Business models and the nature of truth – back to school #2

I am introducing another handful of educational content in the form of video tutorials.

The video recorded on August 23rd, noon sharp, is the first in a separate path of teaching devoted to Political Systems. The link to You Tube is here: PolitSys 2020-08-23 11-16-44 (https://youtu.be/J_78zBEFFNE ). The video introduces two case studies: the constitution of Uganda (https://discoversocialsciences.com/wp-content/uploads/2020/08/Uganda-Constitution.pdf ) and the constitution of India (https://discoversocialsciences.com/wp-content/uploads/2019/10/Con-Of-India-updated-as-31072018.pdf ). In terms of theory, two articles are hinted at: Almond, G. A. (1956). Comparative Political Systems. The Journal of Politics, 18(3), 391-409 (https://www.jstor.org/stable/2127255?seq=1 ), and Easton, D. (1957). An approach to the analysis of political systems. World Politics: A Quarterly Journal of International Relations, 383-400 (https://www.jstor.org/stable/2008920?seq=1 ). General concepts which you will find developed in this video are:

>> Constitution as a double-function tool: the set of rules for the political game, and the foundation of the national legal system

>> Constitutional systems as paradoxes: rules of the very brutal political game put together, in the same document, with ambitious ethical principles for the entire nation.

>> The principle of national sovereignty

>> The method of studying constitutions by simulated removal and negation of rules

Material recorded on Monday, August 24th, 2020 (Econ Basics 1 2020-08-24 08-02-06 ; https://youtu.be/OTGjJGfpdoc ) contains the first, more or less formalized lecture in the fundamentals of economics. I use five essential readings – Netflix Annual Report 2019, Discovery Annual Report 2019, Adam Smith’s ‘Wealth of Nations’, David Ricardo’s ‘Principles of Political Economy and Taxation’, and Carl Menger’s ‘Principles of Economics’ – in order to show the basis axes of approach to economic sciences. Firstly, it is the special social tension between the diversity of skills and social roles, on the one hand, and the fact of them all summing up to one big body of labour (Smith). Secondly, I introduce the distinction between capital and labour, and the importance of capital resources (Ricardo, example Netflix). Thirdly, and finally, I present the concept of economic good (Carl Menger) and the importance of translating technology into products.

The video recorded slightly later on August 24th, 2020 (Renew BM 2 2020-08-24 09-35-20; https://youtu.be/VnwLLCDFXS8 ) is the second educational piece in the stream devoted to Business Models in the industry of Renewable Energies. I stay with the two business cases from the first video, i.e. First Solar Inc. and SMA Solar Technology AG, and I focus on connecting their capital accounts – their respective BALANCE SHEETS – to their business models. In terms of the capital base, First Solar is six times bigger than SMA. First Solar’s business model is based, capital-wise, on using retained earnings and additional paid-in capital to finance property, plant, equipment and a large reserve of cash. As regards SMA Solar, they mostly use retained earnings and long-term, complex contractual debt in order to finance factories and large inventories. What emerges as a common denominator between the two is the stream of capital from retained earnings to the financing of fixed productive assets.

In the second video focused on business models in the media industry (Media BM 2 2020-08-24 13-42-46; https://youtu.be/jZKvNfopShM  ), I keep working with two business cases: Netflix, and Discovery Communications. This time, I focus on deconstructing a business model out of the capital account, i.e. from the balance sheet of a company. I present it in the form of a game, which I frequently practice in class with my students: I ask them to identify the biggest numbers (financial aggregates) on both the active and the passive side of the balance sheet. I demonstrate this exercise in this video and explain how you can use the balance sheet to guess the fundamental traits of a business model.  

I am also putting online a second video in the educational path devoted to the philosophy of science (Phil Science 2 2020-08-24 14-17-58; https://youtu.be/sCI66lARqAI  ). I am investigating the nature of truth, with three basic readings: Philosophical Essay on Probabilities’ by Pierre Simon, marquis de Laplace, ‘Truth and Method’ by Hans Georg Gadamer, and an article entitled ‘Conscious agent networks: Formal analysis and application to cognition’, by Chris Fields, Donald D. Hoffman, Chetan Prakash, and Manish Singh. I briefly discuss the limitations we, humans, encounter when trying to discover truth about reality.

Back to school

More than an entire month has passed since I placed my last update on this blog. I took some time strictly off – some human tribes call it ‘vacation’ – and I have been assiduously doing science. Back from vacation, and imbibed with new science, I am blogging again. Actually, this new science is so fresh that I need to blog about it just to put some order in my findings and my ideas.

I have been doing science, and, in the same time, I have been preparing my teaching content for the new academic year.  As for science, I have been focusing on two things: the general theory of collective intelligence, on the one hand, and the puzzling data on urban density, on the other hand. I am going to develop on that second issue more exhaustively, as these are facts, and facts have disquieting a tendency to bring new insights into the comfortably established theory.

As regards teaching, I have three big curriculums to prepare for the winter semester: economics, management, and economic policy together with political systems. In this update, I am bringing, as sort of test missiles, my first three educational videos for the next semester. In other words, this update on my blog is actually a long, articulated link to those videos on You Tube. Below, I give the links and a short explanation for each of those three.

The video which I recorded around 2 p.m., on August 22nd, 2020, is pertinent to Business Models in the Film and TV production business. In my teaching of management, I have that special path, addressed to students in the Major ‘Film and TV Production’. I am teaching them the basics of management, with a special edge on show business. In this specific video you will see the beginning of two case studies: Netflix and Discovery Inc. You will see the basic tips for finding and retrieving financial reports of those businesses, as well as the first steps into analysing their business in depth. Here is the link to the You Tube video: Media BM 2020-08-22 13-41-17 (https://youtu.be/lR-jX0–1KQ ).  

The video recorded around 2:30 p.m., August 22nd, 2020, regards the Philosophy of Science. It is both extra-curricular content for all those among my students who want to develop their scientific edge, and my auto-reflection on the general issue of collective intelligence, and the possibility to use artificial neural networks for the study thereof. I dive into three readings: ‘Civilisation and Capitalism’ by Fernand Braudel, ‘Philosophical Essay on Probabilities’ by Pierre Simon, marquis de Laplace, and finally ‘Truth and Method’ by Hans Georg Gadamer. I focus on fundamental distinctions between reality such as it is, on the one hand, our perception, and our understanding thereof. The link is here: Phil Science 2020-08-22 14-30-16 (https://youtu.be/Wia0apAOdDQ ).

The video recorded early in the morning of August 23rd, 2020, is devoted to Business Models in the industry of Renewable Energies. The general concept of business models is the overarching common denominator in my teaching of economics and management in the coming academic year (2020/2021). Here, I start with a quick glance on two business cases: FIRST SOLAR and SMA SOLAR. You can see there two different business models, one oriented on big scale in manufacturing, the other one focused on building complex networks and platforms of exchange. Here comes the link to You Tube: Renew BM 2020-08-23 07-52-34 (https://youtu.be/FNOjOMD-OvY ).

Black Swans happen all the time

MY EDITORIAL ON YOU TUBE

I continue with the topic of Artificial Intelligence used as a tool to study collective intelligence in human social structures. In scientific dissertations, the first question, to sort of answer right off the bat, is: ‘Why should anyone bother?’. What is the point of adding one more conceptual sub-repertoire, i.e. that of collective intelligence, to the already abundant toolbox of social sciences? I can give two answers. Firstly, and most importantly, we just can do it. We have Artificial Intelligence, and artificial neural networks are already used in social sciences as tools for optimizing models. From there, it is just one more step to use the same networks as tools for simulation: they can show how specifically a given intelligent adaptation is being developed. This first part of the answer leads to the second one, namely to the scientific value added of such an approach. My essential goal is to explore the meaning, the power, and the value of collective intelligent adaptation as such, and artificial neural networks seem to be useful instruments to that purpose.

We live and we learn. We learn in two different ways: by experimental trial and error, on the one hand, and by cultural recombination of knowledge. The latter means more than just transmission of formalized cultural content: we can collectively learn as we communicate to each other what we know and as we recombine those individual pieces of knowledge. Quite a few times already, I have crossed my intellectual paths with the ‘Black Swan Theory’ by Nassim Nicholas Taleb, and its central claim that we collectively tend to silence information about sudden, unexpected events which escape the rules of normality – the Black Swans – and yet our social structures are very significantly, maybe even predominantly shaped by those unusual events. This is very close to my standpoint. I claim that we, humans, need to find a balance between chaos and order in our existence. Most of our culture is order, though, and this is pertinent to social sciences as well. Still, it is really interesting to see – and possibly experiment with – the way our culture deals with the unpredictable and extraordinary kind of s**t, sort of when history is really happening out there.

I have already had a go at something like a black swan, using a neural network, which I described in The perfectly dumb, smart social structure. The thing I discovered when experimenting with that piece of AI is that black swans are black just superficially, sort of. At the deepest, mathematical level of reality, roughly at the same pub where Pierre Simon Laplace plays his usual poker game, unexpectedness of events is a property of human cognition, and not that of reality as such. The relatively new Interface Theory of Perception (Hoffman et al. 2015[1]; Fields et al. 2018[2]; see also I followed my suspects home) supplies interesting insights in this respect. States of the world are what they are, quite simply. No single state of the world is more expected than others, per se. We expect something to happen, or we don’t although we should. My interpretation of the Nassim Nicholas Taleb’s theory is that Black Swans appear when we have a collective tendency to sort of over-smooth a given chunk of our experience and we collectively commit not to give a f**k about some strange outliers, which sort of should jump to the eye but we cognitively arrange so as they don’t really. Cognitively, Black Swans are qualia rather than phenomena as such.

Another little piece of knowledge I feel like contributing to the theory of Black Swan is that collective intelligence of human societies – or culture, quite simply – is compound and heterogenous. What is unexpected to some people is perfectly normal to others. This is how professional traders make money in financial markets: they are good at spotting recurrence in phenomena which look like perfect Black Swans to the non-initiated market players.

In the branch of philosophy called ‘praxeology’, there is a principle which states that the shortest path to a goal is the most efficient path, which is supposed to reflect the basics of Newtonian physics: the shortest path consumes the least amount of energy. Still, just as Newtonian physics are being questioned by their modern cousins, such as quantum physics, that classical approach of praxeology is being questioned by modern social sciences. I was born in the communist Poland, in 1968, and I spent the first 13 years of my life there. I know by heart the Marxist logic of the shortest path. You want people to be equal? Force them to be equal. You want to use resources in the most efficient way? Good, make a centralized, country-wide plan for all kinds of business, and you know what, make it five-year long. The shortest, the most efficient path, right? Right, there was only one thing: it didn’t work. Today, we have a concept to explain it: hyper-coordination. When a big organization focuses on implementing one, ‘perfect’ plan, people tend to neglect many opportunities to experiment with little things, sort of sidekicks regarding the main thread of the plan. Such neglect has a high price, for a large number of what initially looks like haphazard disturbances is valuable innovation. Once put aside, those ideas seldom come back, and they turn into lost opportunities. In economic theory, lost opportunities have a metric attached. It is called opportunity cost. Lots of lost opportunities means a whole stockpile of opportunity cost, which, in turn, takes revenge later on, in the form of money that we don’t earn on the technologies we haven’t implemented. Translated into present day’s challenges, lost ideas can kick our ass as lost chances to tackle a pandemic, or to adapt to climate change.

The shortest path to a goal is efficient under the condition that we know the goal. In long-range strategies, we frequently don’t know it, and then adaptative change is the name of the game. Here come artificial neural networks, once again. At the first sight, if we assume learning by trial and error and who knows where exactly we are heading, we tend to infer that we don’t know at all. Still, observing neural networks with their sleeves up and doing computational work teaches an important lesson: learning by trial and error follows clear patterns and pathways, and so does adaptative change. Learning means putting order in the inherent chaos of reality. Probably the most essential principle of that order is that error is information, and, should it be used for learning, it needs to be memorized, remembered, and processed.

Building a method of adaptative learning is just as valuable as, and complementary to preparing a plan with clearly cut goals. Goals are cognitive constructs which we make to put some order in the chaos of reality. These constructs are valuable tools for guiding our actions, yet they are in loop with our experience. We stumble upon Black Swans more frequently than we think. We just learn how to incorporate them into our cognition. I have experienced, in my investment strategy, the value and the power of consistent, relentless reformulation and re-description of both my strategic goals and of my experience.

How does our culture store information about events which we could label as errors? If I want to answer that question, I need to ask and answer another one: how do we collectively know that we have made a collective error, which can possibly be used as material for collective learning? I stress very strongly the different grammatical transformations of the word ‘collective’. A single person can know something, by storing information, residual from sensory experience, in the synapses of the brain. An event can be labelled as error, in the brain, when it yields an outcome non-conform to the desired (expected) one. Of course, at this point, a whole field of scientific research emerges, namely that of cognitive sciences. Still, we have research techniques to study that stuff. On the other hand, a collective has no single brain, as a distinct information processing unit. A collective cannot know things in the same way an individual does.

Recognition of error is a combination of panic in front of chaos, on the one hand, and objective measurement of the gap between reality and expected outcomes.  Let’s illustrate it with an example. When I am writing these words, it is July 12th, 2020, and it is electoral day: we are having, in Poland, the second-round ballot in presidential elections. As second rounds normally play out, there are just two candidates, the first two past-the-post in the first-round ballot. Judging by the polls, and by the arithmetic of transfer from the first round, it is going to be a close shave. In a country of about 18 million voters, and with an expected electoral attendance over 50%, the next 5 years of presidency is likely to be decided by around 0,5% of votes cast, roughly 40 ÷ 50 thousand people. Whatever the outcome of the ballot, there will be roughly 50% of the population claiming that our country is on the right track, and another 50% or so pulling their hair out and screaming that we are heading towards a precipice. Is there any error to make collectively, in this specific situation? If so, who and how will know whether the error really occurred, what was its magnitude and how to process the corresponding information?

Observation of neural networks at work provides some insights in that respect. First of all, in order to assess error, we need a gap between the desired outcome and the state of reality such as it is. We can collectively assume that something went wrong if we have a collective take on what would be the perfect state of things. What if the desired outcome is an internally conflicted duality, as it is the case of the Polish presidential elections 2020? Still, that collectively desired outcome could be something else that just the victory of one candidate. Maybe the electoral attendance? Maybe the fact of having elections at all? Whatever it is that we are collectively after, we learn by making errors at nailing down that specific value.

Thus, what are we collectively after? Once again, what is the point of discovering anything in respect to presidential elections? Politics are functional when they help uniting people, and yet some of the most efficient political strategies are those which use division rather than unity. Divide et impera, isn’t it? How to build social cooperation at the ground level, when higher echelons in the political system love playing the poker of social dissent? Understanding ourselves seems to be the key.    

Once again, neural networks suggest two alternative pathways for discovering it, depending on the amount of data we have regarding our own social structure. If we have acceptably abundant and reliable data, we can approach the thing straightforwardly, and test all the variables we have as the possible output ones in the neural network supposed to represent the way our society works. Variables which, when pegged as output ones in the network, allow the neural network to produce datasets very similar to the original one, are probably informative about the real values pursued by the given society. This is the approach I have already discussed a few times on my blog. You can find a scientific example of its application in my paper on energy efficiency.

There is another interesting way of approaching the same issue, and this one is much more empiricist, as it forces to discover more from scratch. We start with the simple observation that things change. When they change a lot, and we can measure change on some kind of quantitative scale, we call it variance. There is a special angle of approach to variance, when we observe it over time. Observable behavioural change – or variance at different levels of behavioural patterns – includes a component of propagated error. How? Let’s break it down.

When I change my behaviour in a non-aleatory way, i.e. when my behavioural change makes at least some sense, anyone can safely assume that I made the change for a reason. I changed my behaviour because my experience tells me that I should. I recognized something I f**ked up or some kind of frustration with the outcomes of my actions, and I change. I have somehow incorporated information about past error into my present behaviour, whence the logical equivalence: Variance in behaviour = Residual behaviour + Adaptive change after the recognition of error + Aleatory component.

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.

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

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

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

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


[1] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[2] Fields, C., Hoffman, D. D., Prakash, C., & Singh, M. (2018). Conscious agent networks: Formal analysis and application to cognition. Cognitive Systems Research, 47, 186-213. https://doi.org/10.1016/j.cogsys.2017.10.003

Cruel and fatalistic? Weelll, not necessarily.

MY EDITORIAL ON YOU TUBE

I am developing on one particular thread in my research, somehow congruent with the research on the role of cities, namely the phenomenon of collective intelligence and the prospects for using artificial intelligence to study human social structures. I am going both for good teaching material and for valuable scientific insight.

In social sciences, we face sort of an embarrassing question, which nevertheless is a fundamental one, namely how should we interpret quantitative data about societies. Simple but puzzling: are those numbers a meaningful representation of collectively pursued desired outcomes, or should we view them as largely random, temporary a representation of something going on at a deeper, essentially unobserved level?

I guess I can use artificial neural networks to try and solve that puzzle, at least to some extent. like starting with empirics, or, in plain human, with facts which I have observed so far. My most general observation, pertinent to every single instance of me meddling with artificial neural networks is that they are intelligent structures. I ground this general claim in two specific observations. Firstly, a neural network can experiment with itself, and come up with meaningful outcomes of experimentation, whilst keeping structural stability. In other words, an artificial neural network can change a part of itself whilst staying the same in its logical frame. Secondly, when I make an artificial neural network observe its own internal coherence, that observation changes the behaviour of the network. For me, that capacity to do meaningful and functional introspection is an important sign of intelligence.

This intellectual standpoint, where artificial neural networks are assumed to be intelligent structures, I pass to the question what kind of intelligence those networks can possibly represent. At this point I assume that human social structures are intelligent, too, as they can experiment with themselves (to some extent) whilst keeping structural stability, and they can functionally observe their own internal coherence and learn therefrom. Those two intelligent properties of human social structures are what we commonly call culture.

As I put those two intelligences – that of artificial neural networks and that of human social structures – back to back, I arrive at a new definition of culture. Instead of defining culture as a structured collection of symbolic representations, I define it as collective intelligence of human societies, which, depending on its exact local characteristics, endows those societies with a given flexibility and capacity to change, through a given capacity for collective experimentation.      

Once again, these are my empirical observations, the most general ones regarding the topic at hand. Empirically, I can observe that both artificial neural networks and human social structures can experiment with themselves in the view of optimizing something, whilst maintaining structural stability, and yet that capacity to experiment with itself has limits. Both a neural network and a human society can either stop experimenting or go haywire when experimentation leads to excessively low internal coherence of the system. Thence the idea of using artificial neural networks to represent the way that human social structures experiment with themselves, i.e. the way we are collectively intelligent. When we think about our civilisation, we intuitively ask what’s the endgame, seen from the present moment. Where are we going? That’s a delicate question, and, according to historians such as Arnold Toynbee, this is essentially a pointless one. Civilisations develop and degenerate, and supplant each other, in multi-secular cycles of apparently some 2500 – 3500 years each. If I ask the question ‘How can our civilisation survive, e.g. how can we survive climate change?’, the most rationally grounded answer is ‘Our civilisation will almost certainly fade away and die out, and then a new civilisation will emerge, and climate change could be as good an excuse as anything else to do that transition’. Cruel and fatalistic? Weelll, not necessarily. Think and ask yourself: would you like to stay the same forever? Probably not. The only way to change is to get out of our comfort zone, and the same is true for civilisations. The death of civilisations is different from extinction: when a civilisation dies, its culture transforms radically, i.e. its intelligent structure changes, yet the human population essentially survives.        

Social sciences are sciences because they focus on the ‘how?’ more than on the ‘why?’. The ‘why?’ implies there is a reason for everything, thus some kind of ultimate goal. The ‘how?’ dispenses with those considerations. The personal future of each individual human is almost entirely connected to the ‘how?’ of civilizational change and virtually completely disconnected from the ‘why?’. Civilisations change at the pace of centuries, and this is a slow pace. Even a person who lives for 100 years can see only a glimpse of human history. Yes, our individual existences are incredibly rich in personal experience, and we can use that existential wealth to make our own lives better, and to give a touch of betterment to the lives of incoming humans (i.e. our kids), and yet our personal change is very different from civilizational change. I will even go as far as claiming that individual human existence, with all its twists and turns, usually takes place inside one single cultural pattern, therefore inside a given civilisation. There are just a few human generations in the history of mankind, whose individual existences happened at the overlapping between a receding civilization and an emerging one.

On the night of July 6th, 2020, I had that strange dream, which I believe could be important in the teaching of social sciences. I dreamt of being pursued by some not quite descript ‘them’, in a slightly gangster fashion. I knew they had guns. I procured a gun for myself by breaking its previous owner neck by surprise. Yes, it is shocking, but it was just the beginning. I was running away from those people who wanted to get me. I was running through something like an urban neighbourhood, slightly like Venice, Italy, with a lot of canals all over the place. As I was running, I was pushing people into those canals, just to have freeway and keep running. I shot a few people dead, when they tried to get hold of me. All the time, I was experiencing intense, nagging fear. I woke up from that dream, shortly after midnight, and that intense fear was still resonating in me. After a few minutes of being awake, and whilst still being awake, I experienced another intense frame of mind, like a realization: me in that dream, doing horrible things when running away from people about whom I think they could try to hurt me, it was a metaphor of quite a long window in my so-far existence. Many a time I would just rush forward and do things I am still ashamed of today, and, when I meditate about it, I was doing it out of that irrational fear that other people could do me harm when they sort of catch on. When this realization popped in my mind, I immediately calmed down, and it was deep serenity, as if a lot of my deeply hidden fears had suddenly evaporated.

Fear is a learnt response to environmental factors. Recently, I have been discovering, and I keep discovering something new about fear: its fundamentally irrational nature. All of my early life, I have been taught that when I am afraid of something, I probably have good reasons to. Still, over the last 3 years, I have been practicing intermittent fasting (combined with a largely paleo-like diet), just to get out of a pre-diabetic state. Month after month, I was extending that window of fasting, and now I am at around 17 – 18 hours out of 24. A little bit more than one month ago, I decided to jump over another hurdle, i.e. that of fasted training. I started doing my strength training when fasting, early in the morning. The first few times, my body was literally shaking with fear. My muscles were screaming: ‘Noo! We don’t want effort without food!’. Still, I gently pushed myself, taking good care of staying in my zone of proximal development, and already after a few days, all changed. My body started craving for those fasted workouts, as if I was experiencing some strange energy inside of me. Something that initially had looked like a deeply organic and hence 100% justified a fear, turned out to be another piece of deeply ingrained bullshit, which I removed safely and fruitfully.

My generalisation on that personal experience is a broad question: how much of that deeply ingrained bullshit, i.e. completely irrational and yet very strong beliefs do we carry inside our body, like literally inside our body? How much memories, good and bad, do we have stored in our muscles, in our sub-cortical neural circuitry, in our guts and endocrine glands? It is fascinating to discover what we can change in our existence when we remove those useless protocols.

So far, I have used artificial neural networks in two meaningful ways, i.e. meaningful from the point of view of what I know about social sciences. It is generally useful to discover what we, humans, are after. I can use a dataset of common socio-economic stats, and test each of them as the desired outcome of an artificial neural network. Those stats have a strange property: some of them come as much more likely desired outcomes than others. A neural network oriented on optimizing those ‘special’ ones is much more similar to the original data than networks pegged on other variables. It is also useful to predict human behaviour. I figured out a trick to make such predictions: I define patterns of behaviour (social roles or parts thereof), and I make a neural network which simulates the probability that each of those patterns happens.

One avenue consists in discovering a hierarchy of importance in a set of socio-economic variables, i.e. in common stats available from external sources. In this specific approach, I treat empirical datasets of those stats as manifestation of the corresponding state spaces. I assume that the empirical dataset at hand describes one possible state among many. Let me illustrate it with an example: I take a big dataset such as Penn Tables. I assume that the set of observations yielded by the 160ish countries in the database, observed since 1964, is like a complex scenario. It is one scenario among many possible. This specific scenario has played out the way it has due to a complex occurrence of events. Yet, other scenarios are possible.      

To put it simply, datasets made of those typical stats have a strange property, possible to demonstrate by using a neural network: some variables seem to reflect social outcomes of particular interest for the society observed. A neural network pegged on those specific variables as output ones produces very little residual error, and, consequently, stays very similar to the original dataset, as compared to networks pegged on other variables therein.

Under this angle of approach, I ascribe an ontological interpretation to the stats I work with: I assume that each distinct socio-economic variable informs about a distinct phenomenon. Mind you, it is just one possible interpretation. Another one, almost the opposite, claims that all the socio-economic stats we commonly use are essentially facets (or dimensions) of the same, big, compound phenomenon called social existence of humans. Long story short, when I ascribe ontological autonomy to different socio-economic stats, I can use a neural network to establish two hierarchies among these variables: one hierarchy is that of value in desired social outcomes, and another one of epistatic role played by individual variables in the process of achieving those outcomes. In other words, I can assess what the given society is after, and what are the key leverages being moved so as to achieve the outcome pursued.

Another promising avenue of research, which I started exploring quite recently, is that of using an artificial neural network as a complex set of probabilities. Those among you, my readers, who are at least mildly familiar with the mechanics of artificial neural networks, know that a neural network needs empirical data to be transformed in a specific way, called standardization. The most common way of standardizing consists in translating whatever numbers I have at the start into a scale of relative size between 0 and 1, where 1 corresponds to the local maximum. I thought that such a strict decimal fraction comprised between 0 and 1 can spell ‘probability’, i.e. the probability of something happening. This line of logic applies to just some among the indefinitely many datasets we can make. If I have a dataset made of variables such as, for example, GDP per capita, healthcare expenditures per capita, and the average age which a person ends their formal education at, it cannot be really considered in terms of probability. If there is any healthcare system in place, there are always some healthcare expenditures per capita, and their standardized value cannot be really interpreted as the probability of healthcare spending taking place. Still, I can approach the same under a different angle. The average healthcare spending per capita can be decomposed into a finite number of distinct social entities, e.g. individuals, local communities etc., and each of those social entities can be associated with a probability of using any healthcare at all during a given period of time.

That other approach to using neural networks, i.e. as sets of probabilities, has some special edge to it. I can simulate things happening or not, and I can introduce a disturbing factor, which kicks certain pre-defined events into existence or out of it. I have observed that once a phenomenon becomes probable, it is not really possible to kick it out of the system, yet it can yield to newly emerging phenomena. In other words, my empirical observation is that once a given structure of reality is in place, with distinct phenomena happening in it, that structure remains essentially there, and it doesn’t fade even if probabilities attached to those phenomena are random. On the other hand, when I allow a new structure, i.e. another set of distinct phenomena, to come into existence with random probabilities, that new structure will slowly take over a part of the space previously occupied just by the initially incumbent, ‘old’ set of phenomena. All in all, when I treat standardized numerical values – which an artificial neural network normally feeds on – as probabilities of happening rather than magnitudes of something existing anyway, I can simulate the unfolding of entire new structures. This is a structure generating other structures.

I am trying to reverse engineer that phenomenon. Why do I use at all numerical values standardized between 0 and 1, in my neural network? Because this is the interval (type) of values that the function of neural activation needs. I mean there are some functions, such as the hyperbolic tangent, which can work with input variables standardized between – 1 and 1, yet if I want my data to be fully digest for any neural activation function, I’d better standardize it between 0 and 1. Logically, I infer that mathematical functions useful for simulating neural activation are mathematically adapted to deal with sets of probabilities (range between 0 and 1) rather than sets of local magnitudes.    

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

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

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

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

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

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

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

What can be wanted only at the collective level

MY EDITORIAL ON YOU TUBE

I am recapitulating on my research regarding cities and their role in our civilization. In the same time, I start preparing educational material for the next semester of teaching, at the university. I am testing somehow new a format, where I precisely try to put science and teaching content literally side by side. The video editorial on You Tube plays an important part here, and I sincerely invite all my readers to watch it.  

I am telling the story of cities once again, from the beginning. Beginning of March 2020. In Poland, we are going into the COVID-19 lockdown. I am cycling through the virtually empty streets of Krakow, my hometown. I slowly digest the deep feeling of weirdness: the last time I saw the city that inanimate, it was during some particularly tense moments in the times of communism, decades ago. A strange question keeps floating on the surface of my consciousness: ‘How many human footsteps per day does this place need to be truly alive?’.

Cities are demographic anomalies. This is particularly visible from space, when satellite imagery serves to distinguish urban areas from rural ones. Cities are abnormally dense agglomerations of man-made architectural structures, paired with just abnormally dense clusters of night-time lights. We, humans, we agglomerate in cities. We purposefully reduce the average social distance, and just as purposefully increase the intensity of our social interactions. Why and how do we do that? The ‘why?’ is an abyssal question. If I attempt to answer it with all the intellectual rigor possible, it is almost impossible to answer. Still, there is hope. I have that little theory of mine – well, not just mine, it is called ‘contextual ethics’ – namely that we truly value the real outcomes we get. In other words, we really want the things which we actually get at the end of the day. This could be a slippery slope. Did Londoners want to have the epidemic of plague, in 1664? I can cautiously say it wasn’t on the top list of their wildest dreams. Yet, acquiring herd immunity and figuring out ways of containing an epidemic outbreak: well, that could be a valuable outcome in the long perspective. That outcome has a peculiar trait: it sort of can be wanted only at the collective level, since it is a collective outcome par excellence. If we pursue an outcome like this one, we are being collectively intelligent. It would be somehow adventurous to try and acquire herd immunity singlehandedly. 

Cities manifest one of the ways we are collectively intelligent. In cities, we get individual outcomes, and collective ones, sort of in layers. Let’s take a simple pattern of behaviour: imitation and personal style. We tend to imitate each other, and frequently, as we are doing so, we love pretending we are reaching the peak or originality. Both imitation and pretention to originality make any sense only when there are other people around, and the more people are there around, the more meaningful it is. Imagine you have a ranch in Texas, like 200 hectares, and in order to imitate anyone, or to pretend being original, you need to drive for 2 hours one way, and then 2 hours back, and, at the end of the day, you have interacted with maybe 20 people.

Our human social structures are machines which make other social structures, and not only sustain the current humans inside. A lot of behavioural patterns make any sense at all when the density of population reaches a reasonably required minimum. Social interactions produce and convey information which our brains use to form new patterns. As I think about it, my take on collective intelligence opens up onto the following claim: we have cities in order to make some social order for the future, and order made of social roles and group identities. We have a given sharpness of social distinction between cities and the countryside, e.g. in terms of density in population, in order to create some social roles and group identities for the future.

We, humans, had discovered – although we might not be aware of what we discovered – that certain types of social interactions (not all of them) can be made into recurrent patterns, and those patterns have the capacity to make new patterns. As long as I just date someone, it is temporary interaction. When I propose, it takes some colours: engagement can turn into marriage (well, it should, technically), thus one pattern of interaction can produce another pattern. When I marry a woman, it opens up a whole plethora of new interactions: parenthood, agreement as for financials (prenuptial contracts or the absence thereof), in-law family relations (parents-in-law, siblings-in-law). Have you noticed that some of the greatest financial fortunes, over centuries, had been accumulated inside family lineages? See? We hit the right pattern of social interactions, and from there we can derive either new copies of the same structure or altogether new structures.

Blast! I have just realized I finally nailed down something which I have been turning around in my mind for months: the logical link between human social structures and artificial neural networks. I use artificial neural networks to simulate collective intelligence in human societies, and I have found one theoretical assumption which I need to put in such a model, namely that consecutive states of society must form a Markov chain, i.e. each individual state must be possible to derive entirely from the preceding state, without any exogenous corrective influence.

Still, I felt I was missing something and now: boom! I figured it out. Once again: among different social interactions there are some which have the property to turn into durable and generative patterns, i.e. they reproduce their general structure in many local instances, each a bit idiosyncratic, yet all based on the same structure. In other words, some among our social interactions have the capacity to be intelligent structures, which experiment with themselves by producing many variations of themselves. This is exactly what artificial neural networks are: they are intelligent structures able to experiment with themselves by generating many local, idiosyncratic variations and thereby nailing down the variation which minimizes error in achieving a desired outcome.

When I use an artificial neural network to simulate social change, I implicitly assume that the social change in question is a Markov chain of states, and that the society under simulation has some structural properties which remain consistent over all the Markov chain of states. Now, I need to list the structural properties of artificial neural networks I use in my research, and to study the conditions of their stability. An artificial neural network is a sequence of equations being run in a loop. Structure of the network is given by each equation separately, and by their sequential order. I am going to break down that logical structure once again and pass its components in review. Just a general, introductory remark: I use really simple neural networks, which fall under the general category of multi-layer perceptron. This is probably the simplest that can be in terms of AI, and this is the logic which I connect to collective intelligence in human societies.

The most fundamental structure of an artificial neural network is given by the definition of input variables – the neural stimuli – and their connection to the output variable(s). I used that optional plural, i.e. the ‘(s)’ suffix, because the basic logic of an artificial neural network assumes defining just one output variable, whilst it is possible to construe that output as the coefficient of a vector. In other words, any desired outcome given by one number can be seen as being derived from a collection of numbers. I hope you remember from your math classes in high school that the Pythagorean theorem, I mean the a2 + b2 = c2 one, has a more general meaning, beyond the simple geometry of a right-angled triangle. Any positive number we observe – our height in centimetres (or in feet and inches), the right amount of salt to season shrimps etc. – any of those amounts can be interpreted as the square root of the sum of squares of two other numbers. I mean, any x > 0 is x = (y2 + x2)0,5. Logically, those shady y and z can be seen, in turn, as derived, Pythagorean way, from even shadier and more mysterious entities. In other words, it is plausible to assume that x = (y2 + x2)0,5 = {[(a2 + b2)0,5]2 + [(c2 + d2)0,5]2}0,5 etc.

As a matter of fact, establishing an informed distinction between input variables on the one hand, and the output variable on the other hand is the core and the purpose of my method. I take a handful of variables, informative about a society or a market, and I make as many alternative neural networks as there are variables. Each alternative network has the same logical structure, i.e. the same equations in the same sequence, but is pegged on a different variable as its output. At some point, I have the real human society, i.e. the original, empirical dataset, and as many alternative versions thereof as there are variables in the dataset. In other words, I have a structure and a finite number of experiments with that structure. This is the methodology I used, for example, in my paper on energy efficiency.

There are human social structures which can make other social structures, by narrowing down, progressively, the residual error generated when trying to nail down a desired outcome and experimenting with small variations of the structure in question. Those structures need abundant social interactions in order to work. An artificial neural network which has the capacity to stay structurally stable, i.e. which has the capacity to keep the Euclidean distance between variables inside a predictable interval, can be representative for such a structure. That predictable interval of Euclidean distance corresponds to predictable behavioural coupling, the so-called correlated coupling: social entity A reacts to what social entity B is doing, and this reaction is like music, i.e. it involves moving along a scale of response in a predictable pattern.

I see cities as factories of social roles. The intensity of social interactions in cities works like a social engine. New businesses emerge, new jobs form in the labour market. All these require new skillsets and yet those skillsets are expected to stop being entirely new and to become somehow predictable and reliable, whence the need for correspondingly new social roles in training and education for those new skills. As people endowed with those new skills progressively take over business and jobs, even more novel skillsets emerge and so the wheel of social change spins. The peculiar thing about social interactions in cities are those between young people, i.e. teenagers and young adults up to the age of 25. Those interactions have a special trait, just as do the people involved: their decision-making processes are marked by significantly greater an appetite for risk and immediate gratification, as opposed to more conservative and more perseverant behavioural patterns in older adults.

Cities allow agglomeration of people very similar as regards the phase of their personal lifecycle, and, in the same time, very different in their cultural background. People mix a lot inside generations. Cities produce a lot of social roles marked with a big red label ‘Only for humans below 30!’, and, in the same time, lots of social roles marked ‘Below 40, don’t even think about it!’. Please, note that I define a generation in sociological terms, i.e. as a cycle of about 20 ÷ 25 years, roughly corresponding to the average age of reproduction (I know, first parenthood sounds kind’a more civilized). According to this logic, I am one generation older than my son.

That pattern of interactions is almost the exact opposite of rural villages and small towns, where people interact much more between generations and less inside generations. Social roles form as ‘Whatever age you are between 20 and 80, you do this’. As we compare those two mechanisms of role-formation, in turns out that cities are inherently prone to creating completely new sets of social roles for each new generation of people coming with the demographic tide. Cities facilitate innovation at the behavioural level. By innovation, I mean the invention of something new combined with a mechanism of diffusing that novelty across the social system.

These are some of my thoughts about cities. How can I play them out into my teaching? I start with a staple course of mine: microeconomics. Microeconomics sort of nicely fit with the topic of cities, and I don’t even have to prove it, ‘cause Adam Smith did. In his ‘Inquiry Into The Nature And Causes of The Wealth of Nations’, Book I, Chapter III, entitled ‘That The Division Of Labour Is Limited By The Extent Of The Market’, he goes: ‘[…] There are some sorts of industry, even of the lowest kind, which can be carried on nowhere but in a great town. A porter, for example, can find employment and subsistence in no other place. A village is by much too narrow a sphere for him; even an ordinary market-town is scarce large enough to afford him constant occupation. In the lone houses and very small villages which are scattered about in so desert a country as the highlands of Scotland, every farmer must be butcher, baker, and brewer, for his own family. In such situations we can scarce expect to find even a smith, a carpenter, or a mason, within less than twenty miles of another of the same trade. The scattered families that live at eight or ten miles distance from the nearest of them, must learn to perform them- selves a great number of little pieces of work, for which, in more populous countries, they would call in the assistance of those workmen. Country workmen are almost everywhere obliged to apply themselves to all the different branches of industry that have so much affinity to one another as to be employed about the same sort of materials. A country carpenter deals in every sort of work that is made of wood; a country smith in every sort of work that is made of iron. The former is not only a carpenter, but a joiner, a cabinet-maker, and even a carver in wood, as well as a wheel-wright, a plough-wright, a cart and waggon-maker. The employments of the latter are still more various. It is impossible there should be such a trade as even that of a nailer in the remote and inland parts of the highlands of Scotland. Such a workman at the rate of a thousand nails a-day, and three hundred working days in the year, will make three hundred thousand nails in the year. But in such a situation it would be impossible to dispose of one thousand, that is, of one day’s work in the year […]’.     

Microeconomics can be seen as a science of how some specific social structures, strongly pegged in the social distinction between cities and the countryside, reproduce themselves in time, as well as produce other social structures. I know, this definition does not really seem to fall close to the classical, Marshallian graph of two curves, i.e. supply and demand, crossing nicely in the point of equilibrium. ‘Does not seem to…’ is distinct from ‘does not’. Let’s think a moment. The local {Supply <> Demand} equilibrium is a state of deals being closed at recurrent, predictable a price. One of the ways to grasp the equilibrium price consists in treating it as the price which clears all the surplus stock of goods in the market. It is the price which people agree upon, at the end of the day. Logically, there is an underlying social structure which allows such a recurrent, equilibrium-making bargaining process. This structure reproduces itself in n copies, over and over again, and each such copy is balanced on different a coupling between equilibrium price and equilibrium product.

Here comes something I frequently remind to those of my students who have enough grit to read any textbook in economics: those nice curves in the Marshallian graph, namely demand and supply, don’t really exist. They represent theoretical states at best, and usually these are more in the purely hypothetical department. We just guess that social reality is being sort bent along them. The thing that really exists, here and now, is the equilibrium price that we strike our deals at, and the corresponding volumes of business we do at this price. What really exists in slightly longer a perspective is the social structure able to produce local equilibriums between supply and demand, which, in turn, requires people in that structure recurrently producing economically valuable, tradable surpluses of physical goods and/or marketable skills.

Question: how can I know there is any point in producing an economically valuable surplus of anything? Answer: where other people make me understand they would gladly acquire said surplus. Mind you, although markets are mostly based on money, there are de facto markets without straightforward monetary payment. The example which comes to my mind is a structure which I regularly observe, every now and then, in people connected to business and politics, especially in Warsaw, the capital of my home country, Poland. Those guys (and gals) sometimes call it ‘the cooperative of information and favour’. You slightly facilitate a deal I want to strike, and I remember that, and later I facilitate the deal you want to strike. We don’t do business together, strictly speaking, we just happen to have mutual leverage on each other’s business with third parties. I observed that pattern frequently, and the thing really works as a market of favours based on social connections and individual knowledge. No one exchanges money (that could be completely accidentally perceived as corruption, and that perfectly accidental false perception could occur in a prosecutor, and no one wants to go to jail), and yet this is a market. There is an equilibrium price for facilitating a $10 million deal in construction. That equilibrium price might be the facilitation of another $10 million deal in construction, or the facilitation of someone being elected to the city council. By the way, that market of favours really stirs it up when some kind of elections is upcoming.

Anyway, the more social interactions I enter into over a unit of time, the more chances I have to spot some kind of economically valuable surplus in what I do and make. The more such social interactions are possible in the social structure of my current residence, the better. Yes, cities allow that. The next step is from those general thoughts to a thread of teaching and learning. I can see a promising avenue in the following scheme:

>>> Step 1: I choose or ask my students to choose any type of normal, recurrent social interaction. It can be interesting to film a bit of city life, just like that, casually, with a phone, and then use it as empirical material.

>>> Step 2: Students decompose that interaction into layers of different consistency, i.e. separate actions and events which change quickly and frequently from those which last and recur.

>>> Step 3: Students connect the truly recurrent actions and events to an existing market of goods or marketable skills. They describe, with as much detail as possible, how recurrent interactions translate into local states of equilibrium.

Good. One carryover done, namely into microeconomics, I try another one, into another one of my basic courses at the university: fundamentals of management. There is something I try to tell my students whenever I start this course, in October: ‘Guys, I can barely outline what management is. You need to go out there, into that jungle, and then you learn. I can tell you what the jungle looks like, sort of in general’. Social interactions and social roles in management spell power, hierarchy, influence, competition and cooperation on the top of all that. Invariably, students ask me: ‘But, sir, wouldn’t it be simpler just to cooperate, without all those games of power and hierarchy inside the organization?’. My answer is that yes, indeed, it would be simpler to the point of being too simple, i.e. simplistic. Let’s think. When we rival inside the organization, we need to interact. There is no competition without interaction. The more we compete, the more we interact, and the more personal resources we need to put in that interaction.

Mind you, competition is not the only way to trigger intense, abundant human interaction. Camaraderie, love, emotional affiliation to a common goal – they all can do the same job, and they tend to be more pleasant than interpersonal competition. There is a caveat, though: all those forms of action-generating emotional bonds between human beings tend to be fringe phenomena. They happen rarely. With how many people, in our existence, can we hope to develop a bond of the type ‘I have your back and you have my back, no matter what’? Just a few, at best. Quite a number of persons walk through their entire life without ever experiencing this type of connection. On the other hand, competition is a mainstream phenomenon. You put 5 random people in any durable social relation – business, teamwork, art etc. – and they are bound to develop competitive behaviour. Competition happens naturally, very frequently, and can trigger tacit coordination when handled properly.

Yes, right, you can legitimately ask what does it mean to handle competition properly. As a kid, or in your teenage years, have you ever played a competitive game, such as tennis, basketball, volleyball, chess, computer games, or even plain infantile fighting? Do you know that situation when other people want to play with you because you sometimes score and win, but kind of not all the time and not at all price? That special state when you get picked for the next game, and you like the feeling? Well, that’s competition handled properly. You mobilise yourself in rivalry with other people, but you keep in mind that the most fundamental rule of any competitive game is to keep the door open for future games.      

Thus, I guess that teaching management in academia, which I very largely do, may consist in showing my students how to compete constructively inside an organisation, i.e. how to be competitive and cooperative in the same time. I can show internal competition and cooperation in the context of a specific business case. I already tend to work a lot, in class, with cases such as Tesla, Netflix, Boeing or Walt Disney. I can use their business description, such as can be found in an annual report, to reconstruct an organisational environment where competition and cooperation can take place. The key learning for management students is to understand what traits of that environment enable constructive competition, likely to engender cooperation, as opposed to situations marked either with destructive competition or with a destructive absence thereof, on the other hand.

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

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

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

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

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

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

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

I followed my suspects home

MY EDITORIAL ON YOU TUBE

I am putting together the different threads of thinking which I have developed over the last weeks. I am trying to make sense of the concept of collective intelligence, and to make my science keep up with the surrounding reality. If you have followed my latest updates, you know I yielded to the temptation of taking a stance regarding current events (see Dear Fanatics and We suck our knowledge about happening into some kind of patterned structure ). I have been struggling, and I keep struggling with balancing science with current observation. Someone could say: ‘But, prof, isn’t science based on observation?’. Yes, indeed it is, but science is like a big lorry: it has a wide turning radius, because it needs to make sure to incorporate current observation into a conceptual network which takes a step back from current observation. I am committed to science, and, in the same time, I am trying to take as tight a turning around the current events as possible. Tires screech on the tarmac, synapses flare… It is exciting and almost painful, all in the same time. I think we need it, too: that combination of involvement and distance.

Once again, I restate my reasons for being concerned about the current events. First of all, I am a human being, and when I see and feel the social structure rattling around me, I become a bit jumpy. Second of all, as the Black Lives Matter protests spill from the United States to Europe, I can see the worst European demons awakening. Fanatic leftist anarchists, firmly believing they will change the world for the better by destroying it, pave the way for right-wing fanatics, who, in turn, firmly believe that order is all we need and order tastes the best when served with a pinchful of concentration camps. When I saw some videos published by activists from the so-called Chaz, or Capitol-Hill-Autonomous-Zone, in Seattle, Washingtion, U.S., I had a déjà vu. ‘F**k!’ I thought ‘This is exactly what I remember about life in a communist country’. A bunch of thugs with guns control the borders and decide who can get in and out. Behind them, a handful of grandstanding, useful idiots with big signs: ‘Make big business pay’, ‘We want restorative justice’ etc. In the background, thousands of residents – people who simply want to live their lives the best they can – are trapped in that s**t. This is what I remember from the communist Poland: thugs with deadly force using enthusiastic idiots to control local resources.

Let me be clear: I know people in Poland who use the expression ‘black apes’ to designate black people. I am sorry when I hear things like that. That one hurts too. Still, people who say stupid s**t can be talked into changing their mind. On the other hand, people who set fire to other people’s property and handle weapons are much harder to engage into a creative exchange of viewpoints. I think it is a good thing to pump our brakes before we come to the edge of the cliff. If we go over that edge, it will dramatically slow down positive social change instead of speeding it up.   

Thirdly, events go the way I was slightly afraid they would when the pandemic started, and lockdowns were being instated. The sense of danger combined with the inevitable economic downturn make a perfect tinderbox for random explosions. This is social change experienced from the uncomfortable side. When I develop my research about cities and their role in our civilisation, I frequently refer to the concept of collective intelligence. I refer to cities as a social contrivance, supposed to work as creators of new social roles, moderators of territorial conflicts, and markets for agricultural goods. If they are supposed to work, you might ask, why don’t they? Why are they overcrowded, polluted, infested with crime and whatnot?

You probably know that you can hurt yourself with a screwdriver. It certainly not the screwdriver’s fault, and it is not even always your fault. S**t happens, quite simply. When a lot of people do a lot of happening, s**t happens recurrently. It is called risk. At the aggregate scale risk is a tangible quantity of damage, not just a likelihood of damage taking place. This is why we store food for later and buy insurance policies. Dense human settlements mean lots of humans doing things very frequently in space and time, and that means more risk. We create social structures, and those structures work. This is how we survived. Those structures always have some flaws, and when we see it, we try to make some change.

My point is that collective intelligence means collective capacity to figure stuff out when we are at a loss as for what to do next. It does not mean coming up with perfect solutions. It means advancing one more step on a path that we have no idea where exactly it leads to. Scientifically, the concept is called adaptive walk in rugged landscape. There is a specific theoretical shade to it, namely that of conscious representation.

Accidents happen, and another one has just happened. I stumbled upon a video on You Tube, entitled ‘This Scientist Proves Why Our Reality Is False | Donald Hoffman on Conversations with Tom’( https://youtu.be/UJukJiNEl4o ), and I went after the man, i.e. after prof. Hoffman. Yes, guys, this is what I like doing. When I find someone with interesting ideas, I tend to sort of follow them home. One of my friends calls it ‘the bulldog state of mind’. Anyway, I went down this specific rabbit hole, and I found two articles: Hoffman et al. 2015[1] and Fields et al. 2018[2]. I owe professor Hoffman for giving me hope that I am not mad, when I use neural networks to represent collective intelligence. I owe him and his collaborators for giving some theoretic polish to my own work. I am like Moliere’s bourgeois turning into a gentleman: I suddenly realize what kind of prose I have been speaking about that topic. That prose is built around the concept of Markov chains, i.e. sequential states of reality where each consecutive state is the result of just the previous state, without exogenous corrections. The neural network I use is a Markovian kernel, i.e. a matrix (= a big table with numbers in it, to be simple) that transforms one Markov space into another.

As we talk about spaces, I feel like calling two other mathematical concepts, important for understanding the concept of Conscious Agents Networks (yes, the acronym is CAN), as developed by professor Hoffman. These concepts are: measurable space and σ-algebra. If I take a set of any phenomenal occurrences – chicken, airplanes, people, diamonds, numbers and whatnot – I can recombine that set by moving its elements around, and I can define subsets inside of it by cherry-picking some elements. All those possible transformations of the set X, together with the way of doing them and the rules of delimiting the set X out of its environment, all that makes the σ-algebra of the set X. The set X together with its σ-algebra is a measurable space.

Fields et al. 2018 represent conscious existence in the world as relation between three essential, measurable spaces: states of the world or W, conscious experiences thereof or X, and actions, designated as G. Each of these is a measurable space because it is a set of phenomena accompanied by all the possible transformations thereof. States of the world are a set, and this set can be recombined through its specific σ-algebra. The same holds for experiences and actions. Conscious existence consists in consciously experiencing states of the world and taking actions on the grounds of that experience.

That brings up an interesting consequence: conscious existence can be represented as a mathematical manifold of 7 dimensions. Why 7? It is simple. States of the world W, for one. Experiences X, two. Actions G, three. Perception is a combination of experiences with states of the world, right? Therefore, perception P is a Markovian kernel (i.e. a set of strings) attaching those two together and can be represented as P: W*X → X. That makes four dimensions. We go further. Decisions are a transformation of experiences into actions, or D: X*G → G. Yes, this is another Markovian kernel, and it is the 5-th dimension of conscious existence. The sixth one is the one that some people don’t like, i.e. the consequences of actions, thus a Markovian kernel that transforms actions into further states of the world, and spells A: G*W →W. All that happy family of phenomenological dimensions, i.e. W, X, G, P, D, A, needs another, seventh dimension to have any existence at all: they need time t. In the theory presented by Fields et al. 2018 , a Conscious Agent (CA) is precisely a 7-dimensional combination of W, X, G, P, D, A, and t.

That paper by Fields et al. 2018 made me understand that representing collective intelligence with neural networks involves deep theoretical assumptions about perception and consciousness. Neural networks are mathematical structures. In simpler words, they are combinations of symmetrical equations, asymmetrical inequalities and logical propositions linking them (such as ‘if… then…’). Those mathematical structures are divided into output variables and input variables. A combination of inputs should stay in a given relation, i.e. equality, superiority or inferiority to a pre-defined output. The output variable is precisely the tricky thing. The theoretical stream represented by Fields et al. 2018 , as well as by: He et al. 2015[3], Hoffman et al. 2015[4], Hoffman 2016[5] calls itself ‘Interface Theory of Perception’ (ITP) and assumes that the output of perception and consciousness consists in payoffs from environment. In other words, perception and consciousness are fitness functions, and organisms responsive only to fitness systematically outcompete those responsive to a veridical representation of reality, i.e. to truth about reality. In still other words, ITP stipulates that we live in a Matrix that we make by ourselves: we peg our attention on phenomena that give us payoffs and don’t give a s**t about all the rest.

Apparently, there is an important body of science which vigorously oppose the Interface Theory of Perception (see e.g. Trivers 2011[6]; Pizlo et al. 2014[7]), by claiming that human perception is fundamentally veridical, i.e. oriented on discovering the truth about reality.

In the middle of that theoretical clash, my question is: can I represent intelligent structures as Markov chains without endorsing the assumptions of ITP? In other words, can I assume that collective intelligence is a sequence of states, observable as sets of quantitative variables, and each such state is solely the outcome of the preceding state? I think it is possible, and, as I explore this particular question, I decided to connect with a review I am preparing right now, for a manuscript entitled ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’, submitted for publication in the International Journal of Energy Sector Management (ISSN1750-6220). As I am just a reviewer of this paper, I think I should disseminate its contents on my blog, and therefore I will break with the basic habit I have to provide a linked access to the sources I quote on my blog. I will be just discussing the paper in this update, with the hope of adding, by the means of review, as much scientific value to the initial manuscript as possible.

I refer to that paper because it uses a neural network, namely a Levenberg–Marquardt Backpropagation Network, for validating a model of interactions between economy, energy, and environment. I want to start my review from this point, namely from the essential logic of that neural network and its application to the problem studied in the paper I am reviewing. The usage of neural networks in social sciences is becoming a fashion, and I like going through the basic assumptions of this method once again, as it comes handy in connection with the Interface Theory of Perception which I have just passed cursorily through.

The manuscript ‘Evolutionary Analysis of a Four-dimensional Energy-Economy- Environment Dynamic System’ explores the general hypothesis that relations between energy, economy and the ecosystem are a self-regulating, complex ecosystem. In other words, this paper somehow assumes that human populations can somehow self-regulate themselves, although not necessarily in a perfect manner, as regards the balance between economic activity, consumption of energy, and environmental interactions.   

Neural networks can be used in social sciences in two essential ways. First of all, we can assume that ‘IT IS INTELLIGENT’, whatever ‘IT’ is or means. A neural network is supposed to represent the way IT IS INTELLIGENT. Second of all, we can use neural networks instead of classical stochastic models so as to find the best fitting values in the parameters ascribed to some variables. The difference between a stochastic method and a neural network, as regards nailing those parameters down, is in the way of reading and utilizing residual errors. We have ideas, right? As long as we keep them nicely inside our heads, those ideas look just great. Still, when we externalize those ideas, i.e. when we try and see how that stuff works in real life, then it usually hurts, at least a little. It hurts because reality is a bitch and does not want to curb down to our expectations. When it hurts, the local interaction of our grand ideas with reality generates a gap. Mathematically, that gap ‘Ideal expectations – reality’ is a local residual error.

Essentially, mathematical sciences consist in finding such logical, recurrent patterns in our thinking, which generate as little residual error as possible when confronted with reality. The Pythagorean theorem c2 = a2 + b2, the π number (yes, we read it ‘the pie number’) etc. – all that stuff consists of formalized ideas which hold in confrontation with reality, i.e. they generate very little error or no error at all. The classical way of nailing down those logical structures, i.e. the classical way of doing mathematics, consists in making a provisional estimation of what real life should look like according to our provisional math, then assessing all the local residual errors which inevitably appear as soon as we confront said real life, and, in a long sequence of consecutive steps, in progressively modifying our initial math so as it fits well to reality. We take all the errors we can find at once, and we calibrate our mathematical structure so as to minimize all those errors in the same time.

That was the classical approach. Mathematicians whom we read about in history books were dudes who would spend a lifetime at nailing down one single equation. With the emergence of simple digital tools for statistics, it has become a lot easier. With software like SPSS or Stata, you can essentially create your own equations, and, provided that you have relevant empirical data, you can quickly check their accuracy. The problem with that approach, which is already being labelled as classical stochastic, is that if an equation you come up with proves statistically inaccurate, i.e. it generates a lot of error, you sort of have to guess what other equation could fit better. That classic statistical software speeds up the testing, but not really the formulation of equations as such.

With the advent of artificial intelligence, things have changed even further. Each time you fire up a neural network, that thing essentially nails down new math. A neural network learns: it does the same thing that great mathematical minds used to do. Each time a neural network makes an error, it learns on that single error, and improves, producing a slightly different equation and so forth, until error becomes negligible. I noticed there is a recent fashion to use neural networks as tools for validating mathematical models, just as classical stochastic methods would be used, e.g. Ordinary Least Squares. Generally, that approach has some kind of bad methodological smell for me. A neural network can process the same empirical data that an Ordinary Least Squares processes, and the neural network can yield the same type of parameters as the OLS test, and yet the way those values are obtained is completely different. A neural network is intelligent, whilst an Ordinary Least Squares test (or any other statistical test) is not. What a neural network yields comes out of a process very similar to thinking. The result of a test is just a number.  

If someone says: ‘this neural network has validated my model’, I am always like: ‘Weeelll, I guess what this network has just done was to invent its own model, which you don’t really understand, on the basis of your model’. My point is that a neural network can optimize very nearly anything, yet the better a network optimizes, the more prone it is to overfitting, i.e. to being overly efficient at justifying a set of numbers which does not correspond to the true structure of the problem.

Validation of the model, and the use of neural network to that purpose leads me to the model itself, such as it is presented in the manuscript. This is a complex logical structure and, as this blog is supposed to serve the popularization of science, I am going to stop and study at length both the model and its connection with the neural network. First of all, the authors of that ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ manuscript are non-descript to me. This is called blind review. Just in case I had some connection to them. Still, man, like really: if you want to conspire, do it well. Those authors technically remain anonymous, but right at the beginning of their paper they introduce a model, which, in order to be fully understood, requires referring to another paper, which the same authors quote: Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019, https://doi.org/10.1155/2019/3941920 .

As I go through that referenced paper, I discover largely the same line of logic. Guys, if you want to remain anonymous, don’t send around your Instagram profile. I am pretty sure that ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ is very largely a build-up the paper they quote, i.e. ‘Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System’. It is the same method of validation, i.e. a Levenberg-Marquardt Backpropagation Network, with virtually the same empirical data, and almost identical a model.

Good. I followed my suspects home, I know who do they hang out with (i.e. with themselves), and now I can go back to their statement. Both papers, i.e. the one I am reviewing, and the one which serves as baseline, follow the same line of logic. The authors build a linear model of relations between economy, energy, and environment, with three main dependent variables: the volume x(t) of pollution emitted, the value of Gross Domestic Product y(t), and environmental quality z(t).

By the way, I can see that the authors need to get a bit more at home with macroeconomics. In their original writing, they use the expression ‘level of economic growth (GDP)’. As regards the Gross Domestic Product, you have either level or growth. Level means aggregate GDP, and growth means percentage change over time, like [GDP(t1) – GDP(t0)] / GDP(t0). As I try to figure out what exactly do those authors mean by ‘level of economic growth (GDP)’, I go through the empirical data they introduce as regards China and its economy. Under the heading y(t), i.e. the one I’m after, they present standardized values which start at y(2000) = 1,1085 in the year 2000, and reach y(2017) = 9,2297 in 2017. Whatever the authors have in mind, aggregate GDP or its rate of growth, that thing had changed by 9,2297/1,1085 = 8,32 times between 2000 and 2017.

I go and check with the World Bank. The aggregate GDP of Cina, measured in constant 2010 US$, made $2 232 billion in 2000, and  $10 131,9 billion in 2017. This is a change by 4,54 times, thus much less than the standardized change in y(t) that the authors present. I check with the rate of real growth in GDP. In 2000, the Chinese economic growth was 8,5%, and in 2017 it yields 6,8%, which gives a change by (6,8/8,5) = 0,8 times and is, once again, far from the standardized 3,06 times provided by the authors. I checked with 2 other possible measures of GDP: in current US$, and in current international $ PPP. The latter indicator provides values for gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parity (PPP) conversion factor. The first of the two yielded a 10,02 times growth in GDP, in China, from 2000 to 2017. The latter gives 5,31 times growth.

Good. I conclude that the authors used some kind of nominal GDP in their data, calculated with internal inflation in the Chinese economy. That could be a serious drawback, as regards the model they develop. This is supposed to be research on the mutual balance between economy, ecosystems, and energy. In this context, economy should be measured in terms of real output, thus after having shaven off inflation. Using nominal GDP is a methodological mistake.

What the hell, I go further into the model. This is a model based on differentials, thus on local gradients of change. The (allegedly) anonymous authors of the ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ manuscript refer their model, without giving much of an explanation, to that presented in: Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019. I need to cross reference those two models in order to make sense of it.

The chronologically earlier model in: Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019 operates within something that the authors call ‘economic cycle’ (to be dug in seriously, ‘cause, man, the theory of economic cycles is like a separate planet in the galaxy of social sciences), and introduces 4 essential, independent variables computed as peak values inside the economic cycle. They are:

>>> F stands for peak value in the volume of pollution,

>>> E represents the peak of GDP (once again, the authors write about ‘economic growth’, yet there is no way it could possibly be economic growth, it has to be aggregate GDP),

>>> H stands for ‘peak value of the impact of economic growth 𝑦(𝑡) on environmental quality 𝑧(𝑡)’, and, finally,

>>> P is the maximum volume of pollution possible to absorb by the ecosystem.

With those independent peak values in the system, that baseline model focuses on computing first-order derivatives of, respectively x(t), y(t) and z(t) over time. In other words, what the authors are after is change over time, noted as, respectively d[x(t)]/ d(t), d[y(t)]/d(t), and d[z(t)]/d(t).

The formal notation of the model is given in a triad of equations:  

d[x(t)]/d(t) = a1*x*[1 – (x/F)] + a2*y*[1 – (y/E)] – a3*z

d[y(t)]/d(t) = -b1*x – b2*y – b3*z

d[z(t)]/d(t) = -c1*x + c2*y*[1 – (y/H)] + c3*z*[(x/P) – 1]

Good. This is the baseline model presented in: Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019. I am going to comment on it, and then I present the extension to that model, which the paper under review, i.e. ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ introduces as theoretical value added.

Thus, I comment. The model generally assumes two things. Firstly, gradients of change in pollution x(t), real output y(t), and environmental quality z(t) are sums of fractions taken out of stationary states. It is like saying: the pace at which this child will grow will be a fraction of its bodyweight plus a fraction of the difference between their current height and their tallest relative’s physical height etc. This is a computational trick more than solid theory. In statistics, when we study empirical data pertinent to economics or finance, we frequently have things like non-stationarity (i.e. a trend of change or a cycle of change) in some variables, very different scales of measurement etc. One way out of that is to do regression on the natural logarithms of that data (logarithms flatten out whatever needs to be flattened), or first derivatives over time (i.e. growth rates). It usually works, i.e. logarithms or first moments of original data yield better accuracy in linear regression than original data itself. Still, it is a computational trick, which can help validate a theory, not a theory as such. To my knowledge, there is no theory to postulate that the gradient of change in the volume of pollution d[x(t)]/d(t) is a sum of fractions resulting from the current economic output or the peak possible pollution in the economic cycle. Even if we assume that relations between energy, economy and environment in a human society are a complex, self-organizing system, that system is supposed to work through interaction, not through the addition of growth rates.

I need to wrap my mind a bit more around those equations, and here comes another assumption I can see in that model. It assumes that the pace of change in output, pollution and environmental quality depends on intra-cyclical peaks in those variables. You know, those F, E, H and P peaks, which I mentioned earlier. Somehow, I don’t follow this logic. The peak of any process depends on the cumulative rates of change rather that the other way around. Besides, if I assume any kind of attractor in a stochastic process, it would be rather the mean-reverted value, and not really the local maximum.

I can see that reviewing that manuscript will be tons of fun, intellectually. I like it. For the time being, I am posting those uncombed thoughts of mine on my blog, and I keep thinking.

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[1] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[2] Fields, C., Hoffman, D. D., Prakash, C., & Singh, M. (2018). Conscious agent networks: Formal analysis and application to cognition. Cognitive Systems Research, 47, 186-213. https://doi.org/10.1016/j.cogsys.2017.10.003

[3] He, X., Feldman, J., & Singh, M. (2015). Structure from motion without projective consistency. Journal of Vision, 15, 725.

[4] Hoffman, D. D., Singh, M., & Prakash, C. (2015). The interface theory of perception. Psychonomic bulletin & review, 22(6), 1480-1506.

[5] Hoffman, D. D. (2016). The interface theory of perception. Current Directions in Psychological Science, 25, 157–161

[6] Trivers, R. L. (2011). The folly of fools. New York: Basic Books

[7] Pizlo, Z., Li, Y., Sawada, T., & Steinman, R. M. (2014). Making a machine that sees like us. New York: Oxford University Press.

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.      

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[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.

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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