doing: I am disrupting the gently predictable flow of theory with some incongruous facts. Yes, facts don’t know how to behave themselves, like really. Here is an interesting fact about energy: between 1999 and 2016, at the planetary scale, there had been more and more new cars produced per each new human being born. This is visualised in the composite picture below. Data about cars comes from https://www.worldometers.info/cars/ , whilst data about the headcount of population comes from the World Bank (https://data.worldbank.org/indicator/SP.POP.TOTL ).

Now, the meaning of all that. I mean, not ALL THAT (i.e. reality and life in general), just all that data about cars and population. Why do we consistently make more and more physical substance of cars per each new human born? Two explanations come to my mind. One politically correct and nicely environmentalist: we are collectively dumb as f**k and we keep overshooting the output of cars over and above the incremental change in population. The latter, when translated into a rate of growth, tends to settle down (https://data.worldbank.org/indicator/SP.POP.GROW ). Yeah, those capitalists who own car factories just want to make more and more money, and therefore they make more and more cars. Yeah, those corrupt politicians want to conserve jobs in the automotive industry, and they support it. Yeah, f**k them all! Yeah, cars destroy the planet!

I checked. The first door I knocked at was General Motors (https://investor.gm.com/sec-filings ). What I can see is that they actually make more and more operational money by making less and less cars. Their business used to be overshot in terms of volume, and now they are slowly making sense and money out of making less cars. Then I checked with Toyota (https://global.toyota/en/ir/library/sec/ ). These guys looks as if they were struggling to maintain their capacity to make approximately the same operational surplus each year, and they seem to be experimenting with the number of cars they need to put out in order to stay in good financial shape. When I say ‘experimenting’, it means experimenting upwards or downwards.

As a matter of fact, the only player who seems to be unequivocally making more operational money out of making more cars is Tesla (https://ir.tesla.com/#tab-quarterly-disclosure). In There comes another explanation – much less politically correct, if at all – for there being more cars made per each new human, and it says that we, humans, are collectively intelligent, and we have a good reason for making more and more cars per each new human coming to this realm of tears, and the reason is to store energy in a movable, possibly auto-movable a form. Yes, each car has a fuel tank or a set of batteries, in the case of them Teslas or other electric f**kers. Each car is a moving reservoir of chemical energy, immediately converted into kinetic energy, which, in turn, has economic utility. Making more cars with batteries pays off better than making more cars with combustible fuel in their tanks: a new generation of movable reservoirs in chemical energy is replacing an older generation thereof. 

Let’s hypothesise that this is precisely the point of each new human being coupled with more and more of a new car being made: the point is more chemical energy convertible into kinetic energy. Do we need to move around more, as time passes? Maybe, although I am a bit doubtful. Technically, with more and more humans being around in a constant space, there is more and more humans per square kilometre, and that incremental growth in the density of population happens mostly in cities. I described that phenomenon in a paper of mine, titled ‘The Puzzle of Urban Density And Energy Consumption’. That means that space available for travelling and needed to be covered, per individual capita of each human being, is actually decreasing. Less space to travel in means less need for means of transportation. 

Thus, what are we after, collectively? We might be preparing for having to move around more in the future, or for having to restructure the geography of our settlements. That’s possible, although the research I did for that paper about urban density indicates that geographical patterns of urbanization are quite durable. Anyway, those two cases sum up to some kind of zombie apocalypse. On the other hand, the fact of developing the amount of dispersed, temporarily stored energy (in cars) might be a manifestation of us learning how to build and maintain large, dispersed networks of energy reservoirs.

Isn’t it dumb to hypothesise that we go out of our way, as a civilisation, just to learn the best ways of developing what we are developing? Well, take the medieval cathedrals. Them medieval folks would keep building them for decades or even centuries. The Notre Dame cathedral in Paris, France, seems to be the record holder, with a construction period stretching from 1160 to 1245 (Bruzelius 1987[1]). Still, the same people who were so appallingly slow when building a cathedral could accomplish lightning-fast construction of quite complex military fortifications. When building cathedrals, the masters of stone masonry would do something apparently idiotic: they would build, then demolish, and then build again the same portion of the edifice, many times. WTF? Why slowing down something we can do quickly? In order to experiment with the process and with the technologies involved, sir. Cathedrals were experimental labs of physics, mathematics and management, long before these scientific disciplines even emerged. Yes, there was the official rationale of getting closer to God, to accomplish God’s will, and, honestly, it came handy. There was an entire culture – the medieval Christianity – which was learning how to learn by experimentation. The concept of fulfilling God’s will through perseverant pursuit, whilst being stoic as regards exogenous risks, was excellent a cultural vehicle to that purpose.

We move a few hundreds of years in time, to the 17th century. The cutting edge of technology is to find in textile and garments (Braudel 1992[2]), and the peculiarity of the European culture consisted in quickly changing fashions, geographically idiosyncratic and strongly enforced through social peer pressure. The industry of garments and textile was a giant experimental lab of business and management, developing the division of labour, the management of supply chains, quick study of subtle shades in customers’ tastes and just as quick adaptation thereto. This is how we, Europeans, prepared for the much later introduction of mechanized industry, which, in turn, gave birth to what we are today: a species controlling something like 30% of all energy on the surface of our planet.       

Maybe we are experimenting with dispersed, highly mobile and coordinated networks of small energy reservoirs – the automotive fleet – just for the sake of learning how to develop such networks? Some other facts, which, once again, are impolitely disturbing, come to the fore. I had a look at the data published by United Nations, as regards the total installed capacity of generation in electricity (https://unstats.un.org/unsd/energystats/ ). I calculated the average electrical capacity per capita, at the global scale. Turns out in 2014 the average human capita on Earth had around 60% more power capacity to tap from, as compared to a similarly human capita in 1999.

Interesting. It looks even more interesting when taken as the first moment of a process. When I divide the annual incremental change in the installed electrical capacity on the planet, and I divide it by the absolute demographic increment, thus when I go ‘Delta capacity / delta population’, that coefficient of elasticity grows like hell. In 2014, it was almost three times more than in 1999. We, humans, keep developing denser a network of cars, as compared to our population, and, at the same time, we keep increasing the relative power capacity which every human can tap into.    

Someone could say it is because we simply consume more and more energy per capita. Cool, I check with the World Bank: https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE . Yes, we increase our average annual consumption of energy per one human being, and yet this is a very gentle increment: barely 18% from 1999 through 2014. Nothing to do with the quick accumulation of generative capacity. We accumulate densifying a global fleet of cars, and growing a reserve of power capacity. What are we doing it for?

This is a deep question, and I calculated two additional elasticities with the data at hand. Firstly, I denominated incremental change in the number of new cars per each new human born over the average consumption of energy per capita. In the visual below, this is the coefficient ‘Elasticity of cars per capita to energy per capita’. Between 1999 and 2014, this elasticity had passed from 0,49 to 0,79. We keep accumulating something like an overhead of incremental car fleet, as compared to the amount of energy we consume.

Secondly, I formalized the comparison between individual consumption of energy and average power capacity per capita. This is the ‘Elasticity of capacity per capita to energy per capita’ column in the visual below.  Once again, it is a growing trend.   

At the planetary scale, we keep beefing up our collective reserves of energy, and we seriously mean business about dispersing those reserves into networks of small reservoirs, possibly on wheels.

Increased propensity to store is a historically known collective response to anticipated shortage. Do we, the human race, collectively and not quite consciously anticipate a shortage of energy? How could that happen? Our biology should suggest it just the opposite. With the climate change being around, we technically have more energy in the ambient environment, not less. What exact kind of shortage in energy are we collectively anticipating? This is the type of riddle I like.


[1] Bruzelius, C. (1987). The Construction of Notre-Dame in Paris. The Art Bulletin, 69(4), 540-569. https://doi.org/10.1080/00043079.1987.10788458

[2] Braudel, F. (1992). Civilization and capitalism, 15th-18th century, vol. II: The wheels of commerce (Vol. 2). Univ of California Press.

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