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

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

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.     

Discover Social Sciences is a scientific blog, which I, Krzysztof Wasniewski, individually write and manage. If you enjoy the content I create, you can choose to support my work, with a symbolic $1, or whatever other amount you please, via MY PAYPAL ACCOUNT.  What you will contribute to will be almost exactly what you can read now. I have been blogging since 2017, and I think I have a pretty clearly rounded style.

In the bottom on the sidebar of the main page, you can access the archives of that blog, all the way back to August 2017. You can make yourself an idea how I work, what do I work on and how has my writing evolved. If you like social sciences served in this specific sauce, I will be grateful for your support to my research and writing.

‘Discover Social Sciences’ is a continuous endeavour and is mostly made of my personal energy and work. There are minor expenses, to cover the current costs of maintaining the website, or to collect data, yet I want to be honest: by supporting ‘Discover Social Sciences’, you will be mostly supporting my continuous stream of writing and online publishing. As you read through the stream of my updates on https://discoversocialsciences.com , you can see that I usually write 1 – 3 updates a week, and this is the pace of writing that you can expect from me.

Besides the continuous stream of writing which I provide to my readers, there are some more durable takeaways. One of them is an e-book which I published in 2017, ‘Capitalism And Political Power’. Normally, it is available with the publisher, the Scholar publishing house (https://scholar.com.pl/en/economics/1703-capitalism-and-political-power.html?search_query=Wasniewski&results=2 ). Via https://discoversocialsciences.com , you can download that e-book for free.

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 .

You might be interested Virtual Summer Camps, as well. These are free, half-day summer camps will be a week-long, with enrichment-based classes in subjects like foreign languages, chess, theater, coding, Minecraft, how to be a detective, photography and more. These live, interactive classes will be taught by expert instructors vetted through Varsity Tutors’ platform. We already have 200 camps scheduled for the summer.   https://www.varsitytutors.com/virtual-summer-camps .

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. 

Discover Social Sciences is a scientific blog, which I, Krzysztof Wasniewski, individually write and manage. If you enjoy the content I create, you can choose to support my work, with a symbolic $1, or whatever other amount you please, via MY PAYPAL ACCOUNT.  What you will contribute to will be almost exactly what you can read now. I have been blogging since 2017, and I think I have a pretty clearly rounded style.

In the bottom on the sidebar of the main page, you can access the archives of that blog, all the way back to August 2017. You can make yourself an idea how I work, what do I work on and how has my writing evolved. If you like social sciences served in this specific sauce, I will be grateful for your support to my research and writing.

‘Discover Social Sciences’ is a continuous endeavour and is mostly made of my personal energy and work. There are minor expenses, to cover the current costs of maintaining the website, or to collect data, yet I want to be honest: by supporting ‘Discover Social Sciences’, you will be mostly supporting my continuous stream of writing and online publishing. As you read through the stream of my updates on https://discoversocialsciences.com , you can see that I usually write 1 – 3 updates a week, and this is the pace of writing that you can expect from me.

Besides the continuous stream of writing which I provide to my readers, there are some more durable takeaways. One of them is an e-book which I published in 2017, ‘Capitalism And Political Power’. Normally, it is available with the publisher, the Scholar publishing house (https://scholar.com.pl/en/economics/1703-capitalism-and-political-power.html?search_query=Wasniewski&results=2 ). Via https://discoversocialsciences.com , you can download that e-book for free.

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 .

You might be interested Virtual Summer Camps, as well. These are free, half-day summer camps will be a week-long, with enrichment-based classes in subjects like foreign languages, chess, theater, coding, Minecraft, how to be a detective, photography and more. These live, interactive classes will be taught by expert instructors vetted through Varsity Tutors’ platform. We already have 200 camps scheduled for the summer.   https://www.varsitytutors.com/virtual-summer-camps .

[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.

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.

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[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.