We suck our knowledge about happening into some kind of patterned structure

MY EDITORIAL ON YOU TUBE

It’s done. I am caught in the here and now with my writing. As I am writing these words, on June 10^th 2020, George Floyd’s funeral in Minneapolis, U.S., is just over. I am Polish, I live in Poland, and I am essentially a bystander as regards the events taking place in United States. Yet, those events resonate in my country, and I think I can express an opinion.

I have a few words to say about the idea of defunding the police force. We had the same idea in Poland, when we were transitioning from communism to democracy, from 1989 on. As communism collapsed, we would intuitively associate police force in general with an oppressive regime. It was a pattern inherited from the communist system: the police force was a tool of oppression in the hands of a totalitarian state. In parallel, the new democratic Poland had to rebuild its fiscal base almost from scratch, and for quite a few years, the government went very largely bankrupt. Defunding the police force came as sort of handy, both economically and politically, and so we did.

We expected more freedom with less cops in the streets. Still, instead of freedom, gangs crept in. Gangsters took control of entire cities, within months. One year after the fall of communism, in summer 1990, it was already impossible to run any substantial business without being racketed, pardon, without paying for “protection services”, and you were lucky if just one gang claimed that tribute from you. Sometimes, you would find yourself in disputed terrain between rivalling gangs, and then you were really f**ked. In 1995, a friend of mine died of a horrible death, in a cartel-style execution, because he was unlucky enough to be a bouncer in a club which two rivalling gangs tried to take over and control. It took us like a decade to re-establish a relatively normal social order, around 2001 – 2002.

Thus, guys, a message to those of you who think that the police force is your worst enemy. With all the due respect, you’re wrong. The police force is like a shield between us, normal folks, and a social fringe of truly evil sociopaths. Once again, believe me, you have no idea what true evil is until you look it straight in the face. You remove the shield, and you get exposed to real monsters, and those monsters are surprisingly well organized. I am tempted to quote Jean – Jacques Rousseau, the French thinker commonly associated with the theory of social contract. Rousseau stated very clearly that what we see as civil rights and freedoms really works only to the extent that we have a government strong enough to guarantee them.

The world is changing. I am doing my best to wrap my mind around those changes. As social media swell with contrary tides of ideas, I try to keep my mind open to all kinds of opinions. A strange memory floats up to the surface of my consciousness. I think I was like 10 years old, so it must have been 1978, communist Poland, of course. We already had in place a system of food rationing, especially as regards meat and fruit. My father was in the communist party and was a fervent acolyte thereof. I remember seeing in the official news, on TV, a reportage on how fantastically buoyant our agriculture and the food sector were. Mountains of delicious fresh food loomed on the TV screen. I asked my father: ‘Dad, how come we have such amounts of food on TV, but in day to day life we have so little meat and fresh fruit, and what we can buy in food stores is mostly industrial sugar and industrial pasta?’. My dad answered: ‘This is because our entire society, in line with the doctrine of the Party, we are committed to support the emancipation of black Americans in the United States’. ‘Oh, so we send them our pork meat, to the United States?’ – I would reply – ‘Cool. I didn’t know. But, dad, couldn’t we help those black Americans a little less and eat a little better? I think it is called a compromise…’. ‘Don’t you dare questioning the policy of the Party! There are no compromises in promoting international social justice’. Yes, it was the usual closure to such conversations, at the time. You never knew who was listening.

Of course, it was bullshit. We were not helping black Americans, we simply had a f**ked up economic system, based on ideology instead of entrepreneurship, and black Americans were just an excuse. I wonder how much of handy excuse are black Americans now, serving to cover various mistakes in people who would lose a lot, should they have to endorse those mistakes, and serving ambitions in other people (or maybe in the same people). As I read business news, I can see, here and there, some top corporate executives, all white, being suddenly fired by other top corporate executives, white as well, because of ‘racial hate speech’ etc.

I can see society slightly shaking around me, and I realize how strongly I am attached, in my psyche, to relative stability in the social space. I realize how easy it is to fall for either path: ‘Let’s do revolution!’ is just as tempting as ‘Western civilization is dying!’. Both offer easy space for unloading stress, which accumulates as I see social rituals changing all of a sudden. Good. This thread of thinking, i.e. thinking about social stability versus social change, is a good avenue to lead me back towards my research on the role of cities in our civilisation. I build up intellectual distance by referring once again to Arnold Toynbee’s ‘Study of History’ (abridged version: Somervell &Toynbee 1946[1]). In the introduction, Arnold Toynbee writes: ‘If the argument of this chapter is accepted it will be agreed that the intelligible unit of historical study is neither a nation state nor (at the other end of the scale) mankind as a whole but a certain grouping of humanity which we have called a society’.

That excursion into Arnold Toynbee’s theory serves me as a pretext to open up on a more current topic: cities in Asia, and more specifically in China. I had the opportunity to visit some of the Chinese cities and I was baffled with how different they are from the European ones. Under a superficial layer of similarity, a completely different social order dwells. When I wrote that cities are made of movement and human connection, I should take it to square power in the Chinese case. In Chinese cities, even buildings go faster than European ones. Hardly anyone conserves buildings in China the way we do it in Europe. In Europe, we are used to maintaining constructed architectural substance as a sort of skeleton, and to organizing our social activity around it. In China, buildings are like cars: when used up, no one bothers to renovate them, they are just being replaced with new ones. Chinese cities are all movement.

Cities have grown, across the globe, in strict economic connection to the surrounding countryside. The city creates social roles, and therefore a market for agricultural products, and the countryside provides a stable food base. That connection by partition is fascinatingly different between China and Europe. European agriculture developed as pretty much a closed loop between people, livestock, and vegetal farming. Livestock eats, livestock shits, and thus livestock fertilizes. In China, historically, there has been much less livestock in agriculture, and much more cereals, mostly rice. There is a historical detail about the connection between rice and cities in China. This is one of those details we just don’t talk about, as it sounds awkward: Chinese cities had been fertilizing their neighbouring rice fields with human excrements from cities, with comparatively little amount of animal manure (see for example Braudel 1992[2]). The kind of loop that European humans made with their livestock and their cereal fields, Chinese humans made directly with their rice fields, without inviting cattle to the party.

As you can easily guess, looping our food base on our own excrements gives clear incentives to increase the amount of the latter. Cities can grow much bigger than in Europe. Bigger cities, and faster growth in their population mean more new social roles being created per unit of time, whence new social space for greater a population. Greater a population defecates more, and the loop spirals up.

Chinese cities, including their ancient, peculiar relation to the rice they buy from the countryside, seem to favour hyper-growth in size. Au & Henderson (2006 [3]) claim that Chinese cities, such as they emerged as the Chinese economy after its progressive transition towards market economy, are still too small regarding the economic incentives for growth they offer (or rather used to offer 15 years ago). Au & Henderson claimed that Chinese cities create exceptional economic incentives for demographic growth. On the other hand, as we observe the way that Chinese cities function today, they have an outstanding ability to attract new investment. The bottom line under this specific thread of my writing is that social difference between cities and the countryside is strongly idiosyncratic. Why?

The ‘why?’ question is usually an abyssal one. You have logical coherence and functional correlation entangled around the assumption that things which happen later are the outcome of things that happened earlier. I prefer tricking myself by asking ‘How?’ instead of ‘Why?’. How does the idiosyncratic social difference between cities and the countryside develop? How does it start? What are the distinctive steps in the process? Is there any threshold of saturation?

How does a city start? The basic answer is: slowly and with a lot of struggle, when a local population needs to organize itself. A demographic anomaly forms: a collection of man-made structures, apparently pointless from the point of view of warfare and agriculture, and yet functional for trade, business and politics. Some folks discover that it pays off to construct a few buildings close to each other instead of spreading them across the countryside. Those folks deliberately shrink their respective physical territories, from farm-wide to store-wide, in order to have additional benefits from exchange.

I jump back to the present and the current, and to the #BlackLivesMatter protests. In Seattle, U.S., protesters created (well, they didn’t create anything, they occupy somebody else’s property, but ‘created’ sounds better) an autonomous zone. They created a town. Similar episodes are happening across Western Europe as well. People who, objectively speaking, are anarchists and therefore postulate to destroy the incumbent social order, put in place their own social structure as soon as they are satisfied with apparently having destroyed the old one. This is amazingly coherent with what I discovered in my experiments with a neural network, which was supposed to simulate a system of social roles. When social cohesion, i.e. social distance between distinct social roles, gets a bit of loose in the shoulders, the incumbent social roles disappear at first. Yet, after an initial phase of entropy, that very simple set of equations learns how to bring social roles back (see The perfectly dumb, smart social structure).

Maybe this is how cities formed in the past, i.e. they formed in momentary windows of social entropy, when nobody new s**t, and some people said: ‘OK, guys, as no one really knows what to do, we are going to do urban life. This is how intelligence works: knowing what to do when we have no clue what to do’.

Now, a few more words of explanation as regards my stance on #BlackLivesMatter. By reading what I have just written, you can guess I am a moderate conservatist. Yes, indeed I am, and, on the top, of that, I like asking embarrassing questions and cutting bullshit out of the answers. When people gather in large numbers, what they want most of all is gathering in itself. They want to experience community. Defining a common enemy – those ugly privileged whites and ugly cops – helps reinforcing the oxytocin loops that gathered people trigger with each other. The pandemic and the lockdown have shaken a bit the sense of social cohesion – people stopped going to work, children stopped going to school, habits got shaken – and slogans like ‘Society must change!’, shouted and yelled, actually reflect a post factum acknowledgment of facts (‘F****k! Society changes! Heeeeelp!’).

Sometimes, I have the impression that anarchist movements like this one are a necessary pain in the ass when we want to absorb important exogenous stressors. Maybe we train for the BIG adaptation to climate change?

Cities are distinctive from the countryside by their abnormally high density of population, which is a proportion between population and the territory it occupies. There are two distinct methods of measuring both: administrative and GPW (Gridded Population of the World). I am sharpening my understanding of these two approaches so as to understand the dynamics of urban structures as such. My approach is empiricist. I hope to understand better the boundary between cities and the countryside through understanding the fine distinctions as regards the way we perceive that boundary. Here, one more excursion into current events. Have you noticed that CHaz (Capitol Hill Autonomous Zone) in Seattle is precisely in Seattle and not in the countryside? Logically, if you want to cut ties with the ugly incumbent social order, forming a commune out there, in the fields and woods, could be a tempting idea. Yet, these specific protesters decided to constitute Chaz in the city. They claimed it because they need it.

Underneath the cognitively acknowledged social rituals, maths dwell. Before we started to remember what we had forgotten about life in the presence of epidemic risk, our set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles was congruent with and logically equivalent to such other sets as, for example, the set IN = {in₁, in₂,…, in_z} of ‘z’ typical levels observable in annual income, or the set R = {r₁, r₂, …, r_o} of ‘o’ places of residence. Social contacts had been kind of going along and coming with the social role at hand. Now, our set of social roles has suddenly become significantly congruent with and logically equivalent to a set ßC = {ßc₁, ßc₂, …, ßc_n} of ‘n’ observable levels in epidemic risk derived from social contacts.

Before, the daily mathematical life of our culture consisted in feeding into itself a set of individual experiences regarding income, housing, cars owned etc., and pitching the resulting mix against the benchmark of what we consider as collectively desired outcomes. Life is made of chaos and order, and we mix it. We have things we know we do, i.e. our relative preference for different social roles SR = {sr₁, sr₂, …, sr_m}, and that preference manifests itself as the probability p(sr_i) that a randomly selected human endorses the social role sr_i. We acknowledge chaos as random, local occurrences ε(t) of something barely conceptualized. Our social roles happen as temporary instances of a general cultural frame, i.e. as SR(t) = {ε(t)*p(sr₁), ε(t)*p(sr₂), …, ε(t)*p(sr_i)}. Each ε(t) in that temporary occurrence SR(t) is different. Remember: that ε(t) is just a civilized mask we put on the pretty scary face of barely acknowledged chaos.

We, humans, we are obstinately ordered. Things happen to us in a hurricane of phenomenal chaos, and we take great care to react in an orderly, patterned way. We don’t have enough money? Good, there are patterns to follow: save, invest, get a better job… The catalogue is actually definite, at least for most of us. We feel a bit down on our physical condition? Good. Exercise, sleep more, pay attention to what you eat. Once again, the repertoire of reactions is finite. We need someone else in charge? Well, let’s see… Elections? No? Then maybe a corporate structure and appointment by the strongest players? No? Doesn’t fit the bill either? Well, then we stay with limited options… Structurally unstable dictatorship disguised as democracy where we buy people’s votes with the money they haven’t earned from other people ‘cause we were the first to snatch that money? Good? We go on with this one? Good…

There is another trait of orderliness in our civilization: we are strongly coherent and cohesive in our social ways. Have you ever noticed how frequently those people, who present themselves as outsiders and non-conformists, take great care of fitting into a precise mould of ready-made ideas and behaviours? I remember going to a wedding, in 2018, where the bride and the groom were much younger than I, just as most of their friends. As the wedding party was starting, the young couple announced that ‘this party is a celebration of freedom and independent thinking, without any false moral limitations; do whatever pleases you to do’. The actual consummation of that principle looked stiffer than a reception at the Buckingham Palace. Everybody was eyeing everybody else, how free and independent they appear, and tried to fit exactly into the same model of freedom and independence. This is what we humans do, socially: we eye each other, and we conform. Even when we claim we don’t conform, we actually conform to some other pattern. This is not some innate stupidity: this is how being a social species manifests itself. We hold parties in the same basic way our distant ancestors would hunt the woolly mammoth. We coordinate, and much of this coordination is tacit, i.e. not expressed explicitly.

The provisional bottom line of this little intellectual excursion into the realm of weddings is that, on the top of distinctive traits observable in particular social roles, our collective intelligence feeds into itself information about mutual coherence between those social roles.

We have those patterns. Whatever happens, we suck our knowledge about happening into some kind of patterned structure. Patterning starts with aggregation of idiosyncrasies. We collectively make some kind of simple metric about reality. Let’s call it h. The simple h can suck reality into itself in many mathematical ways. The h can emerge as h = ε(t)*p(sr₁) + ε(t)*p(sr₂) + … + ε(t)*p(sr_m), or it can go into the fancy realms of matrix maths, like h = [ε(t)*p(sr₁)/ ε(t)*p(sr₂)] + [ε(t)*p(sr₁)/ ε(t)*p(sr₃)] + … + [ε(t)*p(sr₁)/ ε(t)*p(sr_m)]. Whatever. It boils down to taking a lot of largely chaotic reality and squeezing it into the magic hat of culture, so as to pull a nicely structured rabbit afterwards.

Have you noticed that the rabbit always comes up from the magic hat, and never falls down from it? As a collective intelligence, we have patterned ways of drawing conclusions from aggregate existential chaos. There is something at the base – the hat – and something – the rabbit – comes up from that base. As you browse through neural activation functions, which we use in artificial neural networks to represent what we think intelligence is, at the bottom line you most frequently fall either on the mathematical constant e = 2,71828 elevated to the power h of aggregate chaos, with some kind of additional parameters, or on the square root of 1+ h elevated to some arbitrary power. The idea is that our way of being intelligent contains some kind of constant root, such as e = 2,71828 or √(1 + h²). By the way, the constant root e = 2,71828 is a collection of steps towards reverted infinity of dimensions, i.e. e = (1/1) * (1/1) *( ½) * … * 1/(n → ∞).

Thus, when we think about the way that intelligence works, thus when we project our own thinking about our own intelligence, we assume there is a constant root in that intelligent cognition. At the very base of what we think, we sort of always think the same, and aggregate chaos of daily existence comes as a modifier to that constant root. We think almost the same we used to think before, just with a small drift.

I feel like partly summing it up. We go through that chaos called life by being smartly social. We endorse SR = {sr₁, sr₂, …, sr_m} social roles and we discriminate among them by experimenting with their local probabilities p(sr_i), whilst acknowledging random disturbances ε(t) and producing local instances SR(t) = {ε(t)*p(sr₁), ε(t)*p(sr₂), …, ε(t)*p(sr_i)} of our framework social structure. We aggregate our experience with those local variations into simple metrics, like h = [ε(t)*p(sr₁)/ ε(t)*p(sr₂)] + [ε(t)*p(sr₁)/ ε(t)*p(sr₃)] + … + [ε(t)*p(sr₁)/ ε(t)*p(sr_m)], digestible to our big, patterned institutions, which maintain a baseline continuity and allow some drift as circumstances happen.

The inevitable failure to achieve what we collectively want, largely resulting from the apparently intrinsic inability to define what we really, collectively want, generates learning about the margin of error as regards perfect happiness. We feed that error forward in time, into the next episode of existence, and we backpropagate that error along the logical structure of our civilisation, and it all plays out over and over again. Now, we enrich our collectively subconscious, mathematical life with data about epidemic risk attached to individual social roles, and by that means, to general categories of social roles. We feed into our culture our observation of that risk, we make it a functional part of the social order, and we keep pushing.

OK, change of tangent. I think I have pretty much circled the ideas I want to develop in my book on cities and their civilizational role. Here comes the list:

>>> Idea 1: Cities are demographic anomalies, which we, humans, have devised in order to accommodate a growing population.

>>> Idea 2: Seen as social contrivances, cities have three essential functions. Firstly, they allow rapid multiplication of social roles, which facilitates social structuring of a growing population. Secondly, that creation of new social roles allows the existence of many parallel, social hierarchies, which, in turn, facilitates the moderation of social conflicts. Thirdly, the development of cities allows, as strange as it could seem at the first sight, systematic development of agriculture.

>>> Idea 3: Cities enhance the collective intelligence of human societies, i.e. they enhance our collective ability to experiment with many local, alternative instances of our fundamental social structures. Cities allow systematic development of correlated behavioural coupling, which, in turn, largely eliminates randomness and excessive rigidity in the process of collective learning.

>>> Idea 4: Technological change as such is a manifestation of collective intelligence rather than the individual one. Our technologies change at the pace allowed by the intensity of social interaction. Artificial intelligence is a good example of a technology that reflects collective intelligence.

>>> Idea 5: Cultures built around and on the basis of urban life display a characteristic pattern, centred on demonstrable social activity, correlated behavioural coupling supported by financial markets, and complex institutional systems.

>>> Idea 6: Cities form, as demographic anomalies, when a factor of disturbance temporarily distorts social cohesion, and then a new process of defining social roles emerges, with a cohesion of its own.

Those 6 ideas coincide with some basic empirical regularities which I have noticed. Here they come:

*** Fact 1: Since 2008, the global human society has become prevalently urban and the process of urbanisation continues.

*** Fact 2: There is an interesting discrepancy between the administratively defined extent of urban land, on the one hand, and measurements based on satellite imagery, on the other hand. Whilst some cities in the world officially grow in space (i.e. their officially defined territories spread), the total surface of urban land in the world seems to be constant – at least for now – and cities grow into that de facto urban space rather than out of it.

*** Fact 3: The density of urban population, measured on the basis satellite-assessed extent of urban land, demonstrates intriguing properties as socio-economic variables. Those properties become even more interesting when the density of urban population is being denominated in units of general density in population. That compound variable, i.e. urban social density divided by general social density, which essentially measures the social difference between cities and the countryside, grows in an unusually monotonous, linear manner, and demonstrates intriguing correlations with such variables as income per capita, energy consumption per capita or agricultural productivity. When compared cross-sectionally, i.e. between countries, that variable seems to be hitting some kind of sweet spot around the value of 20 ÷ 22, i.e. when urban populations are approximately between twenty and twenty-two times denser than the general population. Anything significantly below or beyond that value seems to be less functional.

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[1] Royal Institute of International Affairs, Somervell, D. C., & Toynbee, A. (1946). A Study of History. By Arnold J. Toynbee… Abridgement of Volumes I-VI (VII-X.) by DC Somervell. Oxford University Press.,

[2] Braudel, F. (1992). Civilization and capitalism, 15th-18th Century, Vol. I: The structure of everyday life (Vol. 1). Univ of California Press., pp. 145 – 158.

[3] Au, C. C., & Henderson, J. V. (2006). Are Chinese cities too small?. The Review of Economic Studies, 73(3), 549-576. http://www.jstor.org/stable/20185020?origin=JSTOR-pdf

How much of a collective intelligence we are? The case of cities and agricultural land

MY EDITORIAL ON YOU TUBE

I continue to work on the role of cities in our civilisation and on the changes that the current COVID-19 pandemic can possibly bring to our ways of living in cities. Initially, when I started writing this update, on June 6^th, I intended to explore the connection between technological change and the civilizational role of cities. Further in this update, I do go down that avenue, yet for now, in those initial paragraphs, I want to share another strand of my thinking, which I already signalled last time, namely my impressions from reading Daniel Defoe’s ‘Journal of The Plague Year’, published in 1665. That book was published in 1665 and gives the account of events which took place in London, in 1664, during the epidemic outbreak of plague. It was 356 years ago, and yet, when I read it, especially the initial chapters, I have the impression of going through news feeds from the last 4 months, like from February until now, of course in relation to the COVID-19 pandemic. The sequence of events described by Daniel Defoe, the patterns of human reactions to the epidemic disease – all that is so incredibly similar to what we experience today that I have hard times to realize that what Daniel Defoe described took place 18 generations ago (if we count 25 years for one generational shift, by sociological standards).

That striking similarity gives tons of hope. Eighteen generations ago, people had just a small fraction of science and technology that we have today, and yet they pushed themselves through that deep shit, and there was another sunrise. It was plague, not COVID-19. It was a monster. Yet, there was another sunrise. What impressed me the most, I think, is the very end of that book, and I allow myself to quote it: “It was a common thing to meet people in the street that were strangers, and that we knew nothing at all of, expressing their surprise. Going one day through Aldgate, and a pretty many people being passing and repassing, there comes a man out of the end of the Minories, and looking a little up the street and down, he throws his hands abroad, ‘Lord, what an alteration is here! Why, last week I came along here, and hardly anybody was to be seen.’ Another man—I heard him—adds to his words, ‘’Tis all wonderful; ’tis all a dream.’ ‘Blessed be God,’ says a third man, and and let us give thanks to Him, for ’tis all His own doing, human help and human skill was at an end.’ These were all strangers to one another. But such salutations as these were frequent in the street every day; and in spite of a loose behaviour, the very common people went along the streets giving God thanks for their deliverance. It was now, as I said before, the people had cast off all apprehensions, and that too fast; indeed we were no more afraid now to pass by a man with a white cap upon his head, or with a cloth wrapt round his neck, or with his leg limping, occasioned by the sores in his groin, all which were frightful to the last degree, but the week before. But now the street was full of them, and these poor recovering creatures, give them their due, appeared very sensible of their unexpected deliverance; and I should wrong them very much if I should not acknowledge that I believe many of them were really thankful”. (Excerpt From: Daniel Defoe. “A Journal of the Plague Year / Written by a Citizen Who Continued All the While in London”. Apple Books.”)

As I am rereading that book by Daniel Defoe, and as I meditate over it, I realize how bloody tough we, humans, are. The city of London (where the events described by Daniel Defoe take place) is still there. It is thriving. We moan, we bicker, we take grand moral stances over events we don’t even have full knowledge about, and yet, at the bottom line, when the shit hits the fan, we just clench our teeth, dig our heels into the sand, and survive. Wonderful.

I am going into a slightly different path of thinking, as compared to my recent updates. The initial hypothesis of that entire thread of research is that technological change that has been going on in our civilisation at least since 1960 is oriented on increasing urbanization of humanity, and more specifically on effective, rigid partition between urban areas and rural ones. I focus on the connection between cities and the countryside, at the aggregate level. In Figure 1, below, you can see indexed trends in three aggregate variables: a) density of urban population denominated in units of general density in population (which I will further designate, for the sake of presentational convenience, as [DU/DG]) b) cereal yield in kg per hectare, and c) total surface of agricultural land. In order to assure comparability, I represented all those three metrics as constant-base indexes, where values from the year 2000 make 1.

As you can see, I provided direct links (to the database of the World Bank) as regards two variables out of the three. I did it because the first variable is a compound construct of my own, made out of primary data supplied by the World Bank. I took the numbers regarding aggregate urban population, and I divided it by the aggregate surface of urban land, which yields the coefficient of density in urban population. In the next step, I want to use that coefficient so as to measure the relative social difference between cities and the countryside. In order to do so, I divide the coefficient of density in urban population by the coefficient of general density in population. In other words, I check how many general densities of population we need in order to have one unit of density in urban population.

Since 1961 through 2016, the relative social distance between cities and the countryside, measured at the planetary level, has been growing steadily, almost in a straight line. As a matter of fact, that line is so straight that it is hardly believable. When you find a straight line of trend, which sort of cuts across waves and bumps in other variables, you are either completely wrong or deeply right. Linear change over time is a rare beast in the realm of measurable phenomena. However, as I measure local growth rates in that [DU/DG] metric, they keep sticking to 1% a year. Yes, since 1961, the average social distance between cities and the countryside has been growing at a nearly constant rate of 1% a year.

Against that almost suspiciously consistent change in the density of urban populations across the planet, agriculture has been changing at two different speeds. Cereal yield per hectare has grown, at the end of the day, yet its growth has been happening at a much more familiar, bumpy rate, sort of two steps forward, one step back. The aggregate surface of agricultural land presents a stairway type of change: two plateaus separated by a sudden jump in the beginning of the 1990ies.

Summing up, as social density in cities has been hyper-consistently drifting away and above general social density, agriculture kept adapting, mostly by consistent growth in agricultural productivity. Interestingly, all three trends, although different in shape, are strongly correlated, which is shown in Table 1, below Figure 1. Those correlations are so strong that it all looks like one compound phenomenon, with just a little entropy inside.

As local expansions of agricultural land have kept happening, yet they also kept being compensated, at the global scale, by decreases in other parts of the world. On the long run, between 1961 and 2016, the total surface of agricultural land in the world has grown by 11,4 millions of square kilometres. Apparently, more than 55% of that aggregate growth happened in the short window between 1989 and 1992 seems to be only moment since 1961 when the total global surface of agricultural land unequivocally went up. That big leap in agricultural land, by about 6,3 millions of additional square kilometres, happened mostly in countries classified as ‘Middle Income’, and was prevalently concentrated in two of them: Kazakhstan and Russian Federation. The long-term geography of change in agricultural land, between 1961 and 2016, is shown in the form of a map in Figure 2. Kazakhstan, Russian Federation and China keep the podium. A freakish idea comes to my mind. Between 1989 and 1992, a dramatic increase happened in the surface of agricultural land on the planet. It happened mostly in the former Soviet Union, which, precisely then, was dissolving. Are the two phenomena connected? Is it possible that the dissolution of the biggest country in the world was a collectively intelligent response of our planetary human species to the necessity of having more land to grow food?

Figure 1

Table 1 – Pearson correlation between density of urban population, agricultural land, and cereal yield per hectare

	Density of urban population, denominated in units of general density in population: World
Surface of agricultural land, km2 : World	0,927149105
Cereal yield, kg per hectare of arable land: World	0,984881004

Figure 2

Now, I focus on the ‘technological change’ part and I formulate two other hypotheses. Firstly, I claim that technological change manifests collective intelligence in human societies. Secondly, Artificial Intelligence, the development of which marks technological change of the last two decades, emulates collective intelligence much more than individual one.

Why do I claim at all that technological change manifests collective human intelligence? Isn’t it rather individual intelligence saying, at some point in time, something like ‘Enough! Enough of those stupid sleighs. We need wheels!’? It is true to some extent, more specifically to the extent that individually expressed ideas really push technology forward. Still, those ideas work similarly to the way that a ball is being played in a team game. When we play basketball, most individual actions with the ball are effective and efficient only to the extent of cooperation from the part of other players in the team. An innovative idea is like that ball: its needs to be passed around and collectively played.

Collective intelligence can be described as the ability to collectively figure out what to do when we collectively have no clue what to do. This is a very synthetic description of mechanisms which require a deeper insight. We collectively experience problems when we share collective beliefs, acceptably grounded in empirical facts, that something happens the way most of us doesn’t want it to happen. This is the gap between expectations and reality. Collective experience is that something doesn’t work as we would like it to work.

Now, let’s introduce the distinction between simple discomfort with reality, on the one hand, and the experience of inefficiency in our behaviour, on the other hand. Life is brutal, in general. Yes, it is beautiful as well, and yet we experience beauty largely by opposition to ugliness. We perceive the brutal beauty of existence mostly as gradients of change, and not really as absolute states of things (see, for example: We really don’t see small change). We are uncomfortable with some changes in reality, and sometimes that discomfort triggers collectively coordinated action. That’s the first moment of assessment as regards us being collectively smart: can we coordinate to take action, or cannot we? The next level is being efficient in that action. Have we achieved the results we expected to achieve?

We have two levels of collective ambition, whence two possible levels of collective frustration, namely with the failure to coordinate, or with the insufficient outcomes of coordination. Both failures incite to do something about that imperfect social coordination of ours. When we dig a bit into the depth of the problem, we usually discover at least one of the two things: we are either too rigid or too random in coupling individual behaviours into the beautiful dance of well-rounded teamwork. Too rigid means that person A always does what person B expects them to do, whence nearly perfect a stationarity of their common action. Too much randomness manifests as the person A hardly ever doing what person B expects them to do, whence a well-understandable frustration in the person B and a lack of trust in coordination.

Good coordination relies on a behavioural pattern called correlated coupling, which manifests as the person A responding flexibly and yet predictably to signals sent by person B, and vice versa. Being both flexible and predictable in my response to other people’s signals means that my own action takes a recurrent form – which sends other people the reassuring signal ‘I get it, guys, carry on’ – and yet that form is somehow scalable. When I am an engineer and my boss asks me ‘to give that engine a bit of nerve’, he or she can trust – if my behaviour is correlatedly coupled with theirs – that I will come up with a range of possible solutions for said nerve, and I will select the most appropriate.

We are collectively intelligent when, as a collective, we have the ability to spot recurrent cases of too rigid behavioural coupling, or too much randomness in collective coordination, and transform those situations into correlated coupling. Let’s take the example of a simple, old technology: the use of windmills to power querns, instead of grinding cereal grain by hand. I think it was some 20 years ago: I messed around a bit with a reconstructed, man-powered quern (you now, two flat stones on a common rotating axis), just to see how it felt, centuries ago. When I gave it a try, I understood why the baking of bread became really widespread across Europe only with the diffusion of windmills and watermills. Grinding grain into flour by the sheer force of human muscle is, at the end of the day, a zero-sum activity, energy-wise. You burn approximately as much energy when grinding as you can have from the flour you obtain.

Technologies give us flexibility and predictability. The wind-powered quern, back in the day, as compared to the man-powered one, assured smooth grinding of grain and scalability: faster or slower, greater quantity per day or a smaller one etc. Technologies allow replacing fixed coupling in behaviour, or a random one, with the functional elegance of correlated coupling.

Now, let’s get into the process of implementing new technologies. When I do it individually, it is a sequence of trials and errors. I try something, and it works smoothly, it works just sort of, or it doesn’t work at all. Depending on the exact outcome, either I say ‘Hooray! Nailed it!’, or I go ‘Well, it needs some improvement’, or, finally, I say things I could be ashamed, later on, of having said at all. When I need improvement, it slows me down, obviously. Still, when my new contrivance seems to be working just perfectly, it can slow me down even more. I am satisfied with immediate outcomes, and a prolonged chain of satisfactory results can prevent me from seeing an entirely different, alternative way of doing things.

When lots of ‘I’ do the same thing – after all, each human is an ‘I’ – it is different. Each ‘I’ comes up with somehow different results, and these can be instantaneously compared. The ‘I’s which do the best job stick out of the crowd. Their ways are likely to be reproduced by other people, whilst the clearly suboptimal methods fall into oblivion. Many humans experimenting with solutions to the same problem are like as many living organisms attempting to mutate in the presence of an exogenous stressor. The more organisms experiment with themselves, the greater the likelihood of successful mutations. In biology, this mechanism is called ‘adaptive walk in rugged landscape’ and can be applied in social sciences. When many social entities experiment with themselves in order to cope with an exogenous pressure, such as the pressure to survive or to climb the ladder of social hierarchy, some of those entities (e.g. persons, businesses, political parties) are more successful than others. Best practices are retained and reproduced in the future. This is collective intelligence in solving collective problems.

Cities facilitate technological change because they reinforce that sort of next-to-my-neighbour innovation. In cities, due to high density of population, it is simply easier to observe others, to emulate their successes or steer clear of the way they failed. It is easier to navigate through the muddy waters between conformism and individuation. Cities are instances of enhanced collective intelligence.

I use simple neural networks to emulate the way that our human collective intelligence works. I used it in this specific thread of research (see The perfectly dumb, smart social structure), you can find it in a published article on energy efficiency, and in another, unpublished paper. As I keep meddling with neural networks, I am more and more convinced that artificial intelligence emulates the collective intelligence of human societies much more than individual intelligence of one human being. Why do I make such a claim? Because neural networks work well when they can experiment with quasi-random combinations of weights assigned to many input variables, i.e. many different phenomena. With just one input variable, a neural network usually goes completely bananas. No learning whatsoever. The necessity of multiple input makes me think about many social entities trying to do something, rather than just one human.

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

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

Fringes and layers: how do cities develop resilience

MY EDITORIAL ON YOU TUBE

There is a collective intelligence, I mean a lot of us, humans, indulge in thinking how smart we are, and this collective intelligence strives to sustain long-term access to cappuccino, which, in turn, most frequently, requires the presence of cities, conveniently disposed across the landscape. The access to cappuccino is put in jeopardy by secondary outcomes of a new pathogen making itself comfortable in the social space made of human interactions. Some human interactions become riskier than others. At first, we think all human interactions are dangerous, we entrench and yield to fear. Then, both science and individual capacity to learn step in and allow defining those reasonably safe social contacts, conveying low risk of infection, and we start distinguishing them, more and more finely every month, from the risky human interactions. As a matter of fact, we had been doing it for millennia, and stopped just recently, around 1970, when widespread vaccination made us progressively forget the terror of typhoid, polio, tuberculosis etc. Before we started forgetting, we used to follow some simple principles. Hang out with people whom you know and can observe for the time sufficient for an infection to manifest itself. Come close to those who are manifestly healthy. Shake hands, hug, kiss, share kitchenware and have sex only with those whom you can expect to be like really healthy. When you need to make acquaintance with complete strangers, select those whom you can be introduced to (or who can be introduced to you, depends on the arrow on the vector) by a person from the former category of knowingly healthy persons in your social circle. When hanging out with strangers, use all kinds of Ninja tricks: veils, hats with large brims, scarves nonchalantly put around the lower part of the face, fancy silken or leather gloves etc. Arrange the space indoors so as to separate rooms with doors and curtains, whilst putting many windows in external walls. That allows partly independent circulation of air in separate spaces. If you happen to become someone important, like a prince or a wealthy merchant, a lot of people will have some business to talk to you about, and then be smart enough to receive their visit in a space with significant social distance, like you sitting on an ornate, elevated stool and them a few yards in front, lower than you and breathing into the floor, not in front of them. It is fascinating and bemusing, how similar is our present experience with COVID-19 to historically documented episodes of plague in European cities. Reading Daniel Defoe’s ‘Journal of The Plague Year’, published in 1665, offers a lot of interesting insights in that respect, including the problem of asymptomatic carriers (!).

All those small smart details of daily life sum up to paying particular attention to epidemic risk. Each of us, average hominids knowing what a cappuccino is, become more and more likely to endorse social roles involving recurrent, predictable interactions with knowingly healthy people, and just a fringe of society, those lords Byron-like types, or the really-f**ked types, as a matter of fact, remain likely to step into shoes that require abundant, haphazard interactions with people of unknown exposure to the pathogen currently in fashion. At the easily conceptualizable level, we strive to sustain systemic access to cappuccino – i.e. to sustain the market-based, open economy which we know just works – whilst progressively modifying our social roles so as to operate in closed social circles, with precisely defined points of contact with other circles, and barriers to contact with non-circled people.

Cities are demographic anomalies, and inside those anomalies the density of population is abnormally high. According to my research, the wealthier the country, and the greater the consumption of energy per capita in that country, the smaller the difference between urban density of population and the non-urban one. That difference shrinks down to a point, which looks like a threshold: developed social structures hardly descend below urban density of population twice as high as the general density of population (see Demographic anomalies – the puzzle of urban density). Cities define themselves, and this is one side of the coin. Throughout history, cities have been emerging in specific places because some people, dwelling in those places, wanted a city to be around. The other side of the coin is visible from space, i.e. from satellites: urban land displays a specific agglomeration of man-made structures visible during the day, and a high concentration of night-time lights. Once again, there is a threshold in that agglomeration of structures and lights, beyond which your average alien, observing Earth from a distance, could informingly say to another alien: ‘Look, Jkitths, they have a city over there! I wonder how much is a four-star hotel night’.

Interestingly, the two sides of this coin usually don’t match. The stretch of land qualifiable as urban satellite-wise is usually larger than the officially proclaimed expanse of urban territory in that place. Cities usually define themselves inside a de facto urban territory, and ‘inside’ means there is a margin between the physical boundaries of that typically urban agglomeration of structures and night-time lights, on the one hand, and the legally defined boundaries of the city. Normally, cities define themselves by acknowledging the urban nature of a place, not by arbitrarily declaring a place urban. There are exceptions, such as the programme of new cities in Egypt (Attia et al. 2019 [1]) or the founding of the city of Gdynia, in my native Poland, in 1926.

An interesting question emerges: to what extent does new epidemic risk, such as that generated by COVID-19, modify the objectively observable agglomeration of structures and night-time lights? On the other hand, how does epidemic risk affect the way that cities define themselves? Intuitively, I would say that acknowledged epidemic risk leads to spreading ourselves over a larger territory, i.e. to temporary slowdown in the speed of growth in the density of urban population, or even to a temporary reversal towards lower density.

Life in the presence of epidemic risk had been city slickers’ daily bread for centuries, and yet cities have grown up from existing as demographic anomalies to being demographically dominant in our today’s civilisation (55,27% of mankind lived in cities in 2018).

In 2016, I visited Colchester , reputedly the oldest city in Britain. I was bemused to observe the contrast between something which, fault of a better expression, I can describe as multiple layers of being a city. There is that old castle, dating back to Middle Ages, surrounded by the Old Town. It all looks like a really old car with new covers on the seats. Really strange. In a wider radius around the old core, various types of peripheral structures stretch. There is the not-as-old-yet-quite-old a part, which I tentatively date at like the 18^th century. There is a district which looks like a model industrial city from the 1970ies, i.e. an expanse of virtually identical, small terraced houses without apparent centre of gravity. There are patches of more modern buildings, like shopping malls or apparently recent residential blocks. There is the academic campus, displaying layers of its own: old concrete architecture from the 1970ies, combined with the most recent forms of wood and concrete structures (those latter ones look like Hobbiton, I swear). Colchester makes me think about people who have been resolute to be a city, in this specific place, and over centuries they were inventing and superimposing different ways and technologies to serve that purpose.

As I think about it, all the cities I know which have some solid history in them are like that. They are layered patchworks of physical structures. It is interesting. People who will come after us, centuries from now, are most likely to superimpose their urban structures over our contemporary ones, rather than put them somewhere else. Cities seem to be like cores of coagulation in civilization. Why? Why does it work this way? How does it apply to the possible adaptations of our urban space to the newly emergent epidemic risk?

As I read Adam Smith’s ‘Lectures on Justice’ (1766, published in 1896[2]), I realize that historically, cities have been allowed to make their own laws, and that legislative power has been so prominent over centuries that some classics of legal sciences, such as, for example, Herbert Hart, use the expression ‘municipal law’ to designate national legal systems, as opposed to international law. For centuries, cities have been largely defining themselves as demographic anomalies endowed with idiosyncratic, local institutions and jurisdictions. As a matter of fact, the last two centuries have seen a progressive transfer of those legislative powers from cities to national governments.

The impression that cities make is not necessarily identical with their real size and importance. In Richard Cantillon’s ‘Essay on Commerce’ , dating from 1755, we can find the following claim: ‘It is generally supposed that half the inhabitants of a State subsist and have their homes in the town, the other half in the countryside’. Yet, in Fernand Braudel’s ‘Civilisation and Capitalism’, we can find a completely different estimation, namely some 16% of the French population being urban in mid-18^th century. What we think is urban, around us, is not necessarily as urban as we think.

Sometimes, cities define their own existence in strange, apparently counterintuitive places, such as the city of Mulhouse AKA Mulhausen, in Alsace, France. According to Albert Metzger (Metzger 1883 [3]), the city of Mulhouse was just in the middle of a territorial conflict since its very beginnings, around the year 1150. Founding a city there was economically logical, with the proximity of the river Rhine, and yet, politically, it was like asking for trouble. On some occasions, such risky locations turn into permanent failures. If one day you visit the city of Frombork, in Northern Poland, where Nicolaus Copernicus wrote his book ‘De revolutionibus orbium coelestium’, you will immediately understand why he was so much into writing this book. Besides the big cathedral, whose estate Nicolaus Copernicus was in charge of, there is hardly anything else there. Yes, there is a commercial port, and quite an old one, by the way, still the city founders were obstinate to locate that port, and make it prosper, between two other big ports: Gdansk and Elblag. As soon as Frombork had established any kind of presence in the trade across the Bay of Gdansk, one of those two big neighbours (sometimes both) would send an armed expedition in order to explain the delicate nuances of trade in a small market. It was like trying to start a small electronic business in a market, where you have just Tesla and General Electric. Doomed to fail. Never been much of a city, Frombork. Ambitions are not enough. The final ‘and yet’ of the story, though, is that despite all the false starts and adversities, Frombork has kept being a city, and it technically still is a city, although it looks like a village with a big church in the middle.

When cities grow and give birth to new social roles, some of those social roles are ugly. This is the dark side of urbanisation: the growth of crime. Here comes an interesting book, written by William Howe and Abraham Hummel, and equipped with arguably one of the longest titles in the history of literature: ‘Danger! A True History of a Great City’s Wiles and Temptations. The Veil Lifted, and Light Thrown on Crime and its Causes, and Criminals and their Haunts. Facts and Disclosures’ (Howe & Hummel 1886). The book focuses on the city of New York – which can be safely deemed as the Incredible Hulk of urban expansion – and shows, in a casual and completely non-scientific way, how cities allow the burgeoning of social pathologies in many ways. Cities provide good shelter against weather, and thus allow the phenomenon of slumming: people living technically indoors, but not quite, some of them just sometimes, some of them homeless and yet not as much exposed to the dangers of sleeping outdoors as they would be in the countryside. You need to be tough like boot to be homeless in Siberia, but all you need in order to be homeless in Paris is a bad divorce or depression.

Cities give shelter to people who could hardly survive, and certainly not thrive in the countryside. Cities give opportunities to sociopaths and psychopaths, too, and this is another thread explored by Howe and Hummel. The abnormally high density of population in cities offers unusual opportunities to people with deranged personalities, prone to violence and manipulation. They can grow as kingpins, or seconds thereto. In the countryside they would much more likely expect the local community to put an end to their vile life through a completely accidental fire in their house.

An interesting, recent article by Kostas Mouratidis (Mouratidis 2019 [4]) suggests that cities develop and change through a cyclical sinewave in the density of urban population. As density grows, in the presence of relatively constant technological base, subjective well-being of city dwellers decreases, and they sprawl around. As they radiate towards comfortable suburbs, said suburbs lose much of their charm, and, with time, living in those suburbs boils down to spending more time in traffic jams. The phenomenon, known as urban sprawl, creates potential energy for a reverse movement, from peripheries back to the city centre, and that movement comes along with significant technological change, mostly in technologies accessible to the average city slicker in the form of urban infrastructure.

I found at least one author who develops a path of research similar to mine: I am talking about Sir Peter Hall (Hall 2000 [5]; Hall 2003 [6]). He argues that cities give peculiar incentives to the emergence of cultural industries, i.e. industries marked by quick race for dominant position, based on creativity and innovation. Emergence is different from continuous development: Peter Hall observes, with the example of selected British cities, that creative industries tend to be an economic fringe in cities, rather than the mainstream of business. There seems to exist a threshold of 5% share in the city’s GDP, which creative industries can grow within. Anything over and above those 5% is apparently doomed to disappear shortly. Interestingly, that creative fringe of economic life in cities tends to specialize. Peter Hall names three big, typical vectors thereof: art (e.g. Paris, Florence), industry (e.g. Silicon Valley or Manchester), and finally urban creativity in itself (once again, Paris comes as an example, although places like Vienna or Prague seem to fit the same mould).

New social roles emerge in cities due to the phenomenon of emergent fringes. Cities allow significant growth at the tails of statistical distribution. Fringe patterns of behaviour can thrive, both as creative industries, and as social pathologies. This is how cities adapt and stay resilient to exogenous disturbances, epidemic risk included. I intuitively feel that cities of today will be adapting to the pandemic of COVID-19 in a similar way: fringe behaviours will emerge, both at the desirable creative end of the spectrum spread over the scale of ethical values, and at the undesirable end of social pathologies. The latter seem to be attached to the former, like the price of progress. Probably, we will temporarily spread in space: urban sprawl will advance for a certain time. We will be seeking more space around us, so as to reduce epidemic risk, and a new generation of technologies, such as vaccines, testing and decontamination, is likely to counter that sprawling propensity, bringing city dwellers a bit more densely together, one more time.

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

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

[1] Attia, S., Shafik, Z., & Ibrahim, A. (2019). New Cities and Community Extensions in Egypt and the Middle East. Springer Berlin Heidelberg,.

[2] Smith, A. (1896). Lectures on justice, police, revenue and arms: Delivered in the University of Glasgow. Oxford: Clarendon Press.

[3] Metzger, A. (1883). La république de Mulhouse, son histoire, ses anciennes familles bourgeoises et admises à résidence, depuis les origines jusqu’à 1798. Henri Georg.

[4] Mouratidis, K. (2019). Compact city, urban sprawl, and subjective well-being. Cities, 92, 261-272, https://doi.org/10.1016/j.cities.2019.04.013

[5] Hall, P. (2000). Creative cities and economic development. Urban studies, 37(4), 639-649.

[6] Hall, P. (2003). Cities in civilization: culture, innovation and urban order. Journal of Irish Urban Studies, 2, 1-14.

The knowingly healthy people

MY EDITORIAL ON YOU TUBE

I am returning to the thread of research devoted to cities and their role in the human society. My goal is to outline an informed prediction as regards the impact of COVID-19 pandemic on our civilisation, and the prediction is based on a stylized fact I can observe: the most severe outbreaks of COVID-19 take place in densely populated areas, cities or conurbations.

As I connect two threads of my writing and blogging, namely research on cities and collective intelligence, on the one hand, and my investment strategy, on the other hand, one big coin dropped, with ‘logistics’ stamped in that place where traditional coins would display the profile of some king or queen. If my intuition is correct, i.e. if COVID-19 is really forcing us and is going to force us even more into a spatial rearrangement of our settlements, logistics will be a pivotal industry. Here comes that funny coincidence. In Poland, we have that express delivery company, Integer Capital Group, which has pretty much revolutionized the landscape of parcel deliveries. In United States, there is another Integer, namely Integer Holdings Corporation, specializing in portable medical devices, such as neuro- and cardio- modulators. Unfortunately, only the second Integer has its stock publicly listed and available to small investors. Still, there are stocks such as Deutsche Post (the mothership of DHL), UPS or Fedex, which are all booming, stock-price-wise, and them booming seems to have strong foundations in the economic environment. Cool. Looks like I have just found another wave to ride (see The moment of reassessment for the underlying logic of the concept). The strategy I am forming in my mind consists in selling out my positions in Airway Medix and Bioton, whose fundamentals seem a tiny bit wobbly, then take the next rent I collect from that apartment in town, and invest it all in a basket of stock made of four companies, all of them doing logistics: Deutsche Post, UPS, Wisetech, and XPO Logistics. This time, under those hyperlinked names of companies, instead of the habitual ‘investors relations’ websites, my readers can find Excel workbooks with the technical analysis I did, i.e. moving average price (the ‘Mov’), mean reverted price and volumes traded (under the ‘MR’ label), and extrapolated return on the last closing price (‘Return’), whilst in the spreadsheet labelled ‘Sheet1’ you can find the source data with equations that I use to transform it.

Right, I went off track. I was supposed to focus on cities, COVID-19 and stuff. Still, what do you want: things just connect in surprising ways. The entire topography of those surprising ways is called life. Now, as my internal curious ape is back on track of the serious science to do, I am formalizing my scientific take on two issues: the measurement of urban space, and geographic patterns of the COVID-19 pandemic. Here comes an interesting paper, still at the stage of preprint: ‘Time, Space and Social Interactions: Exit Mechanisms for the Covid-19 Epidemics’ by Scala et al. 2020 [1]. The authors attempt to trace the possible scenarios of SARS-Cov-2’s epidemic spread in Italy after the lockdowns are lifted. They use a simple compartmental epidemic model, and I use their model as base for my own thinking about the long-term impact of COVID-19 on our society, mostly on the way that cities live their life. What? Cities are not alive? They don’t live any life, they just function? Well, just go, one day, and observe a city at dawn, as people inside it start going about their business. Just look how those streaks of light, at sunrise, move through that giant urban body, akin to a bloodstream. Those things (i.e. cities) are alive, and we are alive in them.

Scala et al. 2020 stroll down the same cognitive avenue which I am taking: they assume that our exposure to COVID-19 is a combination of three types of factors: biological (our biology vs that of the virus), technological (the really available science we have), and social, i.e. the way we interact and hand the virus over to each other. This distinction is useful to remember. The way most countries go through the epidemic curve is mostly social. We observe a mounting wave of contagion, at first, then a peak comes, which we pass over, and the curve starts to flatten. All that plays out over something like 8 ÷ 11 weeks. Technology did not change during those 11 weeks. I mean, even in Star Trek it wouldn’t. Biology stays more or less the same, both on our part and on the part of the virus. What makes that specific shape of the epidemic curve is our behaviour.

This pandemic paved the way to fame for a previously shy coefficient, the R₀. Hello everybody, I am R₀. I am the average number of people that can be infected by one already infected and infectious person. I am the proportion between the coefficient β of transmission, and the coefficient γ of removal. The latter means either recovery or death. Whichever happens, the given person is removed from the ranks of those susceptible to infection. Thus, I, R₀, spell: R₀ = β/γ. The γ is essentially made of biology and technology. It is all about the way our body responds to the pathogen, and the way that doctors can have a few words to say about it. On the other hand, the coefficient β of transmission is a mixture of biology and social behaviour. It is about the way we can infect each other by coughing, and about the odds that we have any opportunity to cough at each other. The β can spell β = Cλ, where C is the rate of social contact between people, and λ is the likelihood of infection once such contact happens.

Lockdowns have driven the C factor down, in response to an alarmingly rapid increase number O of clinically observed patients with acute symptoms of COVID-19. It is important to understand: as societies, we do not react to the number of people infected and we do not even react to the number of people with acute symptoms. When was it the last time we closed all the roads and cancelled all traffic thereon because of the number of people injured in traffic-related accidents? Have we ever been tempted to do so in the view of people getting serious cardio-vascular problems as a result of them spending hours a day in their cars and being, most of those hours, viscerally pissed about the way they are? No, we just make more comfortable cars and roads, because all those bad things in traffic happen at pretty a constant rate. We, humans, are programmed to notice gradients of change rather than absolute states (see e.g. We really don’t see small change and The kind of puzzle that Karl Friedrich was after). The pandemic introduced a new gradient of disquieting change into our social system, and we reacted by taking cover.

Social response to the pandemic can be represented very simply as elasticity of social contacts to change in the occurrence of acute COVID-19 cases, or ∆C/[∆(O/N)] (once again, O stands for the aggregate number of acute, clinically observed cases). In the presence of epidemic danger – and this specific danger is going to stay with us for a while – we react by inducing a sinusoid pattern into our ∆C: we lock down, then we release etc. As I said before, I deeply believe that lockdowns as such manifest panic behaviour at the collective level rather than a rational response. They are not sustainable economically, and even psychologically. Whatever we reward, we reinforce, and whatever we reinforce grows. If we reward fear of social contact, that fear is going to grow and our European history tells us very explicitly what happens next: isolated colonies of (allegedly) sick people, erosion of socially cohesive behaviour, lynching etc. Question: how can we develop a collectively rational reaction to the pandemic, whilst staying functional as a society? Answer: by modifying our set of social roles so as to be flexible in the ∆C department, and so as to get healthier and more resilient to infections, thus to drive down the λ likelihood of serious infection due to social contact.

Question: are there any historical precedents of societies purposefully changing their repertoires of social roles so as to achieve those two outcomes? Well, yes, and we keep doing it all the time. A good person is clean, right? We don’t like interacting with smelly people, and we socialize easily with folks who are visibly clean in their personal hygiene and wear clean clothes. We like the company of manifestly healthy people much more than the company of someone obviously sick. We shake hands only when we have reasonable chances to shake a clean hand. We sustain an elaborate game of social rivalry where a higher position in hierarchy means a bigger personal space indoors, both at work and at home.

We have a set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles. Each social role sr_i is characterized by a frequency of direct, potentially infectious social interactions – the coefficient C(sr_i) – and by a probability p(sr_i) that any given individual endorses that specific role. The overall intensity C of such interactions in the given society is a weighted average of individual intensities and comes as C = ∑ [p(sr_i)*C(sr_i)]. At this point, I return to the assumption I phrased out in ‘City slickers, or the illusion of standardized social roles’: social roles are essentially individual and idiosyncratic. Categorial social roles, such as ‘a doctor’, ‘a housewife’ etc. are cognitive simplifications that we build in order to save bandwidth in our brain. Therefore, the C(sr_i) coefficient is really local and individual, and the summation sign ∑ in the C = ∑ [p(sr_i)*C(sr_i)] expression has a lot of summing work to do.

When we want to cut down our overall C, and do it more sensibly than by closing all hairdressers for 2 years, we need to reshape our C = ∑ [p(sr_i)*C(sr_i)], i.e. we need to increase the prevalence p(sr_i) of social roles with relatively low C(sr_i), and reduce the occurrence of those who go the opposite, contact-abundant way in their C(sr_i). Yes, ‘who go’, and not ‘which go’. They are idiosyncratic phenomena in individual people, remember?

In my update entitled ‘The perfectly dumb, smart social structure’, I sketched a piece of artificial intelligence supposed to simulate the interplay of social roles, and I ran a few experiments with it. Those experiments indicate that it is not really possible to kick selected social roles out of the system. Even if we attempt to, they end up by coming back, through one hole or another. On the other hand, the emergence of new social roles can naturally push the incumbent ones out of the system, as long as the society tries to keep all its marbles together and assures coherence between those newcomers and the incumbent ones.

The way out of the shitty spot which we are currently in, some place between the epidemic spread running amok, with reins dangling loosely on its neck, on the one hand, and the how-much-longer-can-we-stay-in-lockdown absence of sensible strategy, on the other hand, consists in triggering the creation of new social roles, endowed with relatively low incidence C(sr_i) of infectious social contacts, whilst maintaining as much social cohesion as possible.

We are facing a functional paradox. Cities are the only social contrivance that we have invented, so far, in order to speed up the creation of new social roles, and cities are demographic anomalies, displaying abnormally high density of population, thus by abundant social contacts. Now, with the pandemic around, we need to create new social roles with lower typical occurrence C(sr_i) of potentially infectious social contacts. Can we induce lower intensity of social interactions and maintain social cohesion in an environment which is naturally made for rich social interactions?

A thread of observation has come to my mind. We have had cities for quite a long time, right? Quite a long time means centuries and even millennia. We have also had epidemics in the past, and, as a matter of fact, we tend to forget how many of them we had, and how brutal they used to be. We tend to be anti-vaccine because we have been spoilt by the prevalence of vaccines and by the absence of serious epidemic outbreaks. Anyway, cities have been there for a long time, and epidemics had been there for a long time, sort of hand in hand, and cities have been and still are the most privileged spot of infection. Does it make sense? Somehow it does, and I want to understand how exactly.

Potentially infectious social contacts fall in two categories: contacts with people whom we don’t know or haven’t checked on for a long time, for one, and contacts with people heavily exposed to other infectious contacts in their environment. Thus, I need to introduce a scale of infectious risk in social interactions associated with any social role sr_i in the set SR = {sr₁, sr₂, …, sr_m}. I obtain something like the Itô calculus: an integral of social interactions inside the integral of a social role. It looks complicated, but we can simplify it by assuming that any set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles is coupled with a set SC = {sc₁, sc₂, …, sc_n} of ‘n’ social interactions. The set SC is structured over an axis (dimension) of infectious risk. I can approach risk in a classical way, called the VaR method AKA Value-at-Risk: risk is a quantity, which, in turn, results from associating a given magnitude of damage with a probability of happening. In the case of an epidemic disease, the magnitude of damage ranges along a scale of severity in symptoms combined with their durability.

The so-far collective behaviour during the COVID-19 pandemic indicates that societies tend to minimize aggregate epidemic risk, defined as the arithmetical product: ‘likelihood of infection * severity of symptoms’. In the case of each infected person, the real danger are the most acute symptoms, and thus, in our practical perception of epidemic risk, severity of symptoms can be considered as a subjective constant: we are afraid of the worst that can possibly happen to us. When we reduce the epidemic risk by lockdowns and social distancing, we control the likelihood of infection.

In the presence of prolonged pandemic, and COVID-19 is likely to play out precisely this way, we are likely to minimize epidemic risk by remodelling our social roles. We can maximize the occurrence of predictable social interactions with knowingly healthy people, and minimize haphazard interactions with people of unknown exposure to infection. With a bit of science, we can reasonably narrow down the category of ‘knowingly healthy people’ to those whom we can categorize as non-symptomatic of COVID-10 for a sufficiently long time to assume they are non-symptomatic because they are either non-infected or they have successfully battled the infection, and not because they are asymptomatic. In plain terms, we discreetly observe someone for 3 weeks and we can make and educated guess as for what likelihood of infection that person conveys. Of course, this is just partly scientific, as we never quite know, and yet I think this is the way that people in the past – when epidemic diseases were daily bread, so to say – used to identify those whom they can reasonably hang out with.

At this point, I am going back to the very definition of urban structures, and to the strange and interesting discrepancy in the assessment of what actual, present-time cities are (see Demographic anomalies – the puzzle of urban density). Cities are distinctive from the countryside by their abnormally high density of population, which is a proportion between population and the territory it occupies. There are two distinct methods of measuring both: administrative and GPW (Gridded Population of the World). I am sharpening my understanding of these two approaches so as to understand the dynamics of urban structures as such. My approach is empiricist. I hope to understand better the boundary between cities and the countryside through understanding the fine distinctions as regards the way we perceive that boundary.

Administratively, towns and cities are being defined by the law. In Antiquity and in the feudal society, legal definition of a town was that of a general privilege. City dwellers were allowed to do things, which people living in the countryside couldn’t do. It was frequently about holding a regular marketplace, and some sort of local government, incorporated as city council and/or the office of mayor. That privilege-based approach to the legal definition of a city seems to have vanished during the 19^th century, when cities became nests of large-scale industry, and, interestingly, the number of officially defined cities seems to have frozen approximately at the same time. At some point in time – in Europe it could be around 1900 – the process of legal-administrative identification of urban settlements came to a virtual standstill. Further changes consisted in spatial extension of the already defined towns and cities. Interestingly, that pivotal moment coincided with the progressive elimination of epidemic diseases, through sanitation, healthcare, vaccination etc.

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

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

[1] Scala, A., Flori, A., Spelta, A., Brugnoli, E., Cinelli, M., Quattrociocchi, W., & Pammolli, F. (2020). Time, Space and Social Interactions: Exit Mechanisms for the Covid-19 Epidemics. arXiv: Physics and Society.

The perfectly dumb, smart social structure

MY EDITORIAL ON YOU TUBE

I am developing directly on the mathematical model I started to sketch in my last update, i.e. in Social roles and pathogens: our average civilisation. This is an extension of my earlier research regarding the application of artificial neural networks to simulate collective intelligence in human societies. I am digging down one particular rabbit-hole, namely the interaction between the prevalence of social roles, and that of disturbances to the social structure, such as epidemics, natural disasters, long-term changes in natural environment, radically new technologies etc.

Here comes to my mind, and thence to my writing, a mathematical model that generalizes some of the intuitions, which I already, tentatively, phrased out in my last update. The general idea is that society can be represented as a body of phenomena able to evolve endogenously (i.e. by itself, in plain human lingo), plus an external disturbance. Disturbance is anything that knocks society out of balance: a sudden, massive change in technology, a pandemic, climate change, full legalization of all drugs worldwide, Justin Bieber becoming the next president of the United States etc.

Thus, we have the social structure and a likely disturbance to it. Social structure is a set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles, defined as combinations of technologies and behavioural patterns. The set SR can be stable or unstable. Some of the social roles can drop out of the game. Just checking: does anybody among my readers know what did the craft of a town crier consist in, back in the day? That guy was a local media industry, basically. You paid him for shouting your message in one or more public places in the town. Some social roles can emerge. Twenty years ago, the social role of an online influencer was associated mostly with black public relations, and today it is a regular occupation.

Disappearance or emergence of social roles is one plane of social change, and mutual cohesion between social roles is another one. In any relatively stable social structure, the existing social roles are culturally linked to each other. The behaviour of a political journalist is somehow coherent with the behaviour of politicians he or she interviews. The behaviour of a technician with a company of fibreoptic connections is somehow coherent with the behaviour of end users of those connections. Yet, social change can loosen the ties between social roles. I remember the early 1990ies, in Poland, just after the transition from communism. It was an odd moment, when, for example, many public officers, e.g. maires or ministers, were constantly experimenting with their respective roles. That very loose coupling of social roles is frequently observable in start-up businesses, on the other hand. In many innovative start-ups, when you start a new job, you’d better be prepared to its exact essence and form taking shape as you work.

In all that story of social cohesion I essentially tap into swarm theory (see Correlated coupling between living in cities and developing science; Xie, Zhang & Yang 2002 [1] ; Poli, Kennedy & Blackwell 2007 [2] ; Torres 2012 [3]; Stradner et al. 2013 [4]). I assume that each given pair of social roles – e.g. the First Secretary of The Communist Party of China and a professional gambler in Las Vegas – can be coupled at three levels: random, fixed, and correlated. A relative loosening of social cohesion means that random coupling grows in relative importance, at the expense of the fixed, strictly ritualized coupling, and of the correlated one.

All in all, I hypothesise four basic types of social change in an established structure, under the impact of an exogenous disturbance. Scenario A assumes the loosening of cohesion between social roles, under the impact of an exogenous disturbance, with a constant catalogue of social roles in place. Scenario B implies that external stressor makes some social roles disappear, whilst scenarios C and D represent the emergence of new social roles, in two different perspectives. In Scenario C, new social roles are not coherent with the established ones, whilst Scenario D assumes such a cohesion.

Mathematically, I represent the whole thing in the form of a simple neural network, a multi-layer perceptron. I have written a lot about using neural networks as representation of collective intelligence, and now, I feel like generalising my theoretical stance and explaining two important points, namely what exactly I mean by a neural network, and why do I apply a neural network instead of a stochastic model, such as e.g. an Ito drift.

A neural network is a sequence of equations, which can be executed in a loop, over a finite sequence ER = {er₁, er₂, …, er_n} of ‘n’ of experimental rounds, and that recurrent sequence of equations has a scalable capacity to learn. In other words, equation A takes input data, transforms it, feeds the result into equation B, which feeds into equation C etc., and, at some point, the result yielded by the last equation in the sequence gets fed into equation A once again, and the whole sequence runs another round A > B > C > …> A etc.. In each consecutive experimental round er_j, equation A taps into raw empirical data, and into the result of the previous experimental round e_j-1. Another way of defining a neural network is to say that it is a general, logical structure able to learn by producing many specific instances of itself and observing their specific properties. Both definitions meet in the concept of logical structure and learning. It is quite an old observation in our culture that some logical structures, such as sequences of words, have the property of creating much more meaning than others. When I utter a sequence ‘Noun + Verb + Noun’, e.g. ‘I eat breakfast’, it has the capacity to produce more meaning than a sequence of the type ‘Verb + Verb + Verb’, e.g. ‘Eat read walk’. The latter sequence leaves more ambiguity, and the amount of that ambiguity makes that sequence of words virtually useless in daily life, save for online memes.

There are certain peg structures in the sequence of equations that make a neural network, i.e. some equations and sequences thereof which just need to be there, and which the network cannot produce meaningful results. I am going to present the peg structure of a neural network, and then I will explain its parts one by one.

Thus, the essential structure is the following: [Equation of random experimentation  ε* x_i(er₁)] => [Equation of aggregation  h = ∑ ε* x_i (er₁)] => [Equation of neural activation  NA = (a*e^bh ± 1) / (a*e^bh ± 1) ] => {Equation of error assessment  e(er₁) = [O(er₁) – NA(er₁)]*c} => {[Equation of backpropagation]  [Equation of random experimentation + acknowledgement of error from the previous experimental round]  [ε* x_i(er_j) + e(er₁)]} => {Equation of aggregation  h = ∑ [ε* x_i (er_j) + e(er₁)]} etc.

In that short sequential description, I combined mathematical expressions with formal logic. Brackets of different types – round (), square [] and curly {} – serve to delineate distinct logical categories. The arrowed symbols stand for logical connections, with ‘’ being an equivalence, and ‘=>’ and implication. That being explained, I can start explaining those equations and their sequence. The equation of random experimentation expresses what an infant’s brain does: it learns, by trial and error, i.e. my mixing stimuli in various hierarchies and seeing which hierarchy of importance, attached to individual pieces of sensory data, works better. In an artificial neural network, random experimentation means that each separate piece of data is being associated with a random number ε between 0 and 1, e.g. 0,2 or 0,87 etc. A number between 0 and 1 can be interpreted in two ways: as a probability, or as the fraction of a whole. In the associated pair ε* x_i(er_j), the random weight 0 < ε < 1 can be seen as hypothetical probability that the given piece x_i of raw data really matters in the experimental round er_j. From another angle, we can interpret the same pair ε* x_i(er_j) as an experiment: what happens when we cut fraction ε from the piece of data x_i. it can be for one, or as a slice cut out of that piece of data.

Random experimentation in the first experimental round er₁ is different from what happens in consecutive rounds er_j. In the first round, the equation of random experimentation just takes the data x_i. In any following round, the same equation must account for the error of adjustment incurred in previous rounds. The logic is still the same: what happens if we assume a probability of 32% that error from past experiments really matters vs. the probability of 86%?

The equation of aggregation corresponds to the most elementary phase of what we could call making sense of reality, or to language. A live intelligent brain collects separate pieces of data into large semantic chunks, such as ‘the colour red’, ‘the neighbour next door’, ‘that splendid vintage Porsche Carrera’ etc. The summation h = ∑ ε* x_i (er_j) is such a semantic chunk, i.e. h could be equivalent to ‘the neighbour next door’.

Neural activation is the next step in the neural network making sense of reality. It is the reaction to the neighbour next door. The mathematical expression NA = (a*e^bh ± 1) / (a*e^bh ± 1) is my own generalisation of two commonly used activation functions: the sigmoid and the hyperbolic tangent. The ‘e’ symbol is the mathematical constant e, and ‘h’ in the expression e^bh is the ‘h’ chunk of pre-processed data from the equation of aggregation. The ‘b’ coefficient is usually a small integer, e.g. b = 2 in the hyperbolic tangent, and -1 in the basic version of the sigmoid function.

The logic of neural activation consists in combining a constant component with a variable one, just as a live nervous system has some baseline neural activity, e.g. the residual muscular tonus, which ramps up in the presence of stimulation. In the equation of hyperbolic tangent, namely NA = tanh = (e^2h – 1) / (e^2h + 1), the constant part is (e² – 1) / (e² + 1) = 0,761594156. Should my neural activation be the sigmoid, it goes like NA = sig = 1 / (1 + e^-h), with the constant root of 1 / (1 + e^-1) = 0,731058579.

Now, let’s suppose that the activating neuron NA gets excited about a stream of sensory experience represented by input data: x₁ = 0.19, x₂ = 0.86, x₃ = 0.36, x₄ = 0.18, x₅ = 0.93. At the starting point, the artificial mind has no idea how important are particular pieces of data, so it experiments by assigning them a first set of aleatory coefficients – ε₁ = 0.85, ε₂ = 0.70, ε₃ = 0.08, ε₄ = 0.71, ε₅ = 0.20 – which means that we experiment with what happens if x₃ was totally unimportant, x₄ was hardly more significant, whilst x₁, x₂ and x₃ are really important. Aggregation yields h = 0,19*0,85 +0,86*0,70 + 0,36*0,08 + 0,18*0,71 + 0,93*0,20 = 1,10.

An activating neuron based on the hyperbolic tangent gets into a state of NA = tanh = (e^2*1,10 – 1) / (e^2*1,10 + 1) = 0.801620, and another activating neuron working with the sigmoid function thinks NA = sig = 1 / (1 + e^-1,10) = 0,7508457. Another experiment with the same data consists in changing the aleatory coefficients of importance and seeing what happens, thus in saying ε₁ = 0.48, ε₂ = 0.44, ε₃ = 0.24, ε₄ = 0.27, ε₅ = 0.80 and aggregating h = 0,19*0,48 +0,86*0,44 + 0,36*0,24 + 0,18*0,27 + 0,93*0,80 = 1,35. In response to the same raw data aggregated in a different way, the hyperbolic tangent says NA = tanh = (e^2*1,35 – 1) / (e^2*1,35 + 1) = 0,873571 and the activating neuron which sees reality as a sigmoid retorts: ‘No sir, absolutely not. I say NA = sig = 1 / (1 + e^-1,35) = 0,7937956’. What do you want: equations are like people, they are ready to argue even about 0,25 of difference in aggregate input from reality.

Those two neural reactions bear a difference, visible as gradients of response, or elasticities of response to a change in aggregate output. The activating neuron based on hyperbolic tangent yields a susceptibility of (0,873571 – 0,801620) / (1,35 – 1,10) = 0.293880075, which the sigmoid sees as an overreaction, with its well-pondered (0,7937956 – 0,7508457) / (1,35 – 1,10) = 0,175427218. That’s an important thing to know about neural networks: they can be more or less touchy in their reaction. Hyperbolic tangent produces more stir, and the sigmoid is more like ‘calm down’ in its ways.

Whatever the neural activation NA produces, gets compared with a pre-set outcome O, or output variable. Error is assessed as e(er_j) = [O(er_j) – NA(er_j)]*c, where ‘c’ is na additional factor, sometimes the local derivative of NA. It just serves to put c there: it can amplify (c > 1) or downplay (c < 1) the importance of local errors and therefore make the neural network more or less sensitive to making errors.

Before I pass to discussing the practical application of that whole logical structure to the general problem at hand, i.e. the way that a social structure reacts to exogenous disturbances, one more explanation is due, namely the issue of backpropagation of error, where said error is being fed forward. One could legitimately ask how the hell is it possible to backpropagate something whilst feeding it forward. Let’s have a look at real life. When I learn to play piano, for example, I make mistakes in my play, and I utilise them to learn. I learn by repeating over and over again the same sequence of musical notes. Repetition is an instance of feeding forward. Each consecutive time I play the same sequence, I move forward one more round. However, if I want that move forward to be really productive as regards learning, I need to review, each time, my entire technique. I need to go back to my first equation and run the whole sequence of equations again. I need to backpropagate my mistakes over the whole sequence of behaviour. Backpropagating errors and feeding them forward calls two different aspects of the same action. I backpropagate errors across the logical structure of the neural network, and I feed them forward over consecutive rounds of experimentation.

Now, it is time to explain how I simulate the whole issue of disturbed social structure, and the four scenarios A, B, C, and D, which I described a few paragraphs earlier. The trick I used consists in creating a baseline neural network, one which sort of does something but not much really, and then making mutants out of it, and comparing the outcomes yielded by mutants with that produced by their baseline ancestor. For the baseline version, I have been looking for a neural network which learns lightning fast on the short run but remains profoundly stupid on the long run. I wanted quick immediate reaction and no capacity whatsoever to narrow down the error and adjust to it.

The input layer of the baseline neural network is made of the set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles, and one additional variables representative for the hypothetical disturbance. Each social role sr_i corresponds to a single neuron, which can take values between 0 and 1. Those values represent the probability of occurrence in the social role sr_i. If, for example, in the experimental round e = 100, the input value of the social role sr_i is sr_i(e₁₀₀) = 0.23, it means that 23% of people manifest the distinctive signs of that social role. Of course, consistently with what I perceive as the conceptual acquis of social sciences, I assume that an individual can have multiple, overlapping social roles.

The factor of disturbance RB is an additional variable in the input layer of the network and comes with similar scale and notation. It takes values between 0 and 1, which represent the probability of disturbing occurrence in the social structure. Once again, RB can be anything, disturbing positively, negatively, or kind of we have no idea what it is going to bring about.

Those of you who are familiar with the architecture of neural networks might wonder how I am going to represent the emergence of new social roles without modifying the structure of the network. Here comes a mathematical trick, which, fortunately enough, is well grounded in social sciences. The mathematical part of the trick consists in incorporating dormant social roles in the initial set SR = {sr₁, sr₂, …, sr_m}, i.e. social roles assigned with arbitrary 0 value, i.e. zero probability of occurrence. On the historically short run, i.e. at the scale of like one generation, new social roles are largely predictable. As we are now, we can reasonably predict the need for new computer programmers, whilst being able to safely assume a shortage of jobs for cosmic janitors, collecting metal scrap from the terrestrial orbit. In 20 years from now, that perspective can change – and it’d better change, as we have megatons of metal crap on the orbit – yet, for now, it looks pretty robust.

Thus, in the set SR = {sr₁, sr₂, …, sr_m}, I reserve k neurons for active social roles, and l neurons for dormant ones, with, of course, k + l = m. All in all, in the actual network I programmed in Excel, I had k = 20 active social roles, l = 19 dormant social roles, and one neuron corresponding to the disturbance factor RB.

Now, the issue of social cohesion. In this case, we are talking about cohesion inside the set SR = {sr₁, sr₂, …, sr_m}. Mathematically, cohesion inside a set of numerical values can be represented as the average numerical distance between them. Therefore, I couple the input layer of 20k + 19l + RB = 40 neurons is coupled with a layer of meta-input, i.e. with a layer of 40 other neurons whose sole function is to inform about the Euclidean distance between the current value of each input neuron, and the values of the other 39 input neurons.

Euclidean distance plays the role of fitness function (see Hamann et al. 2010 [1]). Each social role in the set SR = {sr₁, sr₂, …, sr_m}, with its specific probability of occurrence, displays a Euclidean distance from the probability of occurrence in other social roles. The general idea behind this specific mathematical turn is that in a stable structure, the Euclidean distance between phenomena stays more or less the same. When, as a society, we take care of being collectively cohesive, we use the observation of cohesion as data, and the very fact of minding our cohesion helps us to maintain cohesion. When, on the other hand, we don’t care about social cohesion, then we stop using (feeding forward) this specific observation, and social cohesion dissolves.

For the purposes of my own scientific writing, I commonly label that Euclidean distance as V, i.e. V(sr_i; e_j) stands for the average Euclidean distance between social role sr_i, and all the other m – 1 social roles in the set SR = {sr₁, sr₂, …, sr_m}, in the experimental round e_j. When input variables are being denominated on a scale from 0 to 1, thus typically standardized for a neural network, and the network uses (i.e. feeds forward) the meta input on cohesion between variables, the typical Euclidean distance you can expect is like 0,1 ≤ V(sr_i; e_j) ≤ 0,3. When the social structure loses it, Euclidean distance between phenomena starts swinging, and that interval tends to go into 0,05 ≤ V(sr_i; e_j) ≤ 0,8. This is how the general idea of social cohesion is translated into a mathematical model.

Thus, my neural network uses, as primary data, basic input about the probability of specific social roles being played by a randomly chosen individual, and metadata about cohesion between those probabilities. I start by assuming that all the active k = 20 social roles occur with the same probability of 0,5. In other words, at the starting point, each individual in the society displays a 50% probability of endorsing any of the k = 20 social roles active in this specific society. Reminder: l = 19 dormant social roles stay at 0, i.e. each of them has 0% of happening, and the RB disturbance stays at 0% probability as well. All is calm. This is my experimental round 1, or e₁. In the equation of random experimentation, each social role sr_i gets experimentally weighed with a random coefficient, and with its local Euclidean distance from other social roles. Of course, as all k = 20 social roles have the same probability of 50%, their distance from each other is uniform and always makes V = 0,256097561. All is calm.

As I want my baseline AI to be quick on the uptake and dumb as f**k on the long-haul flight of learning, I use neural activation through hyperbolic tangent. As you could have seen earlier, this function is sort of prone to short term excitement. In order to assess the error, I use both logic and one more mathematical trick. In the input, I made each of k = 20 social roles equiprobable in its happening, i.e. 0,50. I assume that the output of neural activation should also be 0,50. Fifty percent of being anybody’s social role should yield fifty percent: simplistic, but practical. I go e(er_j) = O(er_j) – NA(er_j) = 0,5 – tanh = 0,5 – [(e^2h – 1) / (e^2h + 1)], and I feed forward that error from round 1 to the next experimental round. This is an important trait of this particular neural network: in each experimental round, it experiments adds up the probability from previous experimental round and the error made in the same, previous experimental round, and with the assumption that expected value of output should be a probability of 50%.

That whole mathematical strategy yields interesting results. Firstly, in each experimental round, each active social role displays rigorously the same probability of happening, and yet that uniformly distributed probability changes from one experimental round to another. We have here a peculiar set of phenomena, which all have the same probability of taking place, which, in turn, makes all those local probabilities equal to the average probability in the given experimental round, i.e. to the expected value. Consequently, the same happens to the internal cohesion of each experimental round: all Euclidean distances between input probabilities are equal to each other, and to their average expected distance. Technically, after having discovered that homogeneity, I could have dropped the whole idea of many social roles sr_i in the database and reduce the input data just to three variables (columns): one active social role, one dormant, and the disturbance factor RB. Still, I know by experience that even simple neural networks tend to yield surprising results. Thus, I kept the architecture ’20k + 19l + RB’ just for the sake of experimentation.

That whole baseline neural network, in the form of an Excel file, is available under THIS LINK. In Table 1, below, I summarize the essential property of this mathematical structure: short cyclicality. The average probability of happening in each social role swings regularly, yielding, at the end of the day, an overall average probability of 0,33. Interesting. The way this neural network behaves, it represents a recurrent sequence of two very different states of society. In odd experimental rounds (i.e. 1, 3, 5,… etc.) each social role has 50% or more of probability of manifesting itself in an individual, and the relative cohesion inside the set of social roles is quite high. On the other hand, in even experimental rounds (i.e. 2, 4, 6, … etc.), social roles become disparate in their probability of happening in a given time and place of society, and the internal cohesion of the network is low. The sequence of those two states looks like the work of a muscle: contract, relax, contract, relax etc.

Table 1 – Characteristics of the baseline neural network

Experimental round	Average probability of input	Cohesion – Average Euclidean distance V in input	Aggregate input ‘h’	Error to backpropagate
1	0,5000	0,2501	1,62771505	-0,4257355
2	0,0743	0,0372	0,02990319	0,47010572
3	0,5444	0,2723	1,79626958	-0,4464183
4	0,0980	0,0490	0,05191633	0,44813027
5	0,5461	0,2732	1,60393868	-0,4222593
6	0,1238	0,0619	0,09320145	0,40706748
7	0,5309	0,2656	1,59030006	-0,4201953
8	0,1107	0,0554	0,07157025	0,4285517
9	0,5392	0,2698	1,49009281	-0,4033418
10	0,1359	0,0680	0,11301796	0,38746079
11	0,5234	0,2618	1,51642329	-0,4080723
12	0,1153	0,0577	0,06208368	0,43799596
13	0,5533	0,2768	1,92399208	-0,458245
14	0,0950	0,0476	0,03616495	0,46385081
15	0,5589	0,2796	1,51645936	-0,4080786
16	0,1508	0,0755	0,13860251	0,36227827
17	0,5131	0,2567	1,29611259	-0,3607191
18	0,1524	0,0762	0,12281062	0,37780311
19	0,5302	0,2652	1,55382594	-0,4144146
20	0,1158	0,0579	0,06391662	0,43617027
…	…	…	…	…
Average over 3000 rounds	0,3316	0,1659	0,8113	0,0000041
Variance	0,0408	0,0102	0,5345	0,162
Variability*	0,6092	0,6092	0,9012	97 439,507

*Variability is calculated as standard deviation, i.e. square root of variance, divided by the average.

Now, I go into the scenario A of social change. The factor of disturbance RB gets activated and provokes a loosening of social cohesion. Mathematically, it involves a few modifications to the baseline network. Activation of the disturbance RB involves two steps. Firstly, numerical values of this specific variable in the network needs to take non-null values: the disturbance is there. I do it by generating random numbers in the RB column of the database. Secondly, there must be a reaction to disturbance, and the reaction consists in disconnecting the layer of neurons, which I labelled meta-data, i.e. the one containing Euclidean distances between the raw data points.

Here comes the overarching issue of sensitivity to disturbance, which goes across all the four scenarios (i.e. A, B, C, and D). As representation of what’s going on in social structure, it is about collective and individual alertness. When a new technology comes out into the market, I don’t necessarily change my job, but when that technology spreads over a certain threshold of popularity, I might be strongly pushed to reconsider my decision. When COVID-19 started hitting the global population, all levels of reaction (i.e. governments, media etc.) were somehow delayed in relation to the actual epidemic spread. This is how social change happens in reaction to a stressor: there is a threshold of sensitivity.

When I throw a handful of random values into the database, as values of disturbance RB, they are likely to be distributed under a bell-curve. I translate mathematically the social concept of sensitivity threshold as a value under that curve, past which the network reacts by cutting ties between errors input as raw data from previous experimental rounds, and the measurement of Euclidean distance between them. Question: how to set this value so as it fits with the general logic of that neural network? I decided to set the threshold at the absolute value of the error recorded in the previous experimental round. Thus, for example, when error generated in round 120 is e₁₂₀ = -0.08, the threshold of activation for triggering the response to disturbance is ABS(-0,08) = 0,08. The logic behind this condition is that social disturbance becomes significant when it is more prevalent than normal discrepancy between social goals and the actual outcomes.

I come back to the scenario A, thus to the hypothetical situation when the factor of disturbance cuts the ties of cohesion between existing, active social roles. I use the threshold condition ‘if RB(er_j) > e(er_j-1), then don’t feed forward V(er_j-1)’, and this is what happens. First of all, the values of probability assigned to all active social roles remain just as uniform, in every experimental round, as they are in the baseline neural network I described earlier. I know, now, that the neural network, such as I designed it, is not able to discriminate between inputs. It just generates a uniform distribution thereof. That being said, the uniform probability of happening in social roles sr_i follows, in scenario A, a clearly different trajectory than the monotonous oscillation in the baseline network. The first 134 experimental rounds yield a progressive decrease in probability down to 0. Somewhere in rounds 134 ÷ 136 the network reaches a paradoxical situation, when no active social role in the k = 20 subset has any chance of manifesting itself. It is a society without social roles, and all that because the network stops feeding forward meta-data on its own internal cohesion when the disturbance RB goes over the triggering point. Past that zero point, a strange cycle of learning starts, in irregular leaps: the uniform probability attached to social roles rises up to an upper threshold, and then descends again back to zero. The upper limit of those successive leaps oscillates and then, at an experimental round somewhere between er₄₀₀ and er₁₀₀₀, probability jumps just below 0,7 and stays this way until the end of the 3000 experimental rounds I ran this neural network through. At this very point, the error recorded by the network gets very close to zero and stays there as well: the network has learnt whatever it was supposed to learn.

Of course, the exact number of experimental rounds in that cycle of learning is irrelevant society-wise. It is not 400 days or 400 weeks; it is the shape of the cycle that really matters. That shape suggests that, when an external disturbance switches off internal cohesion between social roles in a social structure, the so-stimulated society changes in two phases. At first, there are successive, hardly predictable episodes of virtual disappearance of distinct social roles. Professions disappear, family ties distort etc. It is interesting. Social roles get suppressed simply because there is no need for them to stay coherent with other social roles. Then, a hyper-response emerges. Each social role becomes even more prevalent than before the disturbance started happening. It means a growing probability that one and the same individual plays many social roles in parallel.

I pass to scenario B of social change, i.e. the hypothetical situation when the exogenous disturbance straightforwardly triggers the suppression of social roles, and the network keeps feeding forward meta-data on internal cohesion between social roles. Interestingly, suppression of social roles under this logical structure is very short lived, i.e. 1 – 5 experimental rounds, and then the network yields an error which forces social roles to disappear.

One important observation is to note as regards scenarios B, C, and D of social change in general. Such as the neural network is designed, with the threshold of social disturbance calibrated on the error from previous experimental round, error keeps oscillating within an apparently constant amplitude over all the 3000 experimental rounds. In other words, there is no visible reduction of magnitude in error. Some sort of social change is occurring in scenarios B, C, and D, still it looks as a dynamic equilibrium rather than a definitive change of state. That general remark kept in mind, the way that the neural network behaves in scenario B is coherent with the observation made regarding the side effects of its functioning in scenario A: when the factor of disturbance triggers the disappearance of some social roles, they re-emerge spontaneously, shortly after. To the extent that the neural network I use here can be deemed representative for real social change, widely prevalent social roles seem to be a robust part of the social structure.

Now, it is time to screen comparatively the results yielded by the neural network when it is supposed to represent scenarios C and D of social change: I study situations when a factor of social disturbance, calibrated in its significance on the error made by the neural network in previous experimental rounds, triggers the emergence of new social roles. The difference between those two scenarios is in the role of social cohesion. Mathematically, I did it by activating the dormant l = 19 social roles in the network, with a random component. When the random value generated in the column of social disturbance RB is greater than the error observed in the previous experimental round, thus when RB(er_j) > e(er_j-1), then each of the l = 19 dormant social roles gets a random positive value between 0 and 1. That random positive value gets processed in two alternative ways. In scenario C, it goes directly into aggregation and neural activation, i.e. there is no meta-data on the Euclidean distance between any of those newly emerging social roles and other social roles. Each new social role is considered as a monad, which develops free from constraints of social cohesion. Scenario D establishes such a constraint, thus the randomly triggered probability of a woken up, and previously dormant social role is being aggregated, and fed into neural activation with meta-data as for its Euclidean distance from other social roles.

Scenarios C and D share one important characteristic: heterogeneity in new social roles. The k = 20 social roles active from the very beginning, thus social roles ‘inherited’ from the baseline social network, share a uniform probability of happening in each experimental round. Still, as probabilities of new social roles, triggered by the factor of disturbance, are random by default, these probabilities are distributed aleatorily. Therefore, scenarios C and D represent a general case of a new, heterogenous social structure emerging in the presence of an incumbent rigid social structure. Given that specific trait, I introduce a new method of comparing those two sets of social roles, namely by the average probability attached to social roles, calculated over the 3000 experimental rounds. I calculate the average probability of active social roles across all the 3000 experimental rounds, and I compare it with individual, average probabilities obtained for each of the new social roles (or woken up and previously dormant social roles) over 3000 experimental rounds. The idea behind this method is that in big sets of observations, arithmetical average represents the expected value, or the expected state of the given variable.

The process of social change observed, respectively, in scenarios C and D, is different. In the scenario C, the uniform probability attached to the incumbent k = 20 social roles follows a very calm trend, oscillating slightly between 0,2 and 0,5, whilst the heterogenous probabilities of newly triggered l = 19 social roles swing quickly and broadly between 0 and 1. When the network starts feeding forward meta-data on Euclidean distance between each new social role and the others, it creates additional oscillation in the uniform probability of incumbent social roles. The latter gets systematically and cyclically pushed into negative values. A negative probability is logically impossible and represents no real phenomenon. Well, I mean… It is possible to assume that the negative probability of one phenomenon represents the probability of the opposite phenomenon taking place, but this is really far-fetched and doesn’t really find grounding in the logical structure of this specific neural network. Still, the cycle of change where the probability of something incumbent and previously existing gets crushed down to zero (and below) represents a state of society, when a new phenomenon aggressively pushes the incumbent phenomena out of the system.

Let’s see how those two processes of social change, observed in scenarios C and D, translate into expected states of social roles, i.e. into average probabilities. The first step in this analysis is to see how heterogeneous are those average expected states across the new social roles, triggered out of dormancy by the intrusion of the disturbance RB. In scenario C, new social roles display average probabilities between 0,32 and 0,35. Average probabilities corresponding to each individual, new social role differs from others by no more than 0.03, thus by a phenomenological fringe to be found in the tails of the normal distribution. By comparison, the average uniform probability attached to the existing social roles is 0,31. Thus, in the absence of constraint regarding social cohesion between new social roles and the incumbent ones, the expected average probability in both categories is very similar.

In scenario D, average probabilities of new social roles oscillate between 0,45 and 0,49, with just as little disparity as in scenario C, but, in the same time, they push the incumbent social roles out of the nest, so to say. The average uniform probability in the latter, after 3000 experimental rounds, is 0.01, which is most of all a result of the ‘positive probability – negative probability’ cycle during experimentation.

It is time to sum up my observations from the entire experiment conducted through and with a neural network. The initial intention was to understand better the mechanism which underlies one of my most fundamental claims regarding the civilizational role of cities, namely that cities, as a social contrivance, serve to accommodate a growing population in the framework of an increasingly complex network of social roles.

I am focusing on the ‘increasingly complex’ part of that claim. I want to understand patterns of change in the network of social roles, i.e. how can the complexity of that network evolve over time. The kind of artificial behaviour I induced in a neural network allows identifying a few recurrent patterns, which I can transform into hypotheses for further research. There is a connection between social cohesion and the emergence/disappearance of new social roles, for one. Social cohesion drags me back into the realm of the swarm theory. As a society, we seem to be evolving by a cycle of loosening and tightening in the way that social roles are coupled with each other.

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

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

[1] Hamann, H., Stradner, J., Schmickl, T., & Crailsheim, K. (2010). Artificial hormone reaction networks: Towards higher evolvability in evolutionary multi-modular robotics. arXiv preprint arXiv:1011.3912.

[1] Xie, X. F., Zhang, W. J., & Yang, Z. L. (2002, May). Dissipative particle swarm optimization. In Proceedings of the 2002 Congress on Evolutionary Computation. CEC’02 (Cat. No. 02TH8600) (Vol. 2, pp. 1456-1461). IEEE.

[2] Poli, R., Kennedy, J., & Blackwell, T. (2007). Particle swarm optimization. Swarm intelligence, 1(1), 33-57.

[3] Torres, S. (2012). Swarm theory applied to air traffic flow management. Procedia Computer Science, 12, 463-470.

[4] Stradner, J., Thenius, R., Zahadat, P., Hamann, H., Crailsheim, K., & Schmickl, T. (2013). Algorithmic requirements for swarm intelligence in differently coupled collective systems. Chaos, Solitons & Fractals, 50, 100-114.

Social roles and pathogens: our average civilisation

MY EDITORIAL ON YOU TUBE

I am starting this update with a bit of a winddown on my previous excitement, expressed in Demographic anomalies – the puzzle of urban density. I was excited about the apparently mind-blowing, negative correlation of ranks between the relative density of urban population, on the one hand, and the consumption of energy per capita, on the other hand. Apparently, the lower the rank of the {[DU/DG]  [Density of urban population / General density of population]} coefficient, the greater the consumption of energy per capita. All in all, it is not as mysterious as I thought. It is visible, that the average value of the [DU/DG] coefficient decreases with the level of socio-economic development. In higher-middle income countries, and in the high-income ones, [DU/DG] stays consistently below 10, whilst in poor countries it can even flirt with values above 100. In other words, relatively greater a national wealth is associated with relatively smaller a social difference between cities and the countryside. Still, that shrinking difference seems to have a ceiling around [DU/DG] = 2,00. In the realm of [DU/DG] < 2,00, we do not really encounter wealthy countries. In this category we have tropical island states, or entities such as West Bank and Gaza, which are demographic anomalies even against the background of cities in general being demographic anomalies. Among really wealthy countries, the lowest values in the [DU/DG] coefficient are to find with Belgium (2,39) and Netherlands (2,30).

I am taking it from the beginning, ‘it’ being the issue of cities and urbanisation. The beginning was my bewilderment when the COVID-19-related lockdowns started in my country, i.e. in Poland. I remember cycling through the post-apocalyptically empty streets of my hometown, Krakow, Poland, I was turning in my mind the news, regarding the adverse economic outcomes of the lockdown, and strange questions were popping up in my consciousness. How many human footsteps per day does a city need to thrive? How many face-to-face interactions between people do we need, to keep that city working?

I had that sudden realization that city life is all about intensity of human interaction. I reminded another realization, which I experienced in November 2017. I was on a plane that had just taken off from the giant Frankfurt airport. It was a short flight, to Lyon, France – almost like a ballistic curve – and this is probably why the plane was gathering altitude very gently. I could see the land beneath, and I marvelled at the slightly pulsating, intricate streaks of light, down there, on the ground. It took me a few minutes to realize that the lights I was admiring were those of vehicles trapped in the gargantuan traffic jams, typical for the whole region of Frankfurt. Massively recurrent, utterly unpleasant, individual happening – being stuck in a traffic jam – was producing outstanding beauty, when contemplated from far above.

As I rummaged a bit through literature, cities seem to have been invented, back in the day, as social contrivances allowing, on the one hand, relatively peaceful coexistence of many different ethnic groups in fertile lowlands, and, on the other hand, a clear focusing of demographic growth in limited areas, whilst leaving the majority of arable land to the production of food. With time, the unusually high density of population in cities started generating secondary and tertiary effects. Greater a density of population favours accelerated emergence of new social roles, which, in turn, stimulates technological change and the development of markets. Thus, initially, cities tend to differentiate sharply from the surrounding countryside. By doing so, they create a powerful creative power regarding aggregate income of the social group. When this income-generating force concurs, hopefully, with acceptably favourable natural conditions and with political stability, the whole place (i.e. country or region) starts getting posh, and, as it does so, the relative disparity between cities and the countryside starts to diminish down to some kind of no-go-further threshold, where urban populations are a little bit twice as dense as the general average of the country. In other words, cities are a demographic anomaly which alleviates social tensions, and allows social change through personal individuation and technological change, and this anomaly starts dissolving itself as soon as those secondary and tertiary outcomes really kick in.

In the presence of that multi-layer cognitive dissonance, I am doing what I frequently do, i.e. in a squid-like manner I produce a cloud of ink. Well, metaphorically: it is more of a digital ink. As I start making myself comfortable inside that cloud, axes of coordinates emerged. One of them is human coordination in cities, and a relatively young, interesting avenue of research labelled ‘social neuroscience’. As digital imaging of neural processes has been making itself some space, as empirical method of investigation, interesting openings emerge. I am undertaking a short review of literature in the field of social neuroscience, in order to understand better the link between us, humans, being socially dense, and us doing other interesting things, e.g. inventing quantum physics or publishing the ‘Vogue’ magazine.

I am comparing literature from 2010 with the most recent one, like 2018 and 2019. I snatched almost the entire volume 65 of the ‘Neuron’ journal from March 25, 2010, and I passed in review articles pertinent to social neuroscience. Pascal Belin and Marie-Helene Grosbras (2010[1]) discuss the implications of research on voice cognition in infants. Neurologically, the capacity to recognize voice, i.e. to identify people by their voices, emerges long before the capacity to process verbal communication. Apparently, the period stretching from the 3^rd month of life through the 7^th month is critical for the development of voice cognition in infants. During that time, babies learn to be sharper observers of voices than other ambient sounds. Cerebral processing of voice seems to be largely subcortical and connected to our perception of time. In other words, when we use to say, jokingly, that city people cannot distinguish the voices of birds but can overhear gossip in a social situation, it is fundamentally true. From the standpoint of my research it means that dense social interaction in cities has a deep neurological impact on people already in their infancy. I assume that the denser a population is, the more different human voices a baby is likely to hear, and learn to discriminate, during that 3^rd ÷ 7^th month phase of learning voice cognition. The greater the density of population, the greater the data input for the development of this specific function in our brain. The greater the difference between the city and the countryside, social-density-wise, the greater the developmental difference between infant brains as regards voice cognition.

From specific I pass to the general, and to a review article by Ralph Adolphs (2010 [2]). One of the most interesting takeaways from this article is a strongly corroborated thesis that social neurophysiology (i.e. the way that our brain works in different social contexts) goes two ways: our neuro-wiring predisposes us to some specific patterns of social behaviour, and yet specific social contexts can make us switch between neurophysiological patterns. That could mean that every mentally healthy human is neurologically wired for being both a city slicker and a rural being. Depending on the context we are in, the corresponding neurophysiological protocol kicks in. Long-lasting urbanization privileges social learning around ‘urban’ neurophysiological patterns, and therefore cities can have triggered a specific evolutionary direction in our species.

I found an interesting, slightly older paper on risk-taking behaviour in adolescents (Steinberg 2008 [3]). It is interesting because it shows connections between developmental changes in the brain, and the appetite for risk. Risk-taking behaviour is like a fast lane of learning. We take risks when and to the extent that we can tolerate both high uncertainty and high emotional tension in a specific context. Adolescents take risks in order to boost their position in social hierarchy and that seems to be a truly adolescent behaviour from the neurophysiological point of view. Neurophysiological adults, thus, roughly speaking, people over the age of 25, seem to develop increasing preference for strategies of social advancement based on long-term, planned action with clearly delayed rewards. Apparently, there are two distinct, neurophysiological protocols – the adolescent one and the adult one – as regards the quest for individual social role, and the learning which that role requires.

Cities allow more interactions between adolescents than countryside does. More interactions between adolescents stronger a reinforcement for social-role-building strategies based on short-term reward acquired at the price of high risk. That might be the reason why in the modern society, which, fault of a better term, we call ‘consumer society’, there is such a push towards quick professional careers. The fascinating part is that in a social environment rich in adolescent social interaction, the adolescent pattern of social learning, based on risk taking for quick reward, finds itself prolongated deep into people’s 40ies or even 50ies.

We probably all know those situations, when we look for something valuable in a place where we can reasonably expect to find valuable things, yet the search is not really successful. Then, all of a sudden, just next door to that well-reputed location, we find true jewels of value. I experienced it with books, and with people as well. So is the case here, with social neuroscience. As long as I was typing ‘social neuroscience’ in the search interfaces of scientific repositories, more or less the same essential content kept coming to the surface. As my internal curious ape was getting bored, it started dropping side-keywords into the search, like ‘serotonin’ and ‘oxytocin’, thus the names of hormonal neurotransmitters in us, humans, which are reputed to be abundantly entangled with our social life. The keyword ‘Serotonin’ led me to a series of articles on the possibilities of treating and curing neurodevelopmental deficits in adults. Not obviously linked to cities and urban life? Look again, carefully. Cities allow the making of science. Science allows treating neurodevelopmental deficits in adults. Logically, developing the type of social structure called ‘cities’ allows our species to regulate our own neurophysiological development beyond the blueprint of our DNA, and the early engram of infant development (see, for example: Ehninger et al. 2008 [4]; Bavelier at al. 2010 [5]).

When I searched under ‘oxytocin’, I found a few papers focused on the fascinating subject of epigenetics. This is a novel trend in biology in general, based on the discovery that our DNA has many alternative ways of expressing itself, depending on environmental stimulation. In other words, the same genotype can produce many alternative phenotypes, through different expressions of coding genes, and the phenotype produced depends on environmental factors (see, e.g. Day & Sweatt 2011 [6]; Sweatt 2013 [7]). It is a fascinating question: to what extent urban environment can trigger a specific phenotypical expression of our human genotype?

A tentative synthesis regarding the social neuroscience of urban life leads me to develop on the following thread: we, humans, have a repertoire of alternative behavioural algorithms pre-programmed in our central nervous system, and, apparently, at some biologically very primal level, a repertoire of different phenotypical expressions to our genotype. Urban environments are likely to trigger some of those alternative patterns. Appetite for risk, combined with quick learning of social competences, in an adolescent-like mode, seems to be one of such orientations, socially reinforced in cities.

All that neuroscience thing leads me to taking once again a behavioural an angle of approach to my hypothesis on the connection between the development of cities, and technological change, all that dipped in the sauce of ‘What is going to happen due to COVID-19?’. Reminder for those readers, who just start to follow this thread: I hypothesise that, as COVID-19 hits mostly in densely populated urban areas, we will probably change our way of life in cities. I want to understand how exactly it can possibly happen. When the pandemic became sort of official, I had a crazy idea: what if I represented all social change as a case of interacting epidemics? I noticed that SARS-Cov-2 gives a real boost to some technologies and behaviours, whilst others are being pushed aside. Certain types of medical equipment, ethylic alcohol (as disinfectant!), online communication, express delivery services – all that stuff just boomed. There were even local speculative bubbles in the stock market, around the stock of medical companies. In my own investment portfolio, I earnt 190% in two weeks, on the stock of a few Polish biotechs, and it could have been 400%, had I played it better.

Another pattern of collective behaviour that SARS-Cov-2 has clearly developed is acceptance of authoritarian governance. Well, yes, folks. Those special ‘epidemic’ regimes most of us live under, right now, are totalitarian governance by instalments, in the presence of a pathogen, which, statistically, is less dangerous than driving one’s own car. There is quite convincing scientific evidence that prevalence of pathogens makes people much more favourable to authoritarian policies in their governments (see for example: Cashdan & Steele 2013 [8]; Murray, Schaller & Suedfeld 2013 [9]).

On the other hand, there are social activities and technologies, which SARS-Cov-2 is adverse to: restaurants, hotels, air travel, everything connected to mass events and live performative arts. The retail industry is largely taken down by this pandemic, too: see the reports by IDC, PwC, and Deloitte. As for behavioural patterns, the adolescent-like pattern of quick social learning with a lot of risk taking, which I described a few paragraphs earlier, is likely to be severely limited in a pandemic-threatened environment.

Anyway, I am taking that crazy intellectual stance where everything that makes our civilisation is the outcome of epidemic spread in technologies and behavioural patterns, which can be disrupted by the epidemic spread of some real s**t, such as a virus. I had a look at what people smarter than me have written on the topic (Méndez, Campos & Horsthemke 2012 [10]; Otunuga 2019 [11]), and a mathematical model starts emerging.

I define a set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles, defined as combinations of technologies and behavioural patterns. On the other hand, there is a set of ‘k’ pathogens PT = {pt₁, pt₂, …, pt_k}. Social roles are essentially idiosyncratic and individual, yet they are prone to imperfect imitation from person to person, consistently with what I wrote in ‘City slickers, or the illusion of standardized social roles’. Types of social roles spread epidemically through civilization just as a pathogen would. Now, an important methodological note is due: epidemic spread means diffusion by contact. Anything spreads epidemically when some form of contact from human to human is necessary for that thing to jump. We are talking about a broad spectrum of interactions. We can pass a virus by touching each other or by using the same enclosed space. We can contaminate another person with a social role by hanging out with them or by sharing the same online platform.

Any epidemic spread – would it be a social role sr_i in the set SR or a pathogen pt_j – happens in a population composed of three subsets of individuals: subset I of infected people, the S subset of people susceptible to infection, and subset R of the immune ones. In the initial phase of epidemic spread, at the moment t₀, everyone is potentially susceptible to catch whatever there is to catch, i.e. subset S is equal to the overall headcount of population N, whilst I and R are completely or virtually non-existent. I write it mathematically as I(t₀) = 0, R(t₀) = 0, S(t₀) = N(t₀).

The processes of infection, recovery, and acquisition of immune resistance are characterized by 5 essential parameters: a) the rate β of transmission from person to person b) the recruitment rate Λ from general population N to the susceptible subset S c) the rate μ of natural death, d) the rate γ of temporary recovery, and e) ψ the rate of manifestation in immune resistance. The rates γ and ψ can be correlated, although they don’t have to. Immune resistance can be the outcome of recovery or can be attributable to exogenous factors.

Over a timeline made of z temporal checkpoints (periods), some people get infected, i.e. they contract the new virus in fashion, or they buy into being an online influencer. This is the flow from S to I. Some people manifest immunity to infection: they pass from S to R. Both immune resistance and infection can have various outcomes. Infected people can heal and develop immunity, they can die, or they can return to being susceptible. Changes in S, I, and R over time – thus, respectively, dS/dt, dI/dt, and dR/dt, can be described with the following equations:

Equation [I]  [Development of susceptibility]  dS/dt = Λ – βSI – μS + γI

Equation [II]  [Infection]  dI/dt = βSI – (μ + γ)I

Equation [III] [Development of immune resistance]  dR/dt = ψS(t0) = ψN

We remember that equations [I], [II], and [III] can apply both to pathogens and new social roles. Therefore, we can have a social role sr_i spreading at dS(sr_i)/dt, dI(sr_i)/dt, and dR(sr_i)/dt, whilst some micro-beast pt_j is minding its own business at dS(pt_j)/dt, dI(pt_j)/dt, and dR(pt_j)/dt.

Any given civilization – ours, for example – experiments with the prevalence of different social roles sr_i in the presence of known pathogens pt_j. Experimentation occurs in the form of producing many alternative, local instances of civilization, each based on a general structure. The general structure assumes that a given pace of infection with social roles dI(sr_i)/dt coexists with a given pace of infection with pathogens dI(pt_j)/dt.

I further assume that ε stands for the relative prevalence of anything (i.e. the empirically observed frequency of happening), social role or pathogen. A desired outcome O is being collectively pursued, and e represents the gap between that desired outcome and reality. Our average civilization can be represented as:

Equation [IV]  [things that happen]  h = {dI(sr₁)/dt}* ε(sr₁) + {dI(sr₂)/dt}* ε(sr₂) + … + {dI(sr_n)/dt}* ε(sr_n) + {dI(pt_j)/dt}* ε(pt_j)

Equation [V]  [evaluation of the things that happen]  e = O – [(e^2h – 1)/(e^2h + 1)]*{1 – [(e^2h – 1)/(e^2h + 1)]}²

In equation [V] I used a neural activation function, the hyperbolic tangent, which you can find discussed more in depth, in the context of collective intelligence, in my article on energy efficiency. Essentially, the more social roles are there in the game, in equation [IV], the broader will the amplitude of error in equation [V], when error is produced with hyperbolic tangent. In other words, the more complex is our civilization, the more it can freak out in the presence of a new risk factor, such as a pathogen. It is possible, at least in theory, to reach a level of complexity where the introduction of a new pathogen, such as SARS-Covid-19, makes the error explode into such high a register that social learning either takes a crash trajectory and aims at revolution, or slows down dramatically.

The basic idea of our civilization experimenting with itself is that each actual state of things according to equation [IV] produces some error in equation [V], and we can produce social change by utilizing this error and learning how to minimize it.

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

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

[1] Belin, P., & Grosbras, M. H. (2010). Before speech: cerebral voice processing in infants. Neuron, 65(6), 733-735. https://doi.org/10.1016/j.neuron.2010.03.018

[2] Adolphs, R. (2010). Conceptual challenges and directions for social neuroscience. Neuron, 65(6), 752-767. https://doi.org/10.1016/j.neuron.2010.03.006

[3] Steinberg, L. (2008). A social neuroscience perspective on adolescent risk-taking. Developmental review, 28(1), 78-106. https://dx.doi.org/10.1016%2Fj.dr.2007.08.002

[4] Ehninger, D., Li, W., Fox, K., Stryker, M. P., & Silva, A. J. (2008). Reversing neurodevelopmental disorders in adults. Neuron, 60(6), 950-960. https://doi.org/10.1016/j.neuron.2008.12.007

[5] Bavelier, D., Levi, D. M., Li, R. W., Dan, Y., & Hensch, T. K. (2010). Removing brakes on adult brain plasticity: from molecular to behavioral interventions. Journal of Neuroscience, 30(45), 14964-14971. https://www.jneurosci.org/content/jneuro/30/45/14964.full.pdf

[6] Day, J. J., & Sweatt, J. D. (2011). Epigenetic mechanisms in cognition. Neuron, 70(5), 813-829. https://doi.org/10.1016/j.neuron.2011.05.019

[7] Sweatt, J. D. (2013). The emerging field of neuroepigenetics. Neuron, 80(3), 624-632. https://doi.org/10.1016/j.neuron.2013.10.023

[8] Cashdan, E., & Steele, M. (2013). Pathogen prevalence, group bias, and collectivism in the standard cross-cultural sample. Human Nature, 24(1), 59-75. https://doi.org/10.1007/s12110-012-9159-3

[9] Murray DR, Schaller M, Suedfeld P (2013) Pathogens and Politics: Further Evidence That Parasite Prevalence Predicts Authoritarianism. PLoS ONE 8(5): e62275. https://doi.org/10.1371/journal.pone.0062275

[10] Méndez, V., Campos, D., & Horsthemke, W. (2012). Stochastic fluctuations of the transmission rate in the susceptible-infected-susceptible epidemic model. Physical Review E, 86(1), 011919. http://dx.doi.org/10.1103/PhysRevE.86.011919

[11] Otunuga, O. M. (2019). Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model. Heliyon, 5(9), e02499. https://doi.org/10.1016/j.heliyon.2019.e02499

We’d better make that change liveable

My editorial on You Tube

I continue developing my ideas. Most people do, all the time, actually: they keep developing their own ideas, and other people’s ideas, and, on the whole, we just develop our ideas.

Good. Linguistic warm up done, I go to work. I continue what I started in my last update ( Steady inflow of assets and predictable rules ): a workable business concept for restarting local economies after COVID-19 lockdowns, and during the ongoing pandemic. Last time, I studied the early days of the Bitcoin, in the hope of understanding how a completely new economic scheme emerges. As hope crystalizes into something more structured, ideas emerge. I am going to make a quick sketch of what I have come up with, and then I will try give it some shine by using my observations as regards the early infancy of the Bitcoin.

As I observe the present situation, I can see that local communities both need and accumulate some typical goods and assets. The most immediately needed, and semi-instinctively accumulated goods are those serving personal protection and hygiene: gloves, facial protections (masks, covers, googles etc.), scrubs and aprons, bonnets, soap, ethanol-based sanitizers. I wonder, and, honestly, I would gladly do with the consultation of an epidemiologist, to what extent an abundant use of those hygienic goods can be substitute to social distancing. I mean, to what extent can we restart social interactions with adequate protection?

Anyway, I am quite confident that local communities will be accumulating what I provisionally call ‘epidemic assets’. The challenge consists in using that phenomenon, and those assets, so as to give some spin to economies brought down by lockdowns.

Now, I am using basic laws of economics. Whenever and wherever some stock of medical supplies will be accumulated, it will be inventories, i.e. circulating assets subject to storage and endowed with direct economic utility, but not to amortization. Sooner or later, substantial inventories of anything attract the company of some fixed assets, such as buildings, equipment, and intellectual property, on the one hand, as well as the company of other circulating assets (e.g. receivable claims on third parties), and, finally, the company of JOBS, which are the key point here.

Now, let’s imagine the following scenario. A local community, e.g. local hospital plus local city council, need to have a given amount of ‘epidemic assets’ stored and ready to use, just to keep the local epidemic situation under control. They need those epidemic assets, yet, as the local economy is stricken by epidemic lockdown, they don’t have enough money (or no money at all) to pay for those assets. Here starts the gamble. The local community offers the suppliers of epidemic assets to be paid in tokens of a virtual currency, where each token corresponds to a futures contract with claims on a future stock of epidemic assets.

The central idea is that with the virus around, everybody will have a keen interest in having enforceable claims on epidemic assets. That keen interest will be driven by two motives. In the first place, many people will need to use those epidemic assets like directly and personally. Secondly, those assets will be valuable, and futures contracts on them will have monetizable, financial value. It should be possible to create a circulation of those tokens (futures), where the direct supplier of epidemic assets can use those tokens to pay their own suppliers of intermediate goods, as well as to pay a part of the payroll. Those whom he pays will either consume those futures to grab some epidemic assets, or make those futures circulate further.

As those tokenized futures contracts on epidemic assets get developed and put in circulation, we can use the relatively recent invention called ‘smart contract’. A complex contract can be split into separate component parts, like LEGO blocks, each endowed with a different function. Users can experiment with each part separately, and the actual contracts they sign and trade are compound legal schemes. For now, I can see 3 principal LEGO blocks. The first one is the exact substance of the claim incorporated in the tokenized contracts. Futures contracts have this nuance in them: they can embody claims on a certain quantity of specified goods or assets, e.g. 100 kg of something, or on a nominal financial value of those goods or assets, like $100 worth of something. Maturity of the claim is another thing. Futures contracts have a time horizon in them: 1 month, 6 months, 12 months etc. In this specific case, maturity of claims is the same as the lifecycle of one tokenized contract, and, honestly, if this scheme is applied in real life, we will be sailing uncharted waters. Those tokens are supposed to keep local economies going, and therefore they’d better have a long lifecycle. Hardly anyone would trust quasi – monetary tokens with a lifespan of 3 months. On the other hand, the longest futures I have seen, like those on coffee or wheat, stretch over 6 months, rarely longer. Here comes the third building block, namely convertibility of the claim. If we want the system to work smoothly, i.e. inspire trust in exchange, and be realistic in the same time, we can make those tokens convertible into something else. They could convert into similar tokens, just valid over the next window of trade, or into something else, e.g. shares in the equity of newly built local hospitals. Yes, we are certainly going to build more of them, trust me.

Building blocks in hand, we start experimenting. Looking at the phases I distinguished in the early infancy of the Bitcoin (once again, you can look up Steady inflow of assets and predictable rules ), I see three essential steps in the development of this scheme. The first step would consist in creating a first, small batch of those tokenized contracts and test them in deals with whoever would like to try. The experience of the Bitcoin shows that once the thing catches on (and IF the thing catches on), i.e. once and if there are any businesspeople interested, it should spread pretty quickly. Then comes the second phase, that of building large portfolios of those tokenized contracts in a relatively small and select community, sort of Illuminati of medical supplies. In that phase, which is likely to be pretty long, like 1,5 year, said Illuminati will be experimenting with the exact smart structure those contracts, so as to come up with workable, massively reproducible patterns for the third phase, that of democratization. This is when the already hammered and hardened contractual patterns in those tokens will spread to a larger population. Individual balances of those tokens are likely to shrink in that third phase and become sort of standardized. This could be the moment, when our tokenized contracts can start being used as a vehicle for saving economic value over time, and it looks like a necessary condition for driving it out of its so-far autonomous, closed market into exchangeability against money.

That would be all for today. If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com . If anyone wants to bounce this ball off their bat, you are welcome. I am deeply convinced that we need to figure out some new s**t. Our world is changing, and we’d better make that change liveable.

Steady inflow of assets and predictable rules

My editorial on You Tube

Clink! The coin dropped… I have been turning that conceptual coin between my synapses for the last 48 hours, and here it is. I know what I have been thinking about, and what I want to write about today. I want to study the possible ways to restart business and economy in the midst of the COVID-19 pandemic.

There is a blunt, brutal truth: the virus will stay with us until we massively distribute an efficient vaccine against it, and that is going to take many months, most probably more than a year. Until then, we need to live our lives, and we cannot live them in permanent lockdown. We need to restart, somehow, our socio-economic structures. We need to overcome our fears, and start living in the presence of, and in spite of danger.

Here come three experiences of mine, which sum up to the financial concept I am going to expose a few paragraphs further. The first experience is that of observing a social project going on in my wife’s hometown, Starachowice, Poland, population 50 000. The project is Facebook-named ‘The Visible Hand’ (the original Polish is: Widzialna Ręka), and it emerged spontaneously with the COVID-19 crisis. I hope to be able to present the full story of those people, which I find truly fascinating, and now, I just give a short glimpse. That local community has created, within less than two weeks, something like a parallel state, with its supply system for the local hospital, and for people at risk. They even go into developing their own technologies of 3D printing, to make critical medical equipment, such as facial masks. Yesterday, I had a phone conversation with a friend, strongly involved in that project, and my head still resonates with what he said: ‘Look, the government is pretty much lost in all that situation. They pretend a lot, and improvise a lot, and it is all sort of more pretending than actually doing things. Our local politicians either suddenly evaporated, or make clumsy, bitchy attempts to boost their popularity in the midst of all that s**t. But people… Man, people are awesome. We are doing together things that our government thinks it is impossible to do, and we are even sort of having fun with it. The sense of community is nothing short of breath-taking’.

My second experience is about the stock market. If you have been following my updates since the one entitled ‘Back in the game’, you know that I decided to restart investing in the stock market, which I had undertaken to do just before the s**t hit the fan, a few weeks ago. Still, what I am observing right now, in the stock market, is something like a latent, barely contained energy, which just seeks any opportunity to engage into. Investors are really playing the game. Fear, which I could observe two weeks ago, has almost vanished from the market. Once again, there is human energy to exploit positively.

There is energy in people, but it is being locked down, with the pandemic around. The big challenge is to restart it. Right now, many folks lose their jobs, and their small businesses. It is important to create substantial hope, i.e. hope which can be turned into action. Here comes my third experience, which is that of preparing a business plan for an environmental project, which I provisionally call Energy Ponds (see Bloody hard to make a strategy and The collective archetype of striking good deals in exports for latest developments). As I prepare that business plan, I keep returning to the conclusion that I need some sort of financial scheme for situations when a local community, willing to implement the technology I propose, is short of capital and needs to sort of squeeze money out of the surrounding landscape.

Those three experiences of mine, taken together, lead me back to something I studied 3 years ago, when I was taking my first, toddler’s steps in scientific blogging: the early days of the Bitcoin. Today, the Bitcoin is the big, sleek predator of financial markets, yet most people have forgotten how that thing was born. It was an idea for safe financial transactions, based on an otherwise old concept of financial law called ‘endorsement of debt’, implemented in the second year of the big financial crisis, i.e. in 2009, to give some liquidity to small networks of just as small local businesses. Initially, for more than 18 first months of existence, the Bitcoin was a closed system of exchange, without any interface with any established currency. As far as I know, it very much saved the day for many small businesses, and I want to study the pattern of success, so as to see how it can be reproduced today for restarting business in the context of pandemic.

Before I go analytical, two general remarks. Firstly, there is plenty of folks who pretend having the magical recipe for the present s**t we are waist-deep in. I start from the assumption that we have no fresh, general experience of pandemics, and pretending to have figured the best way out is sheer bullshit. Still, we need to explore and to experiment, and this is very much the spirit I pursue.

Secondly, the Bitcoin is a cryptocurrency, based on the technology designated as Blockchain. What I want to take away is the concept of virtual financial instrument focused on liquidity, rather than the strictly spoken technology. Of course, platforms such as Ethereum can be used for the purpose I intend to get across, here below, still they are just an instrumental option.

Three years ago, I used data from https://www.quandl.com/collections/markets/bitcoin-data, which contains the mathematical early story of what has grown, since, into the father of all cryptocurrencies, the Bitcoin. I am reproducing this story, now, so as to grasp a pattern. Let’s walse. I am focusing on the period, during which the Bitcoin started, progressively acquired any exchangeable value against the US dollar, and finished by being more or less at 1:1 par therewith. That period stretches from January 3^rd, 2009 until February 10^th, 2011. You can download the exact dataset I work with, in the Excel format, from this link:

https://discoversocialsciences.com/wp-content/uploads/2020/03/Bitcoin-Early-days-to-share.xlsx .

Before I present my take on that early Bitcoin story, a few methodological remarks. The data I took originally contains the following variables: i) total number of Bitcoins mined, ii) days destroyed non-cumulative, iii) Bitcoin number of unique addresses used per day, and iv) market capitalization of the Bitcoin in USD. On the basis of these variables, I calculated a few others. Still, I want to explain the meaning of those original ones. As you might know, Bitcoins were initially mined (as far as I know, not anymore), i.e. you could generate 1 BTC if you solved a mathematical riddle. In other words, the value you had to bring to the table in order to have 1 BTC was your programming wit plus computational power in your hardware. With time, computational power had been prevailing more and more. The first original variable, i. e. total number of Bitcoins mined, is informative about the total real economic value (computational power) brought to the network by successive agents joining it.

Here comes the first moment of bridging between the early Bitcoin and the present situation. If I want to create some kind of virtual financial system to restart, or just give some spin to local economies, I need a real economic value as gauge and benchmark. In the case of Bitcoin, it was computational power. Question: what kind of real economic value is significant enough, right now, to become the tool for mining the new, hypothetical virtual currency? Good question, which I don’t even pretend to have a ready-made answer to, and which I want to ponder carefully.

The variable ‘days destroyed non-cumulative’ refers to the fact that Bitcoins are crypto-coins, i.e. each Bitcoin has a unique signature, and it includes the date of the last transaction made. If I hold 1 BTC for 2 days, and put it in circulation on the 3^rd day, on the very same 3^rd day I destroy 2 days of Bitcoins. If I hold 5 Bitcoins for 7 days, and kick them back into market on the 8^th day, I destroy, on that 8^th day, 5*7 = 35 days. The more days of Bitcoin I destroy on the given day of transactions, the more I had been accumulating. John Maynard Keynes argued that a true currency is used both for paying and for saving. The emergence of accumulation is important in the shaping of new financial instruments. It shows that market participants start perceiving the financial instrument in question as trustworthy enough to transport economic value over time. Note: this variable can take values, like days = 1500, which seem absurd at the first sight. How can you destroy 1500 days in a currency born like 200 days ago? You can, if you destroy more than one Bitcoin, held for at least 1 day, per day.

The third original variable, namely ‘Bitcoin number of unique addresses used per day’, can be interpreted as the number of players in the game. When you trade Bitcoins, you connect to a network, you have a unique address in that network, and your address appears in the cumulative signature that each of the Bitcoins you mine or use drags with it.

With those three original variables, I calculate a few coefficients of mine. Firstly, I divide the total number of Bitcoins mined by the number of unique addresses, on each day separately, and thus I obtain the average number of Bitcoins held, on that specific day, by one average participant in the network. Secondly, I divide the non-cumulative number of days destroyed, on the given day, by the total number of Bitcoins mined, and present in the market. The resulting quotient is the average number of days, which 1 Bitcoin has been held for.

The ‘market capitalization of the Bitcoin in USD’, provided in the original dataset from https://www.quandl.com/collections/markets/bitcoin-data, is, from my point of view, an instrumental variable. When it becomes non-null, it shows that the Bitcoin acquired an exchangeable value against the US dollar. I divide that market capitalization by the total number of Bitcoins mined, and I thus I get the average exchange rate of Bitcoin against USD.

I can distinguish four phases in that early history of the Bitcoin. The first one is the launch, which seems to have taken 6 days, from January 3^rd, 2009 to January 8^th, 2009. There were practically no players, i.e. no exchange transactions, and the number of Bitcoins mined was constant, equal to 50. The early growth starts on January 9^th, 2009, and last just for 3 days, until January 11^th, 2009. The number of Bitcoins mined grows, from 50 to 7600. The number of players in the game grows as well, from 14 to 106. No player destroys any days, in this phase. Each Bitcoin mined is instantaneously put in circulation. The average amount of Bitcoins per player evolves from 50/14 = 3,57 to 7600/106 = 71,7.

On January 12^th, 2009, something changes: participants in the network start (timidly) to hold their Bitcoins for at least one day. This is how the phase of accelerating growth starts, and will last for 581 days, until August 16^th, 2010. On the next day, August 17^th, the first Bitcoins will get exchanged against US dollars. On that path of accelerating growth, the total number of Bitcoins mined passes from 7600 to 3 737 700, and the daily number on players in the network passes from an average around 106 to about 500 a day. By the end of this phase, the average amount of Bitcoins per player reaches 7475,4. Speculative positions (i.e. propensity to save Bitcoins for later) grow, up to an average of about 1500 days destroyed per address.

Finally, the fourth stage of evolution is reached: entry into the financial market, when we pass from 1 BTC = $0,08 to 1 BTC = $1. This transition from any exchange rate at all to being at par with the dollar takes 189 days, from August 17^th, 2010 until February 10^th, 2011. The total number of Bitcoins grows at a surprisingly steady rate, from 3 737 700 to about 5 300 000, whilst the number of players triples, from about 500 to about 1 500. Interestingly, in this phase, the average amount of Bitcoins per player decreases, from 7475,4 to 3 533,33. Speculative positions grow steadily, from about 1500 days destroyed per address to some 2 400 days per address.

Below, you will find graphs with a birds-eye view of the whole infancy of the Bitcoin. Further below, after the graphs, I try to give some closure, i.e. to guess what we can learn from that story, so as to replicate it, possibly, amid the COVID-19 crisis.

My first general conclusion is that the total number of Bitcoins mined is the only variable, among those studied, which shows a steady, quasi linear trend of growth. It is not really exponential, more sort of a power function. The total number of Bitcoins mined corresponds, in the early spirit of this cryptocurrency, to the total computational power brought to the game by its participants. The real economic value pumped into the new concept was growing steadily, linearly, and to an economist, such as I am, it suggests the presence of exogenous forces at play. In other words, the early Bitcoin was not growing by itself, through sheer enthusiasm of its early partisans. It was growing because some people saw real value in that thing and kept bringing assets to the line. It is important in the present context. If we want to use something similar to power the flywheels of local markets under the COVID-19 restrictions, we need some people to bring real, productive assets to the game, and thus we need to know what those key assets should be. Maybe the capacity to supply medical materials, combined with R&D potential in biotech and 3D printing? These are just loose thoughts, as I observe the way that events are unfolding.

My second conclusion is that everything else I have just studied is very swingy and very experimental. The first behavioural transition I can see is that of a relatively small number of initial players experimenting with using whatever assets they bring to the table in order to generate a growing number of new tokens of virtual currency. The first 7 – 8 months in the Bitcoin show the marks of such experimentation. There comes a moment, when instead of playing big games in a small, select network, the thing spills over into a larger population of participants. What attracts those new ones? As I see it, the attractive force consists in relatively predictable rules of the game: ‘if I bring X $mln of assets to the game, I will have Y tokens of the new virtual currency’, something like that.

Hence, what creates propitious conditions for acquiring exchangeable value in the new virtual currency against the established ones, is a combination of steady inflow of assets, and crystallization of predictable rules to use them in that specific scheme.

I can also see that people started saving Bitcoins before these had any value in dollars. It suggests that even in a closed system, without openings to other financial markets, a virtual currency can start giving to its holders a sense of economic value. Interesting.

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

The games we play with what has no brains at all

Life can be full of surprises, like really. When I was writing my last update, on March 7^th and 8^th, the one entitled ‘Lettres de la zone rouge’, I was already writing about the effects of coronavirus in the stock market. Yet, it was just sort of an external panic, back then. External to me, I mean. Now, I am in, as we all are in Europe. Now, more than ever before, I use blogging, i.e. writing and publishing content, as a device for putting order in my own thoughts.

At the university, I had to switch to online teaching, and I am putting a lot of myself into preparing good stuff for students. By the way, you can assess the quality of my material by yourself. I have two lectures on Vimeo, in a course entitled ‘Fundamentals of Finance’. Both are password-locked and the password is ‘akademia’. Pay attention to the ‘k’. Not ‘academia’, but ‘akademia’. Lecture 1 is available at https://vimeo.com/398464552 and Lecture 2 fires on

https://vimeo.com/398467079.

I can’t help philosophizing. I should be focusing, in my blogging, on melting, hammering, and hardening my investment strategy in the stock market. Yet, financial markets are like an endocrine system, and given the way those hormones just fountain, right now, I am truly interested in studying the way the whole organism works. According to the personal strategy of writing and publishing, which I laid out in the update entitled ‘Back in the game’, as well as those which followed, since February 10^th, 2020, I should be using my blog mostly for writing about strategies to apply for investment in the stock market. Still, life can be surprising, and it is being bloody surprising right now. There is a thin line between consistency and obstinacy, and I want to keep walking on its consistency side. In order to coin up a sensible strategy for investment, I need to understand the socio-economic environment: this is elementary stuff which I teach my students in the first year. Besides, as I observe myself right now, I think I have some affinities with some squids and octopuses: when I sense serious cognitive dissonance coming my way, I release a cloud of ink. Just in case.

When I go deep into thinking, I like starting from what I perceive as my most immediate experience. Now, my most immediate experience consists in observing my own behaviour and the behaviour of other people. On Tuesday the 17^th, I recorded those two video lectures, and I had to go to the campus of my university, where we have a state-of-the-art recording facility. I was cycling through the nearly empty city, and memories popped up. I remember the late 1970ies, when I was a little kid, and lived in the communist Poland. When I would walk the streets, back then, they were similarly empty. It is only now, when human traffic in the streets has gone down to like 5% of what it used to be until recently, that I realized how much more mobile and interactive a society we have become, in Poland, since that communist past.

I am thinking about the way we, humans, adapt to new circumstances. How is social mobility, even that most immediately observable daily traffic, connected to the structure of our social life. How is my GDP per capita – I mean, it is per capita, and thus I can say it is my per capita – related to the number of pedestrians per hour per square kilometre out there, in the streets? My most immediate experience of street traffic is that of human energy, and the intensity of its occurrence. It looks as if the number of human steps on the pavement, together with the stream of vehicles, manifested an underlying flow of some raw, hardly individuated at all, social force. What is the link between this raw social energy, and social change, such as what we have experienced, all over Central Europe, since the collapse of the Berlin wall? Well, this is precisely what I am trying to figure out.

Now, I go deeper, as deep as William James used to go in his ‘Essays in Radical Empiricism’, published, for the first time, in 1912. Human energy, out there, manifests itself both in the streets as such, and in me, in my perception. Phenomenologically, the flow of human traffic is both outside of me, and inside my mind. The collective experience is that of roaming the city, and, in the same time, that of seeing other people doing it (or even knowing they keep doing it). Same for the stock market, real business, teaching etc. All those streams of human activity are both out there, as intersubjectively observable stuff, and inside my mind, as part of my consciousness.

What we do is both in us, and out there. Social life is a collection of observable events, and a collection of corresponding, individual experiences. My experience right now is that of reorganizing my activity, starting with my priorities as for what to work on. It is fully official, the Minister of Science and Higher Education has just signed the emergency ordinance that all classes in universities are suspended until April 10^th and that we are all encouraged to take on any form of distance learning we can use, even if it isn’t officially included in syllabuses. Given that right after April 10^th it will be the Easter break, and that, realistically, classes are highly unlikely to restart afterwards, I have a lot of free time and a lot of things to stream smoothly inside of that sudden freedom.

I start with making a list. I structure my activity into 3 realms: pure science, applied science, and professional occupation.

As for the strictly speaking scientific work, i.e. the action of discovering something, I am working on using artificial intelligence as a tool for simulating collective intelligence in human societies. I have come up with some interesting stuff, but the first exchange I had about it with publishers of scientific journals is like ‘Look, man, it sounds interesting, but it is really foggy, and you are really breaking away from established theory of social sciences. You need to break it down so as to attach the theory you have in mind to the existing theory and to sort. In other words: your theory is not marketable yet’. I humbly accept those criticisms, I know that good science is to be forged in such fire, and I know that science is generally about figuring out something intelligible and workable.

The concept of collective intelligence is even more interesting right now. Honestly, that COVID-19 looks to me as something collectively intelligent. I know, I know: viruses don’t even have anything to be intelligent with, them having no nervous system whatsoever. Still, juts look. COVID-19 is different from his cousins by its very progressive way of invading its host’s body. The COVID-19’s granddad, the SARS virus from 2003, was like Dalton brothers. It would jump on its prey, all guns out, and there was no way to be asymptomatic with this f**ker. Once contaminated, you were lucky if you stayed alive. SARS 2003 was sort of self-limiting its range. COVID-19 is like a jihadist movement: it sort of hangs around, masking its pathogenic identity, and starts reproducing very slowly, sort of testing the immune defences of the organism, and each consecutive step of that testing can lead to ramping up the pace of reproduction.

All this virus has, as a species, is a chain of RNA (ribonucleic acid), which is essentially information about reproducing itself, without any information about any vital function whatsoever. This chain is apparently quite long, as compared to other viruses, so it takes some time to multiply itself. That time, unusually long, allows the host’s body to develop an immune response. The mutual pacing of reproduction in the virus, and of immune kickback in the host creates that strange phase, when the majority of hosts act like postmen for the virus. Their bodies allow the COVID-19 to proliferate just a little, but just enough to become transmissible. Allowing some colonies of itself to be killed, the virus brings a new trait: it is more pervasive than deadly, and it is both in the same time. At the end of the day, COVID-19 achieves an impressive reach across the human species. I think it will turn out, by the end of this year, that COVID-19 is a record holder, among viruses, as for the total human biomass infected per unit of time.

Functionally, COVID-19 looks almost like a civilisation: it is able to expand by adaptation. As I read scientific articles on the topic of epidemics, many biologists anthropomorphise pathogens: they write about those little monsters ‘wanting’ something, or ‘aiming’ for some purpose. Still, there is nothing in a virus that could be wanting anything. There is no personality or culture. There is just a chain of RNA, long enough to produce additional time in proliferation.

Let’s compare it to human civilisation. Any human social structure started, long ago, as a small group of hominids trying to survive in an ecosystem which allows no mistakes. One of the first mistakes that our distant ancestors would make consisted in killing and gathering the shit out of their immediate surroundings, and then starving to death. Hence, they invented the nomadic pattern, i.e. moving from one spot to another before exhausting completely the local resources. Our apish great-grandparents were not nomadic by nature: they probably picked it from other species they observed. Much later, more evolved hominids discovered that nomadism could be replaced by proper management of local resources. If you domesticate a cow, and that cow shits in the fields, it contributes to regenerating the productive capacity of that soil, and so we can stay in one place for longer.

Many generations later, we had figured out still another pattern. Instead of having a dozen children per woman and letting most of them die before the age of 10, we came to having less offspring but taking care to bring that smaller number up, nicely and gently, all the way to adulthood. That allows more learning within one individual lifetime, and thus we can create a much more complex culture, and more complex technologies. In our human evolution, we have been doing very much what the COVID-19 virus does: we increase our own complexity, and, by the same means, we slow down our pace of reproduction. At the end of the day, slowing down pays off through increased range, flexibility and biomass.

My theoretical point is that collective intelligence is something very different from the individual one. The latter requires a brain, the former not at all. All a species needs at the level of collective intelligence is to make an important sequence of actions (such as the action of reproducing a long chain of nucleotides) complex and slow enough for allowing adaptation to environmental response, in that very sequence.

I assume I am a virus. I slow down my action so as to allow some response from outside, and to adapt to that response. It has a name: it is a game. An action involving two or more distinct agents, where each agent pends their action on the action of the other(s) is a game. Let’s take a game of chess. Two players: the collective intelligence of humans vs. the collective intelligence of COVID-19. Someone could say it is a wrong representation, as the human civilisation has a much more complex set of pieces than the virus has, and we can make more different moves. Really? Let’s look. How much complexity and finesse have we demonstrated so far in response to the COVID-19 pandemic? It turns out we are quite cornered: if we don’t temporarily shut down our economy, we will expose ourselves to seeing the same economy imploding when the reasonably predictable 7% of the population develops acute symptoms, i.e. respiratory impairment. What we do is essentially what the virus does: we play on time, and delay the upcoming events, so as to gain some breathing space.

We can change the rules of the game. We can introduce new technologies (e.g. vaccines), which will give is more possible moves. Still, the virus can respond by mutating. The most general rules of the game we play with the virus is given by the epidemic model. I tap into the science published in 2019 by Olusegun Michael Otunuga, in the article entitled ‘Closed-form probability distribution of number of infections at a given time in a stochastic SIS epidemic model’ (Heliyon, 5(9), e02499, https://doi.org/10.1016/j.heliyon.2019.e02499 ).

A crazy scientific idea comes to my mind: as we are facing a pandemic, and that pandemic deeply affects social life, I can study all of social life as concurrent pandemics: a pandemic of going to restaurant, a pandemic of making vehicles and driving them around, a pandemic of making and consuming electricity etc. COVID-19 is just one among those pandemics, and proves being competitive against them, i.e. COVID-19 prevents those other pandemics from carrying on at their normal pace.

What is the cognitive value of such an approach, besides pure intellectual entertainment? Firstly, I can use the same family of theoretical models, i.e. epidemic models, to study all those phenomena in the same time. Epidemic models have been in use, in social sciences, for quite some time, particularly in marketing. The diffusion of a new product or that of a new technology can be studied as the spreading of a new lifeform in an ecosystem. That new lifeform can be considered as candidate for being a pathogen, or a symbiont, depending on the adaptive reaction of other lifeforms involved. A new technology can both destroy older technologies and enter with them in all sorts of alliances.

A pathogen able to kill circa 3% of the population, and temporarily disable around 10%, can take down entire economic systems. In the same time, it stimulates the development of entire industries: 3D printing, biotech, pharmacy, and even basic medical supplies. One year ago, would anyone believe that manufacturing latex gloves could be more strategic than manufacturing guns?

Tag: epidemic

We’d better make that change liveable

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The games we play with what has no brains at all

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