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 3rd month of life through the 7th 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 3rd ÷ 7th 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, …, srm} 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, …, ptk}. 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 sri in the set SR or a pathogen ptj – 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 sri spreading at dS(sri)/dt, dI(sri)/dt, and dR(sri)/dt, whilst some micro-beast ptj is minding its own business at dS(ptj)/dt, dI(ptj)/dt, and dR(ptj)/dt.
Any given civilization – ours, for example – experiments with the prevalence of different social roles sri in the presence of known pathogens ptj. 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(sri)/dt coexists with a given pace of infection with pathogens dI(ptj)/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(srn)/dt}* ε(srn) + {dI(ptj)/dt}* ε(ptj)
Equation [V] [evaluation of the things that happen] e = O – [(e2h – 1)/(e2h + 1)]*{1 – [(e2h – 1)/(e2h + 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.
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[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
