Money being just money for the sake of it

I have been doing that research on the role of cities in our human civilization, and I remember the moment of first inspiration to go down this particular rabbit hole. It was the beginning of March, 2020, when the first epidemic lockdown has been imposed in my home country, Poland. I was cycling through streets of Krakow, my city, from home to the campus of my university. I remember being floored at how dead – yes, literally dead – the city looked. That was the moment when I started perceiving cities as something almost alive. I started wondering how will pandemic affect the mechanics of those quasi-living, urban organisms.

Here is one aspect I want to discuss: restaurants. Most restaurants in Krakow turn into takeouts. In the past, each restaurant had the catering part of the business, but it was mostly for special events, like conferences, weddings and whatnot. Catering was sort of a wholesale segment in the restaurant business, and the retail was, well, the table, the napkin, the waiter, that type of story. That retail part was supposed to be the main one. Catering was an addition to that basic business model, which entailed a few characteristic traits. When your essential business process takes place in a restaurant room with tables and guests sitting at them, the place is just as essential. The location, the size, the look, the relative accessibility: it all played a fundamental role. The rent for the place was among the most important fixed costs of a restaurant. When setting up business, one of the most important questions – and risk factors – was: “Will I be able to attract sufficiently profuse customers to this place, and to ramp up prices sufficiently high to as to pay the rent for the place and still have satisfactory profit?”. It was like a functional loop: a better place (location, look) meant more select a clientele and higher prices, which required to pay a high rent etc.

As I was travelling to other countries, and across my own country, I noticed many times that the attributes of the restaurant as physical place were partly substitute to the quality of food. I know a lot of places where the customers used to pretend that the food is excellent just because said food was so strange that it just didn’t do to say it is crappy in taste. Those people pretended they enjoy the food because the place was awesome. Awesomeness of the place, in turn, was largely based on the fact that many people enjoyed coming there, it was trendy, stylish, it was a good thing to show up there from time to time, just to show I have something to show to others. That was another loop in the business model of restaurants: the peculiar, idiosyncratic, gravitational field between places and customers.

In that business model, quite substantial expenses, i.e.  the rent, and the money spent on decorating and equipping the space for customers were essentially sunk costs. The most important financial outlays you made to make the place competitive did not translate into any capital value in your assets. The only way to do such translation was to buy the place instead of renting it. Advantageous, long-term lease was another option. In some cities, e.g. the big French ones, such as Paris, Lyon or Marseille, the market of places suitable for running restaurants, both legally and physically, used to be a special segment in the market of real estate, with its own special contracts, barriers to entry etc.   

As restaurants turn into takeouts, amidst epidemic restrictions, their business model changes. Food counts in the first place, and the place counts only to the extent of accessibility for takeout. Even if I order food from a very fancy restaurant, I pay for food, not for fanciness. When consumed at home, with the glittering reputation of the restaurant taken away from it, food suddenly tastes differently. I consume it much more with my palate and much less with my ideas of what is trendy. Preparation and delivery of food becomes the essential business process. I think it facilitates new entries into the market of gastronomy. Yes, I know, restaurants are going bankrupt, and my take on it is that places are going bankrupt, but people stay. Chefs and cooks are still there. Human capital, until recently being 50/50 important – together with the real estate aspect of the business – becomes definitely the greatest asset of the restaurants’ sector as they focus on takeout. The broadly spoken cooking skills, including the ability to purchase ingredients of good quality, become primordial. Equipping a business-scale kitchen is not really rocket science, and, what is just as important, there is a market for second-hand equipment of that kind. The equipment of a kitchen, in a takeout-oriented restaurant, is much more of an asset than the decoration of a dining room. The rent you pay, or the market price of the whole place in the real-estate market are much lower, too, as compared to classical restaurants.

What restaurant owners face amidst the pandemic is the necessity to switch quickly, and on a very short notice of 1 – 2 weeks, between their classical business model based on a classy place to receive customers, and the takeout business model, focused on the quality of food and the promptness of delivery. It is a zone of uncertainty more than a durable change, and this zone is

associated with different cash flows and different assets. That, in turn, means measurable risk. Risk in big amounts is an amount, essentially, much more than a likelihood. We talk about risk, in economics and in finance, when we are actually sure that some adverse events will happen, and we even know what is going to be the total amount of adversity to deal with; we just don’t know where exactly that adversity will hit and who exactly will have to deal with it.

There are two basic ways of responding to measurable risk: hedging and insurance. I can face risk by having some aces up my sleeve, i.e. by having some alternative assets, sort of fall-back ones, which assure me slightly softer a landing, should the s**t which I hedge against really happen. When I am at risk in my in-situ restaurant business, I can hedge towards my takeout business. With time, I can discover that I am so good at the logistics of delivery that it pays off to hedge towards a marketing platform for takeouts rather than one takeout business. There is an old saying that you shouldn’t put all your eggs in the same basket, and hedging is the perfect illustration thereof. I hedge in business by putting my resources in many different baskets.

On the other hand, I can face risk by sharing it with other people. I can make a business partnership with a few other folks. When I don’t really care who exactly those folks are, I can make a joint-stock company with tradable shares of participation in equity. I can issue derivative financial instruments pegged on the value of the assets which I perceive as risky. When I lend money to a business perceived as risky, I can demand it to be secured with tradable notes AKA bills of exchange. All that is insurance, i.e. a scheme where I give away part of my cash flow in exchange of the guarantee that other people will share with me the burden of damage, if I come to consume my risks. The type of contract designated expressis verbis as ‘insurance’ is one among many forms of insurance: I pay an insurance premium in exchange o the insurer’s guarantee to cover my damages. Restaurant owners can insure their epidemic-based risk by sharing it with someone else. With whom and against what kind of premium on risk? Good question. I can see like a shade of that. During the pandemic, marketing platforms for gastronomy, such as Uber Eats, swell like balloons. These might be the insurers of the restaurant business. They capitalize on the customer base for takeout. As a matter of fact, they can almost own that customer base.

A group of my students, all from France, as if by accident, had an interesting business concept: a platform for ordering food from specific chefs. A list of well-credentialed chefs is available on the website. Each of them recommends a few flagship recipes of theirs. The customer picks the specific chef and their specific culinary chef d’oeuvre. One more click, and the customer has that chef d’oeuvre delivered on their doorstep. Interesting development. Pas si con que ça, as the French say.     

Businesspeople have been using both hedging and insurance for centuries, to face various risks. When used systematically, those two schemes create two characteristic types of capitalistic institutions: financial markets and pooled funds. Spreading my capitalistic eggs across many baskets means that, over time, we need a way to switch quickly among baskets. Tradable financial instruments serve to that purpose, and money is probably the most liquid and versatile among them. Yet, it is the least profitable one: flexibility and adaptability is the only gain that one can derive from holding large monetary balances. No interest rate, no participation in profits of any kind, no speculative gain on the market value. Just adaptability. Sometimes, just being adaptable is enough to forego other gains. In the presence of significant need for hedging risks, businesses hold abnormally large amounts of cash money.

When people insure a lot – and we keep in mind the general meaning of insurance as described above – they tend to create large pooled funds of liquid financial assets, which stand at the ready to repair any breach in the hull of the market. Once again, we return to money and financial markets. Whilst abundant use of hedging as strategy for facing risk leads to hoarding money at the individual level, systematic application of insurance-type contracts favours pooling funds in joint ventures. Hedging and insurance sort of balance each other.

Those pieces of the puzzle sort of fall together into a pattern. As I have been doing my investment in the stock market, all over 2020, financial markets seems to be puffy with liquid capital, and that capital seems to be avid of some productive application. It is as if money itself was saying: ‘C’mon, guys. I know I’m liquid, and I can compensate risk, but I am more than that. Me being liquid and versatile makes me easily convertible into productive assets, so please, start converting. I’m bored with being just me, I mean with money being just money for the sake of it’.

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.