Tag: urbanization

Correlated coupling between living in cities and developing science

MY EDITORIAL ON YOU TUBE

I continue working on the hypothesis 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 have been meditating on the main threads which I opened up in my previous update entitled ‘City slickers, or the illusion of standardized social roles’. One conclusion comes to my mind, as both an ethical, and a praxeological precept: we, humans, we should really individuate the s**t out of our social roles. Both from the point of individual existence, and that of benefiting to the society we live in, it is of utmost importance to develop unique skillsets in ourselves. Each of us is a distinct experiment in the broad category of ‘human beings’. The more personal development each of us achieves in one’s own individual existence, the further we can advance that local experiment of ours. Standardizing ourselves serves just the purpose of coordination with others, and that of short-term hierarchical advancement. The marginal gains of standardizing our own behaviour tend rapidly towards zero, once we are past the point of efficient coordination.

I think I will be weaving that thought into a lot of my writing. It is one of those cases when science just nails something already claimed as philosophical claim. What science? This time, I will be developing on the science known as ‘swarm theory’, and I will try to bridge between that theory and my meditations on human individuation. The swarm theory – which you can study by yourself by reading, e.g. Xie, Zhang & Yang 2002[1] ; Poli, Kennedy & Blackwell 2007[2] ; Torres 2012[3], and Stradner et al. 2013[4] – takes empirical observations of swarm animals, such as bees, wasps, ants, and applies those observations to the programming of robots and neural networks, just as to studying cooperation in human societies. It is one of those eclectic approaches, hard to squeeze into any particular drawer, in the huge cabinet of science, and this is precisely why I appreciate it so much.

The basic observation of the swarm theory is that collective coordination is based on functional coupling of individual actions. Coupling means that action of the social entity A provokes action in the social entity B, and it can provoke action in three distinct patterns: random, correlated, and coordinated (AKA fixed). Random coupling happens when my action (I am the social entity A) makes someone else do something, but at the moment of performing my action I haven’t the faintest idea how that other person will react. I just know that they are bound to react someone. Example: I walk into a bar and I start asking complete strangers whether they are happy with their lives. Their range of reactions can stretch from politely answering they are truly happy, thank you so much for asking, through telling me to f**k off, all the way up to punching my face.

When I can reasonably predict the type of other people’s reaction to my action, yet I cannot predict it 100% accurately the magnitude of that reaction, it is correlated coupling. Let’s suppose I assign homework to my students. I can reasonably predict their reaction, on a scale. Some will not do their homework (value 0 on the scale), and those who do it will stretch in their accomplishment from just passable to outstanding. I intend to focus a lot on correlated coupling, in the context of collective intelligence, and I will return to that concept. Now, I want to explain the difference between correlated coupling and the third type, the coordinated AKA fixed coupling. The latter means that a given type of behaviour in one social entity always provokes exactly the same kind of reaction in another social entity. Bal dancing, I mean the really trained one, comes as a perfect example here. A specific step in dancer A provokes always the same step in dancer B.

In my update entitled ‘A civilisation of droplets’, I started to outline my theory about the role of correlated coupling in the phenomenon of collective intelligence. Returning to that example of homework which I assign to my students, the assignment I make is a piece of information, and I communicate that piece of information to a group of recipients. Even before the advent of digital technologies, assignment of homework at school had a standardized form. I remember my own school days (long ago): the teacher would open up with something like ‘Attention! This is going to be your homework..’, and then would say the substance of the task(s) to perform or would write it on the blackboard. It was a standardized communication, which provoked, in us, students, non-standardized and yet scalable and predictable reactions. That assignment worked as a portion of some hormone, dropped among potentially receptive entities (students).

The development of social roles in cities works very much through correlated coupling of behaviour. Let’s take the example of the urban job market. People migrate to cities largely because of the career opportunities offered there. If the urban job market worked in random coupling, a job offer communicated to job seekers would have unknown consequences. I run a construction business, I look for staff, I communicate around the corresponding job offers, and I receive job applications from nurses, actors, and professional cooks, but not a single person with credentialed skills in construction. This is a case of random coupling between the substance of jobs I search to staff, and the qualifications of applicants. Let’s suppose I figured out how to train a cook into a construction worker (See? You just make a recipe for that ceiling, just as if you were preparing a sauce, and then you just apply the recipe: the right structure, the right temperature etc. Simple, isn’t it?), and I sign a work contract with that person, and then they call me on their first scheduled day of work just to say they changed their mind and they will not turn up. This is a case of random coupling between contracts and behaviour.   

If the same market of jobs worked in fixed coupling, it would be central planning, which I know perfectly from the times of my childhood and teenage years in the communist Poland. It works as a system of compulsory job assignments and the peculiar thing about that system is that it barely works at all. It was tons of fun, in the communist Poland. People would produce all kinds of fake medical papers in order not to be assigned industrial jobs in factories. The government figured out a system of incentives for those workers: high salaries, additional rations of meat per month etc. Result? A wonderfully blossoming black market of fake job certificates, which would officially certify the given person is a factory worker (= money, meat), whilst the same person would run a small private business on the side.        

It is interesting to study those three possible types of behavioural coupling – the random, the correlated and the fixed – in the field of law and contracts. Let’s suppose that me and you, my readers, we sign a business partnership contract. In the random coupling version, the contract would cover just some most essential provisions, e.g. duration and scope, and would give maximum freedom in any other respect. It is like ‘We do business together and we figure the thing out as events unfold’. I used to do business in this manner, back in the day, and it falls under every possible proverb about easy ways: the easy way is a way to nowhere, sort of. A good contract needs reasons for being signed, i.e. it needs to bring real order in an otherwise chaotic situation. If the contract just says: ‘We do whatever comes to our mind’, it is not really order, it is still chaos, with just a label on it.

Fixed coupling corresponds to contracts which regulate in great detail every footstep that parties might be willing to make. If you have some business experience, you probably now the kind: a 50-page framework agreement with 50 pages of annexes, and it just gives grounds for signing a case-specific agreement of 50 more pages etc. It is frequently practiced, yet good lawyers know there is a subtle razor edge in that game: if you go too specific, the contract can literally jam. You can get into a situation, when terminating the agreement or going to court are the only solutions logical on the grounds of the contractual wording, and yet completely illogical businesswise. A good contract gives some play to parties, so as they can adapt to the surprising and the unusual. Such flexibility can be created, e.g. through a system of contractual score points. If you buy goods from me, for at least $100 000 a month, I give you 2% of rebate, and if you go beyond $500 000 a month, I give you a rebate of 5% etc.

If we think about life in cities, it is all about social interaction. This is the whole point of living in a city: being in interaction with other people. That interaction is intense and is based on correlated coupling. People tend to flock to those cities, which offer that special kind of predictable freedom. Life in those cities is neither random, nor fixed in its ways. I start a business, and by observing other similar businesses I can nail down a business model that gives me reasonable confidence I will make reasonable money. I assume that when I drop into the social organism a relatively standardized droplet of information (advertising, sign over the door etc.), people will react.

Urban life allows figuring out a whole system of correlated coupling in human behaviour. I walk down the street and I can literally see it. Shop signs, traffic lights, cycling lanes, billboards, mobile apps pegged on digital beacons, certificates working as keys that open career doors: all that stuff is correlated coupling. Now, I want to connect the next dot: formation of new social roles. As I hinted the thing in ‘City slickers, or the illusion of standardized social roles’, I deeply, intuitively believe that social roles are much more individual, idiosyncratic ways of behaviour, rather than general categories. I think that I am much more the specific blogger than a general blogger.

Here I step in with another intuition of mine: technological change, such as we have been experiencing it over the period of time I can reasonably study, coincides with the multiplication of social roles. I am following two lines of logic. Firstly, the big generic technologies we have been developing allow growing individuation of social roles. Digital technologies are probably the most marked example thereof. Online content plays the role of semantic droplets, provoking more or less correlated coupling of behaviour in people located all over the planet. Personal electronics (smartphones, tablets, laptop computers) start working, for many of us, as some sort of super-cortex. Yes, that super-cortex can be slightly psychotic (e.g. comments on social media), and yes, it gives additional flexibility of behaviour (e.g. I can learn knew knowledge faster than before).

I frequently use patent applications as phenomenological manifestation of technological change. When I want to have my invention legally protected, I apply for a patent. Before the patent is granted, I need to file a patent application with the proper patent office. Then, I wait for said office to assess whether my invention is unique enough, and my patent application early enough to assume that I truly own truly novel a solution to an important problem. Patent applications are published, in case someone had two words to say about me having tapped into their earlier invention(s). At the aggregate level, the number of patent applications filed during a given window in time is informative about the number of workable and marketable inventions.

The World Bank, my favourite source of data (i.e. of numbers which allow me feeling confident that I have accurate knowledge about reality) provides two aggregates of patent applications: those filed by residents (https://data.worldbank.org/indicator/IP.PAT.RESD ), and those filed by non-residents (https://data.worldbank.org/indicator/IP.PAT.NRES ). A non-resident patent application is that filed by an entity located outside the jurisdiction of the corresponding patent office. I use these two aggregates, calculated for the world as a whole, as nominators, which I divide by the headcount of population, and thus I calculate coefficients of patent applications per 1 million people.

Of course, you could legitimately ask how the hell is it possible to have non-resident patent applications at the scale of the whole planet. Aliens? Not at all. When a Polish company applies for a patent to be granted in the territory of the United States, by the United States Patent and Trademark Office, it is a non-resident patent application, and it remains non-resident when summed up together with patent applications filed by the same Polish company with the European Patent Office. The global count of non-resident patent applications covers all the cases when applicant from country A files for a patent with the patent office of country B.

Good. I calculate the coefficients of patent applications per 1 million people on Earth, split into resident applications and the non-resident ones. The resulting trends in those two coefficients, calculated at the global scale, are shown in the graph below, entitled ‘Trends in the intensity of patentable invention’. Data regarding patent applications is available since 1985, thus its temporal window is shorter than that of urbanization-pertinent aggregates (since 1960). The general reading of the graph is that of an ascending trend. Our average million of people files for patenting more and more inventions. Their average million does the same.

However, as two separate trends are observed in, respectively, resident and non-resident patent applications per 1 million people, their ascent diverges in level and gradient. There have been systematically less non-resident patent applications that the resident ones. It indicates that the early marketing of science developed into tangible solutions takes place mostly in the domestic markets of the science in question. As for the gradients of change in both trends, they had been more or less similar until 2009 – 2010, and since then resident patent applications have taken off much more steeply. Domestic early marketing of developed science started to grow much more in intensity than the internationally played one. Interesting. Anyway, both trends are ascending in the presence of ascending urbanization and even more sharply growing density of urban population (see City slickers, or the illusion of standardized social roles’).   

Both trends in patent applications per 1 million people are ascending in the presence of ascending urbanization and even more sharply growing density of urban population (see City slickers, or the illusion of standardized social roles’). I want to test that apparent concurrence, and, by the same occasion, I want to do the kind of intellectual stunt I love, which consists in connecting human behaviour with directly to mathematics. I intend to use the logic of mathematical correlation, and more specifically of the so-called Pearson correlation, to explain and explore correlated coupling between growing urbanization and growing intensity of patenting, in the global human civilization.  

The first parachute I strap myself to, as I am preparing for that stunt, is the assumption that both urbanization and patenting are aggregate measures of human behaviour. The choice of living in a city is a pattern of behaviour, and spending 5 years in my garage, building that spaceship for going to Mars, and subsequently filing for a patent, is also a pattern of behaviour. My second parachute is statistical: I assume that change in behaviour is observable as deviation from an expected, average behaviour. I take a third parachute, by assuming that behaviours which I want to observe are numerically measurable as magnitudes on their respective scales. At this point, we enter the subtly foggy zone somewhere between individual behaviour and the collective one. The coefficient of urbanization, calculated as the percentage of humanity living in cities, is actually a measure of collective behaviour, indirectly informative about individual decisions. The same is true for all the other quantitative variables we commonly use in social sciences, including variables used in the present study, i.e. density of population in cities, and intensity of patenting. This is an important assumption of the swarm theory, when applied to social sciences: values observed in aggregate socio-economic variables represent cumulative outcomes of past human decisions.     

Correlated coupling means predictable change in the behaviour of social entity B, as social entity A does something. Mathematically, correlated coupling can be observed as concurrent deviations in behaviours A and B from their respective, average expected states. Good. Assumptions are made. Let’s dance on the edge between mathematics and human behaviour. (Local incidence of behaviour A – Average expected behaviour A) * (Local incidence of behaviour B – Average expected behaviour B) = Local covariance of behaviour A and behaviour B. That local covariance is the way two behaviours coincide. In my next step, I generalize: Sum (Local covariances of behaviour A and behaviour B) / Number of cases (Local coincidence of behaviour A and behaviour B) = General covariance of behaviour A and behaviour B.

Covariance of two behaviours is meaningful to the extent that it is compared with the individual, endogenous variance of each behaviour taken separately. For that reason, it is useful to denominate covariance with the arithmetical product of standard deviations observable in each of the two behaviours in question. Mathematically, it goes like: (Local incidence of behaviour A – Average expected behaviour A)2, i.e. square power of local deviation = local absolute variance in the phenomenon A. Elevating to square power serves to get rid of the minus sign, should the local incidence of behaviour A be smaller in magnitude than average expected behaviour A. Once again, I generalize: Sum (Local absolute variances in behaviour A) / Number of cases (Local incidence of behaviour A) = General variance in behaviour A.

Variance is the square power of something that really happened. Square powers are interesting, yet I want to get back to what really happened, and so I take the square root of variance: (General variance in behaviour A)0,5, i.e. square root of its general variance = standard deviation in behaviour A.

Covariance (Behaviour A <> Behaviour B) / (Standard deviation in behaviour A * Standard deviation in behaviour B) = Pearson correlation between behaviour A and B, commonly symbolised as ‘r’. It is mathematically impossible for the absolute value of r to go above 1. What we can reasonably expect from r is that it falls somewhere inside -1 ≤  r  ≤ 1. In statistics, it is assumed that – 0,3 <  r  < 0,3 is nothing to write home about, really. It is not a significant correlation. What we are interested in are the outliers of r, namely: -1 ≤ r ≤ -0,3 (significant negative correlation, behaviours A and B change in counter-step, in opposition to each other) and  0,3 ≤ r ≤ 1 (significant positive correlation, behaviours A and B fall nicely in step with each other).

Let’s have a look at the Pearson correlation between the metrics of urbanization, discussed in City slickers, or the illusion of standardized social roles’, and the coefficients of patent applications per 1 million people. Coefficients of correlation are shown in the table below, and they are really high. These are very strong, positive correlations. Covariances of patenting and urbanization mutually explain almost entirely their combined standard deviations. This is a very strong case of correlated coupling between behaviours that make cities, on the one hand, and behaviours that make iPhones and electric cars, on the other hand.  

Table – Pearson correlations between the metrics of urbanization and the coefficients of patent applications per 1 million people

 Non-resident patent applications per 1 million peopleResident patent applications per 1 million people
Density or urban population (people per 1 km2)0,970,93
Percentage of global population living in cities0,980,92

I sometimes use a metaphor about my own cognition. I say that I am three: a curious ape, a happy bulldog, and an austere monk. Now, my internal bulldog kickstarts, and bites deeper into that bone. There are strong positive correlations, and therefore there is correlated coupling of behaviours which those statistical correlations are informative about. Still, I want to know how exactly that correlated coupling happens. The graph below, entitled ‘Urbanization and intensity of patenting’ presents three coefficients: % of humanity in cities, density of urban population, and total number of patent applications (resident and non-resident together) per 1 million people. All three are driven to a common scale of measurement, by transforming them into constant-base indexes. For each of them, the value observed in the year 2000 stands for 1, i.e. any other value is divided by that value from 2000.    The index of urbanization (% of humanity living in cities), represented by the orange line marked with black triangles, is the flattest of the three. The blue line of indexed density in urban population is slightly steeper, and the red line, marked with green circles, representing indexed patent applications per 1 million people, is definitely the hastiest to ascend. Slight change in human decisions of moving to a city is coupled by correlation to human decisions to live in a progressively shrinking space in cities, and it looks like the former type of decision sort of amplifies the magnitude of change implied in the latter type of decision. That coupled correlation in collective behaviour, apparently working as a self-reinforcing loop, is further coupled by correlation with behaviours relative to developing science into something marketable, i.e. patenting.

Urbanization reinforces density of population in cities, through correlated coupling, and further reinforces the intensity of patenting. The density of population in cities grows faster than the percentage of humans living in cities because we need those cities to be kept in leash as for the territory they take. The more humans we are, the more food we need, whence the need for preserving the agricultural land that serves to make food. We invent more and more technologies, per 1 million people, that both allow those people to live in shrinking individual spaces, and allow people in the countryside to produce more and more food. 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. That hypothesis seems to be holding.

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

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

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

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

City slickers, or the illusion of standardized social roles

MY EDITORIAL ON YOU TUBE

I am developing on the topic which I signalled in the update entitled Stress-tested by a highly infectious micro-organism, i.e. on the role of cities in our human civilization. A big, sharp claim comes to my mind: 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.

Against the background of that claim, which I consider as a general working hypothesis of my research, I want to dig deep into its phenomenological foundations. An observation has really stricken me as odd: the spatial structure of land use. Since 1960 through 2018, the percentage of urban population in the total population of the Earth passed from 33,6% to 55,3%, and the absolute number of people living in cities has grown from around 1 billion up to some 4,2 billion. Yet, as astonishing as it seems, it is really unclear what exact surface of urban land those people inhabit, and many sources, including the World Bank, indicate that the surface in question has been fairly constant over the last 20 years, around 3,6 million km2. 

Yes, as strange as it might seem, we don’t know for sure what is the exact surface that we, humans, use for living, like around in the world. There is a bit more clarity as for agricultural land, although that one is far from being clear, too. Fog starts to sort of hang around already when we ask how much land in general do we have exactly. Estimates of this kind are made on the basis of satellite readings, and satellites are apparently not too good at recognizing land elevated close to the sea level. Apparently, a satellite has hard times to distinguish land in depression (i.e. below the sea level, such as a significant part of Netherlands, for example) from the neighbouring sea. As regards this topic, I recommend getting acquainted with the resources available with the Center for International Earth Science Information Network (CIESIN), at the Columbia University

I found some readings which indicate that the total surface of urban land in the world has been growing for the last 20 years (e.g. Li et al. 2019[1]), yet it is unclear where exactly the data cited there comes from. We are facing a paradox, when truly serious scientific projections allow expecting, in the near future, a dramatic growth in the surface of urban land on Earth, at the expense of agriculture and wildlife, yet there is no tangible evidence of such growth in the recent past.

Thus, apparently, urban people live in a consistently growing number within a fairly constant space. As there is more and more of us, in cities, what we are becoming really good at is increasing our density of population. I am trying to represent that phenomenon in the graphs below. We have two concurrent trends: growing a percentage of humanity living in cities and increasing density of population in those cities. The latter seems to have been progressing faster than the former. The question is: why? Why do we keep cramming ourselves more and more in cities? After all, it is possible – and even quite simple in terms of basic spatial geometry – to take any big city we know, like Paris, Tokyo, or my hometown, Krakow (Poland) – and sort of spread its current population onto a much bigger area. The resulting structure of settlement would look like a giant residential suburb, with people living in medium-sized houses, located on medium-sized plots of land. It would be even possible to associate small-scale agriculture with each such medium-sized real estate and thus have a food base. It is technically possible, but we don’t do it. Why?  

We, humans, we can build incredibly complex social structures, yet we have at hand just a few building blocks for that complexity. We have the capacity to define social groups, and we define them by nailing down an appurtenance function. On the other hand, we define social roles inside the group. If we include into the definition of a social role the possible connections to other social roles in the group, then we have just two building blocks: groups and social roles. I am going to explore the connection between those basic building blocks, and the phenomenon of urbanization with growing a density of population in urban structures.

I have recently begun to develop on the intuition that each individual human being represents a distinct social role. In other words, we have as many social roles in a group as there are individual human beings. Now, it is arguable, even as an intuition. Logically, if I want to say that my social role is X, there must be a categorial set X and I should be inclusive into that set. In other words, the intuitive take on social roles is to see them as general categories rather than as individual instances. Yet, my experience with digital neural networks has taught me that the distinction between general categories and individual instances is much foggier than traditional logic would indicate. There is a category (hic!) of neural networks, designated as convolutional neural networks, used in deep learning, where not only does the network learn to optimize a variable, but also it learns to formulate the best function in the view of optimizing that variable.

I am mildly obsessed with the application of artificial intelligence to simulate the working of collective intelligence in human societies. In this specific case, the logic of AI suggests me that social roles in human societies are a good example of that foggy zone between general categories and individual instances. Instead of saying ‘my role in society belongs to category X’, I could rather say that ‘the category X is a generalisation drawn out of many different, individual social roles, mine included, and this generalisation is a purely cognitive construct’.        

If I adopt this stance, then at least one obvious conclusion forms: the more people are there around, the more different social roles are being played. Just to show you the fundamental difference, I will use an example strongly referring to the current situation: consulting a medical doctor. This is precisely what we use to say: ‘I go to see a doctor’. A doctor, not the doctor. Still, if you have ever had the misfortune of suffering from anything more serious than a common cold, you could have noticed that consulting different doctors is like consulting different artists. Each good doctor, with good credentials, builds his or her reputation on digging on their own, into a specific field of medicine, and figuring out idiosyncratic methods. You can also recognize a good doctor by their capacity to deal with atypical cases.

Interesting: an accomplished social role is a clearly idiosyncratic social role. You can climb the ladder of social hierarchy precisely by developing a unique mix of skills, thus by shaping a highly individualized social role.

Now, imagine that individuation among doctors is prevalently recognized. Imagine a world where, instead of expecting a standardized treatment, and standardized skillsets in all the medical profession, patients commonly expect each physician to approach them differently, and therapeutic idiosyncrasy is the norm. What would public healthcare systems look like if we assumed such a disparity? How to calculate a standard price for a medical procedure, if every medical professional is assumed, by default, to perform this very procedure it their artistically unique manner?   

As I occasionally delve into history books, an example of which you can read in my recent update entitled ‘Did they have a longer breath, those people in the 17th century?’, I discover that we are currently living an epoch of very pronounced social standardization. We have evolved those social systems – healthcare, pensions, police, adjudication – which, whilst being truly beneficial, require us to be very standardized and predictable in our personal demeanour. When I compare Europe today with Europe at the end of the 17th century, it is like comparing someone in a strict uniform with someone dressed in a theatrical outfit. It is uniformity against diversity.

We could be living in an illusion of widely standardized social roles, whence, e.g. the concept of ‘career path’ in life. This is a useful illusion, nevertheless an illusion. I am deeply convinced that what we, individual homo sapiens, commonly do, is individuation. The more we learn, the more idiosyncratic our accumulated learning becomes. Ergo, once again, the more humans are there around, the more distinct social roles are there around. What connection with cities and urban life? Here comes another intuition of mine, already hinted at in ‘Stress-tested by a highly infectious microorganism’, namely that urban environments are much more favourable than rural ones, as far as creating new social roles is concerned. In the rural environment, agricultural production is the name of the game, which requires a certain surface of arable land and pasturage. The more people in the finite habitable space of our planet, the more food we need, and the more stringent we need to be on shielding agricultural land from other uses. This is the spiral we are in: our growing human population needs more agricultural resources, and thus we need to be more and more particular about partitioning between agricultural land and urban space.       

I found an interesting passage in Arnold Toynbee’s ‘Study of History’ (abridged version: Somervell &Toynbee 1946[1]). In Section 3: The Growths of Civilizations, Chapter X: The Nature of the Growths of Civilizations, we can read: ‘We have found by observation that the most stimulating challenge is one of mean degree between an excess of severity and a deficiency of it, since a deficient challenge may fail to stimulate the challenged party at all, while an excessive challenge may break his spirit. […] The real optimum challenge is one which not only stimulates the challenged party but also stimulates him to acquire momentum that carries him a step farther […].

The single finite movement from a disturbance to a restoration of equilibrium is not enough if genesis is to be followed by growth. And, to convert the movement into a repetitive, recurrent rhythm, there must be an élan vital (to use Bergson’s term) which carries the challenged party through equilibrium into an overbalance which exposes him to a fresh challenge and thereby inspires him to make a fresh response in the form of a further equilibrium ending in a further overbalance, and so on in a progression which is potentially infinite.

This élan, working through a series of overbalances, can be detected in the course of the Hellenic Civilization from its genesis up to its zenith in the fifth century B.C. […] The disintegration of the apparented Minoan Society had left a welter of social debris – marooned Minoans and stranded Achaeans and Dorians. […] Would the rare patches of lowland in the Achaean landscape be dominated by the wilderness of highlands that ringed them round? Would the peaceful cultivators of the plains be at the mercy of the shepherds and brigands of the mountains?

This first challenge was victoriously met: it was decided that Hellas should be a world of cities and not of villages, of agriculture and not of pasturage, of order and not of anarchy. Yet the very success of their response to this first challenge exposed the victors to a second. For the victory which ensured the peaceful pursuit of agriculture in the lowlands gave a momentum to the growth of population, and this momentum did not come to a standstill when the population reached the maximum density which agriculture in the Hellenic homeland could support.’

I frequently like reading my readings from the end backwards. In this case, it allows me to decipher the following logic: urbanization is a possible solution to the general problem of acceptably peaceful coexistence between mutually competing, and demographically expansive social groups, in a lowland environment. Makes sense. Virtually all the big cities of humanity are in lowlands, or in acceptably fertile plateaus (which the Greeks did not have). There are practically no big cities in the mountains.

When distinct social groups compete for territories in a relatively flat and fertile terrain, there are two possible games to play in such a competition. The game of constant war consists in delineating separate territories and consistently maintain control over them, possibly striving for expanding them. Another game, the game of influence, consists in creating cities, as markets and political centres, and then rival for influence in those cities.

When the Western Roman Empire collapsed, in the 5th century A.D., the ensuing partition of Western Europe into tribal states, constantly fighting against each other, illustrates the first type of game. Still, when we finally connected the dots as for highly efficient agriculture, in the 9th and the 10th centuries, the situation morphed progressively into the second type of game. I can find a trace of that logic in another favourite reading of mine, which I cite frequently, namely in Fernand Braudel’s ‘Civilisation and Capitalism’, Volume 1.  Section 8 of this book, entitled ‘Towns and Cities’, brings the following narrative: ‘Towns are like electric transformers. They increase tension, accelerate the rhythm of exchange and constantly recharge human life. They were born of the oldest and most revolutionary division of labour: between work in the fields on the one hand and the activities described as urban on the other. “The antagonism between town and country begins with the transition from barbarism to civilization, from tribe to State, from locality to nation, and runs through the whole history of civilization to the present day”, wrote the young Marx.

Towns, cities, are turning-points, watersheds of human history. When they first appeared, bringing with them the written word, they opened the door to what we now call history. Their revival in Europe in the eleventh century marked the beginning of the continent’s rise to eminence. When they flourished in Italy, they brought the age of Renaissance. So it has been since the city-states, the poleis of ancient Greece, the medinas of the Muslim conquest, to our own times. All major bursts of growth are expressed by an urban explosion. […] Towns generate expansion and are themselves generated by it.

[…] The town, an unusual concentration of people, of houses close together, often joined wall to all, is a demographic anomaly. […] There are some towns which have barely begun being towns and some villages that exceed them in numbers of inhabitants. Examples of this are enormous villages in Russia, past and present, the country towns of the Italian Mezzogiorno or the Andalusian south, or the loosely woven clusters of hamlets in Java […]. But these inflated villages, even when they were contiguous, were not necessarily destined to become towns.’

Interesting. Urban structures are a demographic anomaly, and once this anomaly emerges, it brings written culture, which, in turn, allows the development of technology. This anomaly allows demographic growth (thus biological expansion of the human species) in the context of group rivalry for lowland territory. The development of cities appears to be more productive as alternative to constant war. Once this alternative is chosen, cities allow the development of culture and technology. This is how they allow forming a rich palette of social roles. I think I understand. We, the human species, choose to be more and more crammed in cities, because such a demographic anomaly allows us to transmute growing population into a growing diversity of skills. Good example of collective intelligence.

This is the mechanism which allowed Adam Smith to observe, in his ‘Inquiry Into The Nature And Causes of The Wealth of Nations’, Book III, Of The Different Progress Of Opulence In Different Nations, Chapter I, Of The Natural Progress Of Opulence’: THE GREAT COMMERCE of every civilized society is that carried on between the inhabitants of the town and those of the country. It consists in the exchange of rude for manufactured produce, either immediately, or by the intervention of money, or of some sort of paper which represents money. The country supplies the town with the means of subsistence and the materials of manufacture. The town repays this supply, by sending back a part of the manufactured produce to the inhabitants of the country. The town, in which there neither is nor can be any reproduction of substances, may very properly be said to gain its whole wealth and subsistence from the country. We must not, however, upon this account, imagine that the gain of the town is the loss of the country. The gains of both are mutual and reciprocal, and the division of labour is in this, as in all other cases, advantageous to all the different persons employed in the various occupations into which it is subdivided. The inhabitants of the country purchase of the town a greater quantity of manufactured goods with the produce of a much smaller quantity of their own labour, than they must have employed had they attempted to pre- pare them themselves. The town affords a market for the surplus produce of the country, or what is over and above the maintenance of the cultivators; and it is there that the inhabitants of the country exchange it for something else which is in demand among them. The greater the number and revenue of the inhabitants of the town, the more extensive is the market which it affords to those of the country; and the more extensive that market, it is always the more advantageous to a great number’.   

It is worth noticing that urbanization is a workable solution to inter-group rivalry just in an ecosystem of fertile lowlands. In many places on Earth, e.g. large parts of Africa, there is rivalry for territories, there are tribal wars, and yet there is no citification, since there is not enough fertile agricultural land at hand. That brings the topic of climate change. If climate change, as it is now the most common prediction, brings a shortage of agricultural land, we could come to a point when cities will be no longer a viable condenser of social energy, and war can become more workable a path. Frightening, yet possible.

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 .

[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.,

[1] Li, X., Zhou, Y., Eom, J., Yu, S., & Asrar, G. R. (2019). Projecting global urban area growth through 2100 based on historical time series data and future Shared Socioeconomic Pathways. Earth’s Future, 7(4), 351-362.

Stress-tested by a highly infectious microorganism

My editorial on You Tube

I want to go sideways – but just a tiny bit sideways – from the deadly serious discourse on financial investment, which I developed in Partial outcomes from individual tables and in What is my take on these four: Bitcoin, Ethereum, Steem, and Golem?.  I want to try and answer the same question we all try to answer from time to time: what’s next? What is going to happen, with all that COVID-19 crisis?

Question: have we gone into lockdowns out of sheer fear on an unknown danger, or are we working through a deep social change with positive expected outcomes?

What happens to us, humans, depends very largely on what we do: on our behaviour. I am going to interpret current events and the possible future as collective behaviour with economic consequences, in the spirit of collective intelligence, the concept I am very fond of. This is a line of logic I like developing with my students. I keep telling them: ‘Look, whatever economic phenomenon you take, it is human behaviour. The Gross Domestic Product, inflation, unemployment, the balance of payments, local equilibrium prices: all that stuff is just a bunch of highly processed metaphors, i.e. us talking about things we are afraid to admit we don’t quite understand. At the bottom line of all that, there are always some folks doing something. If you want to understand economic theory, you need to understand human behaviour’.

As I will be talking about behaviour, I will be referring to a classic, namely to Burrhus Frederic Skinner, the founding father of behavioural psychology, and one of his most synthetic papers, ‘Selection by Consequences’ (Skinner, B. F.,1981, Selection by consequences, Science, 213(4507), pp. 501-504). This paper had awoken my interest a few months ago, in Autumn 2019, when I was discussing it with my students, in a course entitled ‘Behavioural modelling’. What attracted my attention was the amount of bullshit which has accumulated over decades about the basic behavioural theory that B.F. Skinner presented.

I can summarize the bullshit in question with one sentence: positive reinforcement of behaviour is stronger than negative reinforcement. This is the principle behind policies saying that ‘rewards work better than punishments’ etc. Before I go further into theory, and then even further into the application of theory to predicting our collective future, please, conduct a short mental experiment. Imagine that I want to make you walk 100 yards by putting your feet exactly on a white line chalked on the ground. I give you two reinforcements. When you step out of the line, I electrocute you. When you manage to walk the entire distance of 100 yards exactly along the chalked line, I reward you with something pleasurable, e.g. with a good portion of edible marijuana. Which of those reinforcements is stronger?

If you are intellectually honest in that exercise, you will admit that electrocution is definitely stronger a stimulus. That’s the first step in understanding behaviourism: negative reinforcements are usually much stronger than positive ones, but, in the same time, they are much less workable and flexible. If you think even more about such an experiment, you will say: ‘Wait a minute! It all depends on where exactly I start my walk. If my starting point is exactly on the white chalked line, the negative reinforcement through electrocution could work: I step aside and I get a charge. Yet, if I start somewhere outside the white line, I will be electrocuted all the time (I am outside the allowed zone), and avoiding electrocution is a matter of sheer luck. When I accidentally step on the white line, and electrocution stops, it can give me a clue’. The next wait-a-minute argument is that electrocution works directly on the person, whilst the reward works in much more complex a pattern. I need to know there is a reward at the end of the line, and I need to understand the distance I need to walk etc. The reward works only if I grasp the context.

The behavioural theory by B.F. Skinner is based on the general observation that all living organisms are naturally exploratory in their environment (i.e. they always behave somehow), and that exploratory behaviour is reinforced by positive and negative stimuli. By the way, when I say all living organisms, it really means all. You can experiment with that. Take a lump of fresh, edible yeast, the kind you would use to make bread. Put it in some kind of petri dish, for example on wet cotton. Smear a streak of cotton with a mix of flour, milk, and sugar. Smear another streak with something toxic, like a house cleaner. You will see, within minutes, that yeast starts branching aggressively into the streak of cotton smeared with food (milk, sugar, butter), and will very clearly detract from the area smeared with detergent.

Now, imagine that you are more or less as smart as yeast is, e.g. you have just watched Netflix for 8 hours on end. Negative stimulus (house cleaner) gives you very simple information: don’t, just don’t, and don’t even try to explore this way. Positive stimulus (food) creates more complex a pattern in you. You have a reward, and it raises the question what is going to happen if you make one more step in that rewarding direction, and you make that step, and you reinforce yourself in the opinion that this is the right direction to go etc. Negative stimulation developed in you a simple pattern of behaviour, that of avoidance. It is a very strong stimulus, and an overwhelmingly powerful pattern of behaviour, and this is why there is not much more to do, down this avenue. I know I shouldn’t, right? How much more can I not do something?

Positive stimulation, on the other hand, triggers the building up of a strategy. Positive stimulation is scalable. You can absorb more or less pleasure, depending on how fast you branch into cotton imbibed with nutrients (remember, we are yeast, right?). Positive stimulation allows to build up experience, and to learn complex patterns of behaviour. By the way, if you really mean business with that yeast experiment, here is something to drag you out of Netflix. In the petri dish, once you have placed yeast on that wet cotton, put in front of it a drop of detergent (negative stimulus), and further in the same direction imbibe cotton with that nutritive mix of flour, milk and sugar. Yeast will branch around the drop of detergent and towards food. This is another important aspect of behaviourism: positive reinforcements allow formulating workable goals and strategies, whilst a strategy consisting solely in avoiding negative stimuli is one of the dumbest strategies you can imagine. Going straight into negative and destroying yourself is perhaps the only even dumber way of going through life.

One more thing about behaviourism. When I talk about it, I tend to use terms ‘pleasure’ and ‘pain’ but these are not really behaviourist ones. Pleasure and pain are inside my head, and from the strictly behaviourist point of view, what’s inside my head is unobservable at best, and sheer crap at worst. Behaviourism talks about reinforcements. A phenomenon becomes reinforcement when we see it acting as one. If something that happens provokes in me a reaction of avoidance, it is a negative stimulus, whatever other interpretation I can give it. There are people who abhor parties, and those people can be effectively reinforced out of doing something with the prospect of partying, although for many other people parties are pleasurable. On the other hand, positive reinforcement can go far beyond basic hedonism. There are people who fly squirrel suits, climb mountains or dive into caves, risking their lives. Emotional states possible to reach through those experiences are their positive reinforcements, although the majority of general population would rather avoid freezing, drowning, or crashing against solid ground at 70 miles per hour.

That was the basic message of B.F. Skinner about reinforcements. He even claimed that we, humans, have a unique ability to scale and combine positive reinforcements and this is how we have built that thing we call civilisation. He wrote: ‘A better way of making a tool, growing food, or teaching a child is reinforced by its consequence – the tool, the food, or a useful helper, respectively. A culture evolves when practices originating in this way contribute to the success of the practicing group in solving its problems. It is the effect on the group, not the reinforcing consequences for individual members, which is responsible for the evolution of the culture’.

Complex, civilisation-making patterns of both our individual and collective behaviour are shaped through positive reinforcements, and negative ones serve as alert systems that correct our course of learning. Now, COVID – 19: what does it tell us about our behaviour? I heard opinions, e.g. in a recent speech by Emmanuel Macron, the French president, that lockdowns which we undertook to flatten down the pandemic curve are something unique in history. Well, I partly agree, but just partly. Lockdowns are complex social behaviour, and therefore they can be performed only to the extent of previously acquired learning. We need to have practiced some kind of lockdown-style-behaviour earlier, and probably through many generations, in order to do it massively right now. There is simply no other way to do it. The speed we enter into lockdowns tells me that we are demonstrating some virtually subconscious pattern of doing things. When you want to do something really quickly and acceptably smoothly, you need to have the pattern ingrained through recurrent practice, just as a pianist has their basic finger movements practiced, through hundreds of hours at the piano, into subconscious motor patterns.

In one of my favourite readings, Civilisation and Capitalism by Fernand Braudel, vol. 1, ‘The Structures of Everyday Life. The limits of the possible’, Section I ‘Weight of Numbers’, we can read: ‘Ebb and flow. Between the fifteenth and the eighteenth century, if the population went up or down, everything else changed as well. When the number of people increased, production and trade also increased. […] But demographic growth is not an unmitigated blessing. It is sometimes beneficial and sometimes the reverse. When a population increases, its relationship to the space it occupies and the wealth at its disposal is altered. It crosses ‘critical thresholds’ and at each one its entire structure is questioned afresh’.

There is a widely advocated claim that we, humans, have already overpopulated Earth. I even developed on that claim in my own book, Capitalism and Political Power. Still, in this specific context, I would like to focus on something slightly different: urbanisation. The SARS-Cov-2 virus we have so much trouble with right now seems to be particularly at ease in densely populated urban agglomerations. It might be a matter of pure coincidence, but in 2007 – 2008, the share of urban population in total global population exceeded 50% (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS ). Our ‘critical threshold’, for now, might be precisely that: the percentage of people in urban structures. In 2003, when SARS-Cov-1 epidemic broke out, global urbanisation just passed the threshold of 43%. In 2018 (last data available) we were at 55,27%.

When Ebola broke out in Africa, in 2014 ÷ 2016, three countries were the most affected: Liberia, Guinea, and Sierra Leone. Incidentally, all three were going, precisely when Ebola exploded, through a phase of quick urbanisation. Here are the numbers:

 Percentage of urban population in total population
Country2015201620172018
Liberia49,8%50,3%50,7%51,2%
Guinea35,1%35,5%35,8%36,1%
Sierra Leone40,8%41,2%41,6%42,1%

I know, this is far from being hard science, yet I can see the outline of a pattern. Modern epidemics break out in connection with growing urbanisation. A virus like SARS-Covid-2, with its crazily slow cycle of incubation, and the capacity to jump between asymptomatic hosts, is just made for the city. It is like a pair of Prada shoes in the world of pathogens.    

Why are we becoming more and more urbanized, as a civilisation? I think it is a natural pattern of accommodating a growing population. When each consecutive generation comes with greater a headcount than the preceding ones, new social roles are likely to emerge. The countryside is rigid in terms of structured habitable space, and in terms of social roles offered to the newcomers. Farmland is structured for agricultural production, not for the diversity of human activity. There is an interesting remark to find in another classic, reverend Thomas Malthus. In chapter 4 of An Essay on the Principle of Population (1798), he writes ‘The sons of tradesmen and farmers are exhorted not to marry, and generally find it necessary to pursue this advice till they are settled in some business or farm that may enable them to support a family. These events may not, perhaps, occur till they are far advanced in life. The scarcity of farms is a very general complaint in England. And the competition in every kind of business is so great that it is not possible that all should be successful.’

In other words, the more of us, humans, is there around, the more we need urban environments to maintain relative stability of our social structure. What would happen in the absence of cities to welcome the new-born (and slightly grown) babies from each, ever growing generation? In Europe, we have a good example of that: crusades. In the 10th and 11th centuries, in Europe, we finally figured out an efficient agricultural system, and our population had been growing quickly at the time. Still, in a mostly agricultural society which we were back then, a growing number of people had simply nothing to do. Result: outwards-oriented conquest.

We need cities to accommodate a growing population, still we need to figure out how those cities should work. Healthcare is an important aspect of urban life, as we have a lot of humans, with a lot of health issues, in one place. The COVID-19 crisis has shown very vividly all the weaknesses of healthcare infrastructures in cities. Transportation systems are involved too, and the degree of safety they offer. A pathogen preying on our digestive tract, such as dysentery, should it be as sneaky as SARS-Cov-2, would expose our water and sanitation systems, as well as our food supply system. I know it sounds freaky, but virtually every aspect of urban infrastructure can be stress-tested by a highly infectious microorganism.  

Here comes another passage from Civilisation and Capitalism by Fernand Braudel, vol. 1, ‘The Structures of Everyday Life. The limits of the possible’, Section I ‘Weight of Numbers’: ‘Looking more closely at Western Europe, one finds that there was a prolonged population rise between 1100 and 1350, another between 1450 and 1650, and a third after 1750; the last alone was not followed by a regression. Here we have three broad and comparable periods of biological expansion. The first two […] were followed by recessions, one extremely sharp, between 1350 and 1450, the next rather less so, between 1650 and 1750 (better described as a slowdown than as a recession) […] Every recession solves a certain number of problems, removes pressures and benefits the survivors. It is pretty drastic, but none the less a remedy. Inherited property became concentrated in a few hands immediately after the Black Death in the middle of the fourteenth century and the epidemics which followed and aggravated its effects. Only good land continued to be cultivated (less work for greater yield). The standard of living and real earnings of the survivors rose. […] Man only prospered for short intervals and did not realize it until it was already too late.’

I think we have collective experience in winding down our social business in response to external stressors. This is the reason why we went so easily into lockdowns, during the pandemic. We are practicing social flexibility and adaptability through tacit coordination. You can read more on this topic in The games we play with what has no brains at all, and in A civilisation of droplets.

In many countries, we don’t have problems with food anymore, yet we have problems with health. We need a change in technology and a change in lifestyles, in order to keep ourselves relatively healthy. COVID -19 shows that, first of all, we don’t really know how healthy exactly we are (we don’t know who is going to be affected), second of all that some places are too densely populated (or have too little vital resources per capita) to assure any health security at all (New York), and third of all, that uncertainty about health generates a strategy of bunkering and winding down a large part of the material civilisation.

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 .