Month: February 2021

Still some juice in facts

I am working on improving my manuscript titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, after it received an amicable rejection from the journal Applied Energy, and, in the same time, I am working on other stuff. As usually. Some of that other staff is a completely new method of teaching in the summer semester, sort of a gentle revolution, with glorious prospects ahead, and without guillotines (well, not always).

As for the manuscript, I intend to work in three phases. I restate and reformulate the main lines of the article, and this is phase one. I pass in review the freshest literature in energy economics, as well as in the applications of artificial neural networks therein, and this is phase two. Finally, in phase three, I plan to position my method and my findings vis a vis that latest research.

I start phase one. When I want to understand what I wrote about 1 year ago, it is very nearly as if I was trying to understand what someone else wrote. Yes, I function like that. I have pretty good long-term memory, and it is because I learnt to detach emotions from old stuff. I sort of archive my old thoughts in order to make room for the always slightly disquieting waterfall of new thoughts. I need to dig and unearth my past meaning. I use the technique of reverse reading to achieve that. I read written content from its end back upstream to its beginning, and I go back upstream at two levels of structure: the whole piece of text, and individual sentences. In practical terms, when I work with that manuscript of mine, I take the last paragraph of the conclusion, and I actively write it backwards word-wise (I keep proper names unchanged). See by yourself.

This is the original last paragraph: ‘What if economic systems, inclusive of their technological change, optimized themselves so as to satisfy a certain workstyle? The thought seems incongruous, and yet Adam Smith noticed that division of labour, hence the way we work, shapes the way we structure our society. Can we hypothesise that technological change we are witnessing is, most of all, a collectively intelligent adaptation in the view of making a growing mass of humans work in ways they collectively like working? That would revert the Marxist logic, still, the report by World Bank, cited in the beginning of the article, allows such an intellectual adventure. On the path to clarify the concept, it is useful to define the meaning of collective intelligence’.

Now, I write it backwards: ‘Intelligence collective of meaning the define to useful is it concept the clarify to path the on adventure intellectual an such allows article the of beginning the in cited World Bank by report the still logic Marxist the revert that would that. Working like collectively they ways in work humans of mass growing a making view of the in adaptation intelligent collectively a all of most is witnessing are we change technological that hypothesise we can? Society our structure we way the shapes work we way the hence labour of division that noticed Adam Smith yet and incongruous seems thought the workstyle certain a satisfy to as so themselves optimized change technological their of inclusive systems economic if what?’

Strange? Certainly, it is strange, as it is information with its pants on its head, and this is precisely why it is informative. The paper is about the market of energy, and my last paragraph of conclusions is about the market of labour, and its connection to the market of energy.

I go further upstream in my writing. The before-last paragraph of conclusions goes like: ‘Since David Ricardo, all the way through the works of Karl Marks, John Maynard Keynes, and those of Kuznets, economic sciences seem to be treating the labour market as easily transformable in response to an otherwise exogenous technological change. It is the assumption that technological change brings greater a productivity, and technology has the capacity to bend social structures. In this view, work means executing instructions coming from the management of business structures. In other words, human labour is supposed to be subservient and executive in relation to technological change. Still, the interaction between technology and society seems to be mutual, rather than unidirectional (Mumford 1964, McKenzie 1984, Kline and Pinch 1996; David 1990, Vincenti 1994). The relation between technological change and the labour market can be restated in the opposite direction. There is a body of literature, which perceives society as an organism, and social change is seen as complex metabolic adaptation of that organism. This channel of research is applied, for example, in order to apprehend energy efficiency of national economies. The so-called MuSIASEM model is an example of that approach, claiming that complex economic and technological change, including transformations in the labour market, can be seen as a collectively intelligent change towards optimal use of energy (see for example: Andreoni 2017 op. cit.; Velasco-Fernández et al 2018 op. cit.). Work can be seen as fundamental human activity, crucial for the management of energy in human societies. The amount of work we perform creates the need for a certain caloric intake, in the form of food, which, in turn, shapes the economic system around, so as to produce that food. This is a looped adaptation, as, on the long run, the system supposed to feed humans at work relies on this very work’.

Here is what comes from reverted writing of mine: ‘Work very this on relies work at humans feed to supposed system the run long the on as adaptation looped a is this food that produce to around system economic the shapes turn in which food of form the in intake caloric certain a for need the creates perform we work of amount the societies human in energy of management the for crucial activity human fundamental as seen be can work. Energy of use optimal towards change intelligent collectively a as seen be can market labour the in transformations including change technological and economic complex that claiming approach that of example an is model MuSIASEM called so the economies national of efficiency energy apprehend to order in example for applied is research of channel this. Organism that of adaptation metabolic complex as seen is change social and organism an as society perceives which literature of body a is there. Direction opposite the in restated be can market labour the and change technological between relation the. Unidirectional than rather mutual be to seems society and technology between interaction the still. Change technological to relation in executive and subservient be to supposed is labour human words other in. Structures social bend to capacity the has technology and productivity a greater brings change technological that assumption the is it. Change technological exogenous otherwise an to response in transformable easily as market labour the treating ne to seem sciences economic Kuznets of those and Keynes […], Marks […] of works the through way the all Ricardo […]’.

Good. I speed up. I am going back upstream through consecutive paragraphs of my manuscript. The chain of 35 ideas which I write here below corresponds to the reverted logical structure (i.e. from the end backstream to the beginning) of my manuscript. Here I go. Ideas listed below have numbers corresponding to their place in the manuscript. The higher the number, the later in the text the given idea is phrased out for the first time.

>> Idea 35: The market of labour, i.e. the way we organize for working, determines the way we use energy.

>> Idea 34: The way we work shapes technological change more than vice versa. Technologies and workstyles interact

>> Idea 33: The labour market offsets the loss of jobs in some sectors by the creation of jobs in other sectors, and thus the labour market accommodates the emergent technological change.

>> Idea 32: The basket of technologies we use determines the ways we use energy. work in itself is human effort, and that effort is functionally connected to the energy base of our society

>> Idea 31: Digital technologies seem to have a special function in mediating the connection between technological change and the labour market

>> Idea 30: the number of hours worked per person per year (AVH), the share of labour in the GNI (LABSH), and the indicator of human capital (HC) seem to make an axis of social change, both as input and as output of the collectively intelligent structure.

>> Idea 29: The price index in exports (PL_X) comes as the chief collective goal pursued, and the share of public expenditures in the Gross National Income (CSH_G) appears as the main epistatic driver in that pursuit.

>> Idea 28: The methodological novelty of the article consists in using the capacity of a neural network to produce many variations of itself, and thus to perform evolutionary adaptive walk in rugged landscape.

>> Idea 27: The here-presented methodology assumes: a) tacit coordination b) evolutionary adaptive walk in rugged landscape c) collective intelligence d) observable socio-economic variables are manifestations of the past, coordinated decisions.

>> Idea 26: Variance observable in the average Euclidean distances that each variable has with the remaining 48 ones reflects the capacity of each variable to enter into epistatic interactions with other variables, as the social system studied climbs different hills, i.e. pursues different outcomes to optimize.

>> Idea 25: Coherence: across 48 sets Si out of the 49 generated with the neural network, variances in Euclidean distances between variables are quite even. Only one set Si yields different variances, namely the one pegged on the coefficient of patent applications per 1 million people.

>> Idea 24: the order of phenomenal occurrences in the set X does not have a significant influence on the outcomes of learning.

>> Idea 23: results of multiple linear regression of natural logarithms in the variables observed is compared to the application of an artificial neural network with the same dataset – to pass in review and to rework – lots of meaning there.

>> Idea 22: the phenomena assumed to be a disturbance, i.e. the discrepancy in retail prices of electricity, as well as the resulting aggregate cash flow, are strongly correlated with many other variables in the dataset. Perhaps the most puzzling is their significant correlation with the absolute number of resident patent applications, and with its coefficient denominated per million of inhabitants. Apparently, the more patent applications in the system, the deeper is that market imperfection.

>> Idea 21: Another puzzling correlation of these variables is the negative one with the variable AVH, or the number of hours worked per person per year. The more an average person works per year, in the given country and year, the less likely this local market is to display harmful differences in the retail prices of electricity for households.

>> Idea 20: On the other hand, variables which we wish to see as systemic – the share of electricity in energy consumption and the share of renewables in the output of electricity – have surprisingly few significant correlations in the dataset studied, just as if they were exogenous stressors with little foothold in the market as for yet. 

>> Idea 19: None of the four key variables regarding the European market of energy: a) the price fork in the retail market of electricity (€) b) the capital value of cash flow resulting from that price fork (€ mln) c) the share of electricity in energy consumption (%) and d) the share of renewables in electricity output (%)seems having been generated by a ‘regular’ Gaussian process: they all produce definitely too much outliers for a Gaussian process to be the case.

>> Idea 18: other variables in the dataset, the ‘regulars’ such as GDP or price levels, seem to be distributed quite close to normal, and Gaussian processes can be assumed to work in the background. This is a typical context for evolutionary adaptive walk in rugged landscape. An otherwise stable socio-economic environment gets disturbed by changes in the energy base of the society living in the whereabouts. As new stressors (e.g. the need to switch to electricity, from the direct combustion of fossil fuels) come into the game, some ‘mutant’ social entities stick out of the lot and stimulate an adaptive walk uphill.

>> Idea 17: The formal test of Euclidean distances, according to equation (1), yields a hierarchy of alternative sets Si, as for their similarity to the source empirical set X of m= 300 observations. This hierarchy represents the relative importance of variables, which each corresponding set Si is pegged on.

>> Idea 16: The comparative set XR has been created as a sequence of 10 stacked, pseudo-random permutations of the original set X has been created as one database. Each permutation consists in sorting the records of the original set X according to a pseudo-random index variable. The resulting set covers m = 3000 phenomenal occurrences.

>> Idea 15: The underlying assumption as regards the collective intelligence of that set is that each country learns separately over the time frame of observation (2008 – 2017), and once one country develops some learning, that experience is being taken and reframed by the next country etc. 

>> Idea 14: we have a market of energy with goals to meet, regarding the local energy mix, and with a significant disturbance in the form of market imperfections

>> Idea 13: special focus on two variables, which the author perceives as crucial for tackling climate change: a) the share of renewable energy in the total output of electricity, and b) the share of electricity in the total consumption of energy.

>> Idea 12: A est for robustness, possible to apply together with this method, is based on a category of algorithms called ‘random forest’

>> Idea 11: The vector of variances in the xi-specific fitness function V[xi(pj)] across the n sets Si has another methodological role to play: it can serve to assess the interpretative robustness of the whole complex model. If, across neural networks oriented on different outcome variables, the given input variable xi displays a pretty uniform variance in its fitness function V[xi(pj)], the collective intelligence represented in equations (2) – (5) performs its adaptive walk in rugged landscape coherently across all the different hills considered to walk up. Conversely, should all or most variables xi, across different sets Si, display noticeably disparate variances in V[xi(pj)], the network represents a collective intelligence which adapts in a clearly different manner to each specific outcome (i.e. output variable).

>> Idea 10: the mathematical model for this research is composed of 5 main equations, which, in the same time, make the logical structure of the artificial neural network used for treating empirical data. That structure entails: a) a measure of mathematical similarity between numerical representations of collectively intelligent structure b) the expected state of intelligent structure reverse engineered from the behaviour of the neural network c) neural activation and the error of observation, the latter being material for learning by measurable failure, for the collectively intelligent structure d) transformation of multi-variate empirical data into one number fed into the neural activation function e) a measure of internal coherence in the collectively intelligent structure

>> Idea 9: the more complexity, the more is the hyperbolic tangent, based on the expression e2h, driven away from its constant root e2. Complexity in variables induces greater swings in the hyperbolic tangent, i.e. greater magnitudes of error, and, consequently, longer strides in the process of learning.

>> Idea 8: Each congruent set Si is produced with the same logical structure of the neural network, i.e. with the same procedure of estimating the value of output variable, valuing the error of estimation, and feeding the error forward into consecutive experimental rounds. This, in turn, represents a hypothetical state of nature, where the social system represented with the set X is oriented on optimizing the given variable xi, which the corresponding set Si is pegged on as its output.

>> Idea 7: complex entities can internalize an external stressor as they perform their adaptive walk. Therefore, observable variance in each variable xi in the set X can be considered as manifestation of such internalization. In other words, observable change in each separate variable can result from the adaptation of social entities observed to some kind of ‘survival imperative’.

>> Idea 6: hypothesis that collectively intelligent adaptation in human societies, regarding the ways of generating and using energy, is instrumental to the optimization of other social traits.    

>> Idea 5: Adaptive walks in rugged landscape consist in overcoming environmental challenges in a process comparable to climbing a hill: it is both an effort and a learning, where each step sets a finite range of possibilities for the next step.

>> Idea 4: the MuSIASEM methodological framework – aggregate use of energy in an economy can be studied as a metabolic process

>> Idea 3: human societies are collectively intelligent about the ways of generating and using energy: each social entity (country, city, region etc.) displays a set of characteristics in that respect

>> Idea 2: adaptive walk of a collective intelligence happens in a very rugged landscape, and the ruggedness of that landscape comes from the complexity of human societies

>> Idea 1: Collective intelligence occurs even in animals as simple neurologically as bees, or even as the Toxo parasite. Collective intelligence means shifting between different levels of coordination.

As I look at that thing, namely at what I wrote something like one year ago, I have a doubly recomforting feeling. The article seems to make sense from the end to the beginning, and from the beginning to the end. Both logical streams seem coherent and interesting, whilst being slightly different in their intellectual melody. This is the first comfortable feeling. The second is that I have still some meaning, and, therefore, some possible truth, to unearth out of my empirical findings, and this is always a good thing. In science, the view of empirical findings squeezed out of the last bit of meaning and yet still standing as something potentially significant is one of the saddest perspectives one can have. Here, there is still some juice in facts. Good.  

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.