I needed that

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

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

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

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

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

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

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

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

*Lack of scientific originality/novelty:

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

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

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

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

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

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

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

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

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

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

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

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

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

Out-of-the-lab monsters

That period, end of January, beginning of February, is usually a moment of reassessment for me. This might be associated with my job – I am a scientist and an academic teacher – and right now, it is the turn of semesters in my country, Poland. I need to have some plan of teaching for the next semester, and, with the pandemic still around, I need to record some new video material for the courses of the Summer semester: Macroeconomics, International Trade, and International Management.

That being said, I think that formulating my current research on collective intelligence in terms of teachable material could help me to phrase out those thoughts of mine coherently and intelligibly enough to advance with the writing of my book on the same topic. I feel like translating a few distinct pieces of scientific research into teaching. The theoretical science of Markov chains is the first one. The empirically observed rise of two technologically advanced industry, namely biotechnology and electric vehicles comes as the second big thing. Thirdly, and finally, I want to develop on the general empirical observation that money tends to flow towards those new technologies even if they struggle to wrap themselves into operationally profitable business models. Next comes a whole set of empirical observations which I made à propos of the role of cities in our civilization. Finally, the way we collectively behave amidst the pandemic is, of course, the most obvious piece of empirical science I need connecting to in my teaching. 

In discussing those pieces of science in a teachable form, I feel like using the method I have been progressively forming in my research over the last 2 years or so. I use simple artificial neural networks as simulators of collectively intelligent behaviour. I have singled out a few epistemological regularities I feel like using in my teaching. Large datasets of socio-economic variables seem to have privileged orientations: they sort of wrap themselves around some specific variables rather than others. When disturbed with a random exogenous factor, the same datasets display different ways of learning, depending, precisely, on the exact variable I make them wrap themselves around. One and the same dataset, annoyingly disturbed by the buzz of a random disturbance, displays consistent learning when oriented on some variables, and goes haywire when oriented on others.

On the top of all that, I want to use in my teaching the experience I have collected when investing in the stock market. This is mostly auto-narrative experience, about my own behaviour and my own reactions when sailing in my tiny boat across the big ocean, filled with sharks, of the stock market.

What exactly do I want to teach my students? I mean, I know the labels: Macroeconomics, International Trade, International Management. These are cool labels. Yet, what do I want to teach in terms of real skills and understanding? I think that my core message is that science is f**king amazing, and when we combine scientific thinking with good, old-fashioned perseverance and grit, great things emerge. My students are young people, and having been their age, back in the day, I know that entering adulthood and developing personal independence is a lot about pretending, and a lot about finding one’s place in a fluid, essentially chaotic reality. That place is called a social role. I think I can deliver valuable teaching as for how to use the basic tools of social sciences in order to make ourselves good, functional social roles.

Concurrently to that purpose, I have another one, about mathematics. I can see many of my students the same kind of almost visceral, and yet visibly acquired abhorrence of mathematics, which I used to have in my mind. I think this is one of the failures in our educational system: early at school, we start learning mathematics as multiplication tables, which quite thoroughly kills the understanding that mathematics are a language. It is a language which speaks about the structure of reality, just a bit less convivially than spoken languages do. That language proves being bloody useful when talking about tough and controversial, such as ways of starting a new business from scratch (hence engaging people’s equity into something fundamentally risky), ways of getting out of an economic crisis, or ways of solving a political conflict.     

I think I can teach my students to perceive their existence as if they were travelling engineers in the small patch social reality around them, particularly engineers of their own social role. Look around you, across the surrounding social landscape. Find your bearings and figure out your coordinates on those bearings. Formulate a strategy: set your goals, assess your risks, make the best-case scenario and the worst-case scenario. What is your action? What can you do every day in order to implement that strategy? Therefore, what repetitive patterns of behaviour should you develop and become skilful at, in order to perform your action with the best possible outcomes? Let’s be clear: it is not about being world champion in anything (although it wouldn’t hurt), it is about being constructively optimistic, with a toolbox close at hand.  

What do I really know about macroeconomics, international trade, and international management? This is a fundamental question. Most of what I know, I know from the observation of secondary sources. Periodical financial reports of the companies, coupled with their stock prices, and with general economic reports, such as the World Economic Outlook, published by the International Monetary Fund, are my basic sources of information about what’s up in business and economics. What I know in those fields is descriptive knowledge.    

Where do I start? We, humans, form collectively intelligent structures which learn by experimenting with many alternative versions of themselves. Those versions are built around a fundamental balance between two institutional orders: the institutions of agriculture, which serve as a factory of food, and the institutions of cities, whose function consists in creating and sustaining social roles, whilst speeding up technological change. We collectively experiment with ourselves by creating demographic anomalies: abnormally dense populations in cities, next door to abnormally dispersed populations in the countryside. I think this is the fundamental distinction between the populations of hunters-gatherers, and the populations of settlers. Hunters-gatherers live in just one social density, whilst settlers live in two of them: the high urban density coexisting with low rural density.

I can put it in a different way. We, humans, interact with the natural environment, and interact with each other.  When we interact with each other a lot, in highly dense networks of social relations, we reinforce each other’s learning, and start spinning the wheel of innovation and technological change. Abundant interaction with each other gives us new ideas for interacting with the natural environment.

Cities have peculiar properties. Firstly, by creating new social roles through intense social interaction, they create new products and services, and therefore new markets, connected in chains of value added. This is how the real output of goods and services in a society becomes a complex, multi-layered network of technologies, and this is how social structures become self-propelling businesses. The more complexity in social roles is created, the more products and services emerge, which brings the development in greater a number of markets. That, in turn, gives greater a real output, greater income per person, which incentivizes to create new social roles etc. This how social complexity creates the phenomenon called economic growth.

The phenomenon of economic growth, thus the quantitative growth in complex, networked technologies which emerge in relatively dense human settlements, has a few peculiar properties. You can’t see it, you can’t touch it, and yet you can immediately feel when its pace changes. Economic growth is among the most abstract concepts of social sciences, and yet living in a society with real economic growth at 5% per annum is like a different galaxy when compared to living in a place where real economic growth is actually a recession of -5%. The arithmetical difference is just 10 percentage points, around the top of something underlying which makes the base of 1. Still, lives in those two contexts are completely different. At +5% in real economic growth, starting a new business is generally a sensible idea, provided you have it nailed down with a business plan. At – 5% a year, i.e. in recession, the same business plan can be an elaborate way of committing economic and financial suicide. At +5%, political elections are usually won by people who just sell you the standard political bullshit, like ‘I will make your lives better’ claimed by a heavily indebted alcoholic with no real career of their own. At -5%, politics start being haunted by those sinister characters, who look and sound like evil spirits from our dreams and claim they ‘will restore order and social justice’.

The society which we consider today as normal is a society of positive real economic growth. All the institutions we are used to, such as healthcare systems, internal security, public administration, education – all that stuff works at least acceptably smoothly when complex, networked technologies of our society have demonstrable capacity to increase their real economic output. That ‘normal’ state of society is closely connected to the factories of social roles which we commonly call ‘cities’. Real economic growth happens when the amount of new social roles – fabricated through intense interactions between densely packed humans – is enough for the new humans coming around. Being professionally active means having a social role solid enough to participate in the redistribution of value added created in complex technological networks. It is both formal science and sort of accumulated wisdom in governance that we’d better have most of the adult, able bodied people in that state of professional activity. A small fringe of professionally inactive people is somehow healthy a margin of human energy free to be professionally activated, and when I say ‘small’, it is like no more than 5% of the adult population. Anything above becomes both a burden and a disruption to social cohesion. Too big a percentage of people with no clear, working social roles makes it increasingly difficult to make social interactions sufficiently abundant and complex to create enough new social roles for new people. This is why governments of this world attach keen importance to the accurate measurement of the phenomenon quantified as ‘unemployment’.  

Those complex networks of technologies in our societies, which have the capacity to create social roles and generate economic growth, work their work properly when we can transact about them, i.e. when we have working markets for the final economic goods produced with those technologies, and for intermediate economic goods produced for them. It is as if the whole thing worked when we can buy and sell things. I was born in 1968, in a communist country, namely Poland, and I can tell you that in the absence of markets the whole mechanism just jams, progressively to a halt. Yes, markets are messy and capricious, and transactional prices can easily get out of hand, creating inflation, and yet markets give those little local incentives needed to get the most of human social roles. In the communist Poland, I remember people doing really strange things, like hoarding massive inventories of refrigerators or women’s underwear, just to create some speculative spin in an ad hoc, semi-legal or completely illegal market. It looks as if people needed to market and transact for real, amidst the theoretically perfectly planned society.   

Anyway, economic growth is observable through big sets of transactions in product markets, and those transactions have two attributes: quantities and prices AKA Q an P. It is like Q*P = ∑qi*pi. When I have – well, when we have – that complex network of technologies functionally connected to a factory of social roles for new humans, that thing makes ∑qi*pi, thus a lot of local transactions with quantities qi, at prices pi. The economic growth I have been so vocal about in the last few paragraphs is the real growth, i.e. in quantity Q = ∑qi. On the long run, what I am interested in, and my government is interested in, is to reasonably max out on ∆ Q = ∆∑qi. Quantities change slowly and quite predictably, whilst prices tend to change quickly and, mostly on the short term, chaotically. Measuring accurately real economic growth involving kicking the ‘*pi’ component out of the equation and extracting just ∆ Q = ∆∑qi. Question: why bothering with the observation of Q*P = ∑qi*pi when the real thing we need is just ∆ Q = ∆∑qi? Answer: because there is no other way. Complex networks of technologies produce economic growth by creating increasing diversity in social roles in concurrence with increasing diversity in products and their respective markets. No genius has come up, so far, with a method to add up, directly, the volume of visits in hairdresser’s salons with the volume of electric vehicles made, and all that with the volume of energy consumed.

Cities trade. Initially, they trade with the surrounding farms, out in the countryside, but, with time, the zone of trade relations tends to extend, and, interestingly enough, its extent is roughly proportional to the relative weight of the given city’s real output in the overall economic activity of the whole region. It is as if cities were developing some sort of gravitational field around them. The bigger the city as compared to other cities in the vicinity, the greater share of overall trade it takes, both in terms of exports and imports. Countries with many big cities trade a lot with other countries.     

There is an interesting relationship between exports and imports. Do I, as a person, import anything? Sure, I import plenty of goods. This software I am writing in is an imported good, to start with. Bananas which I ate for breakfast are imported. I drive a Honda, another imported good. My washing machine is a Samsung, my dish washer is a Siemens, and my phone and computer both come from Apple. I am a walking micro-hub of imports. Do I export anything? Almost nothing. One could argue that I export intellectual content with my blog. Still, as I am not being paid (yet) for my blog, it is rather voluntary cultural communication than exports. Well, there is one thing that creates a flow of export and import in me: my investment in the stock market. The money which I invested in the stock market is mostly placed in US-based companies, a few German and Dutch, and just a tiny bit is invested in Poland. Why? Because there is nothing happening in the Polish stock market, really. Boring. Anyway, I sort of export capital.

Cities and countries import a whole diversified basket of goods, but they usually export just a few, which they are really good at making and marketing. There is something like structural asymmetry between exports and imports. As soon as economic sciences started to burgeon, even before they were called economics and had been designated as ‘political economy’, social thinkers were trying to explain that phenomenon. Probably the best known is the explanation by David Ricardo, namely the notion of comparative advantage AKA productive specialization. There are exceptions, called ‘super exporters’, e.g. China or South Korea. These are countries which successfully export virtually any manufactured good, mostly due to low labour costs. However we label that phenomenon, here it is: whilst the global map of imports look like a very tight web, the map of exports is more like a few huge fountains of goods, pouring their output across the world. Practically every known imported good has its specialized big exporters. Thus, if my students ask me what international trade is, I am more and more prone to answer that trade is a structural pattern of the human civilization, where some places on Earth become super-efficient at making and marketing specific goods, and, consequently, the whole planetary civilization is a like team of people, with clearly assigned roles.

What is international management in that context? What is the difference between international management and domestic management, actually? What I can see, for example in the companies whose stock I invest my savings in, there is a special phase in the development of a business. It is when you have developed a product or service which you start marketing successfully at the international scale, thus you are exporting it, and there comes a moment when branching abroad with your organisational structure looks like a good idea. Mind you, there are plenty of business which, whilst growing nicely and exporting a lot, remain firmly domestic. If I run a diamond mine in Botswana – to take one of the most incongruous examples that come to my mind – I mind those diamonds in order to export them. There is no point in mining diamonds in Botswana just to keep those diamonds in Botswana. Export is the name of the game, here. Still, do I need to branch out internationally? My diamonds go to Paris, but is it a sensible idea to open a branch office in Paris? Not necessarily, rents for office space are killers over there. Still, when I run a manufacturing business in Ukraine, and I make equipment for power grids, e.g. electric transformers, and I export that equipment across Europe and to US, it could be a good idea to branch out. More specifically, it becomes a good idea when the value of my sales to a given country makes it profitable to be closer to the end user. Closer means two things. I can clone my original manufacturing technology in the target market, thus instead of making those transformers in Ukraine and shipping them to Texas, I can make them in Texas. On the other hand, closer means more direct human interaction, like customer support. 

Good. I got carried away a bit. I need to return to the things I want to teach my students, i.e. to skills I want to develop in them when teaching those three courses: Macroeconomics, International Trade, and International Management. Here is my take on the thing. These three courses represent three levels of work with quantitative data. Doing Macroeconomics in real life means reading actively macroeconomic reports and data, for the purposes of private business or those of public policy. It means being able to interpret changes in real output, inflation, unemployment, as well as in financial markets.

Doing International Trade for real might go two different ways: either you work in international trade, i.e. you do the technicalities of export and import, on the one hand, or you work about and around international trade, namely you need to nail down some kind of business plan or policy strongly related to export and import. That latter aspect involves working with data much more than the former, which, in turn, is more about documents, procedures and negotiation. I am much more at home with data analysis, contracts, and business planning than with the very technicalities of international trade. My teaching of international trade will go in that direction.

As for International Management, my only real experience is that of advising, doing market research and business planning for people who are about to decide about branching out abroad with their business. This is the only real experience I can communicate to my students.

I want to combine that general drift of my teaching with more specific a take on the current social reality, i.e. that of pandemic, economic recession and plans for recovery, and technological change combined with a modification of established business models. That last phenomenon, namely new technologies coming to the game and forcing a change in business structures is the main kind of understanding I want to provide my students with, as regards current events. Digital technologies, biotechnologies, and complex power systems increasingly reliant on both renewable energies and batteries of all kinds, are the thread of change. On and around that thread, cash is being hoarded, in unusually big cash-oriented corporate balance sheets. Cash is king, and science is the queen, so to say, in those newly developing business models. That’s logical: deep and quick technological change creates substantial risks, and increased financial liquidity is a normal response thereto.

Whatever will be happening over the months and years to come, in terms of economic recovery after the epidemic recession, will be happening through and in businesses which hoard important amounts of cash, and constantly look for the most competitive digital technologies. When governments say ‘We want to support the bouncing back of our domestic businesses’, those governments have to keep in mind that before investing in new property, plant, equipment, and in new intangible intellectual property, those businesses will be bouncing back by accumulating cash. This time, economic recovery will be probably very much non-Keynesian. Instead of unfreezing cash balances and investing them in new productive assets, microeconomic recovery of local business structures will involve them juicing themselves with cash. I think this is to take or to leave, as the French say. Bitching and moaning about ‘those capitalists who just hoard money with no regard for jobs and social gain’ seems as pointless as an inflatable dartboard.

Those cash-rich balance sheets are going to translate into strategies oriented on flexibility and adaptability more than anything else. Business entities are naturally flexible, and they are because they have the capacity to build, purposefully, a zone of proximal development around their daily routines. It is a zone of manageable risks, made of projects which the given business entity can jump into on demand, almost instantaneously. I think that businesses across the globe will be developing such zones of proximal development around themselves: zones of readiness for action rather than action itself. There is another aspect to that. I intuitively feel that we are entering a period of increasingly quick technological change. If you just think about the transformation of manufacturing processes and supply chains in the pharmaceutical industry, so as to supply the entire global population with vaccines, you can understand the magnitude of change. Technologies need to break even just as business models do. In a business model, breaking even means learning how to finance the fixed costs with the gross margin created and captured when transacting with customers and suppliers. In a technology, breaking even means to drive the occurrence of flukes and mistakes, unavoidable in large-scale applications, down to an acceptable level. This, in turn, means that the aggregate costs of said flukes and mistakes, which enters into the fixed costs of the business structure, is low enough to be covered by the gross margin generated from the technological process itself.

That technological breaking even applies to the digital world just as it applies to industrial processes. If you use MS Teams, just as I and many other people do, you probably know that polity enquiry which Teams address you after each video call or meeting: ‘What was the call quality?’. This is because that quality is really poor, with everybody using online connections much more than before the pandemic (much worse than with Zoom, for example), and Microsoft is working on it, as far as I know. Working on something means putting additional effort and expense into that thing, thus temporarily pumping up the fixed costs.

Now, suppose that you are starting up with a new technology, and you brace for the period of breaking even with it. You will need to build up a cushion of cash to finance the costs of flukes and mistakes, as well as the cost of adapting and streamlining your technology as the scale of application grows (hopefully).

We live in a period when a lot of science breaks free out of experimental labs much earlier and faster than it was intended to. Vaccines against COVID-19 are the best example. You probably know those sci fi movies, where some kind of strange experimental creature, claimed to be a super-specimen of a new super-species, and yet strangely ill-adapted to function in the normal world, breaks out of a lab. It wreaks havoc, it causes people to panic, and it unavoidably attracts the attention of an evil businessperson who wants to turn it into a weapon or into a slave. This is, metaphorically, what is happening now and what will keep happening for quite a while. Of course, the Sars-Cov-2 virus could very well be such an out-of-the-lab monster, still I think about all the technologies we deploy in response, vaccines included. They are such out-of-the-lab monsters as well. We have, and we will keep having, a lot of out-of-the-lab monsters running around, which, in turn, requires a lot of evil businesspeople to step in and deploy they demoniac plots.

All that means that the years to come are likely to be bracing, adapting and transforming much more than riding a rising wave crest of economic growth. Recovery will be slower than the most optimistic scenarios imply. We need to adapt to a world of fence-sitting business strategies, with a lot of preparation and build-up in capacity, rather than direct economic bounce-back. When preparing a business plan, we need to prepare for investors asking questions like ‘How quickly and how specifically can you adapt if the competitor A implements the technology X faster than predicted? How much cash do we need to shield against that risk? How do we hedge? How do we insure?’, rather than questions of the type ‘How quickly will I have my money back?’. In such an environment, substantial operational surplus in business is a rarity. Profits are much more likely to be speculative, based on trading corporate stock and other financial instruments, maybe on trading surpluses of inventories.

The right side of the disruption

I am swivelling my intellectual crosshairs around, as there is a lot going on, in the world. Well, there is usually a lot going on, in the world, and I think it is just the focus of my personal attention that changes its scope. Sometimes, I pay attention just to the stuff immediately in front of me, whilst on other times I go wide and broad in my perspective.

My research on collective intelligence, and on the application of artificial neural networks as simulators thereof has brought me recently to studying outlier cases. I am an economist, and I do business in the stock market, and therefore it comes as sort of logical that I am interested in business outliers. I hold some stock of the two so-far winners of the vaccine race: Moderna (https://investors.modernatx.com/ ) and BionTech (https://investors.biontech.de/investors-media ), the vaccine companies. I am interested in the otherwise classical, Schumpeterian questions: to what extent are their respective business models predictors of their so-far success in the vaccine contest, and, seen from the opposite perspective, to what extent is that whole technological race of vaccines predictive of the business models which its contenders adopt?

I like approaching business models with the attitude of a mean detective. I assume that people usually lie, and it starts with lying to themselves, and that, consequently, those nicely rounded statements in annual reports about ‘efficient strategies’ and ‘ambitious goals’ are always bullshit to some extent. In the same spirit, I assume that I am prone to lying to myself. All in all, I like falling back onto hard numbers, in the first place. When I want to figure out someone’s business model with a minimum of preconceived ideas, I start with their balance sheet, to see their capital base and the way they finance it, just to continue with their cash-flow. The latter helps my understanding on how they make money, at the end of the day, or how they fail to make any.

I take two points in time: the end of 2019, thus the starting blocks of the vaccine race, and then the latest reported period, namely the 3rd quarter of 2020. Landscape #1: end of 2019. BionTech sports $885 388 000 in total assets, whilst Moderna has $1 589 422 000. Here, a pretty amazing detail pops up. I do a routine check of proportion between fixed assets and total assets. It is about to see what percentage of the company’s capital base is immobilized, and thus supposed to bring steady capital returns, as opposed to the current assets, fluid, quick to exchange and made for greasing the current working of the business. When I measure that coefficient ‘fixed assets divided by total assets’, it comes as 29,8% for BionTech, and 29% for Moderna. Coincidence? There is a lot of coincidence in those two companies. When I switch to Landscape #2: end of September 2020, it is pretty much the. You can see it in the two tables below:

As you look at those numbers, they sort of collide with the common image of biotech companies in sci fi movies. In movies, we can see huge labs, like 10 storeys underground, with caged animals inside etc. In real life, biotech is cash, most of all. Biotech companies are like big wallets, camped next to some useful science. Direct investment in biotech means very largely depositing one’s cash on the bank account run by the biotech company.

After studying the active side of those two balance sheets, i.e. in BionTech and in Moderna, I shift my focus to the passive side. I want to know how exactly people put cash in those businesses. I can see that most of it comes in the form of additional paid-in equity, which is an interesting thing for publicly listed companies. In the case of Moderna, the bulk of that addition to equity comes as a mechanism called ‘vesting of restricted common stock’. Although it is not specified in their financial report how exactly that vesting takes place, the generic category corresponds to operations where people close to the company, employees or close collaborators, anyway in a closed private circle, buy stock of the company in a restricted issuance.  With Biontech, it is slightly different. Most of the proceeds from public issuance of common stock is considered as reserve capital, distinct from share capital, and on the top of that they seem to be running, similarly to Moderna, transactions of vesting restricted stock. Another important source of financing in both companies are short-term liabilities, mostly deferred transactional payments. Still, I have an intuitive impression of being surrounded by maybies (you know: ‘maybe I am correct, unless I am wrong), and thus I decided to broaden my view. I take all the 7 biotech companies I currently have in my investment portfolio, which are, besides BionTech and Moderna, five others: Soligenix (http://ir.soligenix.com/ ), Altimmune (http://ir.altimmune.com/investors ), Novavax (https://ir.novavax.com/ ) and VBI Vaccines (https://www.vbivaccines.com/investors/  ). In the two tables below, I am trying to summarize my essential observations about those seven business models.

Despite significant differences in the size of their respective capital base, all the seven businesses hold most of their capital in the highly liquid financial form: cash or tradable financial securities. Their main source of financing is definitely the additional paid-in equity. Now, some readers could ask: how the hell is it possible for the additional paid-in equity to make more than the value of assets, like 193%? When a business accumulates a lot of operational losses, they have to be subtracted from the incumbent equity. Additions to equity serve as a compensation of those losses. It seems to be a routine business practice in biotech.

Now, I am going to go slightly conspiracy-theoretical. Not much, just an inch. When I see businesses such as Soligenix, where cumulative losses, and the resulting additions to equity amount to teen times the value of assets, I am suspicious. I believe in the power of science, but I also believe that facing a choice between using my equity to compensate so big a loss, on the one hand, and using it to invest into something less catastrophic financially, I will choose the latter. My point is that cases such as Soligenix smell scam. There must be some non-reported financial interests in that business. Something is going on behind the stage, there.  

In my previous update, titled ‘An odd vector in a comfortably Apple world’, I studied the cases of Tesla and Apple in order to understand better the phenomenon of outlier events in technological change. The short glance I had on those COVID-vaccine-involved biotechs gives me some more insight. Biotech companies are heavily scientific. This is scientific research shaped into a business structure. Most of the biotech business looks like an ever-lasting debut, long before breaking even. In textbooks of microeconomics and management, we can read that being able to run the business at a profit is a basic condition of calling it a business. In biotech, it is different. Biotechs are the true outliers, nascent at the very juncture of cutting-edge science, and business strictly spoken. This is how outliers emerge: there is some cool science. I mean, really cool, the one likely to change the face of the world. Those mRNA biotechnologies are likely to do so. The COVID vaccine is the first big attempt to transform those mRNA therapies from experimental ones into massively distributed and highly standardized medicine. If this stuff works on a big scale, it is a new perspective. It allows fixing people, literally, instead of just curing diseases.

Anyway, there is that cool science, and it somehow attracts large amounts of cash. Here, a little digression from the theory of finance is due. Money and other liquid financial instruments can be seen as risk-absorbing bumpers. People accumulate large monetary balances in times and places when and where they perceive a lot of manageable risk, i.e. where they perceive something likely to disrupt the incumbent business, and they want to be on the right side of the disruption.

Lost in the topic. It sucks. Exactly what I needed.

I keep working on the collective intelligence of humans – which, inevitably, involves working on my intelligent cooperation with other people – in the context of the COVID-19 pandemic. I am focusing on one particular survival strategy which we, Europeans, developed over centuries (I can’t speak for them differently continental folks): the habit of hanging out in relatively closed social circles of knowingly healthy people.

The social logic is quite simple. If I can observe someone for many weeks and months, in a row, I sort have an eye for them. After some time, I know whom that person hangs out with, I can tell when they look healthy, and, conversely, when they look like s**t, hence suspiciously. If I concentrate my social contacts in a circle made of such people, then, even in the absence of specific testing for pathogens, I increase my own safety, and, as I do so, others increase their safety by hanging out with me. Of course, epidemic risk is still there. Pathogens are sneaky, and Sars-Cov-2 is next level in terms of sneakiness. Still, patient, consistent observation of my social contacts, and just as consistent making of a highly controlled network thereof, is a reasonable way to reduce that risk.

That pattern of closed social circles has abundant historical roots. Back in the day, even as recently as in the first half of the 20th century, European societies were very clearly divided in two distinct social orders: that of closed social circles which required introduction, prior to letting anyone in, on the one hand, and the rest of the society, much less compartmentalised. The incidence of infectious diseases, such as tuberculosis or typhoid, was much lower in the former of those social orders. As far as I know, many developing countries, plagued by high incidence of epidemic outbreaks, display such a social model even today.

As I think of it, the distinction between immediate social environment, and the distant one, common in social sciences, might have its roots in that pattern of living in closed social circles made of people whom we can observe on a regular basis. In textbooks of sociology, one can find that statement that the immediate social environment of a person makes usually 20 ÷ 25 people. That might be a historically induced threshold of mutual observability in a closed social circle.

I remember my impressions during a trip to China, when I was visiting the imperial palace in Beijing, and then several Buddhist temples. Each time, the guide was explaining a lot of architectural solutions in those structures as defences against evil spirits. I perceive Chinese people as normal, in the sense they don’t exactly run around amidst paranoid visions. Those evil spirits must have had a natural counterpart. What kind of evil spirit can you shield against by making people pass, before reaching your room, through many consecutive ante rooms, separated by high doorsteps and multi-layered, silk curtains? I guess it is about the kind of evil spirit we are dealing with now: respiratory infections.

I am focusing on the contemporary application of just those two types of anti-epidemic contrivances, namely that of living in close social circles, and that of staying in buildings structurally adapted to shielding against respiratory infections. Both are strongly related to socio-economic status. Being able to control the structure of your social circle requires social influence, which, in turn, and quite crudely, means having the luxury to wait for people who gladly comply with the rules in force inside the circle. I guess that in terms of frequency, our social relations are mostly work-related. The capacity to wait for the sufficiently safe social interactions, in a work environment, means either a job which I can do remotely, like home office, or a professional position of power, when I can truly choose whom I hang out with. If I want to live in an architectural structure with a lot of anterooms and curtains, to filter people and their pathogens, it means a lot of indoor space used just as a filter, not as habitat in the strict sense. Who pays for that extra space? At the end of the day, sadly enough, I do. The more money I have, the more of that filtering architectural space I can afford.

Generally, epidemic protection is costly, and, when used on a regular basis across society, that protection is likely to exacerbate the secondary outcomes of economic inequalities. By the way, as I think about it, the relative epidemic safety we have been experiencing in Europe, roughly since the 1950ies, could be a major factor of another subjective, collective experience, namely that of economic equality. Recently, in many spots of the social space, voices have been rising and saying that equality is not equal enough. Strangely enough, since 2016, we have a rise in mortality among adult males in high-income countries (https://data.worldbank.org/indicator/SP.DYN.AMRT.MA). Correlated? Maybe.

Anyway, I have an idea. Yes, another one. I have an idea to use science and technology as parents to a whole bunch of technological babies. Science is the father, as it is supposed to give packaged genetic information, and that information is the stream of scientific articles on the topic of epidemic safety. Yes, a scientific article can be equated to a spermatozoid. It is relatively small a parcel of important information. It should travel fast but usually it does not travel fast enough, as there is plenty of external censors who cite moral principles and therefore prevent it from spreading freely. The author thinks it is magnificent, and yet, in reality, it is just a building block of something much bigger: life.

Technology is the mother, and, as it is wisely stated in the Old Testament, you’d better know who your mother is. The specific maternal technology here is Artificial Intelligence. I imagine a motherly AI which absorbs the stream of scientific articles on COVID and related subjects, and, generation after generation, connects those findings to specific technologies for enhanced epidemic safety. It is an artificial neural network which creates and updates semantic maps of innovation. I am trying to give the general idea in the picture below.

An artificial neural network is a sequence of equations, at the end of the day, and that sequence is supposed to optimize a vector of inputs so as to match with an output. The output can be defined a priori, or the network can optimize this one too. All that optimization occurs as the network produces many alternative versions of itself and tests them for fitness. What could be those different versions in this case? I suppose each such version would consist in a logical alignment of the type ‘scientific findings <> assessment of risk <> technology to mitigate risk’.

Example: article describing the way that Sars-Cov-2 dupes the human immune system is associated with the risk generated once a person has been infected, and can be mitigated by proper stimulation of our immune system before the infection (vaccine), or by pharmaceuticals administered after the appearance of symptoms (treatment). Findings reported in the article can: a) advance completely new hypotheses b) corroborate existing hypotheses or c) contradict them. Hypotheses can have a strong or a weak counterpart in existing technologies.

The basic challenge I see for that neural network, hence a major criterion of fitness, is the capacity to process scientific discovery as it keeps streaming. It is a quantitative challenge. I will give you an example, with the scientific repository Science Direct (www.sciencedirect.com ), run by the Elsevier publishing group. I typed the ‘COVID’ keyword, and run a search there. In turns out 28 680 peer-reviewed articles have been published this year, just in the journals that belong to the Elsevier group. It has been 28 680 articles over 313 days since the beginning of the year (I am writing those words on November 10th, 2020), which gives 91,63 articles per day.

On another scientific platform, namely that of the Wiley-Blackwell publishing group (https://onlinelibrary.wiley.com/), 14 677 articles and 47 books have been published on the same topic, i.e. The Virus, which makes 14 677/313 = 46,9 articles per day and a new book every 313/47 = 6,66 days.

Cool. This is only peer-reviewed staff, sort of the House of Lords in science. We have preprints, too. At the bioRχiv platform (https://connect.biorxiv.org/relate/content/181 ), there has been 10 412 preprints of articles on COVID-19, which gives 10 412/313 = 33,3 articles per day.

Science Direct, Wiley-Blackwell, and bioRχiv taken together give 171,8 articles per day. Each article contains an abstract of no more than 150 words. The neural network I am thinking about should have those 150-word abstract as its basic food. Here is the deal. I take like one month of articles, thus 30*171,8*150 = 773 100 words in abstracts. Among those words, there are two groups: common language and medical language. If I connect that set of 773 100 words to a digital dictionary, such as Thesaurus used in Microsoft Word, I can kick out the common words. I stay with medical terminology, and I want to connect it to another database of knowledge, namely that of technologies.

You know what? I need to take on something which I should have been taken on already some time ago, but I was too lazy to do it. I need to learn programming, at least in one language suitable for building neural networks. Python is a good candidate. Back in the day, two years ago, I had a go at Python but, idiot of me, I quit quickly. Well, maybe I wasn’t as much of an idiot as I thought? Maybe having done, over the last two years, the walkabout of logical structures which I want to program has been a necessary prelude to learning how to program them? This is that weird thing about languages, programming or spoken. You never know exactly what you want to phrase out until you learn the lingo to phrase it out.

Now, I know that I need programming skills. However strong I cling to Excel, it is too slow and too clumsy for really serious work with data. Good. Time to go. If I want to learn Python, I need an interpreter, i.e. a piece of software which allows me to write an algorithm, test it for coherence, and run it. In Python, that interpreter is commonly called ‘Shell’, and the mothership of Python, https://www.python.org/ , runs a shell at https://www.python.org/shell/ . There are others, mind you: https://www.programiz.com/python-programming/online-compiler/ , https://repl.it/languages/python3 , or https://www.onlinegdb.com/online_python_interpreter .

I am breaking down my research with neural networks into partial functions, which, as it turns out, sum up my theoretical assumptions as regards the connection between artificial intelligence and the collective intelligence of human societies. First things first, perception. I use two types of neural networks, one with real data taken from external databases and standardized over respective maxima for individual variables, another one with probabilities assigned to arbitrarily defined phenomena. The first lesson I need to take – or rather retake – in Python is about the structures of data this language uses.

The simplest data structure in Python is a list, i.a. a sequence of items, separated with commas, and placed inside square brackets, e.g. my_list = [1, 2, 3]. My intuitive association with lists is that of categorization. In the logical structures I use, a list specifies phenomenological categories: variables, aggregates (e.g. countries), periods of time etc. In this sense, I mostly use fixed, pre-determined lists. Either I make the list of categories by myself, or I take an existing database and I want to extract headers from it, as category labels. Here comes another data structure in Python: a tuple. A tuple is a collection of data which is essentially external to the algorithm at hand, immutable, and it can be unpacked or indexed. As I understand, and I hope I understand it correctly, any kind of external raw data I use is a tuple.

Somewhere between a tuple (collection of whatever) and a list (collection of categories), Python distinguishes sets, i.e. unordered collections with no duplicate elements. When I transform a tuple or a list into a set, Python kicks out redundant components.

Wrapping it partially up, I can build two types of perception in Python. Firstly, I can try and extract data from a pre-existing database, grouping it into categories, and then making the algorithm read observations inside each category. For now, the fastest way I found to create and use databases in Python is the sqlite3 module (https://www.tutorialspoint.com/sqlite/sqlite_python.htm ). I need to work on it.

I can see something like a path of learning. I mean, I feel lost in the topic. I feel it sucks. I love it. Exactly the kind of intellectual challenge I needed.

When a best-friend’s-brother-in-law’s-cousin has a specific technology to market

I am connecting two strands of my work with artificial neural networks as a tool for simulating collective intelligence. One of them consists in studying orientations and values in human societies by testing different socio-economic variables as outcomes of a neural network and checking which of them makes that network the most similar to the original dataset. The second strand consists in taking any variable as the desired output of the network, setting an initially random vector of local probabilities as input, adding a random disturbance factor, and seeing how the network is learning in those conditions.

So far, I have three recurrent observations from my experiments with those two types of neural networks. Firstly, in any collection of real, empirical, socio-economic variables, there are 1 – 2 of them which, when pegged as the desired outcome of the neural network, produce a clone of actual empirical reality and that clone is remarkably closer to said reality than any other version of the same network, with other variables as its output. In other words, social reality represented with aggregate variables, such as average number of hours worked per person per year, or energy consumption per person per year, is an oriented reality. It is more like a crystal than like a snowball.

Secondly, in the presence of a randomly occurring disturbance, neural networks can learn in three essential ways, clearly distinct from each other. They can be nice and dutiful, and narrow down their residual error of estimation, down to a negligible level. Those networks just nail it down. The second pattern is that of cyclical learning. The network narrows down its residual error, and then, when I think all is said and done, whoosh!: the error starts swinging again, with a broadening amplitude, and then it decreases again, and the cycle repeats, over and over again. Finally, a neural network prodded with a random disturbance can go haywire. The chart of its residual error looks like the cardiac rhythm of a person who takes on an increasing effort: its swings in an ever-broadening amplitude. This is growing chaos. The funny thing, and the connection to my first finding (you know, that about orientations) is that the way a network learns depends on the real socio-economic variable I set as its desired outcome. My network nails it down, like a pro, when it is supposed to optimize something related to absolute size of a society: population, GDP, capital stock. Cyclical learning occurs when I make my network optimize something like a structural proportion: average number of hours worked per person per year, density of population per 1 km2 etc. Just a few variables put my network in the panic mode, i.e. the one with increasing amplitude of error. Price index in capital goods is one, Total Factor Productivity is another one. Interestingly, price index in consumer goods doesn’t create much of a panic in my network.

There is a connection between those two big observations. The socio-economic variables with come out as the most likely orientations of human societies are those, which seem to be optimized in that cyclical, sort of circular learning, neither with visible growth in precision, nor with visible panic mode. Our human societies seem to orient themselves on those structural proportions, which they learn and relearn over and over again.  

The third big observation I made is that each kind of learning, i.e. whichever of the three signalled above, makes my neural network loosen its internal coherence. I measure that coherence with the local Euclidean distance between variables: j = (1, 2,…, k)[(xi – xj)2]0,5 / k. That distance tends to swing cyclically, as if the network needed to loosen its internal connections in order to absorb a parcel of chaos, and then it tightens back, when chaos is being transformed into order.

I am connecting those essential outcomes of me meddling with artificial neural networks to the research interests I developed earlier this year: the research on cities and their role in our civilisation. One more time, I am bringing that strange thought which came to my mind as I was cycling through the empty streets of my hometown, Krakow, Poland, in the first days of the epidemic lockdown, in March 2020: ‘This city looks dead without people in the streets. I have never seen it as dead as now, even in the times of communism, back in the 1970s. I just wonder, how many human footsteps a day this city needs in order to be truly alive?’. After I had that thought, I started digging and I found quite interesting facts about cities and urban space. Yet, another strand of thinking was growing in my head, the one about the impact of sudden, catastrophic events, such as epidemic outbreaks, on our civilisation. I kept thinking about Black Swans.   

I have been reading some history, I have been rummaging in empirical data, I have been experimenting with neural networks, and I have progressively outlined an essential hypothesis, to dig even further into: our social structures absorb shocks, and we do it artfully. Collectively, we don’t just receive s**t from Mother Nature: we absorb it, i.e. we learn how to deal with it. As a matter of fact, we have an amazing capacity to absorb shocks and to create the impression, on the long run, that nothing bad really happened, and that we just keep progressing gloriously. If we think about all the most interesting s**t in our culture, it all comes from one place: shock, suffering, and the need to get over it.

In 2014, I visited an exposition of Roman art (in Barcelona, in the local Museum of Catalonia). Please, do not confuse Roman with Ancient Roman. Roman art is the early medieval one, roughly until and through the 12th century (historians might disagree with me as regards this periodization, but c’mon guys, this is a blog, I can say crazy things here). Roman art covers everything that happened between the collapse of the Western Roman Empire and the first big outbreak of plague in Europe, sort of. And so I walk along the aisles, in that exposition of Roman art, and I see replicas of frescoes, originally located in Roman churches across Europe. All of them sport Jesus Christ, and in all of them Jesus looks like an archetypical Scottish sailor: big, bulky, with a plump, smiling face, curly hair, short beard, and happy as f**k. On all those frescoes Jesus in happy. Can you imagine The Last Supper where Jesus dances on the table, visibly having the time of his life? Well, it is there, on the wall of a small church in Germany.        

I will put it in perspective. If you look across the Christian iconography today, Jesus is, recurrently, that emaciated guy, essentially mangled by life, hanging sadly from his cross, and apostles are just the same way (no cross, however), and there is all that memento mori stuff sort of hanging around, in the air. Still, this comes from the times after the first big outbreak of plague in Europe. Earlier on, on the same European continent, for roughly 800 years between the fall of the Western Roman Empire and the first big epidemic hit, Jesus and all his iconography had been in the lines of Popeye The Sailor, completely different from what we intuitively associate Christianism with today. 

It is to keep in mind that epidemic diseases have always been around. Traditions such as shaking hands to express trust and familiarity, or spitting in those hands before shaking them to close a business deal, it all comes from those times when any stranger, i.e. someone coming from further than 50 miles away, was, technically, an epidemic threat. For hundreds of years, we had sort of been accepting those pathogens at face value, as the necessary s**t which takes nothing off our joy of life, and then ‘Bang!’, 1347 comes, and we really see how hard an epidemic can hit when that pathogen really means business, and our culture changes deeply.

That’s the truly fundamental question which I want to dig into and discuss: can I at all, and, if so, how can I mathematically model the way our civilisation learns, as a collectively intelligent structure, through and from the experience of COVID-19 pandemic?

Collectively intelligent structures, such as I see them, learn by producing many alternative versions of themselves – each of those versions being like one-mutation neighbour to others –   and then testing each such version as for its fitness to optimize a vector of desired outcomes. I wonder how it can happen now, in this specific situation we are in, i.e. the pandemic? How can a society produce alternative versions of itself? We test various versions of epidemic restrictions. We test various ways of organizing healthcare. We probably, semi-consciously test various patterns of daily social interactions, on the top of official regulations on social mobility. How many such mutations can we observe? What is our desired outcome?

I start from the end. My experiments with neural networks applied as simulators of collective human intelligence suggest that we optimize, most of all, structural proportions of our socio-economic system. The average number of hours worked per person per year, and the amount of human capital accumulated in an average person, in terms of schooling years, come to the fore, by far. Energy consumption per person per year is another important metric.

Why labour? Because labour, at the end of the day, is social interaction combined with expenditure of energy, which, in turn, we have from our food base. Optimizing the amount of work per person, together with the amount of education we need in order to perform that work, is a complex adaptive mechanism, where social structures arrange themselves so as their members find some kind of balance with the grub they can grab from environment. Stands to reason.

Now, one more thing as for the transformative impact of COVID-19 on our civilization. I am participating in a call for R&D tenders, with the Polish government, more specifically with the National Centre for Research and Development (https://www.ncbr.gov.pl/en/ ). They have announced a special edition of the so-called Fast Track call, titled ‘Fast Track – Coronaviruses’. First of all, please pay attention to the plural form of coronaviruses. Second of all, that specific track of R&D goes as broadly as calling for architectural designs supposed to protect against contagion. Yes, if that call is not a total fake (which happens sometimes, when a best-friend’s-brother-in-law’s-cousin has a specific technology to market, for taxpayers’ money), the Polish government has data indicating that pandemic is going to be the new normal.

Stress-tested by a highly infectious microorganism

My editorial on You Tube

I want to go sideways – but just a tiny bit sideways – from the deadly serious discourse on financial investment, which I developed in Partial outcomes from individual tables and in What is my take on these four: Bitcoin, Ethereum, Steem, and Golem?.  I want to try and answer the same question we all try to answer from time to time: what’s next? What is going to happen, with all that COVID-19 crisis?

Question: have we gone into lockdowns out of sheer fear on an unknown danger, or are we working through a deep social change with positive expected outcomes?

What happens to us, humans, depends very largely on what we do: on our behaviour. I am going to interpret current events and the possible future as collective behaviour with economic consequences, in the spirit of collective intelligence, the concept I am very fond of. This is a line of logic I like developing with my students. I keep telling them: ‘Look, whatever economic phenomenon you take, it is human behaviour. The Gross Domestic Product, inflation, unemployment, the balance of payments, local equilibrium prices: all that stuff is just a bunch of highly processed metaphors, i.e. us talking about things we are afraid to admit we don’t quite understand. At the bottom line of all that, there are always some folks doing something. If you want to understand economic theory, you need to understand human behaviour’.

As I will be talking about behaviour, I will be referring to a classic, namely to Burrhus Frederic Skinner, the founding father of behavioural psychology, and one of his most synthetic papers, ‘Selection by Consequences’ (Skinner, B. F.,1981, Selection by consequences, Science, 213(4507), pp. 501-504). This paper had awoken my interest a few months ago, in Autumn 2019, when I was discussing it with my students, in a course entitled ‘Behavioural modelling’. What attracted my attention was the amount of bullshit which has accumulated over decades about the basic behavioural theory that B.F. Skinner presented.

I can summarize the bullshit in question with one sentence: positive reinforcement of behaviour is stronger than negative reinforcement. This is the principle behind policies saying that ‘rewards work better than punishments’ etc. Before I go further into theory, and then even further into the application of theory to predicting our collective future, please, conduct a short mental experiment. Imagine that I want to make you walk 100 yards by putting your feet exactly on a white line chalked on the ground. I give you two reinforcements. When you step out of the line, I electrocute you. When you manage to walk the entire distance of 100 yards exactly along the chalked line, I reward you with something pleasurable, e.g. with a good portion of edible marijuana. Which of those reinforcements is stronger?

If you are intellectually honest in that exercise, you will admit that electrocution is definitely stronger a stimulus. That’s the first step in understanding behaviourism: negative reinforcements are usually much stronger than positive ones, but, in the same time, they are much less workable and flexible. If you think even more about such an experiment, you will say: ‘Wait a minute! It all depends on where exactly I start my walk. If my starting point is exactly on the white chalked line, the negative reinforcement through electrocution could work: I step aside and I get a charge. Yet, if I start somewhere outside the white line, I will be electrocuted all the time (I am outside the allowed zone), and avoiding electrocution is a matter of sheer luck. When I accidentally step on the white line, and electrocution stops, it can give me a clue’. The next wait-a-minute argument is that electrocution works directly on the person, whilst the reward works in much more complex a pattern. I need to know there is a reward at the end of the line, and I need to understand the distance I need to walk etc. The reward works only if I grasp the context.

The behavioural theory by B.F. Skinner is based on the general observation that all living organisms are naturally exploratory in their environment (i.e. they always behave somehow), and that exploratory behaviour is reinforced by positive and negative stimuli. By the way, when I say all living organisms, it really means all. You can experiment with that. Take a lump of fresh, edible yeast, the kind you would use to make bread. Put it in some kind of petri dish, for example on wet cotton. Smear a streak of cotton with a mix of flour, milk, and sugar. Smear another streak with something toxic, like a house cleaner. You will see, within minutes, that yeast starts branching aggressively into the streak of cotton smeared with food (milk, sugar, butter), and will very clearly detract from the area smeared with detergent.

Now, imagine that you are more or less as smart as yeast is, e.g. you have just watched Netflix for 8 hours on end. Negative stimulus (house cleaner) gives you very simple information: don’t, just don’t, and don’t even try to explore this way. Positive stimulus (food) creates more complex a pattern in you. You have a reward, and it raises the question what is going to happen if you make one more step in that rewarding direction, and you make that step, and you reinforce yourself in the opinion that this is the right direction to go etc. Negative stimulation developed in you a simple pattern of behaviour, that of avoidance. It is a very strong stimulus, and an overwhelmingly powerful pattern of behaviour, and this is why there is not much more to do, down this avenue. I know I shouldn’t, right? How much more can I not do something?

Positive stimulation, on the other hand, triggers the building up of a strategy. Positive stimulation is scalable. You can absorb more or less pleasure, depending on how fast you branch into cotton imbibed with nutrients (remember, we are yeast, right?). Positive stimulation allows to build up experience, and to learn complex patterns of behaviour. By the way, if you really mean business with that yeast experiment, here is something to drag you out of Netflix. In the petri dish, once you have placed yeast on that wet cotton, put in front of it a drop of detergent (negative stimulus), and further in the same direction imbibe cotton with that nutritive mix of flour, milk and sugar. Yeast will branch around the drop of detergent and towards food. This is another important aspect of behaviourism: positive reinforcements allow formulating workable goals and strategies, whilst a strategy consisting solely in avoiding negative stimuli is one of the dumbest strategies you can imagine. Going straight into negative and destroying yourself is perhaps the only even dumber way of going through life.

One more thing about behaviourism. When I talk about it, I tend to use terms ‘pleasure’ and ‘pain’ but these are not really behaviourist ones. Pleasure and pain are inside my head, and from the strictly behaviourist point of view, what’s inside my head is unobservable at best, and sheer crap at worst. Behaviourism talks about reinforcements. A phenomenon becomes reinforcement when we see it acting as one. If something that happens provokes in me a reaction of avoidance, it is a negative stimulus, whatever other interpretation I can give it. There are people who abhor parties, and those people can be effectively reinforced out of doing something with the prospect of partying, although for many other people parties are pleasurable. On the other hand, positive reinforcement can go far beyond basic hedonism. There are people who fly squirrel suits, climb mountains or dive into caves, risking their lives. Emotional states possible to reach through those experiences are their positive reinforcements, although the majority of general population would rather avoid freezing, drowning, or crashing against solid ground at 70 miles per hour.

That was the basic message of B.F. Skinner about reinforcements. He even claimed that we, humans, have a unique ability to scale and combine positive reinforcements and this is how we have built that thing we call civilisation. He wrote: ‘A better way of making a tool, growing food, or teaching a child is reinforced by its consequence – the tool, the food, or a useful helper, respectively. A culture evolves when practices originating in this way contribute to the success of the practicing group in solving its problems. It is the effect on the group, not the reinforcing consequences for individual members, which is responsible for the evolution of the culture’.

Complex, civilisation-making patterns of both our individual and collective behaviour are shaped through positive reinforcements, and negative ones serve as alert systems that correct our course of learning. Now, COVID – 19: what does it tell us about our behaviour? I heard opinions, e.g. in a recent speech by Emmanuel Macron, the French president, that lockdowns which we undertook to flatten down the pandemic curve are something unique in history. Well, I partly agree, but just partly. Lockdowns are complex social behaviour, and therefore they can be performed only to the extent of previously acquired learning. We need to have practiced some kind of lockdown-style-behaviour earlier, and probably through many generations, in order to do it massively right now. There is simply no other way to do it. The speed we enter into lockdowns tells me that we are demonstrating some virtually subconscious pattern of doing things. When you want to do something really quickly and acceptably smoothly, you need to have the pattern ingrained through recurrent practice, just as a pianist has their basic finger movements practiced, through hundreds of hours at the piano, into subconscious motor patterns.

In one of my favourite readings, Civilisation and Capitalism by Fernand Braudel, vol. 1, ‘The Structures of Everyday Life. The limits of the possible’, Section I ‘Weight of Numbers’, we can read: ‘Ebb and flow. Between the fifteenth and the eighteenth century, if the population went up or down, everything else changed as well. When the number of people increased, production and trade also increased. […] But demographic growth is not an unmitigated blessing. It is sometimes beneficial and sometimes the reverse. When a population increases, its relationship to the space it occupies and the wealth at its disposal is altered. It crosses ‘critical thresholds’ and at each one its entire structure is questioned afresh’.

There is a widely advocated claim that we, humans, have already overpopulated Earth. I even developed on that claim in my own book, Capitalism and Political Power. Still, in this specific context, I would like to focus on something slightly different: urbanisation. The SARS-Cov-2 virus we have so much trouble with right now seems to be particularly at ease in densely populated urban agglomerations. It might be a matter of pure coincidence, but in 2007 – 2008, the share of urban population in total global population exceeded 50% (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS ). Our ‘critical threshold’, for now, might be precisely that: the percentage of people in urban structures. In 2003, when SARS-Cov-1 epidemic broke out, global urbanisation just passed the threshold of 43%. In 2018 (last data available) we were at 55,27%.

When Ebola broke out in Africa, in 2014 ÷ 2016, three countries were the most affected: Liberia, Guinea, and Sierra Leone. Incidentally, all three were going, precisely when Ebola exploded, through a phase of quick urbanisation. Here are the numbers:

 Percentage of urban population in total population
Country2015201620172018
Liberia49,8%50,3%50,7%51,2%
Guinea35,1%35,5%35,8%36,1%
Sierra Leone40,8%41,2%41,6%42,1%

I know, this is far from being hard science, yet I can see the outline of a pattern. Modern epidemics break out in connection with growing urbanisation. A virus like SARS-Covid-2, with its crazily slow cycle of incubation, and the capacity to jump between asymptomatic hosts, is just made for the city. It is like a pair of Prada shoes in the world of pathogens.    

Why are we becoming more and more urbanized, as a civilisation? I think it is a natural pattern of accommodating a growing population. When each consecutive generation comes with greater a headcount than the preceding ones, new social roles are likely to emerge. The countryside is rigid in terms of structured habitable space, and in terms of social roles offered to the newcomers. Farmland is structured for agricultural production, not for the diversity of human activity. There is an interesting remark to find in another classic, reverend Thomas Malthus. In chapter 4 of An Essay on the Principle of Population (1798), he writes ‘The sons of tradesmen and farmers are exhorted not to marry, and generally find it necessary to pursue this advice till they are settled in some business or farm that may enable them to support a family. These events may not, perhaps, occur till they are far advanced in life. The scarcity of farms is a very general complaint in England. And the competition in every kind of business is so great that it is not possible that all should be successful.

In other words, the more of us, humans, is there around, the more we need urban environments to maintain relative stability of our social structure. What would happen in the absence of cities to welcome the new-born (and slightly grown) babies from each, ever growing generation? In Europe, we have a good example of that: crusades. In the 10th and 11th centuries, in Europe, we finally figured out an efficient agricultural system, and our population had been growing quickly at the time. Still, in a mostly agricultural society which we were back then, a growing number of people had simply nothing to do. Result: outwards-oriented conquest.

We need cities to accommodate a growing population, still we need to figure out how those cities should work. Healthcare is an important aspect of urban life, as we have a lot of humans, with a lot of health issues, in one place. The COVID-19 crisis has shown very vividly all the weaknesses of healthcare infrastructures in cities. Transportation systems are involved too, and the degree of safety they offer. A pathogen preying on our digestive tract, such as dysentery, should it be as sneaky as SARS-Cov-2, would expose our water and sanitation systems, as well as our food supply system. I know it sounds freaky, but virtually every aspect of urban infrastructure can be stress-tested by a highly infectious microorganism.  

Here comes another passage from Civilisation and Capitalism by Fernand Braudel, vol. 1, ‘The Structures of Everyday Life. The limits of the possible’, Section I ‘Weight of Numbers’: ‘Looking more closely at Western Europe, one finds that there was a prolonged population rise between 1100 and 1350, another between 1450 and 1650, and a third after 1750; the last alone was not followed by a regression. Here we have three broad and comparable periods of biological expansion. The first two […] were followed by recessions, one extremely sharp, between 1350 and 1450, the next rather less so, between 1650 and 1750 (better described as a slowdown than as a recession) […] Every recession solves a certain number of problems, removes pressures and benefits the survivors. It is pretty drastic, but none the less a remedy. Inherited property became concentrated in a few hands immediately after the Black Death in the middle of the fourteenth century and the epidemics which followed and aggravated its effects. Only good land continued to be cultivated (less work for greater yield). The standard of living and real earnings of the survivors rose. […] Man only prospered for short intervals and did not realize it until it was already too late.

I think we have collective experience in winding down our social business in response to external stressors. This is the reason why we went so easily into lockdowns, during the pandemic. We are practicing social flexibility and adaptability through tacit coordination. You can read more on this topic in The games we play with what has no brains at all, and in A civilisation of droplets.

In many countries, we don’t have problems with food anymore, yet we have problems with health. We need a change in technology and a change in lifestyles, in order to keep ourselves relatively healthy. COVID -19 shows that, first of all, we don’t really know how healthy exactly we are (we don’t know who is going to be affected), second of all that some places are too densely populated (or have too little vital resources per capita) to assure any health security at all (New York), and third of all, that uncertainty about health generates a strategy of bunkering and winding down a large part of the material civilisation.

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