The balance between intelligence and the way we look in seasoned black leather

MY EDITORIAL ON YOU TUBE

After having devoted some of my personal energy to reviewing other people’s science (see Second-hand observations), I return to my own science, i.e. to my book on the civilizational role of cities. Reviewing that manuscript in the field of energy management gave me some inspiration. I realized that the core message I wanted to convey in the book was that human societies have collective intelligence, that intelligence manifests itself in typical, recurrent patterns, and cities are one of those patterns, where creating a demographic anomaly allows creating new social roles for a growing population, and assuring functional partition between two types of settlements: agricultural land for producing food, on the one hand, and urban land for producing new social roles and new technologies, on the other hand. Moreover, cities are the base of markets, and of the market-based economy. The whole social system based on the development of skills and technologies, so as to produce tradable surpluses, that whole system is precisely based on the role of cities. Maybe there are other social structures to obtain the same result, but we haven’t figured them out yet. With the creation of cities, we developed a pattern of further development, where apparently fundamental markets interweave with the apparently futile ones, and the whole system facilitates technological change and the creation of new social roles. The social system based on cities is like a social vortex, largely powering its own momentum and sucking new people into it.

That overview of my thinking brings me one more time to the issue of collective intelligence and to the big methodological question: to what extent can neural networks be used to simulate collective intelligence in human societies? I know, I know, this is some n-th time I return to that thing. Still, it is important, both methodologically and fundamentally. There is a whole big stream of research, including my own, where neural networks are used as mathematical tool for validating theoretical model. I can see that neural networks tend to replace the classical methods, such as GARCH, ARIMA or the good old Ordinary Least Squares. All that stuff works with the same basic data, i.e. with residual errors which inevitably appear as we try to apply our grand ideas to the brutal reality of life. Still, the way that neural networks process those errors is fundamentally different from stochastic models. The latter just cut through the entire set of data with one line (or curve, for that matter), which minimizes the sum total of residual errors, all in one go. Neural networks are more patient, and they minimize error by error, case by case. Neural networks learn.

The point is that when I use a neural network to validate a theoretical model in social sciences, I should substantiate the claim that the network represents the way of learning in the given society. The best theory of learning which I have found so far is the Interface Theory of Perception (Hoffman et al. 2015[1]; Fields et al. 2018[2]; see also I followed my suspects home). I rephrase it shortly and I try to put it against (or next to) my own methodology.

When an aggregate socio-economic variable, such as e.g. GDP per capita or energy consumption per capita, changes over time, it allows assuming a society doing something differently as time passes. In other words, those aggregate variables are manifestations of collective decisions and collective action. Question: how are those collective decisions being taken and how are they being turned into action? Some sort of a null assumption is that we have no way to guess anything about that process. Still, I think I can make a slightly stronger assumption, namely that we collectively know what we are doing, we just know it imperfectly. Therefore, when I observe a variable such as GDP per capita, or the average number of hours worked per person per year, change over years, I can assume it manifests a collectively intelligent adaptation: we do something together, we contemplate the outcomes, we say ‘Blast! It is not exactly what we meant. Folks! Get ready! We adapt! That rate of secondary education has to change! We are running a civilisation here, aren’t we?’, and we engage into another set of decisions and actions.

Collective decisions and collective action mean that people argue and diverge in what they say they intend to do, in what they really do, and in what they claim they have just done. We diverge from each other and we lie to each other on the top of it, and we lie to ourselves, and yet that whole business of civilisation seems to be working. We have a set N = {se1, se2, …, sen} of n social entities (people, basically, or various agglomerations thereof), and they all cheat, lie, and egoistically get after it, in the presence of a set R = {r1, r2, …, rm} of m external stressors (viruses, presidents, wars, bad crops etc.). Mind you, as long as n > 1, i.e. as long as there are many social entities, probably at least one of them is doing things sufficiently well to survive in the presence of m stressors.

We have those n social entities trying to get by in the presence of m external stressors, and one could wonder how that lot can learn anything? I subtly change the shade of the question ‘how?’ into ‘how can we know that?’. How can we know that a social entity has learnt anything in the presence of external stressors? Learning can be understood from two perspectives: subjective internal impression of having learnt something, on the one hand, and objective, externally observable fact of having acquired new skills. When I prepare the same sauce 30 times, 20 times it is completely spoilt, 9 times it sort of approaches the ideal, and 1 time, the final one, I have the impression I nailed it. I have the impression I have learnt something, however it does not mean other people think the same.  

I need a gauge to assess learning. One possible method, more specifically the one used in artificial neural networks, consists in checking how close my social entities are to a pre-defined desired outcome. In more elaborate artificial neural networks, the pre-defined component might be just the method of setting the desired outcome. That outcome can be simply survival or something more, such as achieving a certain amount of something valuable.

Good, so I have those n social entities, and the stressor they act under is the pressure to achieve a desired outcome, i.e. to obtain a certain payoff. The social entity sei which gets the closest to that outcome, or which, in other words, collects the greatest payoff, can be considered as the most successful. Life is reproduction. People die, and new people are born. Governments change. On the long run our set N = {se1, se2, …, sen} of n social entities is interesting to the extent that it reproduces into another set Nk of n(k) social entities. Social change can be viewed as an (almost) ever-lasting chain of sets, each with social entities inside: N1 transforms into N2, which turns into N3 etc.

I think I have just nailed an important point (involuntarily, to be clear). When I study any kind of social change, I can make one of the two alternative assumptions: continuity of social entities versus their generational reproduction. Social structures can be seen such as I have just described it: as changing sets of social entities. Under that angle, the 38 million people in my native Poland today are a different set of people from the roughly 36 million who were around when I was 10, i.e. in 1978. It does not necessarily mean that each person present in 1978 died and has been replaced by someone else; I am pretty sure I didn’t die. However, some people died, some new people have come to the fore, some people changed significantly etc. On the whole, the set N2020 is different from the set N1978. There is a different angle for looking at the same reality: people in Poland, 2020, are the same big social entity as the one in Poland, 1978, and it is just the internal structure of that entity that has changed. 

What is the practical (well, theoretical) difference between those two angles of approach to the same theatre of social change, i.e. consecutive sets of small entities as opposed to consecutive states of one big entity? When I simulate social change as a sequence of sets, where individual components can change their properties, a long sequence of that type is like a journey of discovery. Each consecutive set Nk comes out of learning that occurred in its predecessor Nk-1. The transformation of one set into another certainly follows some constraints, yet a long sequence of such transformations is like a long hike up a hill: we have to take turns around boulders and ravines, we have to choose between paths of different difficulty, and usually an easier path is a less steep one, thus a longer and slower one. This type of change, social or biological, is known as adaptive walk in rugged landscape in Kaufman & Levin 1987[3]. Mathematically, it is a Markov chain, i.e. a chain of states, where the properties of each consecutive state are determined just by the properties of the previous state as well as by the algebra of transformation from one state to another (the so-called σ-αλγεβρα, oops! Excuse me, I wanted to say σ-algebra).

When I take the other approach to a social structure, i.e. when I study it as one big, perennial social entity which undergoes structural change inside, that change is something like different shapes of the same thing. I noticed strong marks of such an approach in that scientific paper entitled ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’, which I was reviewing recently on the request of  the International Journal of Energy Sector Management (ISSN1750-6220). In that paper, a complex system of relations between economy, energy and society is represented as four gradients of change in, respectively, volume of pollution x, real economic output y, environmental quality z and energy reduction constraints w. I know it is a bit abstract, at this point, yet I want to make an effort and explain it. Imagine an irregular quadrilateral, i.e. a rectangle with broad intellectual horizons. Four angles, four edges, yet the angles do not have to be right and the edges do not have to be parallel in pairs. Just four of each lot. The length of each edge corresponds to the gradient of change in one of those 4 variables: x, y, z, and w. Any substantial change in that system is a change in lengths of those 4 edges, and, as it is a closed polygon, it entails a change in angles between edges.

As I am trying to grasp fundamental differences between those two views upon social change, namely sequence of sets as opposed to an internally changing perennial entity, I think the difference is mostly epistemological. As a matter of fact, I don’t know s**t about reality as it is, and, let’s be honest, neither do you, my dear readers. We just make many possible Matrixes out of the real stuff and settle for the one that offers the greatest rewards. This is the stance adopted in the Interface Theory of Perception (Hoffman et al. 2015[4]; Fields et al. 2018[5]), as well as in classical Western empiricism (see William James’s ‘Essays in Radical Empiricism’, 1912). This holds for social reality as well as for anything else. When I see social change, I see most of all change in itself, and only secondarily, in my spare moments, I can try to figure out what exactly is that thing that changes. This is science, or philosophy, depends on the exact method I adopt, and this is hard, and time-consuming. Most of the times, I just use a ready-made explanation, conveyed in my culture, that what is changing is, for example, the market or the constitutional order, or the set of cultural stereotypes. Still, at the bottom line, those labels are just labels. What I am really experiencing, is change in itself.

When I assume that social change is a Markov chain of sets made of small social entities, I study social change as change in itself, i.e. as the say σ-algebra of that chain. I do not pretend to know exactly what is happening, I just observe and give the account of the passage from one state to another. Conversely, when I assume that social change is structural recombination inside a big, perennial social structure, I pretend to know the limits and the shape of that big structure. This is a strong assumption, probably an overstated one.    

Now, I connect the dots. I am linking my thinking about cities with my thinking about collective intelligence, and all that I serve in a sauce peppered with the possibility to use artificial neural networks for studying the whole business. I can study the civilizational role of cities under those two angles, i.e. as a Markov chain of states which I barely understand, yet which I can observe, on the one hand, or as internal reshuffling inside a finite, big social entity called ‘civilisation’, with nicely outlined contours. I am honest: I am not even going to pretend I can outline the precise contours of the civilisation we live in. With all the s**t going out there, i.e. leftist extremists in Germany erecting an illegal statue of Lenine, in response to #BlackLivesMatter in United States, and neo-Nazi extremists from The Base organization receiving orders from a leader who is an American with an Italian name, currently living in Russia: man, I do not feel up to trace the external contours of that thing.  

I know that I can and want study the phenomenon of cities as change in itself, and I assume that the change I see is an aggregation of local changes in small social entities sei. As those small sei’s change locally, their local transformations translate and manifest as the passage from the aggregate set Nk = {se1, se2, …, sen} into another set Nk+1 = {se1, se2, …, sen}. The next hurdle to jump over is the connection between sets of the type Nk = {se1, se2, …, sen} and aggregate socio-economic variables commonly used as so-called statistics. Those ‘statistics’ tend to have one of the 4 possible, mathematical forms: averages, totals, frequencies, or rates of change. When they are averages, e.g. GDP per capita, they are expected values of something. When the come as aggregate totals, e.g. aggregate headcount of population, they stand for the size of something. As they take the form of frequencies, e.g. the percentage of people with secondary education, they are simple probabilities. Finally, as rates of change, they are local first derivatives over time in some functions, e.g. the function of economic growth.

Each of those mathematical forms can be deemed representative for a heterogenous set of small phenomena, like small social entities sei. I assume that each set Nk = {se1, se2, …, sen} of n social entities manifests its current state in the form of a complex vector of variables: expected mean values, total sizes, simple probabilities of specific phenomena, and first derivatives over time in the underlying functions of change. Any set of socio-economic variables is an imperfect, epistemic representation of current local states in the individual social entities sei included in the set Nk = {se1, se2, …, sen}.  

As I go through my notes and blog updates from the last 2 months, something emerges. The social entities I focus on, thus my sei‘s, are individual people endorsing individual social roles. set Nk = {se1, se2, …, sen} is essentially a set of people, i.e. a population. Each of those people has at least two coordinates: their place of residency (mostly defined as city vs countryside), and their social role. I messed around with a set like that in a neural network (see The perfectly dumb, smart social structure). The current state of the whole set Nk manifests itself as a vector Vse of socio-economic variables.

So far and by far, the most important variable I have identified is the density of population in cities, denominated over (i.e. divided by) the general density of population. I named this variable [DU/DG] and I assume it shows the relative social difference between cities and the countryside (see Demographic anomalies – the puzzle of urban density). The coefficient [DU/DG] enters into interesting correlations with such variables as: consumption of energy per capita, income per capita, surface of agricultural land, cereal yield in kg per hectare of arable land, number of patent applications per 1 million people, and finally the supply of money as % of the GDP. On the other hand, by studying the way that urban land is distinguished from the rural one and from wildlife, I know there is a correlation between density of urban population and the density of man-made structures, as well as the density of night-time lights.

Good. I have a set Nk = {se1, se2, …, sen} of n social entities, which changes all the time, and a vector Vse = {DU/DG; energy per capita; income per capita; surface of agricultural land; cereal yield; patent applications; supply of money} of variables pertinent regarding cities and their role. Between the two I insert my mild obsession, i.e. the set SR = {sr1, sr2, …, srm} of ‘m’ social roles.

Now, I go pictographic. I use pictures to make myself spit out the words I have in mind. I mean, I know I have words in mind, only I don’t know what exact words are these. Pictures help. In Figure 1 I am trying to convey the idea of proportion between the headcount of population and the range of social roles available to endorse. My basic assumption is that we, humans, are fully socialized when we endorse social roles that suit our basic personal traits, such as intelligence, extroversion vs introversion, neuroticism, conscientiousness, the way we look in seasoned black leather etc. The state of society can be explained as a balance between the incremental headcount of humans, on the one hand, and the incremental range of social roles to take. If the headcount of humans is n, and the number of social roles available is m, we are talking about ∆n/∆m.  

When both sets, i.e. Nk and SR change at the same pace, i.e. ∆n/∆m (t0) = ∆n/∆m (t1), the society is in some sort of dynamic equilibrium, like a constant number of humans per one social role available. When the set SR of social roles burgeons faster than the pace of demographic growth, I mean when ∆n/∆m (t0) > ∆n/∆m (t1), logically there is less and less humans per one social role. This is social change by differentiation. New, idiosyncratic skillsets and behavioural patterns emerge. This is like an absorptive state, which can suck new humans in like easy.

On the other hand, when demographic growth in the set Nk races far ahead, and the set SR of social roles lags behind, i.e. ∆n/∆m (t0) < ∆n/∆m (t1), there is consistently more and more humans per one social role. That favours standardization and institutional definition of those roles, in the form of professions, public offices, ritualized social statuses etc. Society settles down into a nice order. Still, each such institutionalized social role grows barriers to entry around itself. You need to pass some exams, you need to be appointed or elected, you need to invest some capital… New humans coming to the world encounter those barriers, and some of them end up by saying: ‘F**k it! I stay outside of everything’.  This is the tipping point, when social change is needed, so as to make social room for new humans.   

Figure 1

Now, I transition into the role of cities in that social pattern. I am trying to picture the idea in Figure 2. If the state of social differentiation, we need some pattern for generating diversity. We need social interaction. Cities can be seen as a social contrivance which facilitates such interaction. Still, it comes to my mind sort of right now, we don’t really know (yet), to what extent digital interaction between humans can replace the real one in that specific respect, i.e. as a mechanism of creating new social roles. My gut feeling is that digital technologies can be at least imperfect substitutes of real social interaction. You Tube or Instagram may replace cities in their civilizational role of creating new social room for new homo sapiens. We might just be evolving from a civilization of city slickers living next to rednecks, into a civilisation of city slickers, rednecks and homo onlinus.  

Figure 2


In the next step, I am wrapping my mind around the math side of the problem, which I try to picture in Figure 3.  I guess that what I have in terms of empirical data to put in a neural network is mostly the vector Vse of social outcomes, which I can enrich with the headcount of population, and that would be the real-life material that a neural network could learn from. What that network could try and optimize could be the gradient ∆n/∆m or some variation thereof, as the exact number of social roles is technically unobservable with the current state of technology. When I think about the practical way of doing it, I can imagine a network pegged on optimizing some sort of hard-nailed output variable, such as the average number of hours worked per person per year (solid stuff, as it comes out of my so-far research). I drop the gradient ∆n/∆m among the input variables, and I try to discover what value thereof the network would yield after a few thousands laborious trials to produce artificial history.

Another angle of approach that comes to my mind is to take all the known empirical variables as input, and the gradient ∆n/∆m as the output. Then I make different clones of the network, with ∆n/∆m going various ways, like gently up, steep up, down a bit etc. I can check which of the clones displays the closest Euclidean distance to the source empirical dataset.    

Figure 3

Now, the final step: I connect the concept of social role with that of conscious agent, as represented in the Interface Theory of Perception (Hoffman et al. 2015[1]; Fields et al. 2018[2]). Figure 4 represents my pictographic thinking about it. Social roles are temporary outcomes of learning and social interaction between Conscious Agents (CA). In other words, social roles form as humans exchange information about their conscious experience, which serves to translate objectively existing states of the world into material possible to process by our brain, so as to decide whether to run away from the tiger or maybe rather kill the tiger and take the antelope. We take action consequently, we contemplate its consequences, and we talk about it, and we show to each other how we can learn new stuff.

Figure 4


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

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

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

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

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

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

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

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

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

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

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

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

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

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

What is my take on these four: Bitcoin, Ethereum, Steem, and Golem?

My editorial on You Tube

I am (re)learning investment in the stock market, and I am connecting the two analytical dots I developed on in my recent updates: the method of mean-reversion, and the method of extrapolated return on investment. I know, connecting two dots is not really something I necessarily need my PhD in economics for. Still, practice makes the master. Besides, I want to produce some educational content for my students as regards cryptocurrencies. I have collected some data as regards that topic, and I think it would be interesting to pitch cryptocurrencies against corporate stock, as financial assets, just to show similarities and differences.

As I return to the topic of cryptocurrencies, I am returning to a concept which I have been sniffing around for a long time, essentially since I started blogging via Discover Social Sciences: the concept of complex financial instruments, possibly combining future contracts on a virtual currency, possibly a cryptocurrency, which could boost investment in new technologies.

Finally, I keep returning to the big theoretical question I have been working on for many months now: to what extent and how can artificial intelligence be used to represent the working of collective intelligence in human societies? I have that intuition that financial markets are very largely a tool for tacit coordination in human societies, and I feel that studying financial markets allows understanding how that tacit coordination occurs.

All in all, I am focusing on current developments in the market of cryptocurrencies. I take on four of them: Bitcoin, Ethereum, Steem, and Golem. Here, one educational digression, and I am mostly addressing students: tap into diversity. When you do empirical research, use diversity as a tool, don’t run away from it. You can have the illusion that yielding to the momentary temptation of reducing the scope of observation will make that observation easier. Well, not quite. We, humans, we observe gradients (i.e. cross-categorial differences and change over time) rather than absolute stationary states. No wonder, we descend from hunters-gatherers. Our ancestors had that acute intuition that when you are not really good at spotting and hitting targets which move fast, you have to eat targets that move slowly. Anyway, take my word on it: it will be always easier for you to draw conclusions from comparative observation of a few distinct cases than from observing just one. This is simply how our mind works.

The four cryptocurrencies I chose to observe – Bitcoin, Ethereum, Steem, and Golem – represent different applications of the same root philosophy descending from Satoshi Nakamoto, and they stay in different weight classes, so to say. As for that latter distinction, you can make yourself an idea by glancing at the table below:

Table 1

CryptocurrencyMarket capitalization in USD, as of April 26th, 2019Market capitalization in USD, as of April 26th, 2020Exchange rate against USD, as of April 26th, 2020
Bitcoin (https://bitcoin.org/en/ )93 086 156 556140 903 867 573$7 679,87 
Ethereum (https://ethereum.org/ )16 768 575 99821 839 976 557$197,32 
Steem (https://steem.com/ )111 497 45268 582 369$0,184049
Golem (https://golem.network/)72 130 69441 302 784$0,042144

Before we go further, a resource for you, my readers: all the calculations and source data I used for this update, accessible in an Excel file, UNDER THIS LINK.

As for the distinctive applications, Bitcoin and Ethereum are essentially pure money, i.e. pure financial instruments. Holding Bitcoins or Ethers allows financial liquidity, and the build-up of speculative financial positions. Steem is the cryptocurrency of the creative platform bearing the same name: it serves to pay creators of content, who publish with that platform, to collect exchangeable tokens, the steems. Golem is still different a take on encrypting currency: it serves to trade computational power. You connect your computer (usually server-sized, although you can go lightweight) to the Golem network, and you make a certain amount of your local computational power available to other users of the network. In exchange of that allowance, you receive Golems, which you can use to pay for other users’ computational power when you need some. Golems are a financial instrument serving to balance deficits and surpluses in a complex network of nested, local capacities. Mind you, the same contractual patterns can be applied to balancing any type of capacities, not just computational. You can use it for electric power, hospital beds etc. – anything that is provided by locally nested fixed assets in the presence of varying demand.

Thus, below we go further, a reminder: Bitcoins and Ethers pure money, Steem Payment for Work, Golems Payment for Access to Fixed Assets. A financial market made of those four cryptocurrencies represents something like an economy in miniature: we have the labour market, the market of productive assets, and we have a monetary system. In terms of size (see the table above), this economy is largely and increasingly dominated by money, with labour and productive assets manifesting themselves in small and decreasing quantities. Compared to a living organism, it would be a monstrous shot of hormones spreading inside a tiny physical body, i.e. something like a weasel.

In the following part of this update, I will be referring to the method of mean-reversion, and to that of extrapolated rate of return. I am giving, below, simplified summaries of both, and I invite those among my readers who want to have more details to my earlier updates. More specifically, as regards the method of mean-reversion, you can read: Acceptably dumb proof. The method of mean-reversion , as well as Fast + slower = compound rhythm, the rhythm of life. As for the method of extrapolated rate of return, you can refer to: Partial outcomes from individual tables .

Now, the short version. Mean-reversion, such as I use it now for financial analysis, means that I measure each daily closing price, in the financial market, and each daily volume of trade, as the difference between the actual price (volume), and the moving cumulative average thereof, and then I divide the residual difference by the cumulative moving standard deviation. I take a window in time, which, in what follows, is 1 year, from April 26th, 2019, through April 26th, 2020. For each consecutive day of that timeframe, I calculate the average price, and the average volume, starting from day 1, i.e. from April 26th, 2019. I do the same for standard deviation, i.e. with each consecutive day, I count standard deviation in price and standard deviation in volume, since April 26th, 2019.

Long story short, it goes like…

May 10th, 2019 Average (April 26th, 2019 –> May 10th, 2019), same for standard deviation

May 20th, 2019 Average (April 26th, 2019 –> May 20th, 2019), same for standard deviation

… etc.

Mean-reversion allows comparing trends in pricing and volumes for financial instruments operating at very different magnitudes thereof. As you could see from the introductory table, those 4 cryptocurrencies really operate at different levels of pricing and volumes traded. Direct comparison is possible, because I standardize each variable (price or volume) with its own average value and its own standard deviation.

The method of extrapolated return is a strongly reductionist prediction of future return on investment, where I assume that financial markets are essentially cyclical, and my future return is most likely to be an extrapolation of the past returns. I take the same window in time, i.e. from April 26th, 2019, through April 26th, 2020. I assume that I bought the given coin (i.e. one of the four studied here) on the last day, i.e. on April 26th, 2020. For each daily closing price, I go: [Price(Day t) – Price(April 26th. 2020)] / Price(April 26th. 2020). In other words, each daily closing price is considered as if it was bound to happen again in the year to come, i.e. from April 26th, 2020 to April 26th, 2021. Over the period, April 26th, 2019 – April 26th, 2020, I count the days when the closing price was higher than that of April 26th, 2020. The number of those ‘positive’ days, divided by the total of 366 trading days (they don’t stop trading on weekends, in the cryptocurrencies business), gives me the probability that I can get positive return on investment in the year to come. In other words, I calculate a very simple, past experience-based probability that buying the given coin on April 26th, 2020 will give me any profit at all over the next 366 trading days.

I start presenting the results of that analysis with the Bitcoin, the big, fat, patient-zero beast in the world of cryptocurrencies. In the graph below, you can see the basic logic of extrapolated return on investment, which, in the case of Bitcoin, yields a 69,7% probability of positive return in the year to come.

In the next graph, you can see the representation of mean-reverted prices and quantities traded, in the Bitcoin market. What is particularly interesting here is the shape of the curve informative about mean-reverted volume. What we can see here is consistent activity. That curve looks a bit like the inside of an alligator’s mouth: a regular dentition made of relatively evenly spaced spikes. This is a behavioural piece of data. It says that the price of Bitcoin is shaped by regular, consistent trade, all year long. This is like a busy market place, and busy market places usually yield a consistent equilibrium price. 

The next in line is Ethereum. As you can see in the next graph, below, the method of extrapolated return yields a probability of any positive return whatsoever, for the year to come, around 36,9%. Not only is that probability lower than the one calculated for the Bitcoin, but also the story told by the graph is different. Partial moral of the fairy tale: cryptocurrencies differ in their ways. Talking about ‘investing in cryptocurrencies’ in general is like talking about investing in the stock market: these are very broad categories. Still, of you pitch those probabilities for the Bitcoin and for the Ethereum against what can be expected in the stock market (see to: Partial outcomes from individual tables), cryptocurrencies look really interesting.

The next graph, further below, representing mean-reversion in price and volume of Ethereum, tells a story similar to that of the Bitcoin, yet just similar. As strange as it seems, the COVID crisis, since January 2020, seems to have brought a new breeze into that house. There had been a sudden spike in activity (volumes traded) in the beginning of 2020, and that spike in activity led to a slump in price. It is a bit as if a lot of investors suddenly went: ‘What? Those old Ethers in my portfolio? Still there? Unbelievable? I need to get rid of them. Jeeves! Please, be as kind and give those old Ethers to poor investors from the village.’. Another provisional lesson: spikes in activity, in any financial market, can lead both to appreciation of a financial instrument, and to its depreciation. This is why big corporations, and stockbrokers working for them, employ the services of market moderators, i.e. various financial underwriters who keep trading in the given stock, sort of back and forth, just to keep the thing liquid enough to make the price predictable. 

Now, we go into the world of niche cryptocurrencies: the Steem and the Golem. I present their four graphs (Extrapolated return *2, Mean-reversion *2) further below, and now a few general observations about those two. Their mean-reverted volumes are like nothing even remotely similar to the dentition of an alligator. An alligator like that couldn’t survive. Both present something like a series of earthquakes, of growing magnitudes, with the greatest spike in activity in the beginning of 2020. Interesting: it looks as if the COVID crisis had suddenly changed something for these two. When combined with the graphs of extrapolated return, mean-reverted prices and quantities tell us a story of two cryptocurrencies which, back in the day, attracted a lot of attention, and started to have sort of a career, but then it all went flat, and even negative. This is the difference between something that aspires to be money (Steem, Golem), and something that really is money (Bitcoin, Ethereum). The difference is in the predictably speculative patterns of behaviour in market participants. John Maynard Keynes used to stress the fact that real money has always two functions: it serves as a means of payment, and it is being used as a speculative asset to save for later. Without the latter part, i.e. without the propensity to save substantial balances for later, a wannabe money has no chance to become real money.   

Now, I am trying to sharpen my thinking in terms of practical investment. Supposing that I invest in cryptocurrencies (which I do not do yet, although I am thinking about it), what is my take on these four: Bitcoin, Ethereum, Steem, and Golem? Which one should I choose, or how should I mix them in my investment portfolio?

The Bitcoin seems to be the most attractive as investment, on the whole. Still, it is so expensive that I would essentially have to sell out all the stock I have now, just in order to buy even a small number of Bitcoins. The remaining three – Ethereum, Steem and Golem – fall into different categories. Ethereum is regular crypto-money, whilst Steem and Golem are niche currencies. In finance, it is a bit like in exploratory travel: if I want to go down a side road, I’d better be prepared for the unexpected. In the case of Steem and Golem, the unexpected consists in me not knowing how they play out as pure investment. To the extent of my knowledge, these two are working horses, i.e. they give liquidity to real markets of something: Steem in the sector of online creation, Golem in the market of networked computational power. Between those two, I know a bit about online creation (I am a blogger), and I can honestly admit I don’t know s**t about the market of networked computation. The sensible strategy for me would be to engage into the Steem platform as a creator, take my time to gain experience, see how those Steems play out in real life as a currency, and then try to build an investment position in them.

Thus, as regards investment strictly I would leave Steem and Golem aside and go for Ethereum. In terms of extrapolated rate of return, Ethereum offers me chances of positive outcomes comparable to what I can expect from the stock of PBKM, which I already hold, higher chances of positive return than other stock I hold now, and lower chances than, for example, the stock of First Solar or Medtronic (as for these considerations, you can consult Partial outcomes from individual tables ).   

OK, so let’s suppose I stay with the portfolio I already hold –11Bit, Airway Medix , Asseco Business Solutions, Bioton, Mercator Medical, PBKM – and I consider diversifying into Ethereum, First Solar , and Medtronic. What can I expect? As I look at the graphs (once again, I invite you to have a look at Partial outcomes from individual tables ), Ethereum, Medtronic and First Solar offer pretty solid prospects in the sense that I don’t have to watch them every day. All the rest looks pretty wobbly: depending on how the whole market plays out, they can become good investments or bad ones. In order to become good investments, those remaining stocks would need to break their individual patterns expressed in the graphs of extrapolated return and engage into new types of market games.

I can see that with the investment portfolio I currently hold, I am exposed to a lot of risk resulting from price volatility, which, in turn, seems to be based on very uneven market activity (i.e. volumes traded) in those stocks. Their respective histories of mean-reverted volumes look very uneven. What I think I need now are investment positions with less risk and more solidity. Ethereum, First Solar , and Medtronic seem to be offering that, and yet I am still a bit wary about coming back (with my money) to the U.S. stock market. I wrapped up my investments there, like one month ago, because I had the impression that I cannot exactly understand the rules of the game. Still, the US dollar seems to be a good investment in itself. If I take my next portion of investment, scheduled for the next week, i.e. the rent I will collect, transferring it partly to the U.S. market and partly to the Ethereum platform will expose just some 15% of my overall portfolio to the kind of risks I don’t necessarily understand yet. In exchange, I would have additional gains from investing into the US dollar, and additional fun with investing into the Ethereum.

Good. When I started my investment games by the end of January, 2020 (see Back in the game), I had great plans and a lot of doubts. Since then, I received a few nasty punches into my financial face, and yet I think I am getting the hang of it. One month ago, I managed to surf nicely the crest of the speculative bubble on biotech companies in the Polish stock market (see A day of trade. Learning short positions), and, in the same time, I had to admit a short-term defeat in the U.S. stock market. I yielded to some panic, and it made me make some mistakes. Now, I know that panic manifests in me both as an urge to act immediately, and as an irrational passivity. Investment is the art of controlling my emotions, as I see.

All I all, I have built an investment portfolio which seems to be taking care of itself quite nicely, at least in short perspective (it has earnt $238 over the last two days, Monday and Tuesday), and I have coined up my first analytical tools, i.e. mean-reversion and extrapolation of returns. I have also learnt that analytical tools, in finance, serve precisely the purpose I just mentioned: self-control.

Partial outcomes from individual tables

My editorial on You Tube

It is time to return to my investment strategy, and to the gradual shaping thereof, which I undertook in the beginning of February, this year (see Back in the game). Every month, as I collect the rent from the apartment I own and rent out, downtown, I invest that rent in the stock market. The date of collecting the next one approaches (it is in 10 days from now), and it is time for me to sharpen myself again for the next step in investment.

By the same occasion, I want to go scientific, and I want to connect the dots between my own strategy, and my research on collective intelligence. The expression ‘shaping my own investment strategy’ comes in two shades. I can understand it as the process of defining what I want, for one, or, on the other hand, as describing, with a maximum of objectivity, what I actually do. That second approach to strategy, a behavioural one, is sort of a phantom I have been pursuing for more than 10 years now. The central idea is that before having goals, I have values, i.e. I pursue a certain category of valuable outcomes and I optimize my actions regarding those outcomes. This is an approach in the lines of ethics: I value certain things more than others. Once I learn how to orient my actions value-wise, I can set more precise goals on the scale of those values.

I have been using a simple neural network to represent that mechanism at the level of collective intelligence, and I now, I am trying to apply the same logic at the level of my own existence, and inside that existence I can phenomenologically delineate the portion called ‘investment strategy in the stock market’. I feel like one of those early inventors, in the 18th or 19th century, testing a new idea on myself. Fortunately, testing ideas on oneself is much safer than testing drugs or machines. That thing, at least, is not going to kill me, whatever the outcome of experimentation. Depends on the exact kind of idea, though.

What meaningful can I say about my behaviour? I feel like saying something meaningful, like a big fat bottom line under my experience. My current experience is similar to very nearly everybody else’s experience: the pandemic, the lockdown, and everything that goes with it. I noticed something interesting about myself in this situation. As I spend week after week at home, more and more frequently I tend to ask myself those existential questions, in the lines of: “What is my purpose in life?”.  The frame of mind that I experience in the background of those questions is precisely that of the needle in my personal compass swinging undecidedly. Of course, asking myself this type of questions is a good thing, from time to time, when I need to retriangulate my personal map in the surrounding territory of reality. Still, if I ask those questions more and more frequently, there is probably something changing in my interaction with reality, as if with the time passing under lockdown I were drifting further and further away from some kind of firm pegs delineating my personal path.

Here they are, then, two of my behavioural variables, apparently staying in mutually negative correlation: the lower the intensity of social stimulation (variable #1), the greater the propensity to cognitive social repositioning (variable #2). This is what monks and hermits do, essentially: they cut themselves from social stimulation, so as to get really serious about cognitive social repositioning. With any luck, if I go far enough down this path, I reposition myself socially quite deeply, i.e. I become convinced that other people have to pay my bills so as I can experience the state of unity with the Divine, but I can even become convinced that I really am in a state of unity with the Divine. Of course, the state of unity lasts only until I need to pay my bills by myself again.

Good. I need to reinstate some social stimulation in my life. I stimulate myself with numbers, which is typical for economists. I take my investment portfolio such as it is now, plus some interesting outliers, and I do what I have already done once, i.e. I am being mean in reverse, pardon, mean-reverting the prices, and I develop on this general idea. This time, I apply the general line of logic to a metric which is absolutely central to any investment: THE RATE OF RETURN ON INVESTMENT. The general formula thereof is: RR = [profit] / [investment]. I am going to use this general equation, together with very basic calculation of probability, in order to build a PREDICTION BASED ENTIRELY ON AN EXTRAPOLATION OF PAST EVENTS. This is a technique of making forecasts, where we make forecasts composed of two layers. The baseline layer is precisely made of extrapolated past, and it is modified with hypotheses as for what new can happen in the future.

The general formula for calculating any rate of return on investment is: RR = [profit] / [investment]. In the stock market, with a given number of shares held in portfolio, and assumed constant, both profit and investment can be reduced to prices only. Therefore, we modify the equation of return into: RR = [closing price – opening price] / [opening price]. We can consider any price observed in the market, for the given stock, as an instance of closing price bringing some kind of return on a constant opening price. In other words, the closing price of any given trading day can be considered as a case of positive or negative return on my opening price. This is a case of Ockham’s razor, thus quite reductionist an approach. I ask myself what the probability is – given the known facts from the past – that my investment position brings me any kind of positive return vs. the probability of having a negative one. I don’t even care how much positive gain could I have or how deep is a local loss. I am interested in just the probability, i.e. in the sheer frequency of occurrence as regards those two states of nature: gain or loss.

In the graph below, I am illustrating this method with the case of Bioton, one of the companies whose stock I currently hold in my portfolio. I chose a complex, line-bar graph, so as to show graphically the distinction between the incidence of loss (i.e. negative return) vs that of gain. My opening price is the one I paid for 600 shares of Bioton on April 6th, 2020, i.e. PLN 5,01 per share. I cover one year of trading history, thus 247 sessions. In that temporal framework, Bioton had 12 days when it went above my opening price, and, sadly enough, 235 sessions closed with a price below my opening. That gives me probabilities that play out as follows: P(positive return) = 12/247 = 4,9% and P(negative return) = 235/247 = 95,1%. Brutal and sobering, as I see it. The partial moral of the fairy tale is that should the past project itself perfectly in the future, this if all the stuff that happens is truly cyclical, I should wait patiently, yet vigilantly, to spot that narrow window in the reality of stock trade, when I can sell my Bioton with a positive return on investment.      

Now, I am going to tell a different story, the story of First Solar, a company which I used to have an investment position in. As I said, I used to, and I do not have any position anymore in that stock. I sold it in the beginning of April, when I was a bit scared of uncertainty in the U.S. stock market, and I saw a window of opportunity in the swelling speculative bubble on biotech companies in Poland. As I do not have any stock of First Solar, I do not have any real opening price. Still, I can play a game with myself, the game of ‘as if…’. I calculate my return as if I had bought First Solar last Friday, April 24th. I take the closing price from Friday, April 24th, 2020, and I put it in the same calculation as my opening price. The resulting story is being told in the graph below. This is mostly positive a story. In strictly mathematical terms, over the last year, there had been 222 sessions, out of a total of 247, when the price of First Solar went over the closing price of Friday, April 24th, 2020. That gives P(positive return) = 222/247 = 89,9%, whilst P(negative return) = 10,1%.

The provisional moral of this specific fairy tale is that with First Solar, I can sort of sleep in all tranquillity. Should the past project itself in the future, most of trading days is likely to close with a positive return on investment, had I opened on First Solar on Friday, April 24th, 2020.  

Now, I generalize this way of thinking over my entire current portfolio of investment positions, and I pitch what I have against what I could possibly have. I split the latter category in two subsets: the outliers I already have some experience with, i.e. the stock I used to hold in the past and sold it, accompanied by two companies I am just having an eye on: Medtronic (see Chitchatting about kings, wars and medical ventilators: project tutorial in Finance), and Tesla. Yes, that Tesla. I present the results in the table below. Linked names of companies in the first column of the table send to their respective ‘investor relations’ sites, whilst I placed other graphs of return, similar to the two already presented, under the links provided in the last column.      

Company (investment position)Probability of negative returnProbability of positive returnLink to the graph of return  
  My current portfolio
11BitP(negative) = 209/247 = 84,6%P(positive) = 15,4%11Bit: Graph of return  
Airway Medix (243 sessions)P(negative) = 173/243 = 71,2%P(positive) = 70/243 = 28,8%Airway Medix: Graph of return  
Asseco Business SolutionsP(negative) = 221/247 = 89,5%P(positive) = 10,5%Asseco Business Solutions: Graph of return  
BiotonP(negative) = 235/247 = 95,1%P(positive) = 12/247 = 4,9%Bioton: Graph of return  
Mercator MedicalP(negative) = 235/247 = 95,1%P(positive) = 12/247 = 4,9%Mercator: graph of return  
PBKMP(negative) = 138/243 = 56,8%P(positive) = 105/243 = 43,2%  PBKM: Graph of return
  Interesting outliers from the past
Biomaxima (218 sessions)P(negative) = 215/218 = 98,6%P(positive) = 3/218 = 1,4%Biomaxima: Graph of return  
Biomed LublinP(negative) = 239/246 = 97,2%P(positive) = 7/246 = 2,8%Biomed Lublin: graph of return  
OAT (Onco Arendi Therapeutics)P(negative) = 205/245 = 83,7%P(positive) = 40/245 = 16,3%OAT: Graph of return  
Incyte CorporationP(negative) = 251/251 = 100%P(positive) = 0/251 = 0%Incyte: Graph of return  
First SolarP(negative) = 10,1%P(positive) = 222/247 = 89,9%First Solar: Graph of return  
  Completely new interesting outliers
TeslaP(negative) = 226/251 = 90%P(positive) = 25/251 = 10%Tesla: Graph of return  
MedtronicP(negative) = 50/250 = 20%P(positive) = 200/250 = 80%  Medtronic: Graph of return

As I browse through that table, I can see that extrapolating the past return on investment, i.e. simulating the recurrence of some cycle in the stock market, sheds a completely new light on both the investment positions I have open now, and those I think about opening soon. Graphs of return, which you can see under those links in the last column on the right, in the table, tell very disparate stories. My current portfolio seems to be made mostly of companies, which the whole COVID-19 crisis has shaken from a really deep sleep. The virus played the role of that charming prince, who kisses the sleeping beauty and then the REAL story begins. This is something I sort of feel, in my fingertips, but I have hard times to phrase it out: the coronavirus story seems to have awoken some kind of deep undertow in business. Businesses which seemed half mummified suddenly come to life, whilst others suddenly plunge. This is Schumpeterian technological change, if anybody asked me.

In mathematical terms, what I have just done and presented reflects the very classical theory of probability, coming from Abraham de Moivre’s ‘The doctrine of chances: or, A method of calculating the probabilities of events in play’, published in 1756. This is probability used for playing games, when I assume that I know the rules thereof. Indeed, when I extrapolate the past and use that extrapolation as my basic piece of knowledge, I assume that past events have taught me everything I need to understand the present. I used exactly the same approach as Abraham De Moivre did. I assumed that each investment position I open is a distinct gambling table, where a singular game is being played. My overall outcome from investment is the sum total of partial outcomes from individual tables (see Which table do I want to play my game on?).   

Important announcement for my students with the Frycz university

Dear Students,

We are supposed to keep working by distance learning. I have made a provisional schedule of our work up until May 22nd, 2020. This time, the backbone of our common work will be a schedule of ZOOM meetings, as provided in the table below. If you are not familiar with ZOOM, get acquainted with it. Essentially, you go to https://zoom.us and there you click ‘Join a meeting’ (top right of the page). When asked, you authenticate yourself (Name), and you provide the proper meeting ID, and the password, as specified in the table below. Read the text after the table as well: it is important.

Schedule of ZOOM classes until May 22nd, 2020

DateHours of ZOOM classDedicated SubjectDedicated groupMeeting IDPassword
16/04/2020ZOOM 11:00 – 12:30Foundations of finance Z/M-ang/19/SS 884-6631-3234afmclass
16/04/2020ZOOM 13:00 – 14:00Macroeconomics SM/IB/19/1/SS 861-7687-7064890398
17/04/2020ZOOM 13:00 – 14:00Macroeconomics SM/IT/19/1/SS 832-3205-9764063629
22/04/2020ZOOM 10:30 – 11:30International management  Z/M-ang/18/SS 857-0624-2561218335
22/04/2020ZOOM 13:00 – 14:30International management  Z/M-ang/18/SS 892-0410-3913327541
23/04/2020ZOOM 10:00 – 11:30Foundations of finance Z/M-ang/19/SS 859-6907-6622afmclass
23/04/2020ZOOM 13:00 – 14:30International Trade SM/IB/18/SS , SM/IT/18/SS , SM/IR&CD/18/SS 856-3907-1671601531
30/04/2020ZOOM 11:00 – 12:30Foundations of finance Z/M-ang/19/SS 810-9958-0322816031
30/04/2020ZOOM 13:00 – 14:30International Trade SM/IB/18/SS , SM/IT/18/SS , SM/IR&CD/18/SS 871-1234-4440200795
06/05/2020ZOOM 10:00 – 11:30International management  Z/M-ang/18/SS 864-8741-3349736263
06/05/2020ZOOM 12:00 – 13:00International management  Z/M-ang/18/SS 810-0220-6075450869
07/05/2020ZOOM 11:00 – 12:30Foundations of finance Z/M-ang/19/SS 825-5416-3530389190
07/05/2020ZOOM 13:00 – 14:30International Trade SM/IB/18/SS , SM/IT/18/SS , SM/IR&CD/18/SS 848-7174-8214073419
13/05/2020ZOOM 10:30 – 11:30International management  Z/M-ang/18/SS 890-0156-8782138985
13/05/2020ZOOM 12:00 – 13:00International management  Z/M-ang/18/SS 822-3899-2134862134
14/05/2020ZOOM 10:00 – 11:30Foundations of finance Z/M-ang/19/SS 854-8943-3613079806
14/05/2020ZOOM 12:00 – 13:00Foundations of finance Z/M-ang/19/SS 893-8431-4288416195
14/05/2020ZOOM 13:00 – 14:30International Trade SM/IB/18/SS , SM/IT/18/SS , SM/IR&CD/18/SS 870-5811-0884234869
20/05/2020ZOOM 10:00 – 11:30International management  Z/M-ang/18/SS 848-8416-7085788524
20/05/2020ZOOM 12:00 – 13:00International management  Z/M-ang/18/SS 881-9216-6055162890
21/05/2020ZOOM 11:00 – 12:30Foundations of finance Z/M-ang/19/SS 843-1135-2446850647
21/05/2020ZOOM 13:00 – 14:30Foundations of finance Z/M-ang/19/SS 840-0479-6888779164

In our ZOOM classes, we will be talking about a lot of things, yet the discussion will be structured around topics, which I briefly present further, in the order of their importance Priority #1 will be to discuss your progress in the preparation of your graduation projects for the semester. It applies to the students of 3 courses: International Management, Foundations of Finance, and International Trade.  

Priority #2 is to talk about the tasks that you still have to perform on the basis of materials I placed on the e-learning platform before Easter. Priority #3, to animate our common work, will be loose, creative discussion around topics to find on my blog, or around questions brought up by you.  

Just as a reminder, below I am providing the full list of links to the copies of materials placed at the e-learning platform before Easter:

Foundations of Finance

https://discoversocialsciences.com/wp-content/uploads/2020/03/Fundamentals-of-Finance-Spring-Summer-2020-lecture-1.pptx

https://discoversocialsciences.com/wp-content/uploads/2020/03/Fundamentals-of-Finance-Spring-Summer-2020-lecture-2.pptx

https://discoversocialsciences.com/wp-content/uploads/2020/03/Fundamentals-of-Finance-Spring-Summer-2020-lecture-3-Crisis-Finance.pptx

International Management

https://discoversocialsciences.com/wp-content/uploads/2020/03/International-Management-Spring-Summer-2020-lecture-1.pptx

https://discoversocialsciences.com/wp-content/uploads/2020/03/International-Management-Spring-Summer-2020-lecture-2.pptx

Macroeconomics

https://discoversocialsciences.com/wp-content/uploads/2020/03/Macroeconomics-Spring-Summer-2020-workshop-1.pptx

https://discoversocialsciences.com/wp-content/uploads/2020/03/Macroeconomics-Summer-Spring-2020-workshop-2-crisis-economics.pptx

International Trade

https://discoversocialsciences.com/wp-content/uploads/2020/03/International-Trade-Spring-Summer-2020-lecture-1.pptx

https://discoversocialsciences.com/wp-content/uploads/2020/03/International-Trade-Spring-Summer-2020-lecture-2-trade-in-crisis.pptx

Which table do I want to play my game on?

My editorial on You Tube

There is so much going on, I mean, sort of in the world, that I have hard times focusing on a tangible thread of research, writing and blogging. The plan, for 2020, was to focus on three things: my personal strategy of investment in the stock market, business plan for my technological concept of Energy Ponds, and scientific research on the application of artificial intelligence to represent and study collective intelligence in human societies. There is like a fourth path, temporarily of secondary importance, namely the creation of new, 100% educational material for distance learning.

I like reviewing things from the end, and so I take a stance, in the first place, on that educational thing. Education should teach people something useful. I think that with all the COVID-19 crisis happening around, teaching social sciences should help my students to understand the events, and when I say ‘understand’, I follow its most fundamental aspect: that of creating some personal and collective order amongst the surrounding chaos. With that assumption in mind, I decided to prepare teaching material as much connected to the current events as possible. When financial markets go amok, teaching my students the fundamental principles of finance is a bit useless, as long as I don’t demonstrate how, and whether at all, those principles apply here and now. In some countries, e.g. Poland, investors in the stock market (including myself, to be clear) suddenly rush on the stock of biotech and medical companies, this is a financial change and a managerial change, as the managers of those life-science businesses will need to do something about that sudden interest. What they will do is going to impact the job which my son, age 24, is going to have, as IT engineer, in the near future.

Still, as much as I value the lore preached in the Church of What’s Going On Right Now, I value historical perspective. Akin to a squid, in my life, whenever I face chaos, I intuitively dive into a cloud of ink, namely into classical writings of philosophers, social thinkers, and historians. Uncertainty is the name of the game right now, and so I decide to dive into some foundational writings of Western statistics. There are two of them. The first is Abraham de Moivre’s ‘The doctrine of chances: or, A method of calculating the probabilities of events in play’, published in 1756, printed, as its cover page announces, for A.Millar, in the Strand. The second is the posthumous publication of ‘An Essay towards Solving a Problem in the Doctrine of Chances’, by the Late Reverend Mr. Thomas Bayes, published in 1763 as an article in the Royal Society’s journal ‘Philosophical Transactions’ (1683-1775, Vol. 53 1763).  As I read the chronologically earlier treaty by de Moivre, I realise there is something even older, and not European at all: I Ching, or the Book of Changes, written and rewritten many times in China, during the entire millennium before Christ.

There is a common denominator to all those three classical writings. They all start from the assumption that reality is made of parts, and that we truly like just some of those parts. There are favourable events, and there is all the rest. The most fundamental message stemming from all those three seminal intellectual stances is that reality is essentially chaotic to us. We partition chaos into successes and failures, whilst being aware how gross a partition is that. The three books I am studying present three different approaches to structuring reality into what we like, and what we don’t really. I Ching proposes to define typical patterns – states of reality – defined at different levels of complexity, as individual happenings, combinations of three events in a row, then trigrams made of said sequences of three, and finally hexagrams made of two trigrams each. The central thought behind that classification is that some states of reality are more prone to change than others, and, in the same time, some sequences of change are much more likely to occur than others.

Abraham de Moivre’s ‘doctrine of chances’ focuses on the rationale behind playing and betting on some states of reality rather than others. Yes, of course, in his letter to Lord Carpenter, placed at the very beginning of the book, as some sort of pre-introduction, de Moivre stresses firmly that he is not writing about gambling: he just illustrates his method with examples from the world of gambling. Still, when I read what’s further, it just jumps to the eye: Abraham de Moivre was a savvy of gambling, like really. His method starts with the assumption that uncertain future is like a gambling house, with many tables. A different game is played at each table, with different rules and different payoffs. The central question that de Moivre tries to answer is how to use our knowledge of rules in the game so as to establish the well-founded of playing the game. From there on it is more or less what most of us studied at high school as basics of probability. There is a total number of events possible, everything put together in the same basket, and this number is n. In the world made of n, there are situations which we would like to take place, and the total number of such situations is k. The fraction k/n is informative about the general likelihood that in the cruel world of n, we find ourselves in the cosy k place.

Thomas Bayes’s, in his own ‘doctrine of chances’, claims that reality is a finite space of happenings and presents a method of slicing that space so as to narrow down our chances of getting what we want. Bayes, by an otherwise very explicit opposition to de Moivre, stressed strongly the point that we never completely now the rules of the game which we play with reality (reality plays with us?). His method is very much a way of making informed hypothesis about those rules. He distinguishes, for example, mutually consistent events from the mutually inconsistent ones, thus turning the reader’s attention towards the fact that opportunities rarely happen twice, whilst s**t usually comes in bundles. He construes that intriguingly empiricist example of throwing two   

Page 17 of the published article: ‘1. I suppose the square table or plane ABCD to be so made and levelled that if either of the balls o r W be thrown upon it, there shall be the same probability that it rests upon any one equal part of the plane as another, and that it must necessarily rest somewhere upon it. 2. I suppose that the ball W shall be first thrown and through the point where it rests a line os shall be drawn parallel to AD, and meeting CD and AB in s and o; and that afterwards the ball O shall be thrown p + q or n times, and that it resting between AD and os after a single throw be called the happening of the event M in a single trial […]’.

Good. It is time to use the wisdom of sages from the past so as to solves some riddles of the present times. Here is the situation with my personal strategy of investment in the stock market. Until recently, i.e. until I made the decision which I am describing further below, I had two portfolios: one in Poland, with a local brokerage house, and another one international, run through the DEGIRO investment platform. When the coronavirus panic broke in financial markets, much blood trickled out of both of those portfolios of mine. Yet, for like 2 last weeks, events have been unfolding differently in those two worlds. In the Polish stock market, a crazy ride upwards has developed on anything even vaguely medical: biotech, medical equipment, even on trade in wood (wood ó cellulose ó toilet paper). On Friday, March 27th, 2020, I bought stock of Biomed Lublin. Nothing big. They have been dabbling for years in vaccines and bio-serums but used to be the mostly known for having completely failed on developing synthetic human blood serum. Last Friday, April 3rd, after less than one full week of trade, I had 331% of profit on that stock. Madness.

Against that Polish background, my international positions had a grim look. As for yesterday, only one – Incyte Corporation – was bringing me a slight profit, around 9%. All the rest – First Solar, Macrogenics, Norsk Hydro, Virgin Galactic, Vivint Solar, tracker Amundi Epra, tracker Invesco A QQQ – each of those guys was more or less sucked dead by the vampire of market reality. The decision I made yesterday, and which I expect to consume on Monday 6th, was to sell out everything from the international portfolio at DEGIRO, transfer the money as quickly as possible to my Polish account (as quickly as possible is bloody slow with DEGIRO; I don’t know why, but it takes them like 3 days to pay me my money back), and invest in the Polish biotech sector.

It was a normal decision in the world of investment: wrap up whatever you have left after a lost battle and move it to a field where you can win. Cut your losses short etc. Still, I experienced really strong emotions about that decision. I know, by science and by personal experience, that when I get emotional about something, there is a lot of information my brain tries to process in as short a time as possible. I want to learn investment, and to use writing and blogging so as to enhance my learning. Therefore, I take those classical views, from ancient China and much less ancient Europe, and I apply them to my own investment strategy.

In which of the three worlds was I when I was making that particular decision: “Sell out international, move to domestic, national biotech and medical”? Was I in the world of I Ching, in that sketched by Abraham de Moivre, or maybe was I in Thomas Bayes’s rectangle with two balls? Intuitively, I would say that I was mostly Bayesian. I mean, yes, I was perceiving the situation slightly de Moivrian: as if I had two gambling tables in front of me, each with different rules and payoffs. Still, I felt that I don’t really understand the rules of at least one of the two games, the international one. I had most of my stock in the US market, and in the German market. Those governments released huge packages of economic support for their locked-down economies, so why isn’t that stuff growing? In Poland, what our government proudly announced as Anti-Crisis Shield looks more like a bath robe, if you want my opinion. The real shield, pretty much unavoidable, will consist in amending the budget and passing from the planned zero deficit to something akin the budget of 2018, with like €10 bln of deficit, and that money should be pumped into the economy. Why is biotech and medical growing like hell in Poland, and not really growing elsewhere (at least not even remotely as fast)?

I had that feeling of playing two different games at two distinct tables. At the national Polish table, I understand the rules: we run short of medical supplies and short of ideas for cures and vaccines against COVID, and so the market turns towards anything related. Stands to reason. At the international table, I felt lost. I felt that I didn’t understand the game, which looks like a random hiccup more than like anything organized. I had one game in de Moivre’s lines (Polish market), and another one more Bayes (international). I quit the Bayesian one and prefer staying in Abraham de Moivre’s world of gambling when knowing the rules.

Aha! I see. I am obsessed (mildly) with perceiving mathematics as a reflection of how we experience and digest the stuff called reality. With that situation, and those dead guys (anonymous in the case of I Ching) lurking over my shoulder, I can distinguish two different types of statistics and probability: the data science of the known as opposed to the data science of the unknown. The former sums up and structures the acquired knowledge, whilst the latter attempts to put some order in the untidy happening of things.

Right. I made a move: sell out international, move to Polish life sciences business. What’s my next step? Which of the three worlds of probability am I unfolding in the backyard of my thinking, right now, as I write these words? As I focus on the Polish market, I notice that, in my thoughts, I am partitioning reality, once again, into the cosy world of known rules de Moivre style, and into the exploratory excitement, which accompanies me as I trace that Bayesian rectangle over reality. What are my border lines, which I cannot trespass? What is my elementary way of exploration, i.e. how do I throw the ball W, so as to cut my acceptable reality into distinct fields?

I know there is technical growth in the stock market – when some buyers outbid other buyers – and there is fundamental growth, driven by something happening to the real, productive assets, which financial securities give a claim on. I gladly jump of technical waves in the market, yet in the long run, I want to invest in business rather than in financial deeds. What fundamental direction is the market going to take. Will there really be a massive development in Polish biotech businesses. We live, we see, as our saying says.

I return to my take on collective intelligence. In two updates on my blog, one rather written ( The collective archetype of striking good deals in exports ), and another one more sort of You-Tube-spoken (A civilisation of droplets), I developed on what I think are the differences between individual intelligence, and the collective one. The role played by individual representation of reality seems to be amongst the most important distinctions. Question: how does my representation of the social environment play out in my individual intelligence regarding investment decisions?

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

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]

x

20 [head in meters, by ram pumping]

x

9,81 [Newtonian acceleration]

x

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?