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.

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