A test pitch of my ‘Energy Ponds’ business concept

I am returning to a business concept I have been working on for many months, and which I have provisionally labelled ‘Energy Ponds’. All that thinking about new economic solutions for a world haunted by insidious pathogens – no, not selfie sticks, I am talking about the other one, COVID-19 – pushed me to revisit fundamentally the concept of Energy Ponds, and you, my readers, you are my rubber duck.

The rubber duck (Latin: anas flexilis), also known as bath duck (anas balneum) is a special semi-aquatic avian species, whose valour I know from my son, IT engineer by profession. Every now and then, he says, on the phone: ‘Dad, focus, you are going to be my rubber duck’. The rubber duck is an imaginary animal. It feeds on discursive waters. You talk to it in order to get your own thoughts straight. When I am my son’s rubber duck, he explains me some programming problems and solutions, he checks if I understand what he says, and when I test positive, it means that he can get the message across to any moderately educated hominid.

I am going to proceed along the path of discursive equilibrium, in a cycle made of three steps. First, I will try to describe my idea in 1 – 2 sentences, in a simple and intelligible way. Then, I develop on that short description, with technical details. In the third step, I look for gaps and holes in the so-presented concept, and then I go again: short description, development, critical look etc. I think I will repeat the cycle until I reach the Subjective Feeling of Having Exhausted the Matter. Nelson Goodman and John Rawls proposed something slightly similar (Goodman 1955[1]; Rawls 1999[2]): when I talk long enough to myself, and to an imaginary audience, my concepts sharpen.   

Here I go. First attempt. I synthesize. The concept of ‘Energy Ponds’ consists in ram-pumping water from rivers into retentive, semi-natural wetlands, so as to maximize the retention of water, and, in the same time, in using the elevation created through ram-pumping so as to generate hydroelectricity. At the present stage of conceptual development, ‘Energy Ponds’ require optimization at two levels, namely that of adequately choosing and using the exact geographical location, and that of making the technology of ram-pumping economically viable.  

I develop. We are increasingly exposed to hydrological effects of climate change, namely to recurrent floods and droughts, and it starts being a real pain in the ass. We need to figure out new ways of water management, so as to retain a maximum of rainwater, whilst possibly alleviating occasional flood-flows. Thus, we need to figure out good ways of capturing rainwater, and of retaining it. Rivers are the drainpipes of surrounding lands, whence the concept of draining basin: this is the expanse of land, adjacent to a river, where said river collects (drains) water from. That water comes from atmospheric precipitations. When we collect water from rivers, we collect rainwater, which fell on the ground, trickled underground, and then, under the irresistible force of grandpa Newton, flew towards the lowest point in the whereabouts, that lowest point being the river.

Thus, when we collect water from the river, we collect rainwater, just drained through land. We can collect it in big artificial reservoirs, which has been done for decades. An alternative solution is to retain water in wetlands. This is something that nature has been doing for millions of years. We have sort of a ready-made recipe from. Wetlands are like sponges covered with towels. A layer of spongy ground, allowing substantial accumulation of water, is covered with a dense, yet not very thick layer of shallowly rooted vegetation. That cover layer prevents the evaporation of water.  

Now, I go into somehow novel a form of expression, i.e. novel for me. The age I am, 52, I have that slightly old school attachment to writing, and for the last 4 years, I have been mostly writing on my blog. Still, as a university professor, I work with young people – students – and those young people end up, every now and then, by teaching me something. I go more visual in my expression, which this whole written passage can be considered as an introduction to. Under the two links below, you will find:

  1. The Power Point Presentation with a regular pitch of my idea

That would be all in this update. Just as with my other ideas, in the times we have, i.e. with the necessity to figure out new s**t in the presence of pathogens, you are welcome to contact me with any intellectual contribution you feel like supplying.  

If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .

[1] Goodman, N. (1955) Fact, Fiction, and Forecast, Cambridge, Mass., Harvard University Press, pp. 65–68

[2] Rawls J. (1999) A Theory of Justice. Revised Edition, President and Fellows of Harvard College, ISBN 0-674-00078-1, p. 18

We’d better make that change liveable

My editorial on You Tube

I continue developing my ideas. Most people do, all the time, actually: they keep developing their own ideas, and other people’s ideas, and, on the whole, we just develop our ideas.

Good. Linguistic warm up done, I go to work. I continue what I started in my last update ( Steady inflow of assets and predictable rules ): a workable business concept for restarting local economies after COVID-19 lockdowns, and during the ongoing pandemic. Last time, I studied the early days of the Bitcoin, in the hope of understanding how a completely new economic scheme emerges. As hope crystalizes into something more structured, ideas emerge. I am going to make a quick sketch of what I have come up with, and then I will try give it some shine by using my observations as regards the early infancy of the Bitcoin.  

As I observe the present situation, I can see that local communities both need and accumulate some typical goods and assets. The most immediately needed, and semi-instinctively accumulated goods are those serving personal protection and hygiene: gloves, facial protections (masks, covers, googles etc.), scrubs and aprons, bonnets, soap, ethanol-based sanitizers. I wonder, and, honestly, I would gladly do with the consultation of an epidemiologist, to what extent an abundant use of those hygienic goods can be substitute to social distancing. I mean, to what extent can we restart social interactions with adequate protection?

Anyway, I am quite confident that local communities will be accumulating what I provisionally call ‘epidemic assets’. The challenge consists in using that phenomenon, and those assets, so as to give some spin to economies brought down by lockdowns.

Now, I am using basic laws of economics. Whenever and wherever some stock of medical supplies will be accumulated, it will be inventories, i.e. circulating assets subject to storage and endowed with direct economic utility, but not to amortization. Sooner or later, substantial inventories of anything attract the company of some fixed assets, such as buildings, equipment, and intellectual property, on the one hand, as well as the company of other circulating assets (e.g. receivable claims on third parties), and, finally, the company of JOBS, which are the key point here.   

Now, let’s imagine the following scenario. A local community, e.g. local hospital plus local city council, need to have a given amount of ‘epidemic assets’ stored and ready to use, just to keep the local epidemic situation under control. They need those epidemic assets, yet, as the local economy is stricken by epidemic lockdown, they don’t have enough money (or no money at all) to pay for those assets. Here starts the gamble. The local community offers the suppliers of epidemic assets to be paid in tokens of a virtual currency, where each token corresponds to a futures contract with claims on a future stock of epidemic assets.

The central idea is that with the virus around, everybody will have a keen interest in having enforceable claims on epidemic assets. That keen interest will be driven by two motives. In the first place, many people will need to use those epidemic assets like directly and personally. Secondly, those assets will be valuable, and futures contracts on them will have monetizable, financial value. It should be possible to create a circulation of those tokens (futures), where the direct supplier of epidemic assets can use those tokens to pay their own suppliers of intermediate goods, as well as to pay a part of the payroll. Those whom he pays will either consume those futures to grab some epidemic assets, or make those futures circulate further.

As those tokenized futures contracts on epidemic assets get developed and put in circulation, we can use the relatively recent invention called ‘smart contract’. A complex contract can be split into separate component parts, like LEGO blocks, each endowed with a different function. Users can experiment with each part separately, and the actual contracts they sign and trade are compound legal schemes. For now, I can see 3 principal LEGO blocks. The first one is the exact substance of the claim incorporated in the tokenized contracts. Futures contracts have this nuance in them: they can embody claims on a certain quantity of specified goods or assets, e.g. 100 kg of something, or on a nominal financial value of those goods or assets, like $100 worth of something.     Maturity of the claim is another thing. Futures contracts have a time horizon in them: 1 month, 6 months, 12 months etc. In this specific case, maturity of claims is the same as the lifecycle of one tokenized contract, and, honestly, if this scheme is applied in real life, we will be sailing uncharted waters. Those tokens are supposed to keep local economies going, and therefore they’d better have a long lifecycle. Hardly anyone would trust quasi – monetary tokens with a lifespan of 3 months. On the other hand, the longest futures I have seen, like those on coffee or wheat, stretch over 6 months, rarely longer. Here comes the third building block, namely convertibility of the claim. If we want the system to work smoothly, i.e. inspire trust in exchange, and be realistic in the same time, we can make those tokens convertible into something else. They could convert into similar tokens, just valid over the next window of trade, or into something else, e.g. shares in the equity of newly built local hospitals. Yes, we are certainly going to build more of them, trust me.  

Building blocks in hand, we start experimenting. Looking at the phases I distinguished in the early infancy of the Bitcoin (once again, you can look up Steady inflow of assets and predictable rules ), I see three essential steps in the development of this scheme. The first step would consist in creating a first, small batch of those tokenized contracts and test them in deals with whoever would like to try. The experience of the Bitcoin shows that once the thing catches on (and IF the thing catches on), i.e. once and if there are any businesspeople interested, it should spread pretty quickly. Then comes the second phase, that of building large portfolios of those tokenized contracts in a relatively small and select community, sort of Illuminati of medical supplies. In that phase, which is likely to be pretty long, like 1,5 year, said Illuminati will be experimenting with the exact smart structure those contracts, so as to come up with workable, massively reproducible patterns for the third phase, that of democratization. This is when the already hammered and hardened contractual patterns in those tokens will spread to a larger population. Individual balances of those tokens are likely to shrink in that third phase and become sort of standardized. This could be the moment, when our tokenized contracts can start being used as a vehicle for saving economic value over time, and it looks like a necessary condition for driving it out of its so-far autonomous, closed market into exchangeability against money.

That would be all for today. If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com . If anyone wants to bounce this ball off their bat, you are welcome. I am deeply convinced that we need to figure out some new s**t. Our world is changing, and we’d better make that change liveable.

Steady inflow of assets and predictable rules

My editorial on You Tube

Clink! The coin dropped… I have been turning that conceptual coin between my synapses for the last 48 hours, and here it is. I know what I have been thinking about, and what I want to write about today. I want to study the possible ways to restart business and economy in the midst of the COVID-19 pandemic.

There is a blunt, brutal truth: the virus will stay with us until we massively distribute an efficient vaccine against it, and that is going to take many months, most probably more than a year. Until then, we need to live our lives, and we cannot live them in permanent lockdown. We need to restart, somehow, our socio-economic structures. We need to overcome our fears, and start living in the presence of, and in spite of danger.

Here come three experiences of mine, which sum up to the financial concept I am going to expose a few paragraphs further. The first experience is that of observing a social project going on in my wife’s hometown, Starachowice, Poland, population 50 000. The project is Facebook-named ‘The Visible Hand’ (the original Polish is: Widzialna Ręka), and it emerged spontaneously with the COVID-19 crisis. I hope to be able to present the full story of those people, which I find truly fascinating, and now, I just give a short glimpse. That local community has created, within less than two weeks, something like a parallel state, with its supply system for the local hospital, and for people at risk. They even go into developing their own technologies of 3D printing, to make critical medical equipment, such as facial masks. Yesterday, I had a phone conversation with a friend, strongly involved in that project, and my head still resonates with what he said: ‘Look, the government is pretty much lost in all that situation. They pretend a lot, and improvise a lot, and it is all sort of more pretending than actually doing things. Our local politicians either suddenly evaporated, or make clumsy, bitchy attempts to boost their popularity in the midst of all that s**t. But people… Man, people are awesome. We are doing together things that our government thinks it is impossible to do, and we are even sort of having fun with it. The sense of community is nothing short of breath-taking’.

My second experience is about the stock market. If you have been following my updates since the one entitled ‘Back in the game’, you know that I decided to restart investing in the stock market, which I had undertaken to do just before the s**t hit the fan, a few weeks ago. Still, what I am observing right now, in the stock market, is something like a latent, barely contained energy, which just seeks any opportunity to engage into. Investors are really playing the game. Fear, which I could observe two weeks ago, has almost vanished from the market. Once again, there is human energy to exploit positively.

There is energy in people, but it is being locked down, with the pandemic around. The big challenge is to restart it. Right now, many folks lose their jobs, and their small businesses. It is important to create substantial hope, i.e. hope which can be turned into action. Here comes my third experience, which is that of preparing a business plan for an environmental project, which I provisionally call Energy Ponds (see Bloody hard to make a strategy and The collective archetype of striking good deals in exports for latest developments). As I prepare that business plan, I keep returning to the conclusion that I need some sort of financial scheme for situations when a local community, willing to implement the technology I propose, is short of capital and needs to sort of squeeze money out of the surrounding landscape.

Those three experiences of mine, taken together, lead me back to something I studied 3 years ago, when I was taking my first, toddler’s steps in scientific blogging: the early days of the Bitcoin. Today, the Bitcoin is the big, sleek predator of financial markets, yet most people have forgotten how that thing was born. It was an idea for safe financial transactions, based on an otherwise old concept of financial law called ‘endorsement of debt’, implemented in the second year of the big financial crisis, i.e. in 2009, to give some liquidity to small networks of just as small local businesses. Initially, for more than 18 first months of existence, the Bitcoin was a closed system of exchange, without any interface with any established currency. As far as I know, it very much saved the day for many small businesses, and I want to study the pattern of success, so as to see how it can be reproduced today for restarting business in the context of pandemic.

Before I go analytical, two general remarks. Firstly, there is plenty of folks who pretend having the magical recipe for the present s**t we are waist-deep in. I start from the assumption that we have no fresh, general experience of pandemics, and pretending to have figured the best way out is sheer bullshit. Still, we need to explore and to experiment, and this is very much the spirit I pursue.

Secondly, the Bitcoin is a cryptocurrency, based on the technology designated as Blockchain. What I want to take away is the concept of virtual financial instrument focused on liquidity, rather than the strictly spoken technology. Of course, platforms such as Ethereum can be used for the purpose I intend to get across, here below, still they are just an instrumental option.  

Three years ago, I used data from https://www.quandl.com/collections/markets/bitcoin-data,  which contains the mathematical early story of what has grown, since, into the father of all cryptocurrencies, the Bitcoin. I am reproducing this story, now, so as to grasp a pattern. Let’s walse. I am focusing on the period, during which the Bitcoin started, progressively acquired any exchangeable value against the US dollar, and finished by being more or less at 1:1 par therewith. That period stretches from January 3rd, 2009 until February 10th, 2011. You can download the exact dataset I work with, in the Excel format, from this link:

https://discoversocialsciences.com/wp-content/uploads/2020/03/Bitcoin-Early-days-to-share.xlsx .

Before I present my take on that early Bitcoin story, a few methodological remarks. The data I took originally contains the following variables: i) total number of Bitcoins mined, ii) days   destroyed non-cumulative, iii) Bitcoin number of unique addresses used per day, and iv) market capitalization of the Bitcoin in USD. On the basis of these variables, I calculated a few others. Still, I want to explain the meaning of those original ones. As you might know, Bitcoins were initially mined (as far as I know, not anymore), i.e. you could generate 1 BTC if you solved a mathematical riddle. In other words, the value you had to bring to the table in order to have 1 BTC was your programming wit plus computational power in your hardware. With time, computational power had been prevailing more and more. The first original variable, i. e. total number of Bitcoins mined, is informative about the total real economic value (computational power) brought to the network by successive agents joining it.  

Here comes the first moment of bridging between the early Bitcoin and the present situation. If I want to create some kind of virtual financial system to restart, or just give some spin to local economies, I need a real economic value as gauge and benchmark. In the case of Bitcoin, it was computational power. Question: what kind of real economic value is significant enough, right now, to become the tool for mining the new, hypothetical virtual currency? Good question, which I don’t even pretend to have a ready-made answer to, and which I want to ponder carefully.

The variable ‘days destroyed non-cumulative’ refers to the fact that Bitcoins are crypto-coins, i.e. each Bitcoin has a unique signature, and it includes the date of the last transaction made. If I hold 1 BTC for 2 days, and put it in circulation on the 3rd day, on the very same 3rd day I destroy 2 days of Bitcoins. If I hold 5 Bitcoins for 7 days, and kick them back into market on the 8th day, I destroy, on that 8th day, 5*7 = 35 days. The more days of Bitcoin I destroy on the given day of transactions, the more I had been accumulating. John Maynard Keynes argued that a true currency is used both for paying and for saving. The emergence of accumulation is important in the shaping of new financial instruments. It shows that market participants start perceiving the financial instrument in question as trustworthy enough to transport economic value over time. Note: this variable can take values, like days = 1500, which seem absurd at the first sight. How can you destroy 1500 days in a currency born like 200 days ago? You can, if you destroy more than one Bitcoin, held for at least 1 day, per day.

The third original variable, namely ‘Bitcoin number of unique addresses used per day’, can be interpreted as the number of players in the game. When you trade Bitcoins, you connect to a network, you have a unique address in that network, and your address appears in the cumulative signature that each of the Bitcoins you mine or use drags with it.  

With those three original variables, I calculate a few coefficients of mine. Firstly, I divide the total number of Bitcoins mined by the number of unique addresses, on each day separately, and thus I obtain the average number of Bitcoins held, on that specific day, by one average participant in the network. Secondly, I divide the non-cumulative number of days destroyed, on the given day, by the total number of Bitcoins mined, and present in the market. The resulting quotient is the average number of days, which 1 Bitcoin has been held for.

The ‘market capitalization of the Bitcoin in USD’, provided in the original dataset from https://www.quandl.com/collections/markets/bitcoin-data, is, from my point of view, an instrumental variable. When it becomes non-null, it shows that the Bitcoin acquired an exchangeable value against the US dollar. I divide that market capitalization by the total number of Bitcoins mined, and I thus I get the average exchange rate of Bitcoin against USD.

I can distinguish four phases in that early history of the Bitcoin. The first one is the launch, which seems to have taken 6 days, from January 3rd, 2009 to January 8th, 2009. There were practically no players, i.e. no exchange transactions, and the number of Bitcoins mined was constant, equal to 50. The early growth starts on January 9th, 2009, and last just for 3 days, until January 11th, 2009. The number of Bitcoins mined grows, from 50 to 7600. The number of players in the game grows as well, from 14 to 106. No player destroys any days, in this phase. Each Bitcoin mined is instantaneously put in circulation. The average amount of Bitcoins per player evolves from 50/14 = 3,57 to 7600/106 = 71,7.

On January 12th, 2009, something changes: participants in the network start (timidly) to hold their Bitcoins for at least one day. This is how the phase of accelerating growth starts, and will last for 581 days, until August 16th, 2010. On the next day, August 17th, the first Bitcoins will get exchanged against US dollars. On that path of accelerating growth, the total number of Bitcoins mined passes from 7600 to 3 737 700, and the daily number on players in the network passes from an average around 106 to about 500 a day. By the end of this phase, the average amount of Bitcoins per player reaches 7475,4. Speculative positions (i.e. propensity to save Bitcoins for later) grow, up to an average of about 1500 days destroyed per address.

Finally, the fourth stage of evolution is reached: entry into the financial market, when we pass from 1 BTC = $0,08 to 1 BTC = $1. This transition from any exchange rate at all to being at par with the dollar takes 189 days, from August 17th, 2010 until February 10th, 2011. The total number of Bitcoins grows at a surprisingly steady rate, from 3 737 700 to about 5 300 000, whilst the number of players triples, from about 500 to about 1 500. Interestingly, in this phase, the average amount of Bitcoins per player decreases, from 7475,4 to 3 533,33. Speculative positions grow steadily, from about 1500 days destroyed per address to some 2 400 days per address.

Below, you will find graphs with a birds-eye view of the whole infancy of the Bitcoin. Further below, after the graphs, I try to give some closure, i.e. to guess what we can learn from that story, so as to replicate it, possibly, amid the COVID-19 crisis.  

My first general conclusion is that the total number of Bitcoins mined is the only variable, among those studied, which shows a steady, quasi linear trend of growth. It is not really exponential, more sort of a power function. The total number of Bitcoins mined corresponds, in the early spirit of this cryptocurrency, to the total computational power brought to the game by its participants. The real economic value pumped into the new concept was growing steadily, linearly, and to an economist, such as I am, it suggests the presence of exogenous forces at play. In other words, the early Bitcoin was not growing by itself, through sheer enthusiasm of its early partisans. It was growing because some people saw real value in that thing and kept bringing assets to the line. It is important in the present context. If we want to use something similar to power the flywheels of local markets under the COVID-19 restrictions, we need some people to bring real, productive assets to the game, and thus we need to know what those key assets should be. Maybe the capacity to supply medical materials, combined with R&D potential in biotech and 3D printing? These are just loose thoughts, as I observe the way that events are unfolding.

My second conclusion is that everything else I have just studied is very swingy and very experimental. The first behavioural transition I can see is that of a relatively small number of initial players experimenting with using whatever assets they bring to the table in order to generate a growing number of new tokens of virtual currency.  The first 7 – 8 months in the Bitcoin show the marks of such experimentation. There comes a moment, when instead of playing big games in a small, select network, the thing spills over into a larger population of participants. What attracts those new ones? As I see it, the attractive force consists in relatively predictable rules of the game: ‘if I bring X $mln of assets to the game, I will have Y tokens of the new virtual currency’, something like that.  

Hence, what creates propitious conditions for acquiring exchangeable value in the new virtual currency against the established ones, is a combination of steady inflow of assets, and crystallization of predictable rules to use them in that specific scheme.

I can also see that people started saving Bitcoins before these had any value in dollars. It suggests that even in a closed system, without openings to other financial markets, a virtual currency can start giving to its holders a sense of economic value. Interesting.

That would be it for today. If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .

The collective archetype of striking good deals in exports

My editorial on You Tube

I keep philosophizing about the current situation, and I try to coin up a story in my mind, a story meaningful enough to carry me through the weeks and months to come. I try to figure out a strategy for future investment, and, in order to do that, I am doing that thing called ‘strategic assessment of the market’.

Now, seriously, I am profiting from that moment of forced reclusion (in Poland we have just had compulsory sheltering at home introduced, as law) to work a bit on my science, more specifically on the application of artificial neural networks to simulate collective intelligence in human societies. As I have been sending around draft papers on the topic, to various scientific journals (here you have a sample of what I wrote on the topic << click this link to retrieve a draft paper of mine), I have encountered something like a pretty uniform logic of constructive criticism. One of the main lines of reasoning in that logic goes like: ‘Man, it is interesting what you write. Yet, it would be equally interesting to explain what you mean exactly by collective intelligence. How does it or doesn’t it rhyme with individual intelligence? How does it connect with culture?’.

Good question, truly a good one. It is the question that I have been asking myself for months, since I discovered my fascination with the way that simple neural networks work. At the time, I observed intelligent behaviour in a set of four equations, put back to back in a looping sequence, and it was a ground-breaking experience for me. As I am trying to answer this question, my intuitive path is that of distinction between collective intelligence and the individual one. Once again (see The games we play with what has no brains at all ), I go back to William James’s ‘Essays in Radical Empiricism’, and to his take on the relation between reality and our mind. In Essay I, entitled ‘Does Consciousness Exist?’, he goes: “My thesis is that if we start with the supposition that there is only one primal stuff or material in the world, a stuff of which everything is composed, and if we call that stuff ‘pure experience,’ then knowing can easily be explained as a particular sort of relation towards one another into which portions of pure experience may enter. The relation itself is a part of pure experience; one of its ‘terms’ becomes the subject or bearer of the knowledge, the knower, the other becomes the object known. […] Just so, I maintain, does a given undivided portion of experience, taken in one context of associates, play the part of a knower, of a state of mind, of ‘consciousness’; while in a different context the same undivided bit of experience plays the part of a thing known, of an objective ‘content.’ In a word, in one group it figures as a thought, in another group as a thing. And, since it can figure in both groups simultaneously, we have every right to speak of it as subjective and objective both at once.”

Here it is, my distinction. Right, it is partly William James’s distinction. Anyway, individual intelligence is almost entirely mediated by conscious experience of reality, which is representation thereof, not reality as such. Individual intelligence is based on individual representation of reality. By opposition, my take on collective intelligence is based on the theory of adaptive walk in rugged landscape, a theory used both in evolutionary biology and in the programming of artificial intelligence. I define collective intelligence as the capacity to run constant experimentation across many social entities (persons, groups, cultures, technologies etc.), as regards the capacity of those entities to achieve a vector of desired social outcomes.

The expression ‘vector of desired social outcomes’ sounds as something invented by a philosopher and mathematician, together, after a strong intake of strong spirits. I am supposed to be simple in getting my ideas across, and thus I am translating that expression into something simpler. As individuals, we are after something. We have values that we pursue, and that pursuit helps us making it through each consecutive day. Now, there is a question: do we have collective values that we pursue as a society? Interesting question. Bernard Bosanquet, the British philosopher who wrote ‘The Philosophical Theory of The State[1], claimed very sharply that individual desires and values hardly translate into collective, state-wide values and goals to pursue. He claimed that entire societies are fundamentally unable to want anything, they can just be objectively after something. The collective being after something is essentially non-emotional and non-intentional. It is something like a collective archetype, occurring at the individual level somewhere below the level of consciousness, in the collective unconscious, which mediates between conscious individual intelligence and the external stuff of reality, to use William James’ expression.

How to figure out what outcomes are we after, as a society? This is precisely, for the time being, the central axis of my research involving neural networks. I take a set of empirical observations about a society, e.g. a set of country-year observation of 30 countries across 40 quantitative variables. Those empirical observations are the closest I can get to the stuff of reality. I make a simple neural network supposed to simulate the way a society works. The simpler this network is, the better. Each additional component of complexity requires making ever strengthening assumptions about the way societies works. I use that network as a simple robot. I tell the robot: ‘Take one variable from among those 40 in the source dataset. Make it your output variable, i.e. the desired outcome of collective existence. Treat the remaining 39 variables as input, instrumental to achieving that outcome’.  I make 40 such robots, and each of them produces a set of numbers, which is like a mutation of the original empirical dataset, and I can assess the similarity between each such mutation and the source empirical stuff. I do it by calculating the Euclidean distance between vectors of mean values, respectively in each such clone and the original data. Other methods can be used, e.g. kernel functions.

I worked that method through with various empirical datasets, and my preferred one, for now, is Penn Tables 9.1. (Feenstra et al. 2015[2]), which is a pretty comprehensive overview of macroeconomic variables across the planetary board. The detailed results of my research vary, depending on the exact set of variables I take into account, and on the set of observations I select, still there is a tentative conclusion that emerges: as a set of national societies, living in separate countries on that crazy piece of rock, speeding through cosmic space with no roof whatsoever, just with air condition on, we are mostly after terms of trade, and about the way we work, we prepare for work, and the way we remunerate work. Numerical robots which I program to optimize variables such as average price in exports, the share of labour compensation in Gross National Income, the average number of hours worked per year per person, or the number of years spent in education before starting professional activity: all these tend to win the race for similarity to the source empirical data. These seem to be the desired outcomes that our human collective intelligence seems to be after.

Is it of any help regarding the present tough s**t we are waist deep in? If my intuitions are true, whatever we will do regarding the COVID-19 pandemic, will be based on an evolutionary, adaptive choice. Path #1 consists in collectively optimizing those outcomes, whilst trying to deal with the pandemic, and dealing with the pandemic will be instrumental to, for example, the deals we strike in international trade, and to the average number of hours worked per person per year. An alternative Path #2 means to reshuffle our priorities completely and reorganize so as to pursue completely different goals. Which one are we going to take? Good question, very much about guessing rather than forecasting. Historical facts indicate that so far, as a civilization, we have been rather slow out of the gate. Change in collectively pursued values had occurred slowly, progressively, at the pace of generations rather than press conferences.  

In parallel to doing research on collective intelligence, I am working on a business plan for the project I named ‘Energy Ponds’ (see, for example: Bloody hard to make a strategy). I have done some market research down this specific avenue of my intellectual walk, and here below I am giving a raw account of progress therein.

The study of market environment for the Energy Ponds project is pegged on one central characteristic of the technology, which will be eventually developed: the amount of electricity possible to produce in the structure based on ram pumps and relatively small hydroelectric turbines. Will this amount be sufficient just to supply energy to a small neighbouring community or will it be enough to be sold in wholesale amounts via auctions and deals with grid operators. In other words, is Energy Ponds a viable concept just for the off-grid installations or is it scalable up to facility size?

There are examples of small hydropower installations, which connect to big power grids in order to exploit incidental price opportunities (Kusakana 2019[3]).

That basic question kept in mind, it is worth studying both the off-grid market for hydroelectricity, as well as the wholesale, on-grid market. Market research for Energy Ponds starts, in the first subsection below, with a general, global take on the geographical distribution of the main factors, both environmental and socio-economic. The next sections study characteristic types of markets

Overview of environmental and socio-economic factors 

Quantitative investigation starts with the identification of countries, where hydrological conditions are favourable to implementation of Energy Ponds, namely where significant water stress is accompanied by relatively abundant precipitations. More specifically, this stage of analysis comprises two steps. In the first place, countries with significant water stress are identified[4], and then each of them is checked as for the amount of precipitations[5], hence the amount of rainwater possible to collect.

Two remarks are worth formulating at this point. Firstly, in the case of big countries, such as China or United States, covering both swamps and deserts, the target locations for Energy Ponds would be rather regions than countries as a whole. Secondly, and maybe a bit counterintuitively, water stress is not a strict function of precipitations. When studied in 2014, with the above-referenced data from the World Bank, water stress is Pearson-correlated with precipitations just at r = -0,257817141.

Water stress and precipitations have very different distributions across the set of countries reported in the World Bank’s database. Water stress strongly varies across space, and displays a variability (i.e. quotient of its standard deviation divided by its mean value) of v = 3,36. Precipitations are distributed much more evenly, with a variability of v = 0,68. With that in mind, further categorization of countries as potential markets for the implementation of Energy Ponds has been conducted with the assumption that significant water stress is above the median value observed, thus above 14,306296%. As for precipitations, a cautious assumption, prone to subsequent revision, is that sufficient rainfall for sustaining a structure such as Energy Ponds is above the residual difference between mean rainfall observed and its standard deviation, thus above 366,38 mm per year.      

That first selection led to focusing further analysis on 40 countries, namely: Kenya, Haiti, Maldives, Mauritania, Portugal, Thailand, Greece, Denmark, Netherlands, Puerto Rico, Estonia, United States, France, Czech Republic, Mexico, Zimbabwe, Philippines, Mauritius, Turkey, Japan, China, Singapore, Lebanon, Sri Lanka, Cyprus, Poland, Bulgaria, Germany, South Africa, Dominican Republic, Kyrgyz Republic, Malta, India, Italy, Spain, Azerbaijan, Belgium, Korea, Rep., Armenia, Tajikistan.

Further investigation focused on describing those 40 countries from the standpoint of the essential benefits inherent to the concept of Energy Ponds: prevention of droughts and floods on the one hand, with the production of electricity being the other positive outcome. The variable published by the World Bank under the heading of ‘Droughts, floods, extreme temperatures (% of population, average 1990-2009)[6] has been taken individually, and interpolated with the headcount of population. In the first case, the relative importance of extreme weather phenomena for local populations is measured. When recalculated into the national headcount of people touched by extreme weather, this metric highlights the geographical distribution of the aggregate benefits, possibly derived from adaptive resilience vis a vis such events.

Below, both metrics, i.e. the percentage and the headcount of population, are shown as maps. The percentage of population touched by extreme weather conditions is much more evenly distributed than its absolute headcount. In general, Asian countries seem to absorb most of the adverse outcomes resulting from climate change. Outside Asia, and, of course, within the initially selected set of 40 countries, Kenya seems to be the most exposed.    

Another possible take on the socio-economic environment for developing Energy Ponds is the strictly business one. Prices of electricity, together with the sheer quantity of electricity consumed are the chief coordinates in this approach. Prices of electricity have been reported as retail prices for households, as Energy Ponds are very likely to be an off-grid local supplier. Sources of information used in this case are varied: EUROSTAT data has been used as regards prices in European countries[1] and they are generally relevant for 2019. For other countries sites such as STATISTA or www.globalpetrolprices.com have been used, and most of them are relevant for 2018. These prices are national averages across different types of contracts.

The size of electricity markets has been measured in two steps, starting with consumption of electricity per capita, as published by the World Bank[2], which has been multiplied by the headcount of population. Figures below give a graphical idea of the results. In general, there seems to be a trade-off between price and quantity, almost as in the classical demand function. The biggest markets of electricity, such as China or the United States, display relatively low prices. Markets with high prices are comparatively much smaller in terms of quantity. An interesting insight has been found, when prices of electricity have been compared with the percentage of population with access to electricity, as published by the World Bank[3]. Such a comparison, shown in Further below, we can see interesting outliers: Haiti, Kenya, India, and Zimbabwe. These are countries burdened with significant limitations as regards access to electricity. In these locations, projects such as Energy Ponds can possibly produce entirely new energy sources for local populations. 

The possible implementation of Energy Ponds can take place in very different socio-economic environments. It is worth studying those environments as idiosyncratic types. Further below, the following types and cases are studied more in detail:

  1. Type ‘Large cheap market with a lot of environmental outcomes’: China, India >> low price of electricity, locally access to electricity, prevention of droughts and floods,
  • Type ‘Small or medium-sized, developed European economy with high prices of electricity and relatively small a market’
  • Special case: United States ‘Large, moderately priced market, with moderate environmental outcomes’: United States >> moderate price of electricity, possibility to go off grid with Energy Ponds, prevention of droughts and floods 
  • Special case: Kenya > quite low access to electricity (63%) and moderately high retail price of electricity (0,22/ kWh), big population affected by droughts and floods, Energy Ponds can increase access to electricity

Table 1, further below, exemplifies the basic metrics of a hypothetical installation of Energy Ponds, in specific locations representative for the above-mentioned types and special cases. These metrics are:

  1. Discharge (of water) in m3 per second, in selected riverain locations. Each type among those above is illustrated with a few specific, actual geographical spots. The central assumption at this stage is that a local installation of Energy Ponds abstracts 20% of the flow per second in the river. Of course, should a given location be selected for more in-depth a study, specific hydrological conditions have to be taken into account, and the 20%-assumption might be verified upwards or downwards.
  • Electric power to expect with the given abstraction of water. That power has been calculated with the assumption that an average ram pump can create elevation, thus hydraulic head, of about 20 metres. There are more powerful ram pumps (see for example: https://www.allspeeds.co.uk/hydraulic-ram-pump/ ), yet 20 metres is a safely achievable head to assume without precise knowledge of environmental conditions in the given location. Given that 20-meter head, the basic equation to calculate electric power in watts is:
  • [Flow per second, in m3, calculated as 20% of abstraction from the local river]


20 [head in meters, by ram pumping]


9,81 [Newtonian acceleration]


75% [average efficiency of hydroelectric turbines]

  • Financial results to expect from the sales of electricity. Those results are calculated on the basis of two empirical variables: the retail price of electricity, referenced as mentioned earlier in this chapter, and the LCOE (Levelized Cost Of Energy). The latter is sourced from a report by the International Renewable Energy Agency (IRENA 2019[1]), and provisionally pegged at $0,05 per kWh. This is a global average and in this context it plays the role of simplifying assumption, which, in turn, allows direct comparison of various socio-economic contexts. Of course, each specific location for Energy Ponds bears a specific LCOE, in the phase of implementation. With those two source variables, two financial metrics are calculated:
    • Revenues from the sales of electricity, as: [Electric power in kilowatts] x [8760 hours in a year] x [Local retail price for households per 1 kWh]
    • Margin generated over the LCOE, equal to: [Electric power in kilowatts] x [8760 hours in a year] x {[Retail price for households per 1 kWh] – $0,05}

Table 1

Country Location (Flow per second, with 20% abstraction from the river)   Electric power generated with 20% of abstraction from the river (Energy for sale) Annual revenue (Annual margin over LCOE)  
China Near Xiamen,  Jiulong River (26 636,23 m3 /s)   783,9 kW (6 867 006,38 kWh a year)   $549 360,51 ($206 010,19)
China   Near Changde, Yangtze River (2400 m3/s)     353,16 kW (3 093 681,60 kWh a year)     $247 494,53 ($92 810,45
India   North of Rajahmundry, Godavari River (701 m3/s)   103,15 kW (903 612,83 kWh a year) $54 216,77 ($9 036,13) 
India   Ganges River near Patna (2400 m3/s)   353,16 kW (3 093 681,60 kWh a year) $185 620,90  ($30 936,82)
Portugal Near Lisbon, Tagus river (100 m3/s)   14,72 kW (128 903,40 kWh a year)   € 27 765,79 (€22 029,59)
Germany   Elbe river between Magdeburg and Dresden (174 m3/s)   25,6 kW (224 291,92 kWh a year) €68 252,03 (€58 271,04)
  Poland   Vistula between Krakow and Sandomierz (89,8 m3/s)     13,21 kW (115 755,25 kWh a year)   € 18 234,93 (€13 083,82)
France   Rhone river, south of Lyon (3400 m3/s)   500,31 kW   (4 382 715,60 kWh a year)  € 773 549,30  (€ 582 901,17)
United States, California   San Joaquin River (28,8 m3/s)   4,238 kW (37 124,18 kWh a year) $ 7 387,71 ($5 531,50)
United States, Texas   Colorado River, near Barton Creek (100 m3/s)   14,72 kW (128 903,40 kWh a year) $14 643,43 ($8 198,26)
United States, South Carolina   Tennessee River, near Florence (399 m3/s)   58,8 kW   (515 097,99 kWh a year)    $66 499,15  ($40 744,25)
Kenya   Nile River, by the Lake Victoria (400 m3/s)   58,86 kW (515 613,6 kWh a year)  $113 435  ($87 654,31)
Kenya Tana River, near Kibusu (81 m3/s)   11,92 kW (104 411,75 kWh a year)   $22 970,59 ($17 750)

China and India are grouped in the same category for two reasons. Firstly, because of the proportion between the size of markets for electricity, and the pricing thereof. These are huge markets in terms of quantity, yet very frugal in terms of price per 1 kWh. Secondly, these two countries seem to be representing the bulk of populations, globally observed as touched damage from droughts and floods. Should the implementation of Energy Ponds be successful in these countries, i.e. should water management significantly improve as a result, environmental benefits would play a significant socio-economic role.

With those similarities to keep in mind, China and India display significant differences as for both the environmental conditions, and the economic context. China hosts powerful rivers, with very high flow per second. This creates an opportunity, and a challenge. The amount of water possible to abstract from those rivers through ram pumping, and the corresponding electric power possible to generate are the opportunity. Yet, ram pumps, as they are manufactured now, are mostly small-scale equipment. Creating ram-pumping systems able to abstract significant amounts of water from Chinese rivers, in the Energy Ponds scheme, is a technological challenge in itself, which would require specific R&D work.

That said, China is already implementing a nation-wide programme of water management, called ‘Sponge Cities’, which shows some affinity to the Energy Ponds concept. Water management in relatively small, network-like structures, seems to have a favourable economic and political climate in China, and that climate translates into billions of dollars in investment capital.

India is different in these respects. Indian rivers, at least in floodplains, where Energy Ponds can be located, are relatively slow, in terms of flow per second, as compared to China. Whilst Energy Ponds are easier to implement technologically in such conditions, the corresponding amount of electricity is modest. India seems to be driven towards financing projects of water management as big dams, or as local preservation of wetlands. Nothing like the Chinese ‘Sponge Cities’ programme seems to be emerging, to the author’s best knowledge.

European countries form quite a homogenous class of possible locations for Energy Ponds. Retail prices of electricity for households are generally high, whilst the river system is dense and quite disparate in terms of flow per second. In the case of most European rivers, flow per second is low or moderate, still the biggest rivers, such as Rhine or Rhone, offer technological challenges similar to those in China, in terms of required volume in ram pumping.

As regards the Energy Ponds business concept, the United States seem to be a market on their own right. Local populations are exposed to moderate (although growing) an impact of droughts and floods, whilst they consume big amounts of electricity, both in aggregate, and per capita. Retail prices of electricity for households are noticeable disparate from state to state, although generally lower than those practiced in Europe[2]. Prices range from less than $0,1 per 1 kWh in Louisiana, Arkansas or Washington, up to $0,21 in Connecticut. It is to note that with respect to prices of electricity, the state of Hawaii stands out, with more than $0,3 per 1 kWh.

The United States offer quite a favourable environment for private investment in renewable sources of energy, still largely devoid of systematic public incentives. It is a market of multiple, different ecosystems, and all ranges of flow in local rivers.    

[1] IRENA (2019), Renewable Power Generation Costs in 2018, International Renewable Energy Agency, Abu Dhabi. ISBN 978-92-9260-126-3

[2] https://www.electricchoice.com/electricity-prices-by-state/ last access March 6th, 2020

[1] https://ec.europa.eu/eurostat/

[2] https://data.worldbank.org/indicator/EG.USE.ELEC.KH.PC

[3] https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS

[1] Bosanquet, B. (1920). The philosophical theory of the state (Vol. 5). Macmillan and Company, limited.

[2] Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at http://www.ggdc.net/pwt

[3] Kusakana, K. (2019). Optimal electricity cost minimization of a grid-interactive Pumped Hydro Storage using ground water in a dynamic electricity pricing environment. Energy Reports, 5, 159-169.

[4] Level of water stress: freshwater withdrawal as a proportion of available freshwater resources >> https://data.worldbank.org/indicator/ER.H2O.FWST.ZS

[5] Average precipitation in depth (mm per year) >> https://data.worldbank.org/indicator/AG.LND.PRCP.MM

[6] https://data.worldbank.org/indicator/EN.CLC.MDAT.ZS

The games we play with what has no brains at all

Life can be full of surprises, like really. When I was writing my last update, on March 7th and 8th, the one entitled ‘Lettres de la zone rouge’, I was already writing about the effects of coronavirus in the stock market. Yet, it was just sort of an external panic, back then. External to me, I mean. Now, I am in, as we all are in Europe. Now, more than ever before, I use blogging, i.e. writing and publishing content, as a device for putting order in my own thoughts.

At the university, I had to switch to online teaching, and I am putting a lot of myself into preparing good stuff for students. By the way, you can assess the quality of my material by yourself. I have two lectures on Vimeo, in a course entitled ‘Fundamentals of Finance’. Both are password-locked and the password is ‘akademia’. Pay attention to the ‘k’. Not ‘academia’, but ‘akademia’. Lecture 1 is available at https://vimeo.com/398464552 and Lecture 2 fires on 

I can’t help philosophizing. I should be focusing, in my blogging, on melting, hammering, and hardening my investment strategy in the stock market. Yet, financial markets are like an endocrine system, and given the way those hormones just fountain, right now, I am truly interested in studying the way the whole organism works. According to the personal strategy of writing and publishing, which I laid out in the update entitled ‘Back in the game’, as well as those which followed, since February 10th, 2020, I should be using my blog mostly for writing about strategies to apply for investment in the stock market. Still, life can be surprising, and it is being bloody surprising right now. There is a thin line between consistency and obstinacy, and I want to keep walking on its consistency side. In order to coin up a sensible strategy for investment, I need to understand the socio-economic environment: this is elementary stuff which I teach my students in the first year. Besides, as I observe myself right now, I think I have some affinities with some squids and octopuses: when I sense serious cognitive dissonance coming my way, I release a cloud of ink. Just in case.   

When I go deep into thinking, I like starting from what I perceive as my most immediate experience. Now, my most immediate experience consists in observing my own behaviour and the behaviour of other people. On Tuesday the 17th, I recorded those two video lectures, and I had to go to the campus of my university, where we have a state-of-the-art recording facility. I was cycling through the nearly empty city, and memories popped up. I remember the late 1970ies, when I was a little kid, and lived in the communist Poland. When I would walk the streets, back then, they were similarly empty. It is only now, when human traffic in the streets has gone down to like 5% of what it used to be until recently, that I realized how much more mobile and interactive a society we have become, in Poland, since that communist past. 

I am thinking about the way we, humans, adapt to new circumstances. How is social mobility, even that most immediately observable daily traffic, connected to the structure of our social life. How is my GDP per capita – I mean, it is per capita, and thus I can say it is my per capita – related to the number of pedestrians per hour per square kilometre out there, in the streets? My most immediate experience of street traffic is that of human energy, and the intensity of its occurrence. It looks as if the number of human steps on the pavement, together with the stream of vehicles, manifested an underlying flow of some raw, hardly individuated at all, social force. What is the link between this raw social energy, and social change, such as what we have experienced, all over Central Europe, since the collapse of the Berlin wall? Well, this is precisely what I am trying to figure out.

Now, I go deeper, as deep as William James used to go in his ‘Essays in Radical Empiricism’, published, for the first time, in 1912. Human energy, out there, manifests itself both in the streets as such, and in me, in my perception. Phenomenologically, the flow of human traffic is both outside of me, and inside my mind. The collective experience is that of roaming the city, and, in the same time, that of seeing other people doing it (or even knowing they keep doing it). Same for the stock market, real business, teaching etc. All those streams of human activity are both out there, as intersubjectively observable stuff, and inside my mind, as part of my consciousness.

What we do is both in us, and out there. Social life is a collection of observable events, and a collection of corresponding, individual experiences. My experience right now is that of reorganizing my activity, starting with my priorities as for what to work on. It is fully official, the Minister of Science and Higher Education has just signed the emergency ordinance that all classes in universities are suspended until April 10th and that we are all encouraged to take on any form of distance learning we can use, even if it isn’t officially included in syllabuses. Given that right after April 10th it will be the Easter break, and that, realistically, classes are highly unlikely to restart afterwards, I have a lot of free time and a lot of things to stream smoothly inside of that sudden freedom.

I start with making a list. I structure my activity into 3 realms: pure science, applied science, and professional occupation.

As for the strictly speaking scientific work, i.e. the action of discovering something, I am working on using artificial intelligence as a tool for simulating collective intelligence in human societies. I have come up with some interesting stuff, but the first exchange I had about it with publishers of scientific journals is like ‘Look, man, it sounds interesting, but it is really foggy, and you are really breaking away from established theory of social sciences. You need to break it down so as to attach the theory you have in mind to the existing theory and to sort. In other words: your theory is not marketable yet’. I humbly accept those criticisms, I know that good science is to be forged in such fire, and I know that science is generally about figuring out something intelligible and workable.

The concept of collective intelligence is even more interesting right now. Honestly, that COVID-19 looks to me as something collectively intelligent. I know, I know: viruses don’t even have anything to be intelligent with, them having no nervous system whatsoever. Still, juts look. COVID-19 is different from his cousins by its very progressive way of invading its host’s body. The COVID-19’s granddad, the SARS virus from 2003, was like Dalton brothers. It would jump on its prey, all guns out, and there was no way to be asymptomatic with this f**ker. Once contaminated, you were lucky if you stayed alive. SARS 2003 was sort of self-limiting its range. COVID-19 is like a jihadist movement: it sort of hangs around, masking its pathogenic identity, and starts reproducing very slowly, sort of testing the immune defences of the organism, and each consecutive step of that testing can lead to ramping up the pace of reproduction.

All this virus has, as a species, is a chain of RNA (ribonucleic acid), which is essentially information about reproducing itself, without any information about any vital function whatsoever. This chain is apparently quite long, as compared to other viruses, so it takes some time to multiply itself. That time, unusually long, allows the host’s body to develop an immune response. The mutual pacing of reproduction in the virus, and of immune kickback in the host creates that strange phase, when the majority of hosts act like postmen for the virus. Their bodies allow the COVID-19 to proliferate just a little, but just enough to become transmissible. Allowing some colonies of itself to be killed, the virus brings a new trait: it is more pervasive than deadly, and it is both in the same time. At the end of the day, COVID-19 achieves an impressive reach across the human species. I think it will turn out, by the end of this year, that COVID-19 is a record holder, among viruses, as for the total human biomass infected per unit of time.

Functionally, COVID-19 looks almost like a civilisation: it is able to expand by adaptation. As I read scientific articles on the topic of epidemics, many biologists anthropomorphise pathogens: they write about those little monsters ‘wanting’ something, or ‘aiming’ for some purpose. Still, there is nothing in a virus that could be wanting anything. There is no personality or culture. There is just a chain of RNA, long enough to produce additional time in proliferation.

Let’s compare it to human civilisation. Any human social structure started, long ago, as a small group of hominids trying to survive in an ecosystem which allows no mistakes. One of the first mistakes that our distant ancestors would make consisted in killing and gathering the shit out of their immediate surroundings, and then starving to death. Hence, they invented the nomadic pattern, i.e. moving from one spot to another before exhausting completely the local resources. Our apish great-grandparents were not nomadic by nature: they probably picked it from other species they observed. Much later, more evolved hominids discovered that nomadism could be replaced by proper management of local resources. If you domesticate a cow, and that cow shits in the fields, it contributes to regenerating the productive capacity of that soil, and so we can stay in one place for longer.

Many generations later, we had figured out still another pattern. Instead of having a dozen children per woman and letting most of them die before the age of 10, we came to having less offspring but taking care to bring that smaller number up, nicely and gently, all the way to adulthood. That allows more learning within one individual lifetime, and thus we can create a much more complex culture, and more complex technologies. In our human evolution, we have been doing very much what the COVID-19 virus does: we increase our own complexity, and, by the same means, we slow down our pace of reproduction. At the end of the day, slowing down pays off through increased range, flexibility and biomass.

My theoretical point is that collective intelligence is something very different from the individual one. The latter requires a brain, the former not at all. All a species needs at the level of collective intelligence is to make an important sequence of actions (such as the action of reproducing a long chain of nucleotides) complex and slow enough for allowing adaptation to environmental response, in that very sequence.

I assume I am a virus. I slow down my action so as to allow some response from outside, and to adapt to that response. It has a name: it is a game. An action involving two or more distinct agents, where each agent pends their action on the action of the other(s) is a game. Let’s take a game of chess. Two players: the collective intelligence of humans vs. the collective intelligence of COVID-19. Someone could say it is a wrong representation, as the human civilisation has a much more complex set of pieces than the virus has, and we can make more different moves. Really? Let’s look. How much complexity and finesse have we demonstrated so far in response to the COVID-19 pandemic? It turns out we are quite cornered: if we don’t temporarily shut down our economy, we will expose ourselves to seeing the same economy imploding when the reasonably predictable 7% of the population develops acute symptoms, i.e. respiratory impairment. What we do is essentially what the virus does: we play on time, and delay the upcoming events, so as to gain some breathing space.

We can change the rules of the game. We can introduce new technologies (e.g. vaccines), which will give is more possible moves. Still, the virus can respond by mutating. The most general rules of the game we play with the virus is given by the epidemic model. I tap into the science published in 2019 by Olusegun Michael Otunuga, in the article entitled ‘Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model’ (Heliyon, 5(9), e02499, https://doi.org/10.1016/j.heliyon.2019.e02499 ).

A crazy scientific idea comes to my mind: as we are facing a pandemic, and that pandemic deeply affects social life, I can study all of social life as concurrent pandemics: a pandemic of going to restaurant, a pandemic of making vehicles and driving them around, a pandemic of making and consuming electricity etc. COVID-19 is just one among those pandemics, and proves being competitive against them, i.e. COVID-19 prevents those other pandemics from carrying on at their normal pace.

What is the cognitive value of such an approach, besides pure intellectual entertainment? Firstly, I can use the same family of theoretical models, i.e. epidemic models, to study all those phenomena in the same time. Epidemic models have been in use, in social sciences, for quite some time, particularly in marketing. The diffusion of a new product or that of a new technology can be studied as the spreading of a new lifeform in an ecosystem. That new lifeform can be considered as candidate for being a pathogen, or a symbiont, depending on the adaptive reaction of other lifeforms involved. A new technology can both destroy older technologies and enter with them in all sorts of alliances.      

A pathogen able to kill circa 3% of the population, and temporarily disable around 10%, can take down entire economic systems. In the same time, it stimulates the development of entire industries: 3D printing, biotech, pharmacy, and even basic medical supplies. One year ago, would anyone believe that manufacturing latex gloves could be more strategic than manufacturing guns?

Lettres de la zone rouge

Mon éditorial sur You Tube

Ça fait du temps depuis ma dernière mise à jour en français. Après une période de silence complet, suivie par une période de mises à jour exclusivement en anglais, je retourne à écrire en français aussi. C’était le rythme que je suivais dans le passé et j’aimais bien. Écrire en anglais et en français, sur les mêmes sujets, me donne comme une perspective sous deux angles différents. Oui, je sais, vu que nous avons des traducteurs automatiques, à présent, nous devenons habitués à l’idée que des langues différentes sont des façons alternatives de dire la même chose. À travers mon expérience personnelle, je peux dire que ce n’est pas vrai. Bien sûr, il y a dans chaque langue cette couche parfaitement traduisible, comme les jours de la semaine. Néanmoins, il y a plus : une langue est une structure cognitive que j’utilise pour appréhender la réalité. Mon polonais natal, l’anglais et le français, bien que pas très distants géographiquement, sont pour moi des structures cognitives distinctes.

Le fait de réalité que je suis en train d’appréhender à travers ces paires de lunettes différentes est le fait de perdre de l’argent en Bourse. Douloureux mais éducatif. Je veux faire cette expérience encore plus éducative en la discutant en des langues différentes. C’est le truc ninja que j’ai découvert en bloguant : lorsque je décris et discute par écrit ce que je fais je développe mes compétences dans ce champ d’activité spécifique.  Ça fait quelques semaines que j’avais décidé de retourner dans le jeu boursier, j’utilise mon blog pour développer ma stratégie d’investissement à long terme et j’ai déjà donné un compte rendu progressif de ce retour dans mes mises à jour en anglais : « Back in the game »  , « Fathom the outcomes », « Sharpen myself », « Bloody hard to make a strategy » et enfin « Rowing in a tiny boat across a big ocean ».        

J’avais décidé de retourner dans le jeu boursier juste avant la panique coronavirus. C’est comme dans ces films, où le personnage principal s’arrête dans un hôtel paisible et le jour suivant l’endroit est attaqué par des loup-garous ou bien un volcan explose dans la proximité. La panique explose dans les marchés financiers et moi, je me dis deux choses. Premièrement, bien sûr, je me dis : « Merde ! Fallait vraiment que ça arrive juste au moment où j’ai décidé de redevenir investisseur… ». Ensuite, lorsque je passe au-delà de ce gémissement de base, je me dis : « Des stratégies efficaces à long terme, ça se forge plus que ça s’invente. Il faut de l’adversité pour se faire une idée juste de ce que mes idées valent vraiment. Une bonne stratégie d’investissement sur plusieurs années – donc précisément ce que je veux développer – je ferais mieux de la tester dans un environnement difficile ».

Le contexte, c’est surtout du rouge. Dans mon portefeuille de valeurs, le sang coule abondamment. Cette métaphore veut dire que je note des pertes – habituellement affichées en rouge – sur presque toutes mes positions d’investissement. Presque toutes : je vois une petite tâche verte, donc un retour positif sur l’investissement. C’est Incyte Corporation où je note un gain de +1,01% sur le prix initial d’ouverture. La question intuitive est « pourquoi ? », donc pourquoi est-ce que je gagne sur cette position spécifique pendant que le reste sombre dans le bain de jus de betterave (je ne veux pas répéter le mot « sang » tout le temps ; la betterave, ça peut être tout aussi dramatique, sous certaines conditions). Les questions qui viennent à l’esprit en premier lieu ne sont pas nécessairement les plus pertinentes. La recherche scientifique, ça m’a appris qu’au lieu de demander pourquoi, il vaut mieux demander « comment ? ». Lorsque je développe une compréhension approfondie de la façon dont les évènements surviennent, je peux généraliser en forme des raisons et causalités.

Lorsque je veux comprendre le comment des choses, j’aime bien utiliser la comparaison, bien dans l’esprit des empiristes. Je compare donc ce qui s’est passé avec Incyte Corporation avec ce qui est arrivé à Virgin Galactic Holdings , qui est la perte la plus vertigineuse dans mon portefeuille : – 34,13% en moins de deux semaines. Je teste donc la qualité du service www.bloomberg.com/quote et je fraye mon chemin à travers la première couche du « comment ? » : l’analyse technique. J’étudie les prix transactionnels et les volumes des transactions, pour ces deux valeurs (actions d’Incyte Corporation et celles de Virgin Galactic), afin de trouver des régularités. Puisque je veux expliquer la différence en termes de mon retour sur l’investissement en ces deux positions, je couvre la période de celui-ci, depuis le 18 février 2020 jusqu’à la dernière cotation, vendredi le 6 mars. Les chiffres correspondants à mon analyse se trouvent dans les deux tableaux ci-dessous. Plus loin, donc en-dessous des deux tableaux, je développe mon analyse.       

Incyte Corporation
Date  Volume des transactions  Prix  Valeur totale des transactions
2020, Février 18                 1 209 877   $                     79,30  $       95 943 246,10
2020, Février 19                 1 895 420   $                     82,42  $     156 220 516,40
2020, Février 20                 2 653 348   $                     82,77  $     219 617 613,96
2020, Février 21                 1 413 961   $                     80,89  $     114 375 305,29
2020, Février 24                 1 366 786   $                     78,87  $     107 798 411,82
2020, Février 25                 2 174 593   $                     77,32  $     168 139 530,76
2020, Février 26                 1 442 275   $                     77,97  $     112 454 181,75
2020, Février 27                 1 641 737   $                     75,84  $     124 509 334,08
2020, Février 28                 2 525 548   $                     75,41  $     190 451 574,68
2020, Mars 2                 2 261 393   $                     79,06  $     178 785 730,58
2020, Mars 3                 1 939 411   $                     77,80  $     150 886 175,80
2020, Mars 4                 2 043 844   $                     80,16  $     163 834 535,04
2020, Mars 5                 1 430 221   $                     78,61  $     112 429 672,81
2020, Mars 6                 1 800 123   $                     76,04  $     136 881 352,92
Virgin Galactic
Date  Volume des transactions  Prix  Valeur totale des transactions
2020, Février 18             104 077 763   $                     30,30  $   3 153 556 218,90
2020, Février 19               84 891 132   $                     37,35  $   3 170 683 780,20
2020, Février 20               45 297 426   $                     37,26  $   1 687 782 092,76
2020, Février 21               45 297 426   $                     33,87  $   1 534 223 818,62
2020, Février 24               46 110 165   $                     34,29  $   1 581 117 557,85
2020, Février 25               44 092 654   $                     34,04  $   1 500 913 942,16
2020, Février 26               40 127 391   $                     28,75  $   1 153 662 491,25
2020, Février 27               47 987 693   $                     21,97  $   1 054 289 615,21
2020, Février 28               35 531 152   $                     24,60  $      874 066 339,20
2020, Mars 2               20 098 112   $                     25,98  $      522 148 949,76
2020, Mars 3               20 098 112   $                     24,71  $      496 624 347,52
2020, Mars 4               15 466 394   $                     23,76  $      367 481 521,44
2020, Mars 5               14 124 114   $                     24,09  $      340 249 906,26
2020, Mars 6               12 867 806   $                     21,67  $      278 845 356,02

Le truc de base à se mettre en tête est que ces chiffres représentent, en partie, mais seulement en partie, des comportements humains – des décisions complexes prises en des situations d’incertitude – et la partie non-comportementale (ou plutôt pas immédiatement comportementale) correspond aux transactions automatisées sur la base des logiciels d’intelligence artificielle. Je décris les décisions des logiciels IA comme pas immédiatement comportementales puisque à l’origine, leurs algorithmes sont basés sur une logique imposée par leurs créateurs humains. D’habitude, des logiciels d’investissement contiennent une partie génétique, où l’algorithme s’optimise lui-même en écrivant des lignes de code supplémentaires, donc une fois lâchés de leur laisse, ces trucs peuvent former leur propre logique. Encore, il faut se souvenir que la plupart de ces logiciels achèvent une optimisation linéaire tout à fait simple, du type « donne mois plus de retour que celui offert par l’indice boursier ».

Chaque décision individuelle – donc strictement, humainement comportementale – prise dans le jeu boursier ressemble à surfing. Il y a une force motrice prédominante, soit la tendance temporaire du marché. L’investisseur comprend quelque peu de cette tendance, mais cette compréhension est toujours partielle. Ce que moi je veux comprendre maintenant est la tendance du marché dans ces deux cas – Incyte Corporation et Virgin Galactic Holdings – ainsi que des fines déclinaisons de cette tendance, des déclinaison qui font la différence dans mon retour sur ces deux investissement.

Par vertu de la théorie économique de base j’assume que le comportement de base dans le  marché boursier est la triade sacrée : acheter, garder ou vendre. Moi, pour le moment, vu la panique « coronavirus » je garde mes positions d’investissement comme elles sont, sans acheter plus et sans vendre. Les décisions d’acheter et de vendre sont largement symétriques : elles influencent le prix d’une valeur boursière lorsqu’elles rencontrent l’une l’autre et lorsqu’une transaction est conclue. En termes de comportement, la variable la plus intéressante est le volume des transactions, soit le nombre des valeurs échangées, dans la première colonne numérique de chaque tableau.

Là, dans les cas respectifs d’Incyte Corporation et de Virgin Galactic Holdings, deux modèles se dessinent. Le volume des transactions sur Incyte Corporation oscille, en le dernier volume enregistré, vendredi 6 mars, est en fait supérieur au premier volume observé le 18 février. L’intensité d’échange sur cette valeur démontre quelque chose comme réflexion intense de la part des investisseurs. Ce volume n’est pas du tout corrélé avec les variations des prix : le coefficient de corrélation de Poisson tombe à r = 0,06. Vu la structure mathématique de ce coefficient, il y a probablement trop d’à-coups soudains dans les deux variables. Ça remue, quoi. Si je gagne ou je perds sur cette position dans le marché, c’est précisément parce que ça remue. Dans le cas de Virgin Galactic Holdings, le volume des transactions suit une trajectoire descendante sans équivoque – tout comme le prix – et les deux sont significativement, positivement corrélés, avec r = 0,59. En même temps, bien le volume des transactions strictement dit que la valeur agrégée de ces transactions étaient beaucoup plus élevées dans le cas de Virgin Galactic Holdings que dans celui d’Incyte Corporation.

Je vois donc que les investisseurs se comportent de façon différente vis-à-vis de ces deux valeurs. Le marché boursier est complexe, il marche largement sur anticipation faite sur la base d’information vraiment disparate, et ces différences peuvent être largement le résultat des facteurs autres que les traits individuels de ces sociétés. Ceci dit, les caractéristiques individuelles de ces deux business respectifs peuvent jouer un rôle important pour leur performance boursière. Je passe dont de l’analyse technique à l’analyse fondamentale et jette commence à feuilleter (figurativement, bien sûr, ce sont des documents PDF) leur rapports annuels.   

Incyte Corporation c’est du business bien ancré financièrement. Plus de deux milliards de dollars de revenu en 2019, avec presque 447 millions de bénéfice net, c’est du solide et du hautement profitable. En plus, lorsque j’observe leur compte d’exploitation sur la période 2015 – 2019, je vois in progrès constant et solide. En revanche, Virgin Galactic Holdings c’est plutôt du futur que du présent. Le modèle de business consiste surtout en des dépenses substantielles sur la recherche et le développement des nouvelles technologies (presque 133 millions de dollars) – il s’agit des technologies de voyage spatial commercial – orné ci et là avec des revenus symboliques de 3,8 millions de dollars. Bien sûr, les opérations courantes de ce business sont profondément déficitaires.

Je connecte ces deux observations à la théorie d’investissement formulée par James Tobin et William Brainard, linguistiquement intéressante pour les francophones puisqu’que dans le jargon économique elle est désignée comme la théorie du « q » (regardez, par exemple : Yoshikawa 1980[1]). Dans cette perspective théorique, les titres financiers sujets à l’échange boursier sont surtout et avant tout des titres de contrôle d’actifs productifs exploités par les sociétés émettrices de ces titres. Dans la longue perspective, le marché boursier est donc fortement connecté au marché d’actifs productifs – donc le marché des technologies et de l’immobilier industriel – et cette connexion est plus importante que la mécanique purement interne de la Bourse.

Je me pose la question suivante : comment cette connexion entre les actifs et l’échange boursier d’actions marche dans les deux cas étudiés ici ? Dans le cas d’Incyte Corporation , le 18 février les investisseurs avaient fait des transactions égales à 2,8% d’actifs totaux de la société et le 6 mars les transactions journalières avaient fait 3,99% de la valeurs comptable d’actifs. Avec Virgin Galactic Holdings , c’est une histoire différente : le 18 février le mouvement boursier sue leurs actions avait fait 520,78% de la valeur comptable de leurs actifs, pour descendre à 46,05% desdits actifs dans la journée du 6 mars. Je vois donc deux modèles comportementaux complètement différents dans les décisions d’investissement boursier dans ces deux sociétés. La différence entre ces deux modèles comportementaux peut être liée de la différence sectorielle : Incyte Corporation c’est de la biotechnologie bien tassée et Virgin Galactic Holdings c’est un rêve follement charmant d’organiser des vols orbitaux à l’échelle commerciale et ce rêve semble avoir un fort potentiel de générer des retombées technologiques substantielles.

Maintenant, j’applique la même méthode de comparaison – volume des transactions, valeur totale des transactions – aux deux autres valeurs dans mon portefeuille, toutes les deux dans le même secteur cette fois. Je parle du secteur des technologies photovoltaïques et là-dedans, j’ai investi dans les actions de First Solar (- 21,07% de perte dans mon portefeuille) et dans celles de Vivint Solar (perte – 7,96%). Bien que dans le rouge après vendredi dernier, ces deux valeurs c’étaient défendues longtemps contre la panique « coronavirus ». Encore mardi dernier, j’avais un retour positif sur ces deux positions. Toutes les deux démontrent une proportion similaire entre la valeur totale des transactions boursières et la valeur comptable des actifs. Dans le cas de First Solar , cette proportion était de 1,04% le 18 février et 0,84% vendredi 6 mars. En ce qui concerne Vivint Solar , on parle de 0,54% le 18 février et 0,80% le 6 mars.

Ci-dessous, dans deux autres tableaux, je présente les données détaillées à propos de ces deux sociétés. Je continue mon développement plus loin.  

First Solar
Date  Volume  Prix  Valeur totale des transactions
2020, Fevrier 18   1 407 357   $                     55,65  $       78 319 417,05
2020, Fevrier 19     1 945 614   $                     57,37  $     111 619 875,18
2020, Fevrier 20   4 156 485   $                     59,32  $     246 562 690,20
2020, Fevrier 21   9 774 901   $                     50,59  $     494 512 241,59
2020, Fevrier 24    3 700 847   $                     51,21  $     189 520 374,87
2020, Fevrier 25 2 807 226   $                 48,57  $     136 346 966,82
2020, Fevrier 26 2 670 436   $                     46,11  $     123 133 803,96
2020, Fevrier 27 2 699 288   $                  44,25  $     119 443 494,00
2020, Fevrier 28 2 950 667   $                     45,77  $     135 052 028,59
2020, Mars 2 2 730 409   $                     44,95  $     122 731 884,55
2020, Mars 3 1 569 455   $                     44,21  $       69 385 605,55
2020, Mars 4 1 340 145   $                     45,48  $       60 949 794,60
2020, Mars 5 1 259 142   $                     45,47  $       57 253 186,74
2020, Mars 6 1 448 658   $                     43,37  $       62 828 297,46
Vivint Solar
Date  Volume  Prix  Valeur totale des transactions
2020, Fevrier 18 1 319 176   $                    11,04  $        14 563 703,04
2020, Fevrier 19 2 290 068   $                    11,80  $        27 022 802,40
2020, Fevrier 20 4 279 615   $                    12,85  $        54 993 052,75
2020, Fevrier 21 3 125 854   $                    11,44  $        35 759 769,76
2020, Fevrier 24 2 476 290   $                    11,76  $        29 121 170,40
2020, Fevrier 25 2 004 507   $                    11,59  $        23 232 236,13
2020, Fevrier 26 3 798 572   $                    11,91  $        45 240 992,52
2020, Fevrier 27 3 563 184   $                    11,08  $        39 480 078,72
2020, Fevrier 28 2 526 386   $                    11,24  $        28 396 578,64
2020, Mars 2 3 188 036   $                    11,19  $        35 674 122,84
2020, Mars 3 2 886 017   $                    11,69  $        33 737 538,73
2020, Mars 4 1 539 854   $                    11,91  $        18 339 661,14
2020, Mars 5 1 478 135   $                    11,73  $        17 338 523,55
2020, Mars 6 1 990 593   $                   10,78  $        21 458 592,54

Voilà donc qu’une régularité se dessine. Dans la panique ambiante des marchés financiers, parmi les quatre valeurs que je viens d’analyser point de vue prix et volume, les deux gagnants – Incyte Corporation toujours dans le vert et Vivint Solar juste un peu dans le rouge – démontrent un trait commun intéressant. Dans les deux cas, entre le 18 février et le 6 mars, le coefficient « valeur totale des transactions boursières par jour divisée par la valeur comptable des actifs » démontre une tendance croissante, tout en restant relativement modeste.

Par ailleurs, dans le cas des sociétés du photovoltaïque, on peut remarquer les retombées des derniers développement aux États-Unis. Selon bloomberg.com, en janvier 2020, l’administration du président Donald Trump avait donné le feu vert pour la construction de la première méga-ferme solaire dans le désert californien et cette ferme va être construite précisément par First Solar. On peut voir qu’entre le 18 et le 21 février le volume des transactions en actions de First Solar avait bondi tout à coup, tout en faisant des vagues côté et Vivint Solar.    

Bon, c’est tout dans cette mise à jour. Vous pouvez me contacter à travers la boîte électronique de ce blog : goodscience@discoversocialsciences.com .

[1] Yoshikawa, H. (1980). On the” q” Theory of Investment. The American Economic Review, 70(4), 739-743.

Rowing in a tiny boat across a big ocean

My editorial on You Tube

On February 24th, after I posted my last update ( Bloody hard to make a strategy ), I did what I was declaring I would do: I bought 1 share of Invesco QQQ Trust (QQQ) for $224,4, 5 shares of Square Inc, at $76,85, thus investing $384,25 in this one. Besides, I have just placed an order to buy one share ($33) of Virgin Galactic Holdings, mostly because it is high tech.

These are the small steps I took, but now, as financial markets are freaking out about coronavirus, it is the right moment to figure out my endgame, my strategy. Yes, that’s surely one thing I have already nailed down as regards investment: the more the market is driven by strong emotions, the more I need to stay calm. Another thing I have learnt by experience is that it really pays off, at least for me, to philosophise about the things I do, would it be science or a business strategy. It really pays to take a step back from current events, sit and meditate on said events. Besides, in terms of scientific research, I am currently working on the ways to derive economic value added from environmental projects. I guess that fundamental questioning regarding economic value and decisions we make about it will be interesting.

When I philosophise about anything connected to social sciences, I like talking to dead people. I mean, no candles, no hand-touching, just reading and thinking. I discovered that I can find exceptionally deep insights in the writings of people labelled as ‘classics’. More specifically, books and articles written at an epoch when the given type of economic institution was forming, or changing fundamentally, are particularly insightful. It is a little bit as if I were an astrophysicist and I had a book, written by an alien who watched the formation of a planet. The classic that I want to have a word with right now is Louis Bachelier. I am talking about Bachelier’s ‘Theory of Speculation’ , the PhD thesis from 1900, originally published in French as ‘Théorie de la spéculation’ (Bachelier 1900[1]). Here’s how Louis Bachelier introduces his thesis: ‘INTRODUCTION. The influences which determine the movements of the Stock Exchange are innumerable. Events past, present or even anticipated, often showing no apparent connection with its fluctuations, yet have repercussions on its course. Beside fluctuations from, as it were, natural causes, artificial causes are also involved. The Stock Exchange acts upon itself and its current movement is a function not only of earlier fluctuations, but also of the present market position. The determination of these fluctuations is subject to an infinite number of factors: it is therefore impossible to expect a mathematically exact forecast. Contradictory opinions in regard to these fluctuations are so divided that at the same instant buyers believe the market is rising and sellers that it is falling. Undoubtedly, the Theory of Probability will never be applicable to the movements of quoted prices and the dynamics of the Stock Exchange will never be an exact science. However, it is possible to study mathematically the static state of the market at a given instant, that is to say, to establish the probability law for the price fluctuations that the market admits at this instant. Indeed, while the market does not foresee fluctuations, it considers which of them are more or less probable, and this probability can be evaluated mathematically.

As I see, Louis Bachelier had very much the same feelings about the stock market as I have today. I am talking about the impression to be rowing in a really tiny boat across a bloody big ocean, with huge waves, currents and whatnot, and a general hope that none of these big dangerous things hits me. And yes, there are sharks. A natural question arises: why the hell stepping into that tiny boat, in the first place, and why leaving shore? If it is that risky, why bother at all? I think there is only one sensible answer to that: because I can, because it is interesting, and because I expect a reward, all three in the same time.

This is the general, duly snappy reply, which I need to translate, over and over again, into goals and values. Back in the day, almost 30 years ago, I used to do business, before I went into science. For the last 4 years or so, I am thinking about getting back into business. The difference between doing real business and investing in the stock market is mostly the diversification of risk. I dare say that aggregate risk is the same. When I do real business, as a small businessman, I have the same feeling of rowing in that tiny boat across a big ocean. I have so little control over the way my small business goes that when I manage to get in control of something, I get so excited that I immediately label that controlled thing as ‘successful business model’. Yet, running my own small business is so time and energy absorbing that I have hardly any juice left for anything else. I have all my eggs in the same basket, i.e. I do not hedge my risks. With any luck, I just insure them, i.e. I share them with someone else.

When I invest in the stock market, I almost intuitively spread my equity over many financial assets. I hedge the business-specific risks. Here comes an interesting question: why did I choose to invest in biotechnology, renewable energies and IT? (see Back in the game ). At the time, one month ago, I made that choice very intuitively, following my intellectual interests. Now, I want to understand myself deeper. Logically, I turn to another dead man: Joseph Alois Schumpeter, and to his ‘Business Cycles’. Why am I knocking at this specific door? Because Joseph Alois Schumpeter studied the phenomenon of technological change with the kind of empirical assiduity that even today inspires respect. From Schumpeter I took that idea that once true technological change starts, it is unstoppable, and it inevitably drives resources from the old technologies towards the new ones. There are technologies in the today’s world, precisely such as biotechnology, information technologies, and new sources of energy, which no one can find their way around. Those technologies are already reshaping deeply our everyday existence, and they will keep doing so. If I wanted to start a business of my own in any of these industries, it would take me years to have the thing running, and even more years to see any economic gains. If I invest in those industries via the stock market, I can tap directly into the economic gains of the already existing businesses. Egoistic but honest. I come back to that metaphor of boat and ocean: instead of rowing in a small boat across a big ocean, I hook onto a passing cruiser and I just follow it.

There is more to the difference between entrepreneurship, and investment in the stock market. In the latter case, I can clearly pace myself, and that’s what I do: every month, I invest another parcel of capital, on the grounds of learning acquired in past months. In entrepreneurship, such a pacing is possible, yet much harder to achieve. Capital investment required to start the business usually comes in a lump: if I need $500 000 to buy machines, I just need it. Of course, there is the fine art of engaging my own equity into a business step by step, leveraging the whole thing with credit. It is possible in an incorporated business. Still, the very incorporation of a business requires engaging a minimum equity, which is way greater than the baby steps of investment.

Incidentally, the very present developments in the stock market make an excellent opportunity to discuss more in depth. If you care to have a look at the NASDAQ Composite Index , especially at its relatively longer time window, i.e. from 1 month up, you will see that what we have now is a typical deep trough. In my own portfolio of investment positions, virtually every security is just nosediving. Why does it happen? Because a lot of investors sell out their stock, at whatever price anybody is ready to pay for it.

Have you ever wondered how do stock prices plummet, as it is the case now? I mean, HOW EXACTLY? Market prices are actual transactional prices, i.e. prices that actual deals are closed at. When stock prices dive, people who sell are those who panic, I get it. Who buys? I mean, for a very low stock price to become a market price, someone must be buying at this specific price. Who is that, who buys at low prices when other people freak out and sell out? Interesting question. When the market price of a security falls, the common interpretation is: ‘it is ‘cause that security has become less attractive to investors’. Wait a minute: if the price falls, someone buys at this falling price. Clearly, there are investors who consider that security attractive enough to pay for it even though the price is likely to fall further.

When a whole market index, such as NASDAQ Composite, is skiing downhill, I can see the same phenomenon. Some people – right, lots of people – sell out whatever financial assets they have because they are afraid to see the market prices fall further down. Some other people buy. They see an opportunity in the widespread depreciation. What kind of opportunity? The one that comes out of other people losing control over their behaviour. Now, from there, it is easy to go into the forest of conspiracy theories, and start talking about some ‘them’, who ‘want to..’ etc. Yes, there is a rational core to those theories. The stock market is like an ocean, an there are sharks in it. They just wait patiently for the prey to come close to them. Yes, officially, the spiritus movens of the present trough in stock markets is coronavirus. Wait a minute: is it coronavirus as such, or what we think about it? Wait another minute: is it about what we think as regards the coronavirus, or about what we expect other people (in the stock market) to think about it?

When I look at the hard numbers about coronavirus, they look refreshing. When I divide the number of fatalities by the number of officially diagnosed cases of infection, that f**ker is, under some angle, less deadly than common flu. Take a look at Worldometer: 85 217 official carriers of it, and 2 924 of those carriers dead. The incidence of fatality is 2924/85217 = 3,43%. A study by the Center for Infectious Disease Research and Policy at the University of Minnesota finds an even lower rate: 2,3%. As reported by CNBC, just in the United States of America, during the on-going flu season, the flu virus has infected 19 million people, and caused 10 000 deaths. The incidence of death among people infected with flu is much lower, 10000/19000000 = 0,05%, yet the absolute numbers are much higher.       

Please, notice that when a real panic overwhelms financial markets, there is no visible fall in prices, because there are no prices at all: nobody is buying. This is when pricing is suspended, and the stock market is effectively shut down. As long as there are any prices in the market, some market agents are buying. Here comes my big claim, sourced in my recent conversation with the late Joseph Schumpeter: the present panic in the stock market is just superficially connected to coronavirus, and what is manifesting itself deep underneath that surface is widespread preparation for a bloody deep technological change, coming our way right now. What technological change? Digital technologies, AI, robotization, shift towards new sources of energy, and, lurking from the bottom of the abyss, the necessity to face climate change.

I am deeply convinced that my own investment strategy, should it demonstrate any foresight and long-range planning, should be most of all espousing the process of that technological change. Thus, it is useful to understand the process and to plan my strategy accordingly. I have already done some research in this field and my general observation is that technological change as it is going on right now is most of all marked by increasing diversity in technologies. What we are witnessing is not just a quick replacement of old technologies by new ones: that would be too easy. Owners of technological assets need to think in terms of stockpiling many generations of technologies in the same time, and the same place.

From the point of view of an entrepreneur it is what the French call “l’embarras du choix”, which means embarrassingly wide range of alternative technological decisions to take. I described it partially in ‘4 units of quantity in technological assets to make one unit of quantity in final goods’.  Long story short, there is a threshold speed of technological, up to which older technological assets can me simply replaced by newer ones. Past that threshold, managing technological change at the business level becomes progressively more and more a guessing game. Which specific cocktail of old technologies, and those cutting-edge ones, all that peppered with a pinch of those in between, will work optimally? The more technologies we can choose between, the more aleatory, and the less informed is the guess we make.

I have noticed and studied one specific consequence of that ever-widening choice of technological cocktails: the need for cash. Mathematically, it is observable as correlation between two coefficients: {Amortization / Revenue} on the one hand, and {Cash & cash equivalent / Assets} on the other hand. The greater a percentage of revenues is consumed by amortization of fixed assets, the more cash (in proportion to total assets) businesses hold in their balance sheets. I nailed it down statistically, and it is quite logical. The greater a palette of choices I might have to navigate my way through, the more choices I have to make in a unit of time, and when you need to make a lot of business choices, cash is king, like really. Open credit lines with banks are nice, just as crowdfunding platforms, but in the presence of significant uncertainty there is nothing like a nice, fat bank account with plenty of liquidity at arm’s reach.

When a business holds a lot of cash for a long time, they end up by holding a lot of the so-called ‘cash equivalents’, i.e. a low-risk portfolio of financial securities with a liquidity close to cash strictly spoken. Those securities are listed in some stock market, whence an inflow of capital into the stock market from companies holding a lot of cash just in case a breakthrough technology pokes its head from around the corner. Quick technological change, quick enough to go past the threshold of simple replacement (understood as straightforward shift from older technology towards and into a newer one), generates a mounting wave of capital placed on short-term positions in the stock market.

Those positions are short-term. In this specific financial strategy, entrepreneurs perceive the stock market as one of those garage-size warehouses. In such a place, you can store, for a relatively short time, things which you don’t know exactly what to do with, yet you feel could need them in the future. Logically, growing an occurrence of of short-term positions in the stock market induces additional volatility. Each marginal million of dollars pumped in the stock market via this tube is more restless than the previous one, whence increasing propensity of the market as a whole to panic and run in the presence of any external stressor. Joseph Schumpeter described that: when the economy is really up to a technological leap, it becomes overly liquid financially. The financial hormone gets piled up in the view of going all out.

I come back to thinking about my own strategy. Whatever kind of run we have in the stock market right now, the coronavirus is just a trigger, and the underlying tension it triggered is linked to technological change of apparently unseen speed and complexity.

In my portfolio, just two positions remain positive as for their return rate: Incyte Corporation and Square Inc. All the others have yielded to the overwhelming panic in the market. Why? I can follow the tracks of two hypotheses. One, those companies have particularly good fundamentals, whilst being promisingly innovative: they sort of surf elegantly on the wave of technological change. Two, it is more aleatory. In the times of panic such as we experience now, in any given set of listed securities, investors flock, in a largely random way, towards some businesses, and away from others. Mind you, those two hypotheses are mutually complementary (or rather they are not mutually exclusive): aleatory, panicky behaviour on the part of investors conjoins with a good mix of characteristics in specific businesses.     

Right, so I have the following situation. In my portfolio, I have two champions of survival, as regards the rate of return – the above-mentioned Incyte Corporation and Square Inc. – in the presence of all them other zombies that succumbed to the surrounding panic: First Solar Inc., Macrogenics, Norsk Hydro, SMA Solar Technology AG, Virgin Galactic Holdings, Vivint Solar, 11Bit, Asseco Business Solutions, AMUNDI EPRA DR (ETF Tracker), and Invesco PowerShares EQQQ Nasdaq-100 UCITS (ETF Tracker). I keep in mind the ‘how?’ of the situation. In the case of Incyte Corporation and Square Inc., investors are willing to pay for them more than they were ready to pay in the past, i.e. deals on those securities tend to be closed at a ramping up average price. As for all the others, displaying negative rates of return, presently investors pay for them less than they used to a few days or weeks ago. I stress once again the fact that investors pay. This is how prices are fixed. Whichever of those securities we take, some investors keep buying.

What I can observe are two different strategies of opening new investment positions. The first one, largely dominating, consists in buying into cheap stuff, and forcing that stuff to go even cheaper. The second one, clearly less frequently occurring, displays investors opening new positions in the market at a higher price than before. I am observing two distinct behavioural patterns, and I presume, though I am not sure, that these two patterns of investment are correlated with the intrinsic properties of two supposedly different sets of securities. I know that at this point I am drifting away from the classical ‘supply – demand’ pattern of pricing in the stock market, yet I am not drifting really far. I acknowledge the working of Marshallian equilibrium in that price setting, I just enrich my investigation with the assumption of diverging behavioural patterns.

In my portfolio, I hold securities which somehow are attached to both of those behavioural patterns. I have taken a position on other people’s possible behaviour. This is an important finding about my own investment strategy and the ways I can possibly get better at it. I can be successful in my investment if I make the right guess as regards the businesses or securities that incite the pattern of behaviour manifesting in growing price that deals get closed at.  What I can observe now, in the times of panic in the market, is a selective panic. As a matter of fact, even before that coronavirus story went western, securities in my portfolio were disparate in their rate of return: some of them positive, some others negative. What has changed now is just the proportion between the positive returns and the negative ones.  

Another question comes to my mind: when I open positions on the stock of businesses in some selected technological fields, like solar energy, do I participate in technological change, or do I just surf over the top of financial foam made by that change? There is that theory, called ‘Q theory of investment’ (see for example Yoshikawa 1980[2]), developed by James Tobin and William Brainard, and that theory claims that when I invest in the stock of listed companies, I actually buy claims on their productive assets. In other words, yes, listed shares are just financial instruments, but when I buy them or sell them, I, as an investor, I develop strategies of participation in assets, not just in equity.

When I think about my own behaviour, as investor, I certainly can distinguish between two frames of mind: the gambling one, and the farming one. There are moments, when I fall, unfortunately, into a sort of frantic buying and selling, and I use just small bits of information, and the information I use is exclusively technical, i.e. exclusively the price curves of particular securities. This is the gambling pattern. I do my best to weed out this pattern of behaviour in myself, as it: a) usually makes me lose money, like really and b) is contrary to my philosophy of developing long term strategies for my investment. On the other hand, when I am free of that gambling frenzy, I tend to look at my investment positions in the way I look at roses in my garden, sort of ‘What can I do to make them grow bigger and flourish more abundantly?’. This is my farming frame of mind, it is much less emotional than the gambling one, and I intuitively perceive it as more functional for my long-term goals.

Good, it looks like I should give some provisional closure and put this update online. I think that in the presence of a hurricane, it is good to stay calm, and to meditate over the place to go when the hurricane calms down. I guess that for the weeks to come, until I collect my next rent and invest it in the stock market, no sudden decisions are recommended, given the surrounding panic. I think the best I can do during those weeks is to study the fundamentals of my present portfolio of investment positions and draw some conclusions from it.

If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .

[1] Bachelier, L. (1900). Théorie de la spéculation. In Annales scientifiques de l’École normale supérieure (Vol. 17, pp. 21-86).

[2] Yoshikawa, H. (1980). On the” q” Theory of Investment. The American Economic Review, 70(4), 739-743.