Month: April 2021

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

Alois in the middle

 

I am returning to my syllabuses for the next academic year. I am focusing more specifically on microeconomics. Next year, I am supposed to give lectures in Microeconomics at both the Undergraduate, and the Master’s level. I feel like asking fundamental questions. My fundamental question, as it comes to teaching any curriculum, is the same: what can my students do with it? What is the function and the purpose of microeconomics? Please, notice that I am not asking that frequently stated, rhetorical question ‘What are microeconomics about?’. Well, buddy, microeconomics are about the things you are going to lecture about. Stands to reason. I want to know, and communicate, what is the practical utility, in one’s life, of those things that microeconomics are about.

The basic claim I am focusing on is the following: microeconomics are the accountancy of social structures. They serve exactly the same purpose that any kind of bookkeeping has ever served: to find and exploit patterns in human behaviour, by the means of accurately applied measures. Them ancients, who built those impressive pyramids (who builds a structure without windows and so little free space inside?), very quickly gathered that in order to have one decent pyramid, you need an army of clerks who do the accounting. They used to count stone, people, food, water etc. This is microeconomics, basically.

Thus, you can do with microeconomics if you want to build an ancient pyramid. Now, I am dividing the construction of said ancient pyramid in two stages: Undergraduate, and Master’s. An Undergraduate ancient pyramid requires the understanding of what do you need to keep the accounts of if you don’t want to be thrown to crocodiles. At the Master’s level, you will want to know what are the odds that you find yourself in a social structure, where inaccurate accounting, in connection with a pyramid, will have you thrown to crocodiles.

Good, now some literature, and a little turn by my current scientific work on the EneFin concept (see « Which salesman am I? » and « Sans une once d’utopisme » for sort of a current account of that research). I have just read that sort of transitional form of science, between an article and a book, basically a report, by Bleich and Guimaraes 2016[1]. It regards investment in renewable energies, mostly from the strictly spoken view of investment logic. Return on investment, net present value – that kind of thing. As I was making my notes out of that reading, my mind made a jump, and it landed on the cover of the quite-well-known book by Joseph Schumpeter: ‘Business Cycles’.

Joseph Schumpeter is an intriguing classic, so to say. Born in 1883, he published ‘Business Cycles’ in 1939, being 56 year-old, after the hell of a ride both for him and for the world, and right at the beginning of another ride (for the world). He was studying economics in Austria, in the early 1900, when social sciences in general were sort of different from their today’s version. They were the living account of a world that used to be changing at a breath-taking pace. Young Joseph (well, Alois in the middle) Schumpeter witnessed the rise of Marxism, World War I, the dissolution of his homeland, the Austro-Hungarian Empire, the rise of the German Reich. He moved from academia to banking, and from European banking to American academia.

I deeply believe that whatever kind of story I am telling, whether I am lecturing about economics, discussing a business concept, or chatting about philosophy, at the bottom line I am telling the story of my own existence. I also deeply believe that the same is true for anyone who goes to any lengths in telling a story. We tell stories in order to rationalize that crazy, exciting, unique and deadly something called ‘life’. To me, those ‘Business Cycles’ by Joseph Schumpeter look very much like a rationalized story of quite turbulent a life.

So, here come a few insights I have out of re-reading ‘Business Cycles’ for the n-th time, in the context of research on my EneFin business concept. Any technological change takes place in a chain of value added. Innovation in one tier of the chain needs to overcome the status quo both upstream and downstream of the chain, but once this happens, the whole chain of technologies and goods changes. I wonder how it can apply specifically to EneFin, which is essentially an institutional scheme. In terms of value added, this scheme is situated somewhere between the classical financial markets, and typical social entrepreneurship. It is social to the extent that it creates that quasi-cooperative connexion between the consumers of energy, and its suppliers. Still, as my idea assumes a financial market for those complex contracts « energy + shares in the supplier’s equity », there is a strong capitalist component.

I guess that the resistance this innovation would have to overcome would consist, on one end, in distrust from the part of those hardcore activists of social entrepreneurship, like ‘Anything that has anything to do with money is bad!’, and, on the other hand, there can be resistance from the classical financial market, namely the willingness to forcibly squeeze the EneFin scheme into some kind of established structure, like the stock market.

The second insight that Joseph has just given me is the following: there is a special type of business model and business action, the entrepreneurial one, centred on innovation rather than on capitalizing on the status quo. This is deep, really. What I could notice, so far, in my research, is that in every industry there are business models which just work, and others which just don’t. However innovative you think you are, most of the times either you follow the field-tested patterns or you simply fail. The real, deep technological change starts when this established order gets a wedge stuffed up its ass, and the wedge is, precisely, that entrepreneurial business model. I wonder how entrepreneurial is the business model of EneFin. Is it really as innovative as I think it is?

In the broad theoretical picture, which comes handy as it comes to science, the incidence of that entrepreneurial business model can be measured and assessed as a probability, and that probability, in turn, is a factor of change. My favourite mathematical approach to structural change is that particular mutation that Paul Krugman[2] made out of the classical production function, as initially formulated by Prof Charles W. Cobb and Prof Paul H. Douglas, in their common work from 1928[3]. We have some output generated by two factors, one of which changes slowly, whilst the other changes quickly. In other words, we have one quite conservative factor, and another one that takes on the crazy ride of creative destruction.

That second factor is innovation, or, if you want, the entrepreneurial business model. If it is to be powerful, then, mathematically, incremental change in that innovative factor should bring much greater a result on the side of output than numerically identical an increment in the conservative factor. The classical notation by Cobb and Douglas fits the bill. We have Y = A*F1a*F21-a and a > 0,5. Any change in F1 automatically brings more Y than the identical change in F2. Now, the big claim by Paul Krugman is that if F1 changes functionally, i.e. if its changes really increase the overall Y, resources will flow from F2 to F1, and a self-reinforcing spiral of change forms: F1 induces faster a change than F2, therefore resources are being transferred to F1, and it induces even more incremental change in F1, which, in turn, makes the Y jump even higher etc.

I can apply this logic to my scientific approach of the EneFin concept. I assume that introducing the institutional scheme of EneFin can improve the access to electricity in remote, rural locations, in the developing countries, and, consequently, it can contribute to creating whole new markets and social structures. Those local power systems organized in the lines of EneFin are the factor of innovation, the one with the a > 0,5 exponent in the Y = A*F1a*F21-a function. The empirical application of this logic requires to approximate the value of ‘a’, somehow. In my research on the fundamental link between population and access to energy, I had those exponents nailed down pretty accurately for many countries in the world. I wonder to what extent I can recycle them intellectually for the purposes of my present research.

As I am thinking on this issue, I will keep talking on something else, and the something else in question is the creation of new markets. I go back to the Venerable Man of microeconomics, the Source of All Wisdom, who used to live with his mother when writing the wisdom which he is so reputed for, today. In other words, I am referring to Adam Smith. Still, just to look original, I will quote his ‘Lectures on Justice’ first, rather than going directly to his staple book, namely ‘The Inquiry Into The Nature And Causes of The Wealth of Nations’.

So, in the ‘Lectures on Justice’, Adam Smith presents his basic considerations about contracts (page 130 and on): « That obligation to performance which arises from contract is founded on the reasonable expectation produced by a promise, which considerably differs from a mere declaration of intention. Though I say I have a mind to do such thing for you, yet on account of some occurrences I do not do it, I am not guilty of breach of promise. A promise is a declaration of your desire that the person for whom you promise should depend on you for the performance of it. Of consequence the promise produces an obligation, and the breach of it is an injury. Breach of contract is naturally the slightest of all injuries, because we naturally depend more on what we possess that what is in the hands of others. A man robbed of five pounds thinks himself much more injured than if he had lost five pounds by a contract ».

People make markets, and markets are made of contracts. A contract implies that two or more people want to do some exchange of value, and they want to perform the exchange without coercion. A contract contains a value that one party engages to transfer on the other party, and, possibly, in the case of mutual contracts, another value will be transferred the other way round. There is one thing about contracts and markets, a paradox as for the role of the state. Private contracts don’t like the government to meddle, but they need the government in order to have any actual force and enforceability. This is one of the central thoughts by another classic, Jean-Jacques Rousseau, in his ‘Social Contract’: if we want enforceable contracts, which can make the intervention of the government superfluous, we need a strong government to back up the enforceability of contracts.

If I want my EneFin scheme to be a game-changer in developing countries, it can work only in countries with relatively well-functioning legal systems. I am thinking about using the metric published by the World Bank, the CPIA property rights and rule-based governance rating.

Still another insight that I have found in Joseph Schumpeter’s ‘Business Cycles’ is that when the entrepreneur, introducing a new technology, struggles against the first inertia of the market, that struggle in itself is a sequence of adaptation, and the strategy(ies) applied in the phases of growth and maturity in the new technology, later on, are the outcome of patterns developed during that early struggle. There is some sort of paradox in that struggle. When the early entrepreneur is progressively building his or her presence in the market, they operate under high uncertainty, and, almost inevitably, do a lot of trial and error, i.e. a lot of adjustments to the initially inaccurate prediction of the future. The developed, more mature version of the newly introduced technology is the outcome of that somehow unique sequence of trials, errors, and adjustments.

Scientifically, that insight means a fundamental uncertainty: once the actual implementation of an entrepreneurial business model, such as EneFin, gets inside that tunnel of learning and struggle, it can take on so many different mutations, and the response of the social environment to those mutations can be so idiosyncratic that we get into really serious economic modelling here.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

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[1] Bleich, K., & Guimaraes, R. D. (2016). Renewable Infrastructure Investment Handbook: A Guide for Institutional Investors. In World Economic Forum, Geneva.

[2] Krugman, P. (1991). Increasing returns and economic geography. Journal of political economy, 99(3), 483-499.

[3] Charles W. Cobb, Paul H. Douglas, 1928, A Theory of Production, The American Economic Review, Volume 18, Issue 1, Supplement, Papers and Proceedings of the Fortieth Annual Meeting of the American Economic Association (March 1928), pp. 139 – 165

Les 2326 kWh de civilisation

Mon éditorial sur You Tube

Je reviens à ma recherche sur le marché de l’énergie. Je pense que l’idée théorique a suffisamment mûri. Enfin j’espère.

Dans un marché donné d’énergie il y a N = {i1, i2, …, in} consommateurs finaux, M = {j1, j2, …, jm} distributeurs et Z = {k1, k2, …, kz} fournisseurs primaires (producteurs). Les consommateurs finaux se caractérisent par un coefficient de consommation individuelle directe EC(i). Par analogie, chaque distributeur se caractérise par un coefficient de quantité d’énergie négociée EN(j) et chaque fournisseur primaire se caractérise par un coefficient individuel de production EP(k).

Le marché est à priori ouvert à l’échange avec d’autres marchés, aussi bien au niveau de la fourniture primaire d’énergie qu’à celui du négoce. En d’autres mots, les fournisseurs primaires peuvent exporter l’énergie et les distributeurs peuvent aussi bien exporter leurs surplus qu’importer de l’énergie des fournisseurs étranger pour balancer leur négoce. Logiquement, chaque fournisseur primaire se caractérise par une équation EP(k) = EPd(k) + EPx(k), où EPd signifie fourniture primaire sur le marché local et EPx symbolise l’exportation de l’énergie.

De même, chaque distributeur conduit son négoce d’énergie suivant l’équation EN(j) = ENd(j) + EI(j) + ENx(j) où ENx symbolise l’énergie exportée à l’étranger au niveau des relations entre distributeurs, EI est l’énergie importée et ENd est l’énergie distribuée dans le marché local.

L’offre totale OE d’énergie dans le marché en question suit l’équation OE = Z*[EPd(k) – EPx(k)] = M*[ENd(j) + EI(j) – ENx(j)]. Remarquons qu’une telle équation assume un équilibre local du type marshallien, donc le bilan de l’offre d’énergie et de la demande pour énergie se fait au niveau microéconomique des fournisseurs primaires et des distributeurs.

La consommation totale ET(i) d’énergie au niveau des consommateurs finaux est composée de la consommation individuelle directe EC(i) ainsi que de l’énergie ECT(i) consommée pour le transport et de l’énergie incorporée, comme bien intermédiaire ECB(i), dans les biens et services finaux consommés dans le marché en question. Ainsi donc ET(i) = EC(i) + ECT(i) + ECB(i).

La demande totale et finale DE d’énergie s’exprime donc comme

N*ET(i) = N*[EC(i) + ECT(i) + ECB(i)]

et suivant les assomptions précédentes elle est en équilibre local avec l’offre, donc

Z*[EPd(k) – EPx(k)] = N*[EC(i) + ECT(i) + ECB(i)]

aussi bien que

M*[ENd(j) + EI(j) – ENx(j)] = N*[EC(i) + ECT(i) + ECB(i)].

Avant que j’aille plus loin, une explication. Pour le moment j’assume que les coefficients individuels mentionnés plus haut sont des moyennes arithmétiques donc des valeurs espérées dans des ensembles structurées suivant des distributions normales (Gaussiennes). C’est une simplification qui me permet de formaliser théoriquement des « grosses » idées. Je pense que par la suite, j’aurai à faire des assomptions plus détaillées en ce qui concerne la distribution probabiliste de ces coefficients, mais ça, c’est pour plus tard.

Ça, c’était simple. Maintenant, le premier défi théorique que je perçois consiste à exprimer cette observation que j’avais faite il y a des mois de ça : les pays les plus pauvres sont aussi le moins pourvus en énergie. Au niveau du bilan énergétique la pauvreté se caractérise soit, carrément, par la quasi-absence de la consommation d’énergie niveau transport et niveau énergie incorporée dans les biens et services, soit par une quantité relativement petite dans ces deux catégories. C’est à mesure qu’on grimpe les échelons de richesse relative par tête d’habitant que les coefficients ECT(i) et ECB(i) prennent de la substance.

La seconde observation empirique à formaliser concerne la structure de la fourniture primaire d’énergie. Dans les pays les plus pauvres, l’énergie primaire est très largement fournie par ce que l’Agence Internationale d’Énergie définit élégamment comme « combustion des bio fuels » et qui veut tout simplement dire qu’une grande partie de la société n’a pas d’accès à l’électricité et ils se procurent leur énergie primaire en brûlant du bois et de la paille. Formellement, ça compte comme utilisation d’énergies renouvelables. Le bois et la paille, ça repousse, surtout cette dernière. Encore faut se souvenir que ce type d’énergétique est renouvelable au niveau de la source d’énergie mais pas au niveau du produit : le processus relâche du carbone dans l’atmosphère sans qu’on ait une idée vraiment claire comment faire retourner ce génie dans la lampe. La morale (partielle) du conte des fées est que lorsque vous voyez des nombres agrégés qui suggèrent la prévalence d’énergies renouvelables en Soudan du Sud, par exemple, alors ces renouvelables c’est du feu de paille très littéralement.

La différence empirique entre ces pays les plus pauvres et ceux légèrement plus opulents réside dans le fait que ces derniers ont un réseau de fourniture primaire d’électricité ainsi que de sa distribution et ce réseau dessert une large partie de la population. Ce phénomène se combine avec une percée originale d’énergies renouvelables dans les pays en voie de développement : des populations entières, surtout des populations rurales, gagnent l’accès à l’électricité vraiment 100% renouvelable, comme du photovoltaïque, directement à partir d’un monde sans électricité. Ils ne passent jamais par la phase d’électricité fournie à travers des grosses infrastructures industrielles que nous connaissons en Europe.

C’est justement la percée d’électricité dans une économie vraiment pauvre qui pousse cette dernière en avant sur la voie de développement. Comme j’étudie la base des données de la Banque Mondiale à propos de la consommation finale d’énergie par tête d’habitant, je pose une hypothèse de travail : lorsque ladite tête d’habitant dépasse le niveau de quelques 2326 kilowatt heures de consommation finale d’énergie par an, soit 200 kg d’équivalent pétrole, une société quasiment dépourvue d’économie régulière d’échange se transforme en une société qui produit et fait circuler des biens et des services.

Une fois ce cap franchi, le prochain semble se situer aux environs d’ET(i) égale à 600 ± 650 kg d’équivalent pétrole, soit 6 978,00 ± 7 559,50 kilowatt heures par an par tête d’habitant. Ça, c’est la différence entre des sociétés pauvres et en même temps instables socialement ainsi que politiquement d’une part, et celles dotées d’institutions bien assises et bien fonctionnelles. Rien qui ressemble à du paradis, au-dessus de ces 6 978,00 ± 7 559,50 kilowatt heures par an par tête d’habitant, néanmoins quelque chose qui au moins permet de construire un purgatoire bien organisé.

L’étape suivante est la transgression d’un autre seuil, que je devine intuitivement quelque part entre 16 240 kWh et 18 350 kWh par an par tête d’habitant. C’est plus ou moins le seuil officiel qui marque la limite inférieure de la catégorie « revenu moyen » dans la terminologie de la Banque Mondiale. C’est alors qu’on commence à observer des marchés bien développés est des structures institutionnelles tout à fait stables. Oui, les hommes politiques peuvent toujours faire des conneries, mais ces conneries sont immédiatement projetées contre un fonds d’ordre institutionnel et de ce fait sont possibles à contrecarrer de façon autre qu’une guerre civile. Une fois dans la catégorie « revenu moyen », une économie semble capable de transition secondaire vers les énergies renouvelables. C’est le passage des réseaux typiquement industriels, basés sur des grosses centrales électriques, coexistantes avec des réseaux de distribution fortement oligopolistes, vers des systèmes de fourniture d’énergie basés sur des installations locales puisant leur jus des sources renouvelables.

Finalement, à partir de quelques 3000 kg d’équivalent pétrole = 34 890 kWh par an par tête d’habitant c’est la catégorie des pays vraiment riches. En ce qui concerne les énergies renouvelables, des investissements vraiment systémiques commencent au-dessus de ce seuil. C’est une transition secondaire à forte vapeur.

Bon, je formalise. Une variable parmi celles que j’ai nommées quelques paragraphes plus tôt vient au premier plan :  la consommation totale d’énergie par tête d’habitant ou ET(i) = EC(i) + ECT(i) + ECB(i). Les observations empiriques que je viens de décrire indiquent que dans le processus de développement économique des sociétés, le côté droit de l’équation ET(i) = EC(i) + ECT(i) + ECB(i) se déploie de gauche à droite. D’abord, il y a du EC(i). Les gens consomment de l’énergie pour leurs besoins le plus individuels et le plus directement possible. On brûle du bois ou de la paille et on a de l’énergie thermique pour faire de la cuisine, pour décontaminer l’eau et pour se chauffer. Si ça marche, des habitats humains permanents s’établissent.

Je sais que ça sonne comme le compte rendu d’évènements qui se passèrent à l’aube de la civilisation, mais après que j’ai étudié la situation des nations les plus pauvres du monde je sais aussi que c’est bien ce qui se passe dans des pays comme Niger ou Soudan. Le premier défi de ces populations consiste à faire marcher la structure sociale de base, donc à arriver au point quand les communautés locales sont capables de se développer et pour se développer lesdites communautés locales ont tout simplement besoin de s’établir sur une base relativement stable de nourriture et d’énergie.

Une fois que ce cap est franchi, donc une fois qu’ET(i) passe un seuil critique ET1(i), il y a un surplus d’énergie qui peut se traduire comme le développement du transport, ainsi que celui des marchés des biens et des services. En d’autres mots :

ET1(i) = 2 326 kWh

[EC(i) ≤ EC1(i)] => [ET(i) = EC(i) et ECT(i) ≈ 0 et ECB(i) ≈ 0]

[EC(i) > EC1(i)] => [ET(i) = EC(i) + ECT(i) + ECB(i) ; ECT(i) > 0 et ECB(i) > 0]

[EC(i) > EC1(i)] <=> [ECT(i) + ECB(i) = ET(i) – 2 326 kWh]

La seconde valeur critique, que je nomme ET2(i), donne lieu à l’émergence d’une structure institutionnelle suffisamment stable pour être appelée « ordre institutionnel ». Je sais que :

6 978,00 kWh ≤ ET2(i) ≤ 7 559,50 kWh

et que

4652 kWh < [ET2(i) – ET1(i)] ≤ 5233,5 kWh

et de même

{4652 kWh < [ECT(i) + ECB(i)] ≤ 5233,5 kWh}

ainsi que

[6 978,00 kWh ≤ ET2(i) ≤ 7 559,50 kWh] => ordre institutionnel

Alors vient ce troisième seuil, 16 240 kWh ≤ ET3(i) ≤ 18 350 kWh où la transition secondaire vers les énergies renouvelables devient possible. Cette transition prend donc lieu lorsque

13 914 kWh ≤ [ECT(i) + ECB(i)] ≤ 16 024 kWh

Je continue à vous fournir de la bonne science, presque neuve, juste un peu cabossée dans le processus de conception. Je vous rappelle que vous pouvez télécharger le business plan du projet BeFund (aussi accessible en version anglaise). Vous pouvez aussi télécharger mon livre intitulé “Capitalism and Political Power”. Je veux utiliser le financement participatif pour me donner une assise financière dans cet effort. Vous pouvez soutenir financièrement ma recherche, selon votre meilleur jugement, à travers mon compte PayPal. Vous pouvez aussi vous enregistrer comme mon patron sur mon compte Patreon . Si vous en faites ainsi, je vous serai reconnaissant pour m’indiquer deux trucs importants : quel genre de récompense attendez-vous en échange du patronage et quelles étapes souhaitiez-vous voir dans mon travail ?

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The mathematics of whatever you want: some educational content regarding political systems

My editorial on You Tube

This time, I go educational, and I go educational about political systems, and more specifically about electoral regimes. I generally avoid talking politics with my friends, as I want them to keep being my friends. Really, politics have become so divisive a topic, those last years. I remember, like 20 years ago, talking politics was like talking about the way to organize a business, or to design a machine. Now, it has become more like an ideological choice. Personally, I find it deplorable. There are always people who have more power than other people. Democracy allows us to have some control over those people in power, and if we want to exercise effective control, we need to get your own s**t together, emotionally too. If we become so emotional about politics that we stop thinking rationally, there is something wrong with us.

OK, enough ranting and moaning. Let’s get into facts and method. So, I start as I frequently do: I make a structure, and I drop numbers casually into it, just like that. Later on, I will work through the meaning of those numbers. My structure is a simple political system made of a juxtaposition of threes. There are 3 constituencies, equal in terms of incumbent voters: each constituency has 200 000 of them incumbent voters. Three political parties – Party A, Party B, and Party C – rival for votes in those 3 constituencies. Each political party presents three candidates in the electoral race. Party A presents its candidate A.1. in Constituency 1, candidate A.2. runs in Constituency 2, and Candidate A.3 in Constituency 3. Party B goes sort of the opposite way, and makes its candidates run like: B.1. in Constituency 3, B.2. in Constituency 2, and B.3. in Constituency 1. Party C wants to be original and makes like a triangle: its candidate C.1. runs in Constituency 2, C.2. tries their luck in Constituency 3, and C.3. is in the race in Constituency 1.

Just to recapitulate that distribution of candidates as a choice presented to voters, those in Constituency 1 choose between candidates A.1., B.3., and C.3., voters in Constituency 2 split their votes among A.2., B.2., and C.1.; finally, voters in Constituency 3 have a choice between A.3., B.1., and C.2. It all looks a bit complicated, I know, and, in a moment, you will read a table with the electoral scores, as number of votes obtained. I am just signalling the assumption I made when I was making those scores up: as we have 3 candidates in each constituency, voters do not give, under any circumstance, more than 50% of their votes (or more than 100 000 in absolute numbers) to one candidate. Implicitly, I assume that candidates already represent, somehow, their local populations. It can be achieved through some kind of de facto primary elections, e.g. when you need a certain number of officially collected voters’ signatures in order to register a candidate as running in a given constituency. Anyway, you have those imaginary electoral scores in Table 1, below. Save for the assumption about ‘≤ 50%’, those numbers are random.

 

  Table 1 – Example of electoral score in the case studied (numbers are fictional)

Number of votes obtained
Party Candidate Constituency 1 Constituency 2 Constituency 3
Party A Candidate A.1 23 000
total score [votes]

              174 101    

Candidate A.2 99 274
Candidate A.3 51 827
Party B Candidate B.1 6 389
total score [votes]

              111 118    

Candidate B.2 40 762
Candidate B.3 63 967
Party C Candidate C.1 13 580
total score [votes]

              134 691    

Candidate C.2 33 287
Candidate C.3 87 824
Total 174 791 153 616 91 503

 

On the whole, those random numbers had given quite a nice electoral attendance. In a total population of 600 000 voters, 419 910 had gone to the ballot, which makes 70%. In that general landscape, the three constituencies present different shades. People in the 1 and the 2 are nicely dutiful, they turned up to that ballot at the respective rates of 87,4%, and 76,8%. On the other hand, people in Constituency 3 seem to be somehow disenchanted: their electoral attendance was 45,8%. Bad citizens. Or maybe just bloody pissed.

Now, I apply various electoral regimes to that same distribution of votes. Scenario 1 is a simple one. It is a strictly proportional electoral regime, where votes from all three constituencies are pooled together, to allocate 5 seats among parties. The number of seats going to each party are calculated as: “Total score of the party/ Total number of votes cast”. Inside each party, seats go specific candidates according to their individual scores. The result is a bit messy. Party A gets 2 seats, for its candidates A.2. and A.3., party B passes its B.3. man, and Party C gets C.3. into the Parliament. The first, tiny, little problem is that we had 5 seats to assign, and just 4 got assigned. Why? Simple: the parties acquired fractions of seats. In the strictly proportional count, Party A got 2,073075183 seats, Party B had 1,323116858, and Party’s C score was 1,603807959. I agree that we could conceivably give 0,32 of one seat to a party. People can share, after all. Still, I can barely conceive assigning like 0,000000058 of one seat. Could be tricky for sharing. That is a typical problem with strictly proportional regimes: they look nice and fair at the first sight, but in real life they have the practical utility of an inflatable dartboard.

Scenario 2 is once again a strictly proportional regime, with 6 seats to distribute, only this time,  in each constituency, 2 seats are to be distributed among the candidates with the best scores. The result is a bit of an opposite of Scenario 1: it looks suspiciously neat. Each party gets an equal number of seats, i.e. 2. Candidates A.2., A.3., B2., B.3., C.2., and C.3. are unfolding their political wings. I mean, I have nothing against wings, but it was supposed to be proportional, wasn’t it? Each party got a different electoral score, and each gets the same number of seats. Looks a bit too neat, doesn’t it? Once again, that’s the thing with proportional: growing your proportions does not always translate into actual outcomes.

Good. I go for the 3rd scenario: a strictly plural regime, 3 seats to allocate, in each constituency just one candidate, the one with the best score, gets the seat. This is what the British people call ‘one past the post’, in their political jargon. Down this avenue, Party A pushes it’s A.2. and A.3. people through the gate, and Party C does so with C.3. That looks sort of fair, still there is something… In Constituency 1, 87 000 of votes, with a small change, got the voters one representative in the legislative body. In constituencies 2 and 3, the same representation – 1 person in the probably right place – has been acquired with, respectively, 99 274, and 33 287 votes. Those guys from constituencies 1 and 2 could feel a bit disappointed. If they were voting in constituency 3, they would need much less mobilisation to get their man past the post.

Scenario 4 unfolds as a mixed, plural-proportional regime, with 5 seats to allocate; 3 seats go to the single best candidate in each constituency, as in Scenario 3, and 2 seats go to the party with the greatest overall score across all the 3 constituencies. Inside that party, the 2 seats in question go to candidates with the highest electoral scores. The results leave me a bit perplex: they are identical to those in Scenario 3. The same people got elected, namely A.2., A.3., and C.3., only this time we are left with 2 vacancies. Only 3 seats have been allocated, out of the 5 available. How could it have happened? Well, we had a bit of a loop, here. The party with the highest overall score is Party A, and they should get the 2 seats in the proportional part of the regime. Yet, their two best horses, A.2. and A.3. are already past the post, and the only remaining is A.1. with the worst score inside their party. Can a parliamentary seat, reserved for the best runner in the winning party, be attributed to actually the worst one? Problematic. Makes bad publicity.

Scenarios 5 and 6 are both variations on the d’Hondt system. This is a special approach to mixing plural with proportional, and more specifically, to avoiding those fractional seats as in Scenario 1. Generally, the total number of votes cast for each party is divided by consecutive denominators in the range from 1 up to the number of seats to allocate. We get a grid, out of which we pick up as many greatest values as there are seats to allocate. In Scenario 5, I apply the d’Hondt logic to votes from all the 3 constituencies pooled together, and I allocate 6 seats. Scenario 6 goes with the d’Hondt logic down to the level of each constituency separately, 2 seats to allocate in each constituency. The total number of votes casted for each party is divided by consecutive denominators in the range from 1 up to the number of seats to allocate (2 in this case). The two greatest values in such a grid get the seats. Inside each party, the attribution of seats to candidates is proportional to their individual scores.

Scenario 5 seems to work almost perfectly. Party A gets 3 seats, thus they get all their three candidates past the post, Party C acquires 2 seats for C.2. and C.3., whilst Party B has one seat for candidate B.3. In a sense, this particular mix of plural and proportional seems even more fairly proportional that Scenario 1. The detailed results, which explain the attribution of seats, are given in Table 2, below.

 

Table 2 – Example of application of the d’Hondt system, Scenario 5

Number of votes obtained divided by consecutive denominators
Denominator of seats Party A Party B Party C
1        174 101            111 118            134 691    
2          87 051              55 559          67 346     
3          58 034              37 039          44 897
4          43 525          27 780          33 673
5          34 820          22 224          26 938
6          29 017          18 520          22 449

 

On the other hand, Scenario 6 seems to be losing the proportional component. Table 3, below, shows how exactly it is dysfunctional. As there are 2 seats to assign in each constituency, electoral scores of each party are being divided by, respectively, 1 and 2. In Constituency 1, the two best denominated scores befall to parties C and B, thus to their candidates C.3. and B.3. In Constituency 2, both of the two best denominated scores are attributed to Party A. The trouble is that Party A has just one candidate in this constituency, the A.2. guy, and he (she?) gets the seat. The second seat in this constituency must logically befall to the next best party with any people in the game, and it happens to be Party B and its candidate B.2. Constituency 3, in this particular scenario, gives two best denominated scores to parties A and C, thus to candidates A.3. and C.2. All in all, each party gets 2 seats out of the 6. Uneven scores, even distribution of rewards.

 

Table 3 – Application of the d’Hondt logic at the level of separate constituencies: Scenario 6.

Party A Party B Party C
Denominator of seats Constituency 1
1        23 000        63 967            87 824    
2        11 500        31 984        43 912
Constituency 2
1        99 274            40 762  (?)        13 580
2        49 637            20 381          6 790
Constituency 3
1        51 827              6 389        33 287    
2        25 914          3 195        16 644

 

Any mechanism can be observed under two angles: how it works, and how it doesn’t. It applies to electoral regimes, too. An electoral regime doesn’t work in two respects. First of all, it does not work if it does not lead to electing anyone. Second of all, it does not work if it fails to represent the votes cast in the people actually elected. There is a term, in the science of electoral systems: the wasted votes. They are votes, which do not elect anyone. They have been cast on candidates who lost the elections. Maybe some of you know that unpleasant feeling, when you learn that the person you voted for has not been elected. This is something like frustration, and yet, in my own experience, there is a shade of relief, as well. The person I voted for lost their electoral race, hence they will not do anything stupid, once in charge. If they were in charge, and did something stupid, I could be kind of held accountable. ‘Look, you voted for those idiots. You are indirectly responsible for the bad things they did’, someone could say. If they don’t get elected, I cannot be possibly held accountable for anything they do, ‘cause they are not in a position to do anything.

Wasted votes happen in all elections. Still, an efficient electoral regime should minimize their amount. Let’s compare those six alternative electoral regimes regarding their leakiness, i.e. their tendency to waste people’s voting power. You can see the corresponding analysis in Table 4 below. The method is simple. Numbers in the table correspond to votes from Table 1, cast on candidates who did not get elected in the given constituency, under the given electoral regime. You can see that the range of waste is quite broad, from 4,8% of votes cast, all the way up to 43% with a small change. It is exactly how real electoral regimes work, and this is, in the long run, the soft spot of any representative democracy. In whatever possible way you turn those numbers, you bump on a dilemma: either the race is fair for the candidates, or the ballot is fair for voters. A fair race means that essentially the best wins. There is no point in making an electoral regime, where inefficient contenders have big chances to get elected. On the other hand, those who lose the race represent people who voted for them. If we want all the voters to be accurately represented in the government, no candidate should be eliminated from the electoral contest, only then it would not be a contest.

 

Table 4

Number of votes, which do not elect any candidate
Constituency 1 Constituency 2 Constituency 3 Total elections
Scenario 1 23 000 40 762 33 287 97 049
Scenario 2 0 13 580 6 389 19 969
Scenario 3 23 000 54 342 33 287 110 629
Scenario 4 63 967 54 342 39 676 157 985
Scenario 5 (d’Hondt method, pooled) 0 54 342 6 389 60 731
Scenario 6 (d’Hondt method, separately by constituency) 23 000 54 342 39 676 117 018
Percentage of votes cast, which do not elect any candidate
Constituency 1 Constituency 2 Constituency 3 Total elections
Scenario 1 13,2% 26,5% 36,4% 23,1%
Scenario 2 0,0% 8,8% 7,0% 4,8%
Scenario 3 13,2% 35,4% 36,4% 26,3%
Scenario 4 36,6% 35,4% 43,4% 37,6%
Scenario 5 (d’Hondt method, pooled) 0,0% 35,4% 7,0% 14,5%
Scenario 6 (d’Hondt method, separately by constituency) 13,2% 35,4% 43,4% 27,9%
Average 12,7% 29,5% 28,9% 22,4%

 

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

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

An enlightened grandfather

I am going personal in my writing, or at least in the piece of writing which follows. It is because I am going through an important change in my life. I mean, I am going through another important change in my life, and, as it is just one more twist among many, I already know a few things about change. I know that when I write about it, I can handle it better as compared to a situation, when I just try to shelf it somewhere in my head and write about something else. The something else I technically should be writing about is science, and here comes the next issue: science and life. I believe science is useful, and it is useful when I have the courage to implement it in real life. I am a social scientist. Changes in my life are social changes in microscale: it is all about me being connected in a certain way to other people. I can wrap my mind around my existential changes both honestly, as a person, and scientifically, as that peculiar mix of a curious ape, a happy bulldog living in the moment, and an austere monk equipped with an Ockham’s razor to cut bullshit out.

The change I am going through is about me and my son. Junior, age (almost) 25, has just left Poland, for Nice, France, to start a new job. In Poland, he was leaving with us, his parents. High time to leave, you would say. Yes, you’re right. I think the same. Still, here is the story. Every story needs proper characters, and thus I am going to name my son. His name is Mikołaj, or Nicolas, from the point of view of non-Slavic folks. Mikołaj used to study computer science and live with us, his parents, until Summer 2019. Steam was building up. As you can easily guess, Mikołaj was 23 in 2019. When a guy in his early twenties lives with his parents, friction starts. His nervous system is already calibrated on social expansion, sex, procreation and generally on thrusting himself into life head-first. None of these things matches with a guy living with his parents. In Summer 2019, Mikołaj left home for one year, and went on an Erasmus+ academic exchange to Nice, France, where, by a strange chain of coincidences, as well as by a lot of his own wit and grit he completed a graduated, at the Sophia Antipolis University, a separate Master’s program of studies. A nice prospect for professional career in France was sketching itself, with a job with the same company where Mikołaj had been doing his internship with.

When Mikołaj was away, we spent hundreds of hours on the phone. I swear, it was him more than I. We just git that vibe on the phone which we seldom could hit when talking face to face. I had been having those distance conversations with a guy who was turning, at a turbo speed, from an over-age teenager into an adult. I was talking to a guy who learnt to cook, who was keeping his apartment clean and tidy, who was open to talk about his mistakes and calmly pointed out at my mistakes. It was cool.

The pandemic changed a lot. The nice professional prospect faded away, as the company in question is specialized in IT services for hotels, airports and airlines, which is not really a tail wind right now. Mikołaj came back home on October 1st, 2020, with the purpose of completing the Polish Master’s program – which he initially started the whole Erasmus adventure with – and another purpose of finding a job. His plan was to wrap it all up – graduation and job seeking – in about 3 months. As plans like doing, this one went sideways, and what was supposed to take three months took a bit more than six. During those 6 months which Mikołaj spent with us, in Poland, both we and him had the impression of having gone back in time, in a weirdly painful and unpleasant way. Mikołaj went back to being the overgrown teenager he had been before leaving for the Erasmus exchange. Our cohabitation was a bit tense. Still, things can change for the better. Around Christmas, they started to. As I was coaching and supporting Mikołaj with his job seeking, we sort of started working together, as if it was a project we would run as a team. It was cool.

Yesterday, on April 10th, 2021, early in the morning, Mikołaj left again, to start a job he found, once again in Nice, France. Splitting up was both painful and liberating. Me and my wife experienced – and still are experiencing – the syndrome of empty nest which, interestingly, rhymes with emptiness. This is precisely what I am trying to wrap my mind around in order to produce some useful, almost new wisdom. As Mikołaj called us from Nice, yesterday in the evening, he said openly he experienced the same. Still, things can change for the better. When I heard Mikołaj’s voice on the phone, yesterday, I knew he is an adult again, and happy again. I wonder what I will cook for lunch, tomorrow, he said. In his voice, he had that peculiar vibe I know from his last stay in Nice. That ‘I am lost as f**k and happy as f**k, and I am kicking ass’ vibe. It was cool.

I am still in the process of realizing that my son is happier and stronger when being away from me than what he used to be when being close to me. It is painful, liberating, and I think it is necessary. Here comes the science. Those last years, I almost obsessively do research about the social role of social roles. What is my social role, after I have realized that from now on, being a father for my son is going to be a whole lot different? First of all, I think that my social role is partly given by external circumstances, and partly created by myself as I respond to those external stimuli. I have the freedom of shaping some part of my social role. Which part exactly? As I look at it from inside, I guess the only way to know it is to try, over and over again. I am trying, over and over again, to be the best possible version of myself. Doesn’t everybody try the same, by the way? Basing on my own life experience, I can cautiously say: ‘No’. Not everybody, or at least not always. I know I haven’t always tried to be the best version of myself. I know I am trying now because I know it has paid me off over the last 6 years or so. This is the window in time when I really started to work purposefully on being the best human I can, and I can tell you, there was a lot to do. I was 46 at the time (now, I am 53).

A bit late for starting personal development, you could say. Well, yes and no. Yes, it is late. Still, there is science behind it. During the reproductive age bracket, i.e. roughly between the age of 20 and that of 45÷50, young men are driven mostly by their sexual instinct, because that instinct is overwhelming and we have the capacity to translate it into elaborate patterns of social behaviour. Long story short, between 20 and 50, we build a position in the social hierarchy. This is how sexual instinct civilizes itself. In their late 40ies, most males start experiencing a noticeable folding down in their levels of testosterone, and the strength of sexual drive follows in step. All the motivation based on it is sort of crumbling down, too. This is what we call mid-life crisis, or, in Polish, the Faun’s afternoon.

I remember a conversation I had with a data scientist specialized in Artificial Intelligence. She told me there are AI-based simulations of the human genome, which demonstrate that said genome is programmed to work until we are 50. Anything after that is culture-based. Our culture takes a lot of pains to raise and educate young humans during the first two decades of their lives. Someone has to take care of that secondary socialization, and the most logical way of assigning that role is to take someone who is post-reproductive as a person. This is how grandparents are made by culture.

As I am meditating about the best possible version of myself, right now, this is precisely what comes to my mind. I can be and I think I want to be an enlightened grandfather. There is a bit of a problem, here, ‘cause my son has no kids for the moment. It is hard to be an actual grandfather in the absence of grandchildren, and this is why I said I want to be an enlightened one. I mean that I take the essence of the social role that a grandfather plays in society, and I try to coin it up into a mission statement.  

A good grandfather should provide wisdom. It means I need to have wisdom, and I need to communicate it intelligibly. How do I know I have wisdom? I think there are two components to that. I need to be aware of and accountable for my own mistakes. I need to work through my personal story with as much objectivism as I can, for one. How can I be objective about myself? Here is a little trick. As I live and make mistakes, I learn to observe other people’s response to my own f**k-ups. I learn there is a different, external perspective on my own actions, and with a bit of effort I can reconstruct that external perspective. I can make good, almost new wisdom about myself by combining thorough introspection of my personal experience with that intersubjective reading of my actions.   

There is more to wisdom than just my personal story. I need to collect information about my cultural surroundings, and aggregate it into intelligible a narrative, and I need to do it in the same spirit of critical observation, with curiosity, love and cold objectivism, all in one. I need to be like a local server in a digital network, with enough content stored on my hard drive, and enough efficiency in retrieving that content to be a valuable node in the system.

A good grandfather should support others and accept to act in the backstage. This is what I have been experiencing since I got that job of fundraising and coordination of research projects in my home university. I take surprisingly great a pleasure in supporting other people’s work and research. I remember that 10 years ago I would approach things differently. I would take care, most of all, about putting myself in the centre and at the top of collective projects. Now, I take pleasure in outcomes more than in my own position within those outcomes.   

Now, by antithesis, what a good grandfather shouldn’t be? I think the kind of big existential mistake I could make now would be to become a burden for other people, especially for my son. How can I make such a mistake? It is simple, I observed it in my own father. I convince myself that all good things in life are over for me, because the falling level of testosterone leaves a gaping hole in my emotional structure. I stop taking care of myself, I let myself sink into depression and cynicism, and Bob’s my uncle: I have become a burden for others. Really simple. Don’t try it at home, even under the supervision of qualified professionals.

That brings me to still another positive aspect of being a good grandfather: grit. For me, grit is something that has the chance to supplant fear and anger, under favourable circumstances. When I was young, and even when I was a mature adult, I did not really know how to fight and stand up against existential adversities. It is mostly by observing other people – who developed that skill – that I progressively learnt some of it. Grit is the emotional counterpart of resilience, and I think that conscious, purposeful resilience requires the perspective of time. I need to know, by experience, how that really crazy kind of s**t unfolds over years, in human existence, in order to develop cognitive and emotional structures for coping with it.  

Summing up, an enlightened grandfather is a critical teller of his own story, a good source of knowledge about the general story of his culture, and a supportive mentor for other people. An enlightened grandfather takes care of his body and his mind, to stay healthy, strong and happy as long as possible. An enlightened grandfather keeps himself sharp enough to help those youngsters keep their s**together when things go south. This is my personal mission statement. This is who I want to be over the 20 – 25 years to come, which is what I reasonably expect to be the time I have left for being any good in this world. What next? Well, next, it is time to say goodbye.   

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