Tag: cities

The perfectly dumb, smart social structure

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The logic of neural activation consists in combining a constant component with a variable one, just as a live nervous system has some baseline neural activity, e.g. the residual muscular tonus, which ramps up in the presence of stimulation. In the equation of hyperbolic tangent, namely NA = tanh = (e2h – 1) / (e2h + 1), the constant part is (e2 – 1) / (e2 + 1) = 0,761594156. Should my neural activation be the sigmoid, it goes like NA = sig = 1 / (1 + e-h), with the constant root of 1 / (1 + e-1) = 0,731058579.

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

An activating neuron based on the hyperbolic tangent gets into a state of NA = tanh = (e2*1,10 – 1) / (e2*1,10 + 1) = 0.801620, and another activating neuron working with the sigmoid function thinks NA = sig = 1 / (1 + e-1,10) = 0,7508457. Another experiment with the same data consists in changing the aleatory coefficients of importance and seeing what happens, thus in saying  ε1 = 0.48, ε2 = 0.44, ε3 = 0.24, ε4 = 0.27, ε5 = 0.80 and aggregating h = 0,19*0,48 +0,86*0,44 + 0,36*0,24 + 0,18*0,27 + 0,93*0,80 = 1,35. In response to the same raw data aggregated in a different way, the hyperbolic tangent says NA = tanh = (e2*1,35 – 1) / (e2*1,35 + 1) = 0,873571 and the activating neuron which sees reality as a sigmoid retorts: ‘No sir, absolutely not. I say NA = sig = 1 / (1 + e-1,35) = 0,7937956’. What do you want: equations are like people, they are ready to argue even about 0,25 of difference in aggregate input from reality.

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

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

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

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

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

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

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

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

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

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

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

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

As I want my baseline AI to be quick on the uptake and dumb as f**k on the long-haul flight of learning, I use neural activation through hyperbolic tangent. As you could have seen earlier, this function is sort of prone to short term excitement. In order to assess the error, I use both logic and one more mathematical trick. In the input, I made each of k = 20 social roles equiprobable in its happening, i.e. 0,50. I assume that the output of neural activation should also be 0,50. Fifty percent of being anybody’s social role should yield fifty percent: simplistic, but practical. I go e(erj) = O(erj) – NA(erj) = 0,5 – tanh = 0,5 – [(e2h – 1) / (e2h + 1)], and I feed forward that error from round 1 to the next experimental round. This is an important trait of this particular neural network: in each experimental round, it experiments adds up the probability from previous experimental round and the error made in the same, previous experimental round, and with the assumption that expected value of output should be a probability of 50%.

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

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

Table 1 – Characteristics of the baseline neural network

Experimental roundAverage probability of input  Cohesion – Average Euclidean distance V in input  Aggregate input ‘h’  Error to backpropagate
1           0,5000 0,25011,62771505-0,4257355
2           0,0743 0,03720,029903190,47010572
3           0,5444 0,27231,79626958-0,4464183
4           0,0980 0,04900,051916330,44813027
5           0,5461 0,27321,60393868-0,4222593
6           0,1238 0,06190,093201450,40706748
7           0,5309 0,26561,59030006-0,4201953
8           0,1107 0,05540,071570250,4285517
9           0,5392 0,26981,49009281-0,4033418
10           0,1359 0,06800,113017960,38746079
11           0,5234 0,26181,51642329-0,4080723
12           0,1153 0,05770,062083680,43799596
13           0,5533 0,27681,92399208-0,458245
14           0,0950 0,04760,036164950,46385081
15           0,5589 0,27961,51645936-0,4080786
16           0,1508 0,07550,138602510,36227827
17           0,5131 0,25671,29611259-0,3607191
18           0,1524 0,07620,122810620,37780311
19           0,5302 0,26521,55382594-0,4144146
20           0,1158 0,05790,063916620,43617027
……………
Average over 3000 rounds0,33160,16590,81130,0000041
Variance0,04080,01020,53450,162
Variability*0,60920,60920,901297 439,507

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Social roles and pathogens: our average civilisation

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

I am comparing literature from 2010 with the most recent one, like 2018 and 2019. I snatched almost the entire volume 65 of the ‘Neuron’ journal from March 25, 2010, and I passed in review articles pertinent to social neuroscience. Pascal Belin and Marie-Helene Grosbras (2010[1]) discuss the implications of research on voice cognition in infants. Neurologically, the capacity to recognize voice, i.e. to identify people by their voices, emerges long before the capacity to process verbal communication. Apparently, the period stretching from the 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.

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] 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

Demographic anomalies – the puzzle of urban density

MY EDITORIAL ON YOU TUBE

I am returning to one particular topic connected my hypothesis, stating 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 am returning to a specific metric, namely to the DENSITY OF URBAN POPULATION, which I calculated my myself on the basis of three component datasets from the World Bank, namely: i) percentage of general population living in cities AKA coefficient of urbanization, ii) general headcount of population, and iii) total urban land area. I multiply the coefficient of urbanization by the general headcount of population, and thus I get the total number of people living in cities. In the next step, I divide that headcount of urban population by the total urban land area, and I get the density of urban population, measured as people per 1 km2 of urban land. 

That whole calculation is a bit of a mindfuck, and here is why. According to the World Bank, the total area of urban land, i.e. the two-dimensional total size of cities in the world has remained constant since 1990. Counter-intuitive? Hell, yes, especially that the same numerical standstill is officially recorded not only at the planetary level but also at the level of particular countries. It seems so impossible that my calculations regarding the density of urban population should be meaningless. Yet, the most interesting is to come. That DIY coefficient of mine, the density of urban population is significantly, positively correlated, at least at the level of the whole world, with another one: the coefficient of patent applications per 1 million people, which represents the intensity of occurrence in marketable scientific inventions. The corresponding Pearson correlation is r = 0,93 for resident patent applications (i.e. filed in the same country where the corresponding inventions have been made), and r = 0,97 for non-resident patent applications (i.e. foreign science searching legal protection in a country). You can read the details of those calculations in ‘Correlated coupling between living in cities and developing science’. 

That strong Pearson correlations are almost uncanny. Should it hold to deeper scrutiny, it would be one of the strongest correlations I have ever seen in social sciences. Something that is suspected not to make sense (the assumption of constant urban surface on the planet since 1990) produces a coefficient correlated almost at the 1:1 basis with something that is commonly recognized to make sense. F**k! I love science!

I want to sniff around that one a bit. My first step is to split global data into individual countries. In my native Poland, the coefficient of density in urban population, such as I calculate it on the basis of World Bank data, was 759,48 people per 1 km2, against 124,21 people per 1 km2 of general population density. I confront that metric with official data, published by the Main Statistical Office of Poland (www.stat.gov.pl ), regarding three cities, in 2012: my native and beloved Krakow with 5 481 people per 1 km2 of urban land, and not native at all but just as sentimentally attached to my past Gdansk, yielding 4 761 people per 1 km2. Right, maybe I should try something smaller: Myslenice, near Krakow, sort of a satellite town. It is 3 756 people per 1 km2. If smaller does not quite work, it is worth trying bigger. Poland as a whole, according to the same source, has 2 424 people in its average square kilometre of urban space. All these numbers are one order of magnitude higher than my own calculation.

Now, I take a look at my own country from a different angle. The same site, www.stat.gov.pl says that the percentage of urban land in the total surface of the country has been gently growing, from 4,6% in 2003 to 5,4% in 2017. The total surface of Poland is 312 679 km2, and 5,4% makes 16 884,67 km2, against  30 501,34 km2 reported by the World Bank for 2010. All in all, data from the World Bank looks like an overly inflated projection of what urban land in Poland could possibly grow to in the distant future.

I try another European country: France. According to the French website Actu-Environnement: urban areas in France made 119 000 km2 in 2011, and it had apparently grown from the straight 100 000 km2 in 1999. The World Bank reports 86 463,06 km2, thus much less in this case. Similar check for United Kingdom: according to https://www.ons.gov.uk , urban land makes 1,77 million hectares, thus 17 700 km2, against  58 698,75 km2 reported by the World Bank. Once again, a puzzle: where that discrepancy comes from?

The data reported on https://data.worldbank.org/ , as regards the extent of urban land apparently comes from one place: the Center for International Earth Science Information Network (CIESIN), at the Columbia University, and CIESIN declares to base their estimation on satellite photos. The French statistical institution, INSEE, reports a similar methodological take in their studies, in a  paper available at: https://www.insee.fr/fr/statistiques/fichier/2571258/imet129-b-chapitre1.pdf . Apparently, urban land seen from the orbit of Earth is not exactly the same as urban land seen from the window of an office. The latter is strictly delineated by administrative borders of towns and cities, whilst the former has shades and tones, e.g. are 50 warehouse, office and hotel buildings, standing next to each other in an otherwise rural place, an urban space? That’s a tricky question. We return here to the deep thought by Fernand Braudel, in his ‘Civilisation and Capitalism’, Volume 1, Section 8:‘Towns and Cities’: The town, an unusual concentration of people, of houses close together, often joined wall to all, is a demographic anomaly.  

Yes, that seems definitely the path to follow in order to understand those strange, counterintuitive results which I got, regarding the size and human density of urban spaces across the planet: the town is a demographic anomaly. The methodology used by CIESIN, and reproduced by the World Bank, looks for demographic anomalies of urban persuasion, observable on satellite photos. The total surface of those anomalies can be very different from officially reported surface of administrative urban entities within particular countries and seems remaining constant for the moment.

Good. I can return to my hypothesis: 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. The discourse about defining what urban space actually is, and the assumption that it is a demographic anomaly, leads me into investigating how much of an anomaly is it across the planet. In other words: are urban structures anomalous in the same way everywhere, across all the countries on the planet? In order to discuss this specific question, I will be referring to a small database I made, out of data downloaded from the World Bank, and which you can view or download, in Excel format, from this link: Urban Density Database. In my calculations, I assumed that demographic anomaly in urban settlements is observable quantitatively, among others, as abnormal density of population. Official demographic databases yield average, national densities of population, whilst I calculate densities of urban populations, and I can denominate the latter in units of the former. For each country separately, I calculate the following coefficient: [Density of urban population] / [Density of general population]. Both densities are given in the same units, i.e. in people per 1 km2. With the same unit in both the nominator and the denominator of my coefficient, I can ask politely that unit to go and have a break, so as to leave me with what I like: bare numbers.

Those bare numbers, estimated for 2010, tell me a few interesting stories. First of all, there is a bunch of small states where my coefficient is below 1, i.e. the apparent density of urban populations in those places is lower than their general density. They are: San Marino (0,99), Guam (0,98), Puerto Rico (0,98), Tonga (0,93), Grenada (0,72), Mauritius (0,66), Micronesia Fed. Sts. (0,64), Aruba (0,45), Antigua and Barbuda (0,43), St. Kitts and Nevis (0,35), Barbados (0,33), St. Lucia (0,32). These places look like the dream of social distancing: in cities, the density of population is lower than what is observable in the countryside. Numbers in parentheses are precisely the fractions [Density of urban population / Density of general population]. If I keep assuming that urban settlements are a demographic anomaly, those cases yield an anomalous form of an anomaly. These are mostly small island states. The paradox in their case is that officially, their populations mostly urban: more than 90% of their respective populations are technically city dwellers.

I am going once again through the methodology, in order to understand the logic of those local anomalies in the distribution of a general anomaly. Administrative data yields the number of people living in cities. Satellite-based data from the Center for International Earth Science Information Network (CIESIN), at the Columbia University, yields the total surface of settlements qualifiable as urban. The exact method used for that qualification is described as follows: ‘The Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) urban extent grid distinguishes urban and rural areas based on a combination of population counts (persons), settlement points, and the presence of Night-time Lights. Areas are defined as urban where contiguous lighted cells from the Night-time Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons’.  

Night-time lights manifest a fairly high use of electricity, and this is supposed to combine with the presence of specific settlement points. I assume (it is not straightforwardly phrased out in the official methodology) that settlement points mean residential buildings. I guess that a given intensity of agglomeration in such structures allows guessing a potentially urban area. A working hypothesis is being phrased out: ‘This place is a city’. The next step consists in measuring the occurrence of Night-time Lights, and those researchers from CIESIN probably have some scale of that occurrence, with a threshold on it. When the given place, running up for being a city, passes that threshold, then it is definitely deemed a city.

Now, I am returning to those strange outliers with urban populations being apparently less dense than general populations. In my mind, I can see three maps of the same territory. The first map is that of actual human settlements, i.e. the map of humans staying in one place, over the whole territory of the country. The second map is that of official, administratively defined urban entities: towns and cities. Officially, those first two maps overlap in more than 90%: more than 90% of the population lives in places officially deemed as urban settlements. A third map comes to the fore, that of urban settlements defined according to the concentration of residential structures and Night-Time Lights. Apparently, that third map diverges a lot from the second one (administratively defined cities), and a large part of the population lives in places which administratively are urban, but, according to the CIESIN methodology, they are rural, not urban. 

Generally, the distribution of coefficient [Density of urban population] / [Density of general population], which, for the sake of convenience, I will further designate as [DU/DG], is far from the normal bell curve. I have just discussed outliers to be found at the bottom of the scale, and yet there are outliers on its opposite end as well. The most striking is Greenland, with [DU/DG] = 10 385.81, which is not that weird if one thinks about their physical geography. Mauritania and Somalia come with [DU/DG] equal to, respectively, 622.32 and 618.50. Adverse natural conditions apparently make towns and cities a true demographic anomaly, with their populations being several hundred times denser than the general population of their host countries.

The more I study the geographical distribution of the [DU/DG] coefficient, the more I agree with the claim that towns are a demographic anomaly. The coefficient [DU/DG] looks like a measure of civilizational difference between the city and the countryside. Table 1, below, introduces the average values of that coefficient across categories of countries, defined according to income per capita. An interesting pattern emerges. The wealthier a given country is, the smaller the difference between the city and the countryside, in terms of population density. Most of the global population seems to be living in neighbourhoods where that difference is around 20, i.e. where city slickers live in a twentyish times more dense populations than the national average.

I have been focusing a lot on cities as cradles to hone new social roles for new people coming to active social life, and as solutions for peacefully managing the possible conflicts of interests, between social groups, as regards the exploitation of fertile lowland areas on the planet. The abnormally high density of urban population is both an opportunity for creating new social roles, and a possible threshold of marginal gains. The more people there are per 1 km2, the more social interactions between those people, and the greater the likelihood for some of those interactions turning into recurrent patterns, i.e. into social roles. On the other hand, abundant, richly textured social structure, with a big capacity to engender new social roles – in other words, the social structure of wealthy countries – seems to be developing on the back of an undertow of diminishing difference between the city and the countryside.          

Table 1 – Average values of coefficient [Density of urban population] / [Density of general population] across categories of countries regarding wealth and economic development

Category of countriesDensity of urban population denominated over general density of population, 2010Population, 2010
Fragile and conflict affected situations91,98 618 029 522
Heavily indebted poor countries (HIPC)84,96 624 219 326
Low income74,24577 274 011
Upper middle income26,422 499 410 493
Low & middle income22,885 765 121 055
Middle income20,875 187 847 044
Lower middle income15,392 688 436 551
High Income15,811 157 826 206
Central Europe and the Baltics9,63104 421 447
United States9,21309 321 666
European Union5,65441 532 412
Euro area5,16336 151 479

Table 2 represents a different take on the implications of density in urban population. Something old and something new: the already known coefficient of patent applications per 1 million people, and a new one, of fundamental importance, namely the mean consumption of energy per capita, in kilograms of oil equivalent. One kilogram of oil equivalent stands for approximately 11,63 kilowatt hours.  Those two variables are averaged across sextiles (i.e. sets representing 1/6th of the total sample n = 221 countries), in 2010. Consumption of energy presents maybe the clearest pattern: its mean value decreases consistently across sextiles 1 ÷ 5, just to grow slightly in the sixth one. That sixth sextile groups countries with exceptionally tough natural conditions for human settlement, whence an understandable need for extra energy to consume. Save for those outliers, one of the most puzzling connections I have ever seen in social sciences emerges: the less difference between the city and the countryside, in terms of population density, the more energy is being consumed per capita. In other words: the less of a demographic anomaly cities are, in a given country (i.e. the less they diverge from rural neighbourhoods), the more energy people consume. I am trying to wrap my mind around it, just as I try to convey this partial observation graphically, in Graph 2, further below Table 2.

Table 2 – Mean coefficients of energy use per capita, and patent applications per 1 mln people, across sextiles of density in urban population, data for 2010        

Sextiles (Density of urban population denominated over general density of population)Mean [Energy use (kg of oil equivalent per capita)], 2010Mean [Patent applications total per 1 million people], 2010
50,94 ≤ [DU/DG] ≤ 10 385,812 070,5468,35
23,50 ≤ [DU/DG] < 50,941 611,73596,464
12,84 ≤ [DU/DG] < 23,502 184,039218,857
6,00 ≤ [DU/DG] < 12,842 780,263100,097
2,02 ≤ [DU/DG]  < 6,003 288,468284,685
0,00 ≤ [DU/DG] < 2,024 581,108126,734

Final consumption of energy is usually represented as a triad of main uses: production of goods and services, transportation, and the strictly spoken household use (heating, cooking, cooling, electronics etc.). Still, another possible representation comes to my mind: the more different technologies we have stacked up in our civilization, the more energy they all need. I explain. Technological change is frequently modelled as a process when newer generations of technologies supplant the older ones. However, what happens when those generations overlap? If they do, quick technological change makes us stack up technologies, older ones together with the newer ones, and the faster new technologies get invented, the richer the basket of technologies we hold. We could, possibly, strip our state of possessions down, just to one generation of technologies – implicitly it would be the most recent one – and yet we don’t. We keep them all. I look around my house, and around my close neighbourhood. Houses like mine, built in 2001, with the construction technologies of the time, are neighbouring houses built just recently, much more energy-efficient when it comes to heating and thermal insulation. In a theoretically perfect world, when new generation of technologies supplants the older one, my house should be demolished and replaced by a state-of-the-art structure. Yet, I don’t do it. I stick to the old house.

The same applies to cars. My car is an old Honda Civic from 2004. As compared to the really recent cars some of my neighbours bought, my Honda gently drifts in the direction of the nearest museum. Still, I keep it. Across the entire neighbourhood of some 500 households, we have cars stretching from the 1990s up to now. Many generations of technologies coexist. Once again, we technically could shave off the old stuff and stick just to the most recent, yet we don’t. All those technologies need to be powered with at least some energy. The more technologies we have stacked up, the more energy we need.  

I think about that technological explanation because of the second numerical column in Table 2, namely that informative about patent applications per 1 million people. Patentable invention coincides with the creation of new social roles for new people coming with demographic growth. Data in table 2 suggests that some classes of the coefficient [Density of urban population] / [Density of general population] are more prone to such creation than others, i.e. in those specific classes of [DU/DG] the creation of new social roles is particularly intense.

Good. Now comes the strangest mathematical proportion I found in that data about density of urban population and energy. For the interval 1972 ÷ 2014, I could calculate a double-stack coefficient: {[Energy per capita] / [DU/DG]}. The logic behind this fraction is to smooth out the connection between energy per capita and the relative density of urban population, as observed in Table 2 on a discrete scale. As I denominate the density of urban population in units of density in the general population, I want to know how much energy per capita is consumed per each such unit. As it is, that fraction {[Energy per capita] / [DU/DG] is a compound arithmetical construct covering six levels of simple numerical values. After simplification, {[Energy per capita] / [DU/DG] = [Total energy consumed / Population in cities] * [Surface of urban land / Total surface of land]. Simplification goes further. As I look at the numerical values of that complex fraction, computed for the whole world since 1972 through 2014, it keeps oscillating very tightly around 100. More specifically, its average value for that period is AVG{[Energy per capita] / [DU/DG]} = 102.9, with a standard deviation of 3.5, which makes that standard deviation quasi inexistent. As I go across many socio-economic indicators available with the World Bank, none of them follows so flat a trajectory over that many decades. It looks as if there was a global equilibrium between total energy consumed and density of population in cities. What adds pepper to that burrito is the fact that cross-sectionally, i.e. computed for the same year across many countries, the same coefficient {[Energy per capita] / [DU/DG] swings wildly. There are no local patterns, but there is a very strong planetary pattern. WTF?

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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.

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] 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.

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It warms my heart to know I am not totally insane

My editorial

This is a rainy morning in Amplepuis, France, where I am staying until tomorrow. I am meditating, which means I am thinking without pretending to think anything particularly clever. Just basic, general flow of thinking, enough not to suck my thumb, sitting in a corner. I am mentally reviewing that report by Sebastiano Rwengabo (Rwengabo 2017[1]), which I commented on in my last two updates, and in some strange way I keep turning in returning in my mind that formula of multiple probability by Thomas Bayes (Bayes, Price 1763[2]), namely that if I want more than one success over n trials, at some uncertain, and therefore interesting action, I have always more than one way to have those p successes over n trials. Thomas Bayes originally equated that ‘more than one’ to (pq)/q!, where q is the tolerable number of failures. You can try by yourself: as long as you want more than one success, your (pq)/q! is always greater than one. There is always more than one way to have more than one success. Interesting intuition.

I am digging into the topic of local power systems based on renewable energies, possibly connected to a local cryptocurrency. I want to prepare something like a business plan for that idea. I want to know how many ways of being successful in this type of endeavour are reported in the literature. I start with a leaflet I found and archived on my https://discoversocialsciences.com website under this link . It is entitled ‘100% – RES communities’. As usually, I start at the end, and the end is sub-headed ‘Stay in The Game’. Good. If it is important to stay in the game, then logically it is important not to drop off, which, in turn, means that dropping off is an observable end to local efforts at going 100% renewable. One of the ways to stay in the game consists in joining other people who want to. There is a network, the Global Covenant of Mayors (http://www.globalcovenantofmayors.org ), which currently unites 7 477 cities with almost 685 million people living in them and which has been created quite recently by the merger of the Covenant of Mayors, mentioned in that ‘100% – RES communities’ leaflet, with the Compact of Mayors, in June, 2016. Having more than one success in going 100% means, thus, staying in the game with others, in networks, and those networks tend to merge and create even bigger networks. I have a nice conditional probability, here: my probability of successfully going 100% green, as a local community, depends on the probability we manage to stick to our commitments, which, in turn, depends on our ability to join a network of other communities with similar goals. There are other networks, besides the Covenant of Mayors, such as the RES League (http://www.res-league.eu ), 100% RES Communities (http://www.100-res-communities.eu ), or the French RURENER (http://fr.rurener.eu ).

The next thing, which apparently helps to stay in the game is a SEAP, or Sustainable Energy Action Plan. As I am writing this paragraph, I am browsing the Internet in the search of details about this approach, and I am simultaneously reading that leaflet. SEAP seems to be a general line of approach, which sums up to assuming that we can induce only as much change as we can really plan, i.e. that we can translate into real action on a given date and in a given place. If I am grasping well the concept, it means that whenever a ‘we will do it somehow’ pokes its head out of our action plan, it indicates we have no proper SEAP. I like the approach, I have experienced its soundness in other areas of life: we can usually achieve more than we think we can, but we need to understand very precisely what is the path to cover, step by step. Here, the coin drops: if we need a good SEAP, it is important to tap into other people’s experience, whence the point of forming networks. Being maybe a bit less ambitious, but more realistic and more in touch with what the local community really can do, is apparently helpful in going 100% green.

That was a leaflet, now I take a scientific paper: “Exploring residents’ willingness to pay for renewable energy supply: Evidences from an Italian case study” by Grilli et al.[3] , an unpublished working paper accessible via the Social Sciences Research Network . The paper explored the attitudes of people living in the Gesso and Vermenagna valleys, towards the prospect of paying higher energy bills as long as those bills will be 100% green. The case study suggests an average acceptance for a 5,1€, or 13% increase in the monthly energy bill. Knowledge about renewable energies seems to be a key factor in shaping those attitudes. Good, so I have another nice, conditional probability: having more than one success in going 100% green locally depends on the local acceptance of higher energy bills, which, in turn, depends on the general awareness of the population involved.

I move forward along that financial path, and I am having a look at a published article, entitled “Is energy efficiency capitalized into home prices? Evidence from three US cities.“, by Walls et al.[4] . Margaret Walls and the team of associated researchers found a positive impact of ‘green certification’ of residential properties upon their market price, with a premium ranging from 2 to 8%. Still, this premium remains strongly local, thus largely idiosyncratic. Staying in this path of thinking, i.e. thinking about money whilst thinking about grand green initiatives, I am having a look at an article by Patrick Hartmann and Vanessa Apaolaza-Ibáñez as for the consumers’ attitude towards the so-called ‘green energy brands’[5]. In this case, the most interesting thing is the methodology, as the results are quite tentative, based on a total of 726 street interviews in six towns and villages in northern Spain. The methodology is based on a set of assumed benefits that an individual can derive from purchasing energy from suppliers certified as 100% green in their process of generation. There is a nice piece of fine reasoning from the part of Patrick Hartmann and Vanessa Apaolaza-Ibáñez. They hypothesise that altruistic environmental concerns and their satisfaction are just one among the many psychological factors affecting the decision of purchasing renewable energy. There is a bunch of egoistic factors, slightly in the lines of Thorstein Veblen’s theory of the leisure class: consuming green energy can provide something described as ‘warm glow’, or personal satisfaction derived from experiencing a subjectively positive impact on the social and natural environment, as well as from the social recognition of that impact that we experience as a feedback from other people. In other words, if a person can expect interactions like ‘Oh! You are buying that 100% green energy? Fantastic! You are such a precious member of the community!’. Let’s face it: each of us would like to be praised like that, from time to time. On the top of that, the purchase may be further affected by the general reputation of the given brand, and by individual attitudes towards experiencing the contact with nature. Whilst tentative, the results of those interviews suggest, quite interestingly that the general attitude towards the suppliers of green energy is strongly influenced by that personal, individual experience of nature in general.

Still following the money, but moving from the small money spent by consumer towards the big money held by banks, I am browsing through an unpublished paper by Karen Wendt, from MODUL University in Vienna[6]. This particular paper is precious, from my point of view, mostly because of the interesting stylized facts it presents. In science, stylized facts are facts that we can express in a graph, but we cannot exactly explain why the graph looks the way it looks. So, Karen Wendt lets me learn, for example, that a large part of the known reserves in fossil fuels, probably between 60 and 80% of them, must stay nicely in the ground if we are to meet the 2°C limit of temperature jump. These reserves are accounted for as assets in the balance sheet of your average Exxon Mobil. If they are to stay where they are, they will have to be kicked the hell out of those balance sheets, and that’s gonna hurt. The same is valid for carbon-intensive infrastructure, like chains of petrol stations or oil-refining plants. If we turn green, all that stuff will have to be written off someone’s equity, and this, once again, is likely to make some people nervous. It shows that if we really want to go green, we really could do with some capitalistic mechanism of transition, which would allow, sadly but realistically, to switch relatively smoothly from a carbon-intensive balance sheet, with the corresponding capital profits financing the corresponding private islands, to a balance sheet based on renewable energies. It warms my heart, those observations from Karen Wendt, as it suggests I am not totally insane when I think about monetary systems specifically oriented on giving market value to green energy.

[1] Rwengabo, S., 2017, Efficiency, Sustainability, and Exit Strategy in the Oil and Gas Sector: Lessons from Ecuador for Uganda, ACODE Policy Research Series No.81, 2017, Kampala, ACODE, ISBN: 978-9970-567-01-0

[2] Mr. Bayes, and Mr Price. “An essay towards solving a problem in the doctrine of chances. by the late rev. mr. bayes, frs communicated by mr. price, in a letter to john canton, amfrs.” Philosophical Transactions (1683-1775) (1763): 370-418

[3] Grilli, G., Balest, J., Garengani, G., Paletto, A., 2015, Exploring residents’ willingness to pay for renewable energy supply: Evidences from an Italian case study, https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2669975

[4] Walls, M., Palmer, K., Geranden, T, Xian Bak, 2017, Is energy efficiency capitalized into home prices? Evidence from three US cities, Journal of Environmental Economics and Management vol. 82 (2017), pp. 104-124

[5] Hartmann, P., Apaolaza-Ibáñez, V., 2012, Consumer attitude and purchase intention toward green energy brands: The roles of psychological benefits and environmental concern, Journal of Business Research, vol. 65.9 (2012), pp. 1254-1263

[6] Wendt, K., 2016, Decarbonizing Finance – Recent Developments and the Challenge Ahead, Available at SSRN: https://ssrn.com/abstract=2965677