Both needed and impossible

I return to and focus on the issue of behavioural change under the impact of an external stressor, and the use of an artificial neural network to simulate it. I somehow connect to what I wrote in ‘Cross breeding my general observations’, and I want to explore the outcomes to expect from the kind of s**t which is happening right now: climate change, pandemic, rapid technological change with the rise of digital technologies, urbanisation, social unrest…name it. I want to observe Black Swans and study the way they make their way into our normal (see Black Swans happen all the time). I intend to dissect situations when exogenous stressors trigger the so-far dormant patterns of behaviour, whilst randomly pushing the incumbent ones out of the system, and, in the process, those initially exogenous stressors become absorbed (AKA endogenized) by the society.

Back to plain human lingo, I assume that we, humans, do stuff. We can do only what we have learnt to do, therefore anything we do is a recurrent pattern of behaviour, which changes constantly in the process of learning. We differ in our individual patterns, and social life can be represented as the projection of a population into a finite set of behavioural patterns, which, further in this development, I will label as ‘social roles’. You probably know what a pool table looks like. Imagine a pretty continuous stream of pool balls, i.e. humans, spilling over an immense pool table with lots of holes in it. Each hole is a social role, and each human ball finally ends up in one of the holes, i.e. endorsing one among a finite number of social roles. Probabilistically, each social role can be described with the probability that the average homo sapiens being around endorses that role.

Thus, I study a human population projecting itself into a finite set SR = {sr1, sr2, …, srm} of m social roles, coupled with the set PSR = {p(sr1), p(sr2), …, p(srm)} of probabilities that each given social role is being endorsed. Those two coupled sets, i.e. SR and PSR, make a collectively intelligent social structure, able to learn by experimenting with many alternative versions of itself. This, in turn, implies two processes, namely production of and selection from among those alternative versions. Structural intelligence manifests as the capacity to produce and select alternative versions whilst staying coherent laterally and longitudinally. Lateral coherence is observable as functional connection between social roles in the set SR, whilst the longitudinal one is continuity in the structure of the set SR. Out of those two coherences, the lateral one is self-explanatory and assumed a priori: no social role can exist in total abstraction from other social roles, i.e. without any functional connection whatsoever. On the other hand, I assume that longitudinal coherence can be broken, in the sense that under some conditions the set SR can turn into a new set SR’, which will contain very different a repertoire of social roles.

I go into maths. Each social role sri, besides being associated with its probability of endorsement p(sri), is associated with a meta-parameter, i.e. its lateral coherence LC(sri) with m – 1 other social roles in the set SR, and that coherence is defined as the average Euclidean distance between p(sri) and the probabilities p(srj) of other social roles, as in Equation (1) below.

Equation (1)

Logically, we have one more component of the collectively intelligent social structure, namely the set LCSR = {LC(sr1), LC(sr2), …, LC(sri)} of lateral coherences between social roles.

The collectively intelligent social structure, manifest as three mutually coupled sets, i.e. SR, PSR, and LCSR, optimizes a vector of social outcomes. In order to keep some sort of methodological purity, I will further designate that vector as a set, namely the set O = {o1, o2, …, ok} of k social outcomes. Still, we keep in mind that in mathematics, the transition from set to vector and back is pretty simple and common-sense-based. A set has the same variables in it as the vector made out of that set, only we can cherry-pick variables from a set, whilst we cannot really do it out of a vector, on the account of them variables being bloody entangled in the vector. When a set turns into a vector, its variables go mousquetaire, Dumas style, and they are one for all and all for one, sort of.

With the above assumptions, a collectively intelligent social structure can be represented as the coupling of four sets: social roles SR, probabilities of endorsement as regards those social roles PSR, lateral coherences between social roles LCSR, and social outcomes O. Further, the compound notation {SR, PSR, LCSR, O} is used to designate such a structure.

Experimental instances happen one by one, and therefore they can be interpreted as consecutive experiments, possible to designate mathematically as units t of time. For the sake of clarity, the current experimental instance of the structure {SR, PSR, LCSR, O} is designated with ‘t’, past instances are referred to as t – l, where ‘l’ stands for temporal lag, and the hypothetical first state of that structure is t0. Any current instance of {SR, PSR, LCSR, O} is notated as {SR(t), PSR(t), LCSR(t),O(t)}.

Consistently with the Interface Theory of Perception (Hoffman et al. 2015[1], Fields et al. 2018[2]), as well as the theory of Black Swans (Taleb 2007[3]; Taleb & Blyth 2011[4]), it is assumed that the structure {SR, PSR, LCSR, O} internalizes exogenous stressors, both positive and negative, transforming them into endogenous constraints, therefore creating an expected vector E(O) of outcomes. Each consecutive instance {SR(t), PSR(t), LCSR(t),O(t)} of the structure {SR, PSR, LCSR, O} learns by pitching its real local outcomes O(t) against their expected local state E[O(t)].

Internalization of exogenous stressors allows studying the whole sequence of l states, i.e. from  instance {SR(t0), PSR(t0), LCSR(t0),O(t0)} to {SR(t), PSR(t), LCSR(t),O(t)} as a Markov chain of states, which transform into each other through a σ-algebra. The current state {SR(t), PSR(t), LCSR(t),O(t)} and its expected outcomes E[O(t)] contain all the information from past learning, and therefore the local error in adaptation, i.e. e(t) = {E[O(t)] – O(t)}*dO(t), where dO(t) stands for the local derivative (local first moment) of O(t) conveys all that information from past learning. That factorisation of error in adaptation into a residual difference and a first moment is based on the intuition that collective intelligence is always on the move, and any current state instance {SR(t0), PSR(t0), LCSR(t0),O(t0)} is just a snapshot of an otherwise constantly changing social structure.

With the assumptions above, {SR(t), PSR(t), LCSR(t),O(t)} = {SR(t-1) + e(t-1), PSR(t-1) + e(t-1), LCSR(t-1) + e(t-1), O(t-1) + e(t-1)} and E[O(t)] = E[O(t-1)] + e(t-1). The logic behind adding the immediately past error to the present state {SR(t), PSR(t), LCSR(t),O(t)} is that collective learning is essentially incremental, and not revolutionary. Each consecutive state {SR(t), PSR(t), LCSR(t),O(t)} is a one-mutation neighbour of the immediately preceding state {SR(t-1), PSR(t-1), LCSR(t-1),O(t-1)} rather than its structural modification. Hence, we are talking about arithmetical addition rather than multiplication or division. Of course, it is to keep in mind that subtraction is a special case of addition, when one component of addition has a negative sign.

Exogenous stressors act upon human behaviour at two levels: recurrent and incidental. Recurrent exogenous stressors make people reconsider, systematically, their decisions to endorse a given social role, in the sense that those decisions, besides taking into account the past state of the structure {SR(t0), PSR(t0), LCSR(t0),O(t0)}, incorporate randomly distributed, current exogenous information X(t). That random exogenous parcel of information affects all the people susceptible to endorse the given social role sri which, in turn, means arithmetical multiplication rather than addition, i.e. PSR(t) = X(t)*[PSR(t-1) + e(t-1)].

Incidental exogenous stress, in this specific development, is very similar to Black Swans (Taleb 2007 op. cit.; Taleb & Blyth 2011 op. cit.)., i.e. it consists of short-term, violently disturbing events, likely to put some social roles extinct or, conversely, trigger into existence new social roles. Extinction of a social role means that its probability becomes null: P(sri) = 0. The birth of a new social role is more complex. Social roles are based on pre-formed skillsets and socially tested strategies of gaining payoffs from those skillsets. A new social role appears in two phases. In the first phase, skills necessary to endorse that role progressively form in the members of a given society, yet those skills have not played out sufficiently, yet, in order to be endorsed as the social identity of an individual. Just to give an example, the recent and present development of cloud computing as a distinct digital business encourage the formation of skills in trading, at the business level, large datasets, such as those collected via the cookie algorithms. Trade in datasets is real, and the skills required are just as real, yet there is no officially labelled profession of data trader yet. Data trader is something like a dormant social role: the skills are there, in the humans involved, and still there is nothing to endorse officially. A more seasoned social role, which followed a similar trajectory, is an electricity broker. As power grids have been evolving towards increasing digitalisation and liquidity in the transmission of power, it became possible to do daily trade in power capacity, at first, and then a distinct profession, that of a power broker, emerged together with institutionalized power exchanges.

That first phase of emergence, in a new social role, creates dormant social roles, i.e. ready-to-use skillsets which need just a small encouragement, in the form of socially recognized economic incentives, to kick into existence. Mathematically, it means that the set SR of social roles entails two subsets: active and dormant. Active social roles display p(sri;t) > 0, and, under the impact of a local, Black-Swan type event, they can turn p(sri;t) = 0. Dormant social roles are at p(sri;t) = 0 for now, and can turn into display p(sri;t) > 0 in the presence of a Black Swan.

In the presence of active recurrent stress upon the structure {SR, PSR, LCSR, O}, thus if we assume X(t) > 0, I can present a succinct mathematical example of Black-Swan-type exogenous disturbance, with just two social roles, sr1 and sr2. Before the disturbance, sr1 is active and sr2 is dormant. In other words, P(sr1; t -1)*X(t-1) > 0 whilst P(sr2; t -1)*X(t-1) = 0 . With the component of learning by incremental error in a Markov chain of states, it means [P(sr1; t – 2) + e(t-2)]*X(t-1) > 0 and [P(sr2; t -1) + e(t-2)]*X(t-1) = 0, which logically equates to P(sr1; t – 2) > – e(t-2) and P(sr2; t -1) = – e(t – 2).

After the disturbance, the situation changes dialectically, namely P(sr1; t -1)*X(t-1) = 0 and P(sr2; t -1)*X(t-1) > 0, implying that P(sr1; t – 2) = – e(t-2) and P(sr2; t -1) > – e(t – 2). As you can probably recall from math classes in high school, there is no way a probability can be negative, and therefore, if I want the expression ‘– e(t-2)’ to make any sense at all in this context, I need e(t – 2) ≤ 0. As e(t) = {E[O(t)] – O(t)}*dO(t), e(t) ≤ 0 occurs when E[O(t)] ≤ O(t) or dO(t) ≤ 0.

Therefore, the whole construct of Black-Swan-type exogenous stressors such as presented above seems to hold logically when:

>> the structure {SR, PSR, LCSR, O} yields local real outcomes O(t) greater than or equal to expected outcomes E[O(t)]; in other words, that structure should yield no error at all (i.e. perfect match between actual outcomes and expected ones), thus should a perfect adaptation, or it should overshoot actual outcomes beyond expectations…

…or…

>> …the first moment of local real outcomes is perfectly still (i.e. equal to zero) or negative

Of course, there is open possibility of such instances, in the structure {SR, PSR, LCSR, O}, which yield negative error, thus E[O(t)] > O(t), with dO(t) > 0. In these instances, according to the above-deployed logic of collective intelligence, the next experimental round t+1 can yield negative probabilities p(sri) of endorsing specific social roles, thus an impossible state.  Can collective intelligence of a human society go into those impossible states? I admit I have no clear answer to that question, and therefore I asked around. I mean, I went to Google Scholar. I found three articles, all of them, interestingly, in the field of physics. In an article by Feynman, R. P. , published in 1987, and titled ‘Negative probability. Quantum implications: essays in honour of David Bohm’ (pages: 235-248, https://cds.cern.ch/record/154856/files/pre-27827.pdf), I read: ‘[…] conditional probabilities and probabilities of imagined intermediary states may be negative in a calculation of probabilities of physical events or states. If a physical theory for calculating      probabilities yields a negative probability for a given situation under certain assumed conditions, we need not conclude the theory is incorrect. Two other possibilities of interpretation exist. One is that the conditions (for example, initial conditions) may not be capable of being realized in the physical world. The other possibility is that the situation for which the probability appears to be negative is not one that can be verified directly. A combination of these two, limitation of verifiability and freedom of initial conditions, may also be a solution to the apparent difficulty’.

This sends me back to my economics and to the concept of economic equilibrium, which assumes that societies can be in a state of economic equilibrium or in a lack thereof. In the former case, they can sort of steady themselves, and in the latter… Well, when you have no balance, man, you need to move so as to gain some.  If a collectively intelligent social structure yields negative probability attached to the occurrence of a given social role, it can indicate truly impossible a state, yet impossibility is understood in the lines of quantum physics. It is a state, from which our society should get the hell out of, ‘cause it is not gonna last, on the account of being impossible. An impossible state is not a state that cannot happen: it is a state which cannot stay in place.

Well, I am having real fun with that thing. I started from an innocent model of collective intelligence, I found myself cornered with negative probabilities, and I guess I found my way out by referring to quantum physics. The provisional moral I draw from this fairy tale is that a collectively intelligent social structure, whose learning and adaptation can be represented as a Markov chain of states, can have two types of states: the possible AKA stable ones, on the one hand, and the impossible AKA transitory ones, on the other hand.

The structure {SR, PSR, LCSR, O} is in stable, and therefore in possible a state, it yields local real outcomes O(t) greater than or equal to expected outcomes E[O(t)]; it is perfectly fit to fight for survival or it overshoots expectations. Another possible state is that of real outcomes O(t) being perfectly still or negative in its first moment. On the other hand, when the structure {SR, PSR, LCSR, O} yield real outcomes O(t) smaller than expected outcomes E[O(t)], in the presence of positive local gradient of change in those real outcomes, it is an impossible, unstable state. That thing from quantum physics surprisingly well fits to a classical economic theory, namely the theory of innovation by Joseph Schumpeter: economic systems transition from one neighbourhood of equilibrium to another one, and they transition through states of disequilibrium, which are both needed for social change, and impossible to hold for a long time.

When the structure {SR, PSR, LCSR, O} hits an impossible state, where some social roles happen with negative probabilities, that state is an engine which powers accelerated social change.     


[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] Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.

[4] Taleb, N. N., & Blyth, M. (2011). The black swan of Cairo: How suppressing volatility makes the world less predictable and more dangerous. Foreign Affairs, 33-39.

Time for a revolution

I am rethinking the economics of technological change, especially in the context of cloud computing and its spectacular rise, as, essentially, a new and distinct segment of digital business. As I am teaching microeconomics, this semester, I am connecting mostly to that level of technological change. I want to dive a bit more into the business level of cloud computing, and thus I pass in review the annual reports of heavyweights in the IT industry: Alphabet, Microsoft and IBM.

First of all, a didactic reminder is due. When I want to study the business, which is publicly listed in a stock market, I am approaching that business from its investor-relations side, and more specifically the investor-relations site. Each company listed in the stock market runs such a site, dedicated to show, with some reluctance to full transparency, mind you, the way the business works. Thus, in my review, I call by, respectively: https://abc.xyz/investor/ for Alphabet (you know, the mothership of Google), https://www.microsoft.com/en-us/investor as regards Microsoft, and https://www.ibm.com/investor as for them Ibemians.

I start with the Mother of All Clouds, i.e. with Google and its mother company, namely Alphabet. Keep in mind: the GDP of Poland, my home country, is roughly $590 billions, and the gross margin which Alphabet generated in 2019 was $89 857 million, thus 15% of the Polish GDP. That’s the size of business we are talking about and I am talking about that business precisely for that reason. There is a school in economic sciences, called new institutionalism. Roughly speaking, those guys study the question why big corporate structures exist at all. The answer is that corporations are a social contrivance which allows internalizing a market inside an organization. You can understand the general drift of that scientific school if you study a foundational paper by O.D. Hart (Hart 1988[1]). Long story short, when a corporate structure grows as big as Alphabet, I can assume its internal structure is somehow representative for the digital industry as a whole. You could say: but them Google people, they don’t make hardware. No, they don’t, and yet they massively invest in hardware, mostly in servers. Their activity translates into a lot of IT hardware.

Anyway, I assume that the business structure of Alphabet is informative about the general structure and the drift of the digital business globally. In the two tables below, I show the structure of their revenues. For the non-economic people: revenue is the value of sales, or, in analytical terms, Price multiplied by Quantity.     


Semi-annual revenue of Alphabet Inc.

The next step is to understand specifically the meaning of categories defined as ‘Segments’, and the general business drift. The latter is strongly rooted in what the Google tribe cherishes as ‘Moonshots’, and which means technological change seen as revolution rather than evolution. Their business develops by technological leaps, smoothed by exogenous economic conditions. Those exogenous conditions translate into the Alphabet’s business mostly as advertising. In the subsection titled ‘How we make money’, you can read it explicitly. By the way, under the mysterious categories of ‘Google other’ and ‘Other Bets revenues’, Alphabet understands, respectively:

>> Google other: Google Play, including sales of apps and in-app purchases, as well as digital content sold in the Google Play store; hardware, including Google Nest home products, Pixelbooks, Pixel phones and other devices; YouTube non-advertising, including YouTube Premium and YouTube TV subscriptions and other services;

>> Other Bet revenues are, in the Google corporate jargon, young and risky businesses, slightly off the main Googly track; right now, they cover the sales of Access internet, TV services, Verily licensing, and R&D services.

Against that background, Google Cloud, which most of us are not really familiar with, as it is a business-to-business functionality, shows interesting growth. Still, it is to keep in mind that Google is cloud: ‘Google was a company built in the cloud. We continue to invest in infrastructure, security, data management, analytics and AI’ (page 7 of the 10K annual report for 2019). You Tube ads, which show a breath-taking ascent in the company’s revenue, base their efficiency and attractiveness on artificial intelligence operating in a huge cloud of data regarding the viewers’ activity on You Tube.

Now, I want to have a look at Alphabet from other financial angles. Their balance sheet, i.e. their capital account, comes next in line. In two tables below, I present that balance sheet one side at a time, and I start with the active side, i.e. with assets. I use the principle that if I know what kind of assets a company invests money in, I can guess a lot about the way their business works. When I look at Alphabet’s assets, the biggest single category is that of ‘Marketable securities’, closely followed by ‘Property and Equipment’. They are like a big factory with a big portfolio of financial securities, and the portfolio is noticeably bigger than the factory. This is a pattern which I recently observe in a lot of tech companies. They hold huge reserves of liquid financial assets, probably in order to max out on their flexibility. You never know when exactly you will face both the opportunity and the necessity to invest in the next technological moonshot. Accounts receivable and goodwill come in the second place, as regards the value in distinct groups of assets. A bit of explanation is due as for that latter category. Goodwill might suggest someone had good intentions. Weeell, sort of. When you are a big company and you buy a smaller company, and you obviously overpay for the control over that company, over the market price of that stock, the surplus you have overpaid you call ‘Goodwill’. It means that this really expensive purchase is, in the same time, very promising, and there is likely to be plenty of future profits. When? In the future, stands to reason.

Now, I call by the passive side of Alphabet’s balance sheet, i.e. by their liabilities and equity, which is shown schematically in the next table below. The biggest single category here, i.e. the biggest distinct stream of financial capital fuelling this specific corporate machine is made of ‘Retained Earnings’, and stock equity comes in the second place. Those two categories taken together made 73% of the Alphabet’s total capital base, by the end of 2019. Still, by the end of 2018, that share was of 77%. Whilst Alphabet retains a lot of its net profit, something like 50%, there is a subtle shift in their financing. They seem to be moving from an equity-based model of financing towards more liability-based one. It happens by baby steps, yet it happens. Some accrued compensations and benefits (i.e. money which Alphabet should pay to their employees, yet they don’t, because…), some accrued revenue share… all those little movements indicate a change in their way of accumulating and using capital.   

The next two tables below give a bird’s eye view of Alphabet in terms of trends in their financials. They have a steady profitability (i.e. capacity to make money out of current business), their capacity to bring return on equity and assets steadily grows, and they shift gently from equity-based finance towards more complex a capital base, with more long-term liabilities. My general conclusion is that Alphabet is up to something, like really. They claim they constantly do revolution, but my gut feeling is that they are poising themselves for a really big revolution, business-wise, coming shortly. Those reserves of liquid financial assets, that accumulation of liabilities… All that stuff is typical in businesses coiling for a big leap.  There is another thing, closely correlated with this one. In their annual report, Alphabet claims that they mostly make money on advertising. In a narrow, operational sense, it might be true. Yet, when I have a look at their cash-flow, it looks different. What they have cash from, first and most of all, are maturities and sales of financial securities, and this one comes as way a dominant, single source of cash, hands down. They make money on financial operations in the stock market, in somehow plainer a human lingo. Then, in the second place, come two operational inflows of cash: amortization of fixed assets, and tax benefits resulting from the payment of stock-based compensations. Alphabet makes real money on financial operations and tax benefits. They might be a cloud in their operations, but in their cash-flows they are a good, old-fashioned financial scheme.  

Now, I compare with Microsoft (https://www.microsoft.com/en-us/Investor/sec-filings.aspx). In a recent update, titled ‘#howcouldtheyhavedoneittome’, I discussed the emerging position of cloud computing in the overall business of Microsoft. Now, I focus on their general financials, with a special focus on their balance sheet and their cash-flow. I show a detailed view of both in the two tables that follow. Capital-wise, Microsoft follows slightly different a pattern as compared to Alphabet, although some common denominators appear. On the active side, i.e. as regards the ways of employing capital, Microsoft seems to be even more oriented on liquid financial than Alphabet. Cash, its equivalents, and short-term investments are, by far, the biggest single category of assets in Microsoft. The capital they have in property and equipment is far lower, and, interestingly, almost equal to goodwill. In other words, when Microsoft acquires productive assets, it seems to be like 50/50 their own ones, on the one hand, and those located in acquired companies, on the other hand. As for the sources of capital, Microsoft is clearly more debt-based, especially long-term debt, than Alphabet, whilst retaining comparatively lower a proportion of their net income. It looks as if Alphabet was only discovering, by now, the charms of a capital structure which Microsoft seems to have discovered quite a while ago. As for cash-flows, both giants are very similar. In Microsoft, as in Alphabet, the main single source of cash is the monetization of financial securities, through maturity or by sales, with operational tax write-offs coming in the second place. Both giants seem to be financially bored, so to say. Operations run their way, people are interested in the company’s stock, from time to time a smaller company gets swallowed, and it goes repeatedly, year by year. Boring. Time for a revolution.      

Edit: as I was ruminating my thoughts after having written this update, I recorded a quick video (https://youtu.be/ra2ztH3k0M0 ) on the economics of technological change, where I connect my observations about Alphabet and Microsoft with a classic, namely with the theory of innovation by Joseph Schumpeter.

[1] Hart, O. D. (1988). Incomplete Contracts and the Theory of the Firm. Journal of Law, Economics, & Organization, 4(1), 119-139.

 


[1] Hart, O. D. (1988). Incomplete Contracts and the Theory of the Firm. Journal of Law, Economics, & Organization, 4(1), 119-139.

Cross breeding my general observations

I return to the project which I started in Spring this year (i.e. 2020), and which I had put aside to some extent: the book I want to write on the role and function of cities in our civilization, including the changes, which we, city slickers, can expect in the foreseeable future. As I think about it now, I guess I had to digest intellectually both my essential method of research for that book, and the core empirical findings which I want to connect to. The method consists in studying human civilization as collective intelligence, thus a collection of intelligent structures, able to learn by experimenting with many alternative versions of themselves. Culture, laws and institutions, technologies: I consider all those anthropological categories as cognitive constructs, which we developed over centuries to study our own collective intelligence and being de facto parts thereof.

Collective intelligence, in that perspective, is an overarching conceptual frame, and as overarching frames frequently do, the concept risks to become a cliché. The remedy I want and intend to use is mathematics. I want to write the book as a collection of conceptual developments and in-depth empirical insights into hypotheses previously formulated with the help of a mathematical model. This is, I think, a major originality of my method. In social sciences, we tend to go the other way around: we formulate hypotheses by sort of freestyling intellectually, and then we check them with mathematical models. I start with just a little bit of intellectual freestyling, then I formulate my assumptions mathematically, and I use the mathematical model which results from those assumptions to formulate hypotheses for further research.

I adopt such a strongly mathematical method because we have a whole class of mathematical models which seem to fit the bill perfectly: artificial neural networks. Yes, I consider artificial neural networks as mathematical models in the first place, and only then as algorithms. The mathematical theory which I associate artificial neural networks the most closely with is that of state space, combined with the otherwise related theory of Markov chains. In other words, whatever happens, I attempt to represent it as a matrix of values, which is being transformed into another matrix of values. The artificial neural network I use for that representation reflects both the structure of the matrix in question, and the mechanism of transformation, which, by the way, is commonly called σ – algebra. By ‘commonly’ I mean commonly in mathematics.

My deep intuition – ‘deep’ means that I understand that intuition just partly – is that artificial neural networks are the best mathematical representation of collective intelligence we can get for now. Therefore I use them as a mathematical model, and here comes a big difference between the way I use them and a typical programmer does. Programmers of artificial intelligence are, as far as I know (my son is a programmer, and, yes, sometimes we speak human lingo to each other), absolutely at home with considering artificial neural networks as black boxes, i.e. as something that does something, yet we don’t really need to understand what exactly that thing is, which neural networks do, and we essentially care about those networks being accurate and quick in whatever they do.

I, in my methodological world, I adopt completely different a stance. I care most of all about understanding very specifically what the is the neural network doing, and I draw my conclusions from the way it does things. I don’t need the neural network I use to be super-fast neither super accurate: I need to understand how it does whatever it does.

I use two types of neural networks in that spirit, both 100% hand made. The first one serves me to identify the direction a social system (collective intelligence) follows in its collective learning. You can see an application in this draft paper of mine, titled ‘Climbing the right hill’. The fundamental logic of that network is to take an empirical dataset and use the neural network to produce as many alternative transformations of that dataset as there are variables in it. Each transformation takes a different variable from the empirical dataset as its desired output (i.e. it optimizes all the other variables as instrumental input). I measure the Euclidean similarity (Euclidean distance) between each individual transformation and the source dataset. I assume that the transformation which falls relatively the closest to source empirical data is the best representation of the collective intelligence represented in that data. Thus, at the end of the day, this specific type of neural network serves me to discover what we are really after, as a society.

The second type of network is built as a matrix of probabilities, modified by a quasi-random factor of disturbance. I am tempted to say that this network attempts to emulate coincidence and quasi-randomness of events. I made it and I keep using it as pure simulation: there is no empirical data which the network learns on. It starts with a first, controlled vector of probabilities, and then it transforms that vector in a finite number of experimental iterations (usually I make that network perform 3000 experimental rounds). In the first application I made of that network, probabilities correspond to social roles, and more specifically to the likelihood that a random person in the society studied endorses the given social role (see ‘The perfectly dumb, smart social structure’). At a deeper, and, in the same time, more general a level, I assume that probability as such is a structural variable of observable reality. A network which simulates changes in a vector of probabilities simulated change in the structure of events.

Long story short, I have two neural networks for making precise hypotheses: one uncovers orientations and pursued values in sets of socio-economic data, whilst the other simulates structural change in compound probabilities attached to specific phenomena. When I put that lot to real computational work, two essential conclusions emerge, sort of across the board, whatever empirical problem I am currently treating. Firstly, all big sets of empirical socio-economic data are after something specific. I mean, when I take the first of those two networks, the one that clones an empirical dataset into as many transformations as there are variables, a few of those transformations, like 1 ÷ 3 of them, are much closer to the original, in Euclidean terms, than all the rest. When I say closer, it is several times closer. Secondly, vectors of probabilities are tenacious and resilient. When I take the second of those networks, the one which prods vectors of probabilities with quasi-random disturbances, those probabilities tend to resist. Even if, in some 100 experimental rounds, some of those probabilities get kicked out of the system, i.e. their values descend to 0, they reappear a few hundred of experimental rounds later, as if by magic. Those probabilities can be progressively driven down if the factor of disturbance, which I include in the network, consists in quasi-randomly dropping new events into the game. The phenomenological structure of reality seems to be something very stable, once set in place, however simple I make that reality a priori. It yields to increasing complexity (new phenomena, with their probabilities coming to the game) rather than to arbitrary reduction of the pre-set phenomena.

I generalize those observations. A collective intelligence, i.e. an intelligent social structure, able to learn by experimenting with many alternative versions of itself, can stay coherent in tat experimentation and seems to stay coherent because it pursues very clear collective outcomes. I am even tempted to reframe it as a condition: a human social structure can evolve as a collectively intelligent structure under the condition of having very clear collectively pursued values. If it doesn’t, it is doomed to disintegrate and to be replaced by another collectively intelligent social structure, which, in turn, is sufficiently oriented to stay internally coherent whilst experimenting with itself. As I descend to the level of human behaviour, observed as the probability of an average individual endorsing specific patterns of behaviour, those behavioural patterns are resilient to exogenous destruction, and, in the same time, quite malleable when new patterns emerge and start to compete with the old ones. When a culture starts from a point A, defined as a set of social roles and behavioural patterns with assorted probabilities of happening, that point A needs a bloody long time, or, in other words, a bloody big lot of collectively intelligent experimentation, to vanish completely.   

Now, I want to narrow down the scope of hypotheses I intend to formulate, by specifying the basic empirical findings which I have made so far, and which make the foundations of my research on cities. The first empirical finding does not come from me, but from the CIESIN centre at the Columbia University, and it is both simple and mind blowing: however the formal boundaries of urban areas are being redefined by local governments, the total surface of urban areas, defined as abnormally dense agglomerations of man-made structures and night-time lights, seems to have been constant over the last 30 years, maybe even more. In other words, whilst we have a commonly shared impression that cities grow, they seem to be growing only at the expense of other cities. You can check those numbers via the stats available with the World Bank (https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2 ). As you will be surfing with the World Bank, you can also call by another metric, the total surface of agricultural land on the planet (https://data.worldbank.org/indicator/AG.LND.AGRI.K2 ) and you will see that it has been growing, by hiccups, since 1960, i.e. since that stat is being collected. 

To complete the picture, you can check the percentage of urban population in the total human population on the planet (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS ) and you will see that we have been becoming more and more urban, and right now, we are prevalently urban. Long story short, there are more and more urban humans, who apparently live in a constant urban space, and feed themselves out of a growing area of agricultural land. At the end of the day, cities seem to become increasingly different from the countryside, as regards the density of population: urban populations on Earth are becoming systematically more dense than rural ones.

I am cross breeding my general observations from what my two neural networks tend to do, with those main empirical findings about cities, and I am trying to formulate precise hypotheses for further research. Hypothesis #1: cities are purposeful demographic anomalies, with a clear orientation on optimizing specific social outcomes. Hypothesis #2: if and to the extent that the purpose of cities is to create new social roles, through intense social interaction in a limited physical space, the creation of new social roles involves their long coexistence with older social roles, and, therefore, the resulting growth in social complexity is exponential. Hypothesis #3: the COVID-19 pandemic, as an exogenous factor of disturbance, is likely to impact us in three possible ways: a) it can temporarily make disappear some social roles b) on the long run, it is likely to increase social complexity, i.e. to make us create a whole new set of social roles and c) it can change the fundamental orientation (i.e. the pursued collective values) of cities as demographic anomalies.  

In your spare time you can also watch this video I made a few weeks ago: ‘Urban Economics and City Management #1 Lockdowns in pandemic and the role of cities’ : https://youtu.be/fYIz_6JVVZk . It recounts and restates my starting point in this path of research. I browse through the main threads of connection between the pandemic of COVID-19 and the civilisational role of cities. The virus, which just loves densely populated places, makes us question the patterns of urban life, and makes us ask question as for the future of cities.

Waiting for interesting offers

In this update, I am attempting to introduce a general concept of social sciences, namely that of pooled risks, and, associated with them, pooled resources, and combine it with my research regarding collective intelligence. I open up by connecting to my last update, namely that titled ‘My NORMAL and my WEIRD’. Things happen all the time. Some of that happening we deem as normal, and anything outside normal is weird. We, humans, we have built a whole civilization around saving stuff for later. We started by saving bodily energy for later, and thus we invented shelter as a complex method of saving and investment. Today, we build shelter, and thus we expend more energy than strictly needed for survival, and yet the whole business pays off, because shelter allows us to save even more energy on the long run (lower heat loss when sleeping and resting, more stable conditions for keeping a fire up etc.). As we started building shelters for ourselves, we figured out that food could be conserved for later, too. Once again, spend a little bit more energy today, bro, to conserve that beautiful sabre-tooth tiger, as well as that root, which your distant descendants will call turnip, and tomorrow you will have food which you do not have to run after nor away from.

Over thousands of years, we, humans, have developed that pattern of countering adverse events by saving resources for later and investing them in something that pays off. At some point in time, we noticed, withing the realm of all the nasty s**t that happens, different grades of normality and weirdness. There is daily adversity, such as cold in winter, or the basic need for food (all year long), which needs to be addressed recurrently and sort of locally. Everyone needs local protection from cold, right? If, in a city, there is one shelter, it can work just for a tiny handful of homeless people, not for all the citizens. The latter need their individual places. Still, there is a different type of adversity, such as a flood, a hurricane, or a wedding, which all happen incidentally and are randomly local: they hit a specific subset of population, in a specific place. Although these events are abnormal, they happen abnormally with a pattern. The Black Swan theory, by Nassim Taleb (Taleb 2007[1]; Taleb & Blyth 2011[2]; see Black Swans happen all the time), argues that we, humans, have developed an otherwise amazing skill of incorporating into our collective culture the memories of unusual, catastrophic events so as to be prepared for the next time the s**t hits the fan. A lot of our structures and institutions are made for countering and compensating the sudden, and yet somehow predictable advent of those Black Swans. One of the simplest examples are buildings. The toughest architectural structures have historically developed, a bit surprisingly, in places with an abundant history of warfare and sieging, and not, as someone could expect, in places haunted by hurricanes and earthquakes. I could see that sort of in my front yard, when, in the early 2000s, I met Croatians who migrated abroad. Their default expectation for a building was reinforced concrete, brick was barely acceptable, and the American wooden structure was completely out of the question. It wasn’t until 2004, when I went to Croatia for the first time, and I saw the still-standing corpses of buildings damaged by bombs, that I understood where that preference for reinforced concrete was coming from.

What is even more puzzling is that abundant history of warfare and sieging has developed in places, where heavy construction materials, such as stone, lime, clay for making bricks, and wood for fuelling ovens to bake bricks, were just as abundant. In other words, we historically developed big, solid buildings in places where we used to destroy those buildings a lot through our own military effort, and, apparently, we used to destroy them a lot because we had a lot of raw materials at hand for rebuilding them. That’s what I call incorporating Black Swans in a culture.

Anyway, we have that thing about purposefully investing our resources in structures and institutions supposed to shield us against big, catastrophic events. There is another thing about those Black Swans: they happen in sort of a floating manner. If you know your climate, and that climate is prone to floods, you know there will be floods, you can estimate the magnitude of damage they are likely to inflict, you just don’t know where and when exactly it is going to flood. What you need is a s**t-shielding structure akin to an immune system: a lot of resources, hanging around, sort of held at the ready, able to channel themselves into the specific place where damage occurs.

Now, I jump. Like really. Intellectually, I mean. You probably know that governments of many countries are borrowing lots of money, this year, to counter the adverse impact of the COVID-19 pandemic. You can read more about it in the Fiscal Monitor published by the International Monetary Fund in September 2020, under the title ‘Fiscal Monitor: Policies for the Recovery

September 2020’ (https://www.imf.org/en/Publications/FM/Issues/2020/09/30/october-2020-fiscal-monitor ). I think this is the right move to make, right now, and right now, you, my readers, are, as we say in Poland, in your holy bandit’s right to ask ‘WTF?’. What does public borrowing have to do with floating adversities? Hold on, dear readers. One step at a time. We have those governments borrowing money, right? The first interesting thing is the way they borrow. When I borrow money from a bank, I do it against a written promise of paying back, possibly backed with a conditional claim I grant to the bank on some assets of mine. When governments borrow, they prevalently do it by issuing tradable financial securities called sovereign bonds AKA treasury bonds, which incorporate the government’s obligation to pay back the capital borrowed in more than 12 months. A minor part of public borrowing takes place through the issuance of sovereign notes (treasury notes), which differ from bonds just by their time of maturity, always shorter than 12 months. Hardly any public borrowing takes place in the form of classical loans, the kind which me or any other, non-political mortal could contract. Why is it so?

The first basic difference between a classical bank loan and a loan extended in exchange of tradable securities is that governments can do the latter, cities can do it, too, as well as large corporations. On the other hand, if I go to a bank and I propose to borrow money from them in exchange of bonds which I will issue, they will tell me: ‘Mr Wasniewski, with all the due respect, you have no business issuing any bonds, for one, and even if you had, no one would be really interested in buying your bonds from us, and so there is no point for us to accept bonds from you. If you really like making securities, you can sign a bill of exchange, if it is all the same for you, yet it will be just an additional collateral in the framework of a normal lending contract’.

I could go bitching and ranting about those horrible bankers. Yes, I could. Yet, I prefer understanding. Why can governments borrow in exchange of bonds, and I cannot? ‘Cause their bonds are wanted, whilst mine are not. This is called demand, and when demand pertains to a financial instrument, we call it liquidity. Financial institutions willingly buy sovereign bonds, thus give them liquidity, because these bonds have very low credit risk attached to it. Most governments are good payers, especially when they can schedule those payments way in advance.

Thus, we have a new concept in this update: credit risk. If you are a bank, and you extend 1000 loans of $5000 each, to 1000 different clients, some of them will simply not pay, or, as bankers say, will default on their loans. Realistically, you can expect between 4% and 7% of them to default. Let’s make it average between 4% and 7%, thus 5,5%, which makes 5,5% * 1000 borrowers = 55 loans in default, or 55 * $5000 = $275 000. When someone doesn’t pay, someone else has to pay for them, and the basic financial strategy is to spread those $275 000 over the remaining 94,5% of customers, dutiful and solvable. In other words, those remaining 945 customers will have to pay, in the interest the bank charges them, those $275 000 in default. That makes $291 per customer, over the top of the $5000 principal capital borrowed, or 5,82% of that capital. In even more other words, in this specific population of customers, there is a floating credit risk amounting to 5,82% of capital engaged in lending, or $275 000 for each $5000 000 of credit extended. The interest charged on each individual loan comprises, among other things, those 5,82% of spread credit risk.

The strategy of spreading credit risk is common sense, yet a bit unfair and easily tiltable out of its base. When credit risk in a population of clients rises sharply, e.g. when there is a wave of insolvencies and bankruptcies among small businesses, that spread credit risk starts spiralling up in an uncontrolled manner, and soon enters into a dysfunctional loop. Each additional 1% of customers defaulting on their loans creates the need to make credit even more expensive for all the others, which makes those others more likely to default etc. Someone could say that it is all because of bad banking. Just don’t lend to customers who default, that’s all. Yes, cool, it is an excellent advice and yet it is functional just in a certain number of cases. The historically accumulated wisdom of finance is that credit risk is essentially exogenous, i.e. it is a characteristic of the entire market rather than bad credit decisions in individual cases.

Banks lend to the non-financials the money they have borrowed from other banks. This is something to remember: when a bank extends you credit, they are trading you that money, not producing it. Banks don’t lend out of their equity; they lend out of their liabilities vis a vis other banks. That’s why there is a big market of interbank credit. If you are a bank and another bank asks you for a loan, you will easily do it. Instead of dealing with 1000 small loans of $5000 each, you just deal with one big loan of $5000 000, extended to another bank, thus to an organization which, you, a bunch of bankers, can much better predict the financial stance of than it is the case with the non-financial businesses. You just need to see that this other bank has financial reserves for credit risk, i.e. they do not rely exclusively on the market in managing its own credit risk. You’ve got 5,82% of credit risk, bro? Cool, happens all the time. Still, show me, in your official accounts, that you have secured a financial reserve for that, and the next thing you know, I lend you, no problem.

Thus, when you are exposed to credit risk, and you inevitably are, as a bank, you should keep financial reserves for that risk. Risk is a quantity, see? Now, you can be smart and do something else. You can invest part of your total capital in financial assets which are technically lending, and yet are way away, in terms of credit risk, from your average loan. Most governments are good payers, especially when they can schedule those payments way in advance. Lending to them, especially in exchange of tradable sovereign bonds, is low risk, usually estimated way below 1%. Let’s suppose that you, a bank, can buy sovereign bonds with a credit risk at 0,5%. You lend to all the others with a risk coefficient of 5,82%. You spread your total lending so as to have 30% extended to governments, at 0,5% risk, 30% to other banks, let’s say at 1% risk, and the remaining 40% lent to all them other folks, at a credit risk of 5,82%. Your compound weighted average credit risk amounts to 0,3*0,05% + 0,3*1% + 0,4*5,82% = 2,78%. See, with that portfolio of financial assets, you need to make financial reserves for just 2,78% of your total lending, instead of 5,82%. Moreover, in the distinguished company of government bonds, the baseline credit risk to be spread over loans granted to ordinary clients gets driven down from the initial 5,82% to just 0,4*5,82% = 2,33%. By acquiring and holding solid government bonds, you can stop and revert the upwards spiral of credit risk in the times of economic trouble among your clients. Government bonds suck in and hold part of the baseline credit risk induced by the market.

Governments know all that, and we all know there is a price to pay for anything valuable. Thus, when a government approaches you with an initial, usually unofficial proposal of credit against sovereign bonds, you are likely to hear something in the lines of: ‘Look, man, we offer those bonds with an interest rate of 2% a year, and those bonds have a credit risk of 0,5%. We know your baseline credit risk is 5,82%. Thus, by lending us money in exchange of our bonds, you gain 2% in the nominal interest we offer, and 5,82% – 0,5% = 5,32% of reduction in your nominal credit risk. That makes you a total gain of 2% + 5,32% = 7,32%. C’mon, man, let’s share 50/50. Out of that total gain of 7.32%, you take 3,66% and we take 3,66%. How do we take it? Simple. Nominally, we hand you bonds for $10 000 000, but you actually advance to us, in cash, $10 000 000 * (1 + 3,66%) = $10 366 000. Yes, that 3,66% is called discount and you give us that discount because we give you lower credit risk’.

You can take that proposal such as it is or you can bargain. If you take it, you will get 2% in nominal interest on those bonds and you will give away 3,66% in discount. At the end of the day, you get those sovereign bonds at a negative yield of 2% – 3,66% = – 1,66%. Ridiculous? Not at all. You can go through Fiscal Monitors, published by the International Monetary Fund (https://www.imf.org/en/Publications/FM ), and you will see by yourself: the total value of sovereign bonds endowed with a negative yield, after all is said and done as regards discount for low credit risk, has been growing rapidly over the last decade. You can also consult http://www.worldgovernmentbonds.com/ and see the actual numbers. The French government currently borrows at -0,339% on 10 years, and at -0,715% on 3 years. Germany is the big boss of that lot: they borrow at – 0,619% over 10 years.

You can bargain, too. Instead of accepting that initial proposal of 3,66% discount, you say: ‘Look, government. We like each other, and we acknowledge your bonds give us lower risk. Still, please remember that your bonds have that low risk attached to them just as long as we, all the banks, give you a high credit rating. When we decide to downgrade your rating, your credit risk will go from 0,5% to 1,5%, and that will be bad for everyone. Let’s stay reasonable and share 70% for us and 30% for you, instead of 50/50. You get a discount of 7,32% * 0,3 = 2,2%, which, with the nominal interest of 2,5% you offer, gives us a real positive yield of 2,5% – 2,2% = 0,3%, and then we don’t look as total losers’.

You bargain or you don’t, thus. As a banker, you can bargain with a government when you are strong and they are weak. In the world, there are two completely different markets of public debt. The debt of strong governments, which have a lot of political and economic leverage on banks, is something very distinct from the debt of weak, economically wobbly governments, who are clients to banks rather than equivalent players. Against that background, there is that claim: ‘Our children will have to pay the debts we are contracting now’. Let’s discuss it.

You are a bank. You have lent money to a big solid government, who sort of raped you financially, into negative yield on their bonds, and yet you stay on the top of it with the low credit risk they give you, their bonds. That government comes to you, one day, and they say: ‘Time’s up. The maturity of our bonds was 5 years, which have just elapsed, and thus we are buying our bonds back. Here is the cash. Bye, bye. Have fun’. For you, as a banker, it is a disaster. For years, you have been constructing that financial portfolio where sovereign bonds were compensating the credit risk attached to risky business loans, and all that thing sort of kept itself together. Now, you stay with a pile of cash, which you need to invest into something, only anything you will invest it in will have a higher credit risk, and thus you will have to charge a higher interest, and thus you will be less attractive as lender, and you will be more and more doomed to work with people really desperate for cash, and that will drive your overall credit risk even higher, and here the loop spirals into hell.

What you can do is to agree, with the government, for a deal called ‘roll-over of debt’. When sovereign bonds come to their maturity, the government can offer you to swap them against a next generation of bonds, just to keep your balance sheet stable risk-wise, and to keep their cash-flow stable. In that roll-over swap, the same game of nominal interest and discount recurs. When you, as a bank, deal with a strong government with a solid economic base, they are very likely to swap like $100 of face value in bonds against $98 of face value in the next generation of bonds, or 2,5% of nominal interest a year against 1,9% a year etc. At the end of the day, those strong governments, as long as they stay within the limits of reasonable, can keep borrowing without burdening any future generation with the necessity of paying back the debt, because no one really wants to see it paid back. On the other hand, weak governments, ruling over wobbly economies, are in the opposite position. They have to roll their debt over at systematically worse financial conditions than the previous generation of bonds was based on. Those ones, yes, they fall into a true debt trap.

The key, thus, is to have a strong economy. When business runs well, everything else runs well, too. A strong economy, today, means an innovative one, with a lot of real technological change going on. By ‘real’ I mean technological change which actually develops your national technological base and doesn’t just outsource to China. Technological change involves a lot of uncertainty, and therefore a lot of business risk to face, which, at the level of banks, translates into a lot of credit risk. The latter needs to be compensated by low-risk sovereign bonds, which bear the lowest risk when they come from the government sitting on an economic base endowed with quick technological change. The loop closes. Strong economies generate a lot of credit risk, and their governments can alleviate that risk by borrowing from their national banks. As long as the money borrowed by governments supports technological change, directly or indirectly, and as long as neither party in that game goes feral, the whole thing works. This is an old intuition, which was already phrased out by Adam Smith, in his ‘Inquiry Into The Nature and Causes of The Wealth of Nations’ (Book V, Chapter III): substantial public borrowing appears when there is a substantial amount of private capital accumulated and waiting for interesting offers.

I made a video lecture, mostly addressed to my students of Economic Policy, as those in the course of Managerial Economics, with active reading of selected passages in the Fiscal Monitor September 2020 (https://youtu.be/ODY6zl1Z1r4 ).


[1] Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.

[2] Taleb, N. N., & Blyth, M. (2011). The black swan of Cairo: How suppressing volatility makes the world less predictable and more dangerous. Foreign Affairs, 33-39.

My NORMAL and my WEIRD

I go deep into mathematical philosophy, i.e. into the kind of philosophy, where I spare some of my words and use mathematical notation instead, or, if you want, the kind of maths where I am sort of freestyling. I go back to my mild obsession about using artificial neural networks as representation of collective intelligence, and more specifically to one of my networks, namely that representing social roles in the presence of an exogenous disturbance.

I am currently encouraging my students to come up with ideas of new businesses, and I gently direct their attention towards the combined influence of a long-term trend – namely that of digital technologies growing in and into the global economy – with a medium-term one which consists in cloud computing temporarily outgrowing other fields of digital innovation, and finally a short-term, Black-Swan-type event, namely the COVID-19 pandemic.  I want to re-use, and generalize the interpretation of the neural network I presented in my update from May 25th, 2020, titled ‘The perfectly dumb, smart social structure’, in order to understand better the way that changes of various temporal span can overlap and combine.

I start with introducing a complex, probabilistic definition of, respectively, continuity and change. Stuff happens all the time and happening can be represented mathematically as the probability thereof. Let’s suppose that my life is really simple, and it acquires meaning through the happening of three events: A, B, and C. I sincerely hope there are no such lives, yet you never know, you know. In Poland, we have that saying that a true man needs to do three things in life: build a house, plant a tree and raise a son. See, just three important things over the whole lifetime of a man. I hope women have more diverse existential patterns. 

Over the 365 days of a year, event A happened 36 times, event B happened 12 times, and event C took place 120 times. As long as I keep the 365 days of the year as my basic timeline, those three events have displayed the following probabilities of them happening: P(A) = 36/365 = 0.0986, P(B) = 12/365 = 0.0329, and P(C) = 12/365 = 0.3288. With these three probabilities computed, I want to make reasonable expectations as for the rest of my life, and define what is normal, and what is definitely weird, and therefore interesting. I define three alternative versions of the normal and the weird, using three alternative maths. Firstly, I use the uniform distribution, and the binomial one, to represent the most conservative approach, where I assume that normal equals constant, and anything else than constant is revolution. Therefore NORMAL = {P(A) = 0.0986 and P(B) = 0.0329 and P(C) = 0.3288} and WEIRD = {P(A) ≠ 0.0986 or P(B) ≠ 0.0329 or P(C) ≠ 0.3288}. Why do I use ‘and’ in the definition of NORMAL and ‘or’ in that of WEIRD? That’s sheer logic. My NORMAL is all those three events happening exactly at the probabilities computed for the base year. All the 3 of them need to be happening precisely at those levels of incidence. All of them means A and B and C. Anything outside that state is WEIRD, therefore WEIRD happens when even one out of three goes haywire. It can be P(A) or P(B) or P(C), whatever.

Cool. You see? Mathematics don’t bite. Not yet, I mean. Let’s go a few steps further. In mathematical logic, conjunction ‘and’ is represented as multiplication, therefore with symbols ‘x’ or ‘*’.  On the other hand, logical alternative ‘or’ is equivalent to arithmetical addition, i.e. to ‘+’. In other words, my NORMAL = {P(A) = 0.0986 * P(B) = 0.0329 * P(C) = 0.3288} and WEIRD = {P(A) ≠ 0.0986 + P(B) ≠ 0.0329 + P(C) ≠ 0.3288}. The NORMAL is just one value, namely  0,0986 * 0,0329 * 0,3288 = 0,0011, whilst the WEIRD is anything else.

From one thing you probably didn’t like in elementary school, i.e. arithmetic, I will pass to another thing you just as probably had an aversion to, i.e. to geometry. I have those three things that matter in my life, A, B, and C, right? The probability of each of them happening can be represented as an axis in a 3-dimensional, finite space. That space is finite because probabilities are shy and never go above and beyond 100%. Each of my three dimensions maxes out at 100% and that’s it. My NORMAL is just one point in that manifold, and all other points are WEIRD. I computed my probabilities at four digits after the decimal point, and my NORMAL is just one point, which represents 0,0001 out of 1,00 on each axis. Therefore, on each axis I have 1 – 0,0001 = 0,9999 alternative probability of WEIRD stuff happening. I have three dimensions in my existence, and therefore the total volume of the WEIRD makes 0,9999 * 0,9999 * 0,9999 = 0,99993 = 0,9997.

Let’s check with arithmetic. My NORMAL = {P(A) = 0.0986 * P(B) = 0.0329 * P(C) = 0.3288} = 0,0011, right? This is the arithmetical probability of all those three probabilities happening. If I have just two events in my universe, the NORMAL and the WEIRD, the probability of WEIRD is equal to the arithmetical difference between 1,00 and the probability of NORMAL, thus P(WEIRD) = 1,00 – 0,0011 = 0,9989. See? The arithmetical probability of WEIRD is greater than the geometrical volume of WEIRD. Not much, I agree, yet the arithmetical probability of anything at all happening in my life outgrows the geometrical volume of all the things happening in my life by 0,9989 – 0,9997 = 0,0008. Still, there are different interpretations. If I see the finite space of my existence as an arithmetical product of three dimensions, it means I see it as a cube, right? That, in turn, means that I allow my universe to have angles and corners. Yet, if I follow the intuition of Karl Friedrich Gauss and I perceive my existence as a sphere around me (see The kind of puzzle that Karl Friedrich was after), that sphere should have a diameter of 100% (whatever happens happens), and therefore a radius of 50%, and a total volume V = (4/3)*π*(r3) = (4/3)*π*(0,53) =  0,5236. In plain, non-math human it means that after I smooth my existence out by cutting all the corners and angles, I stay with just a bit more than one half of what can possibly, arithmetically happen.

WTF? Right you would be to ask. Let’s follow a bit in the footsteps of Karl Friedrich Gauss. That whole story of spherical existence might be sensible. It might be somehow practical to distinguish all the stuff that can possibly happen to me from the things which I can reasonably expect to happen. I mean, volcanoes do not erupt every day, right? Most planes land, right? There is a difference between outliers of the possible and the mainstream of existential occurrence. The normal distribution is a reasonably good manner of partitioning between those two realms, as it explicitly distinguishes between the expected and all the rest. The expected state of things is one standard deviation away from the average, both up and down, and that state of things can be mathematically apprehended as mean-reverted value. I have already messed around with it (see, for example ‘We really don’t see small change’ ).

When I contemplate my life as the normal distribution of what happens, I become a bit more lucid, as compared to when I had just that one, privileged state of things, described in the preceding paragraphs. When I go Gaussian, I distinguish between the stuff which is actually happening to me, on the one hand, and the expected average state of things. I humbly recognize that what I can reasonably expect is determined by the general ways of reality rather than my individual expectations, which I just as humbly convert into predictions. Moreover, I accept and acknowledge that s**t happens, as a rule, things change, and therefore what is happening to me is always some distance from what I can generally expect. Mathematically, that last realization is symbolized by standard deviation from what is generally expected.

All that taken into account, my NORMAL is an interval: (μ – σ) ≤  NORMAL ≤ (μ + σ), and my WEIRD is actually two WEIRDS, the WEIRD ≤ (μ – σ) and the WEIRD ≥  (μ + σ).

The thing about reality in normal distribution is that it is essentially an endless timeline. Stuff just keeps happening and we normalize our perception thereof by mean-reverting everything we experience. Still, life is finite, our endeavours and ambitions usually have a finite timeframe, and therefore we could do with something like the Poisson process, to distinguish the WEIRD from the NORMAL. Besides, it would still be nice to have a sharp distinction between the things I want to happen, and the things that I can reasonably expect to happen, and the Poisson process addresses this one, too.

Now, what all that stuff about probability has to do with anything? First of all, things that are happening right now can be seen as the manifestation of a probability of them happening. That’s the deep theory by Pierre Simon, marquis de Laplace, which he expressed in his ‘Philosophical Essay on Probabilities’: whatever is happening is manifesting an underlying probability of happening, which, in turn, is a structural aspect of reality. Thus, when I run my simplistic perceptron in order to predict the impact of Black-Swan-type disruptions on human behaviour ( see ‘The perfectly dumb, smart social structure’), marquis de Laplace would say that I uncover an underlying structural proclivity, dormant in the collective intelligence of ours. Further down this rabbit hole, I can claim that however we, the human civilization, react to sudden stressors, that reaction is always a manifestation of some flexibility, hidden and available in our collective cultural DNA.  

The baseline mechanism of collective learning in a social structure can be represented as a network of conscious agents moving from one complex state to another inside a state space organized as a Markov chain of states, i.e. each current state of the social structure is solely the outcome of transformation in the preceding state(s). This transformation is constrained by two sets of exogenous phenomena, namely a vector of desired social outcomes to achieve, and a vector of subjectively aleatory stressors acting like Black-Swan events (i.e. both impossible to predict accurately by any member of the society, and profoundly impactful).

The current state of the social structure is a matrix of behavioural phenomena (i.e. behavioural states) in individual conscious agents comprised in that structure. Formal definition of a conscious agent is developed, and then its application to social research is briefly discussed. Consistently with Hoffman et al. 2015[1] and Fields et al. 2018[2], conscious existence in the world is a 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 (CE) consists in consciously experiencing states of the world and taking actions on the grounds of that experience, in a 7-tuple defined along the following dimensions:

  1. States of the world W
  2. Experiences X
  3. Actions G
  4. Perception P defined as a combination of experiences with states of the world, therefore as a Markovian kernel P: W*X → X
  5. Decisions D defined as a Markovian kernel transforming experiences into actions, or D: X*G → G
  6. Consequences A of actions, defined as a Markovian kernel that transforms actions into further states of the world, or A: G*W →W.
  7. Time t

Still consistently with Hoffman et al. 2015 (op. cit.) and Fields et al. 2018 (op. cit.) it is assumed that Conscious Agents (CA), are individuals autonomous enough to align idiosyncratically their perception P, decisions D, and actions G in the view of maximizing the experience of positive payoffs among the consequences A of their actions. This assumption, i.e. maximization of payoffs, rather than quest for truth in perception, is both strongly substantiated by the here-cited authors, and pivotal for the rest of the model and for the application of artificial neural networks discussed further. Conscious Agent perceive states of the world as a combination of rewards to strive for, threats to avoid, and neutral states, not requiring attention. The capacity to maximize payoffs is further designated as Conscious Agents’ fitness to environment, and is in itself a complex notion, entailing maximization of rewards strictly spoken, minimization of exposure to threats, and a passive attitude towards neutral states. Fitness in Conscious Agents is gauged, and thus passed onto consecutive generations against a complex environment made of rewards and threats of different recurrence over time. The necessity to eat is an example of extremely recurrent external stressor. Seasonal availability of food exemplifies more variant a stressor, whilst a pandemic, such as COVID-19, or a natural disaster, are incidental stressors of subjectively unpredictable recurrence, in the lines of Black-Swan events (Taleb 2007[3]; Taleb & Blyth 2011[4]). 

Conscious Agents are imperfect in their maximization of payoffs, i.e. in the given population, a hierarchy of fitness emerges, and the fittest CA’s have the greatest likelihood to have offspring. Therefore, an evolutionary framework is added, by assuming generational change in the population of Conscious Agents. Some CA’s die out and some new CA’s come to the game. Generational change does not automatically imply biological death and birth. It is a broader phenomenological category, encompassing all such phenomena where the recombination of individual traits inside a given population of entities contributes to creating new generations thereof. Technologies recombine and give an offspring in the form of next-generation solutions (e.g. transistors and printed circuits eventually had offspring in the form of microchips). Business strategies recombine and thus create conditions for the emergence of new business strategies. Another angle of theoretical approach to the issue of recombination in the fittest CA’s is the classical concept of dominant strategy, and that of dynamic equilibrium by John Nash (Nash 1953[5]). When at least some players in a game develop dominant strategies, i.e. strategies that maximize payoffs, those strategies become benchmarks for other players.

The social structure, such as theoretically outlined above, learns by trial and error. It is to stress that individual Conscious Agents inside the structure can learn both by trial and error and by absorption of pre-formed knowledge. Yet, the structure as a whole, in the long temporal horizon, forms its own knowledge by experimenting with itself, and pre-formed knowledge, communicated inside the structure, is the fruit of past experimentation. Collective learning occurs on the basis of a Markov-chain-based mechanism: the structure produces a range of versions of itself, each endowed with a slightly different distribution of behavioural patterns, expressed in the measurable space of actions G, as formalized in the preceding paragraphs. Following the same logic, those behavioural patterns loop with states of the world through consequences, perception, experience, and decisions.

The social structure experiments and learns by producing many variations of itself and testing their fitness against the aggregate vector of external stressors, which, in turn, allows social evolutionary tinkering (Jacob 1977[6]) through tacit coordination, such that the given society displays social change akin to an adaptive walk in rugged landscape (Kauffman & Levin 1987[7]; Kauffman 1993[8]). Each distinct state of the given society is a vector of observable properties, and each empirical instance of that vector is a 1-mutation-neighbour to at least one other instance. All the instances form a space of social entities. In the presence of external stressor, each such mutation (each entity) displays a given fitness to achieve the optimal state, regarding the stressor in question, and therefore the whole set of social entities yields a complex vector of fitness to cope with the stressor. The assumption of collective intelligence means that each social entity is able to observe itself as well as other entities, so as to produce social adaptation for achieving optimal fitness. Social change is an adaptive walk, i.e. a set of local experiments, observable to each other and able to learn from each other’s observed fitness. The resulting path of social change is by definition uneven, whence the expression ‘adaptive walk in rugged landscape’.

There is a strong argument that such adaptive walks occur at a pace proportional to the complexity of social entities involved. The greater the number of characteristics involved, the greater the number of epistatic interactions between them, and the more experiments it takes to have everything more or less aligned for coping with a stressor. Formally, with n significant epistatic traits in the social structure, i.e. with n input variables in the state space, the intelligent collective needs at least m ≥ n +1 rounds of learning in order to develop adaptation. A complete round of learning occurs when the intelligent collective achieves two instrumental outcomes, i.e. it measures its own performance against an expected state, and it feeds back, among individual conscious agents, information about the gap from expected state. For the purposes of the study that follows it is assumed that temporization matters, for a social structure, to the extent that it reflects its pace of collective learning, i.e. the number of distinct time periods t, in the 7-tuple of conscious existence, is the same as the number m of experimental rounds in the process of collective learning.    

Epistatic traits E of a social structure are observable as recurrent patterns in actions G of Conscious Agents. Given the formal structure of conscious existence such as provided earlier (i.e. a 7-tuple), it is further assumed that variance in actions G, thus in behavioural patterns, is a manifestation of underlying variance in experiences X, perception P, decisions D, and consequences A.

A set of Conscious Agents needs to meet one more condition in order to be an intelligent collective: internal coherence, and the capacity to modify it for the purpose of collective learning. As regards this specific aspect, the swarm theory is the main conceptual basis (see for example: Stradner et al. 2013[9]). Internal coherence of a collective is observable as the occurrence of three types in behavioural coupling between Conscious Agents: fixed, random, and correlated. Fixed coupling is a one-to-one relationship: when Conscious Agent performs action Gi(A), Conscious Agent B always responds by action Gi(B). Fixed coupling is a formal expression of what is commonly labelled as strictly ritual. By opposition, random coupling occurs when the Conscious Agent B can have any response to action in Conscious Agent A, without any pattern. Across the spectrum that stretches between fixed coupling and random coupling, correlated coupling entails all such cases when Conscious Agent B chooses from a scalable range of behaviours when responding to action performed by Conscious Agent A, and coincidence in the behaviour of conscious agents A and B explains a significant part of combined variance in their respective behaviour.

It is to note that correlation in behavioural coupling, such as provided in the preceding paragraph, is a behavioural interpretation of the Pearson coefficient of correlation, i.e. it is statistically significant coincidence of local behavioural instances. Another angle is possible, when instead of correlation strictly speaking, we think rather about cointegration, thus about functional connection between expected states (expected values, e.g. mean values in scalable behaviour) in Conscious Agents’ actions. 

A social structure dominated by fixed behavioural coupling doesn’t learn, as behavioural patterns in Conscious Agents are always the same. Should random coupling prevail, it is arguable whether we are dealing with a social structure at all. A reasonably adaptable social structure needs to be dominated by correlated behavioural coupling between conscious agents, and its collective learning can be enhanced by the capacity to switch between different strengths of correlation in behaviours.  

Definition: An Intelligent Collective (IC) is a set of z Conscious Agents, which, over a sequence of m distinct time periods, understood as experimental rounds of learning, whilst keeping significant correlation in behavioural coupling between Conscious Agents’ actions and thus staying structurally stable, produces m such different instances of itself that the last instance in the sequence displays a vector of n epistatic traits significantly different from that observable in the first instance, with the border condition m ≥ n + 1.  

When a set of z Conscious Agents behaves as an Intelligent Collective, it produces a set of n significant epistatic traits, and m ≥ n + 1 instances of itself, over a continuum of m time periods and w distinct and consecutive states of the world. Collective intelligence is observable as the correlation between variance in local states of the world W, on the one hand, and variance in epistatic traits of the social structure. The same remarks, as those made before, hold as regards the general concept of correlation and the possibility of combining it with cointegration.

It is to notice that the border condition m ≥ n +1 has another interesting implication. If we want n epistatic traits to manifest themselves in a population of Conscious Agents, we need at least m ≥ n +1 experimental rounds of learning in that population. The longer is the consciously, and culturally owned history of a social structure, the more complex vector of epistatic traits can this structure develop to cope with external stressors.

Now, we enter the more epistemological realm, namely the question of observability. How are Intelligent Collectives observable, notably in their epistatic traits and in their evolutionary tinkering? From the point of view of a social scientist, observability of the strictly speaking individual behaviour in Conscious Agents, would they be individual persons or other social entities (e.g. businesses) is a rare delicacy, usually accessible at the price of creating a tightly controlled experimental environment. It is usually problematic to generalize observations made in such a controlled setting, so as to make them applicable to the general population. Working with the concept of Intelligent Collective requires phenomenological bridging between the data we commonly have access to, and the process of collectively intelligent evolutionary social tinkering.

Here comes an important, and sometimes arguable assumption: that of normal distribution in the population of Conscious Agents. If any type of behaviour manifests as an epistatic trait, i.e. as important for the ability of the social structure to cope with external stressors, then it is most likely to be an important individual trait, i.e. it is likely to be significantly correlated with the hierarchical position of social entities inside the social structure. This, in turn, allows contending that behavioural patterns associated with epistatic traits are distributed normally in the population of Conscious Agents, and, as such, display expected values, representative thereof. With many epistatic traits at work in parallel, the population of Conscious Agents can be characterized by a vector (a matrix) of mean expected values in scalable and measurable behavioural patterns, which, in turn, are associated with the epistatic traits of the whole population.

This assumption fundamentally connects individual traits to those of the entire population. The set of epistatic traits in the population of Conscious Agents is assumed to be representative for the set of mean expected values in the corresponding epistatic traits at the individual level, in particular Conscious Agents in the population. There are 3 σ – algebras, and one additional structural space, which, together, allow mutual transformation between 3 measurable and structurally stable spaces, namely between: the set of behavioural patterns BP = {bp1, bp2, …, bpn}, the set PBP = {p(bp1), p(bp2), …, p(bpn)} of probabilities as regards the occurrence of those patterns, and the set μBP = {μ(bp1), μ(bp2), …, μ(bpn)} of mean expected values in scalable and observable manifestations of those behavioural patterns.

The 3 σ – algebras are designated as, respectively:

  • the σ – algebra SB, transforming BP into PBP, and it represents the behavioural state of the intelligent collective IC
  • the σ – algebra SE, which transforms BP into μBP and is informative about the expected state of the intelligent collective IC
  • the σ – algebra SD, allowing the transition from PBP to μBP and representing the internal distribution of behavioural patterns inside the intelligent collective IC 

The additional measurable space is V = {v1, v2, …, vn} of observable Euclidean distances between measurable aspects of epistatic traits, thus between probabilities PBP, or between μBP mean expected values. Three important remarks are to make as regards the measurable space V. Firstly, as the whole model serves to use artificial neural networks in an informed manner as a tool of virtual social experimentation, the ‘between’ part in this definition is to be understood flexibly. We can talk about Euclidean distances between probabilities, or distances between mean expected values, yet it is also possible to compute Euclidean distance between a probability and a mean expected value. The Euclidean distance per se does not have a fixed denominator, and, therefore, can exist between magnitudes expressed on different scales of measurement.

Secondly, for the sake of keeping mathematical complexity of the problem at hand within the limits of reasonable, Euclidean distance is further understood as mean Euclidean distance of the given epistatic trait from all the other k = n – 1 epistatic traits, i.e. as


It is also assumed that structural stability of the Intelligent Collective can be measured as, respectively, the mean and the variance in vi, both across the n epistatic traits and m ≥ n +1 experimental rounds. Thirdly, the averaging of Euclidean distances could be, technically, considered as an σ – algebra, as we are in the conceptual construct of state space. Still, it is always the same operation, and it would be always the same σ – algebra, and, as such, logically redundant.

Definition: Collective Intelligence is a two-dimensional σ–algebra CI = {n, m}, which transforms the 7-dimensional state space CA = {W, X, G, P, D, A, t} of individual conscious existence (in Conscious Agents) into the 7-dimensional state space IC = {BP, PBP, μBP, SB, SE, SD, V} of Intelligent Collective, and the transformation occurs by wrapping experience X, actions G, perception P, and decisions D into n epistatic traits of the Intelligent Collective, and by structuring states of the world W, and consequences A over the timeline t observable in the individual conscious existence into m ≥ n + 1 experimental instances of the Intelligent Collective IC so as the last instance IC(m) = {BP(m), PBP(m), μBP(m), SB(m), SE(m), SD(m), v(m)} is significantly different from the first instance IC(1) = {BP(1), PBP(1), μBP(1), SB(1), SE(1), SD(1), v(1)}.    

This definition of Collective Intelligence stays mathematically in the world of Markov chains. Each 7-dimensional state IC = {BP, PBP, μBP, SB, SE, SD, v}of the Intelligent Collective is a transformation of the previous state. Such as formulated above, Collective Intelligence can be referred to and pegged on exogenous phenomena, yet, as such, it can be observed as a phenomenon sui generis.

I got carried away, again. I mean, intellectually. Happens all the time, actually. Time to cool down. If you really want, you can watch, on the top of reading this update, you can watch those videos of mine on the philosophy of science:

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: (https://youtu.be/Wia0apAOdDQ ).

In the second video, recorded on August 24th, 2020 (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.

[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] Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.

[4] Taleb, N. N., & Blyth, M. (2011). The black swan of Cairo: How suppressing volatility makes the world less predictable and more dangerous. Foreign Affairs, 33-39.

[5] Nash, J. (1953). Two-person cooperative games. Econometrica: Journal of the Econometric Society, 128-140.

[6] Jacob, F. (1977). Evolution and tinkering. Science, 196(4295), 1161-1166

[7] Kauffman, S., & Levin, S. (1987). Towards a general theory of adaptive walks on rugged landscapes. Journal of theoretical Biology, 128(1), 11-45

[8] Kauffman, S. A. (1993). The origins of order: Self-organization and selection in evolution. Oxford University Press, USA

[9] 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

Practical takeaways

I am trying to develop a coherent line of logic for the most basic courses I teach in the winter semester, namely ‘Microeconomics’ and ‘Management’. This is the hell of an unusual semester. The pandemic makes us pass largely to online teaching, for one. The pandemic itself is fascinating as social phenomenon and I want to include its study into my teaching, for two. Thirdly and finally, over the last 12 months, I developed an acceptably solid hypothesis of collective intelligence in human social structures, together with a method of studying said structures with the use of artificial neural networks.

I teach ‘Microeconomics’ and ‘Management’ to essentially the same group of students, 1st year undergraduate. There might be minor difference between those two subjects as regards the Erasmus students asking to enrol, yet it is really minor. Thus, I decided to combine my teaching in microeconomics and management into one thread, which consists, for my students, in graduating those two courses (i.e. ‘Microeconomics’ and ‘Management’) by preparing business plans as graduation projects. Why do I adopt such a didactic stance? First of all, I have been putting a lot of emphasis on the skill of business planning over the last 5 years or so. I like believing my students have some real takeaways from my classes, i.e. true practical skills, useful in daily life. Being able to put together an acceptably bullet-proof business plan is a skill which is both practical and logically connected to Microeconomics and Management. Yes, management too. In real life, i.e. when a young person starts a corporate career and as soon as he or she stops dreaming about instantly becoming a CEO, they will be climbing the steps of hierarchical ladder in some kind of corporate structure. The first remotely managerial assignment he or she is likely to have will be to manage a project, thus, to build a small team, negotiate a result-based budget, interface with other parts of the organization in a client-supplier manner etc. Once you can prepare a good business plan, you can plan for an intrapreneurial project as well.     

Secondly, when you want to understand how something works, try to build it. Want understand microeconomics? Cool. Build the microeconomics of something: a digital start-up, a food store, a construction business, whatever practical and workable comes to your mind. As soon as you start building up your business concept, you will quickly grasp distinctions such as, for example, that between assets and equity, that between monopolistic pricing and competitive pricing, or, last but not least, your short-term cash-flow, in, respectively, the presence or the absence of amortization. Building a business plan can even help understanding those cherries on the cake of microeconomics, such as the new institutional theory. As soon as you ask yourself the practical question ‘Will it be better for my start up to invest in our own server, or maybe it is more workable to outsource server power?’, you will grasp, lightning fast, the fine niceties of transactional costs.  

Long story short, I combine the teaching of microeconomics with that of management, in the courses I have with 1st year undergraduate students, and I make them graduate both with a project, which, in turn, consists in preparing a business plan. Thus, in the structure of the online course on MS Teams, I give both groups access to the basic course of business planning, on the website of my blog (https://discoversocialsciences.com/the-course-of-business-planning/ ).

From there on, I lead two parallel and concurrent lines of teaching. As regards Microeconomics, I focus on something like a spritzer. What? What is a spritzer? Oh, the youth of today… A sprizter, my dear children, is a drink made of wine, white or rosé, mixed with water and lemon juice, and a zest of ice cubes. Looks innocent, is enormously tempting during the summertime, and, comparatively to its alcohol content, kicks like a mule. My sprizter is made of classics, mostly Adam Smith (https://discoversocialsciences.com/wp-content/uploads/2018/02/adam_smith_wealth-nations1.pdf ) and Carl Menger (https://discoversocialsciences.com/wp-content/uploads/2019/02/Menger_principles_of_economics.pdf ), who come as the gentle and innocent mixture of water and orange juice, combined with wine, in the form of a strong grasp on the present-day crazy ride of digital economy based on cloud computing, the pandemic and the resulting sudden shift towards medical technologies, and all that against the background of a major shift in our energy base, from fossil fuels to renewables as well as towards a possible new generation of nuclear.

I plan to present my teaching of Microeconomics as a combination of quotes from those two big classics, and references to what is happening right now. As for Management, I stick to the spritzer philosophy. The wine is the same, i.e. all the things that are happening around, whilst just one classical name comes as lemon juice and water in one: Nicolo Machiavelli (https://discoversocialsciences.com/wp-content/uploads/2020/10/Machiavelli-the-prince.pdf ).

So far, when I am writing those words, I have prepared 5 video lectures along the lines I laid out in the preceding paragraphs. In Microeconomics & Management. Opening lecture [https://youtu.be/N7u8Hs_KATc ], I introduce the course of ‘Microeconomics’, as well as that of ‘Principles of Organization and Management’, which I will be holding with the Andrzej Frycz – Modrzewski Krakow University (Krakow, Poland). You can download the corresponding Power Point presentation from:  https://discoversocialsciences.com/wp-content/uploads/2020/09/Microeconomics_Management_Opening-Lecture.pptx

In ‘Fundamentals of Economics #1’ (https://youtu.be/OTGjJGfpdoc) I open up with 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. Finally, in Fundamentals of Economics #2 The basic theory of markets [https://youtu.be/1nObCUBWi4E], I present the behavioural essence of markets as structure of tacit coordination between humans.

As regards Management, I have shot two video lectures so far. In Fundamentals of Management #1 [https://youtu.be/j5RmYViqcT4  ], I present the main lines of teaching and study in the path of Management. More specifically addressed to my students in the majors of Management and International Relations. The link to power point: https://discoversocialsciences.com/wp-content/uploads/2020/10/Fundamentals-Management_1.pptx . In Fundamentals of Management #2 Team building [https://youtu.be/1Ho1ZW-9GXY  ], I describe the 4 fundamental tools of team building: recruitment, alignment of values and goals, their proper communication, and the assessment of performance. The link to power point: https://discoversocialsciences.com/wp-content/uploads/2020/10/Fundamentals-Management-2-Team-building.pptx

#howcouldtheyhavedoneittome

I  am considering the idea of making my students – at least some of them – into an innovative task force in order to develop new technologies and/or new businesses. My essential logic is that I teach social sciences, under various possible angles, and the best way of learning is by trial and error. We learn the most when we experiment with many alternative versions of ourselves and select the version which seems the fittest, regarding the values and goals we pursue. Logically, when I want my students to learn social sciences, like really learn, the first step is to make them experiment with the social roles they currently have and make many alternative versions thereof. You are 100% student at the starting point, and now you try to figure out what is it like to be 80% student and 20% innovator, or 50% student and 50% innovator etc. What are your values? Well, as it comes to learning, I advise assuming that the best learning occurs when we get out of our comfort zone but keep the door open for returning there. I believe it can be qualified as a flow state. You should look for situations when you feel a bit awkward, and the whole thing sucks a bit because you feel you do not have all the skills you need for the situation, and still you see like a clear path of passage between your normal comfort zone and that specific state of constructive suck.   

Thus, when I experiment with many alternative versions of myself, without being afraid of losing my identity, thus when I behave like an intelligent structure, the most valuable versions of myself as learning comes are those which push me slightly out of my comfort zone. When you want to learn social sciences, you look for those alternative versions of yourself which are a bit uncomfortably involved in that whole social thing around you. That controlled uncomfortable involvement makes you learn faster and deeper.

The second important thing I know about learning is that I learn faster and deeper when I write and talk about what I am learning and how I am learning. I have just experienced that process of accelerated figuring my s**t out as regards investment in the stock market. I started by the end of January 2020 (see Back in the game or Fathom the outcomes ) and, with a bit of obsessive self-narration, I went from not really knowing what I am doing and barely controlling my emotions to a portfolio of some 20 investment positions, capable to bring me at least 10% a month in terms of return on capital (see Fire and ice. A real-life business case).

Thus, consistently getting out of your comfort zone just enough to feel a bit of suck, and then writing about your own experience in that place, that whole thing has the hell of a propulsive power. You can really burn the (existential) rubber, under just one condition: the ‘consistently’ part. Being relentless in making small everyday steps is the third ingredient of that concoction. We learn by forming habits. Daily repetition of experimenting in the zone of gentle suck makes you be used to that experimentation, and once you are used to that, well, man, you have the turbo boost on, in your existence.

This is precisely what I fathom to talk my students into: experimenting outside of their comfort zone, with a bit of uncomfortably stimulating social involvement into the development of an innovative business concept. The type of innovation I am thinking about is some kind of digital technology or digital product, and I want to start exploration with rummaging a little bit in the investor-relations sites of publicly listed companies, just to see what they are up to and to find some good benchmarks for business modelling. I start with one of the T-Rexes of the industry, namely with Microsoft (https://www.microsoft.com/en-us/investor ). As I like going straight for the kill, I dive into the section of SEC filings (https://www.microsoft.com/en-us/Investor/sec-filings.aspx ), and there, a pleasant surprise awaits: they end their fiscal year by the end of June, them people at Microsoft, and thus I have their annual report for the fiscal year 2020 ready and available even before the calendar year 2020 is over. You can download the report from their site or from my archives: https://discoversocialsciences.com/wp-content/uploads/2020/10/Microsoft-_FY20Q4_10K.docx .

As I grab my machete and my camera and I cut myself a path through that document, I develop a general impression that digital business goes more and more towards big data and big server power more than programming strictly speaking. I allow myself to source directly from that annual report the table from page 39, with segment results. You can see it here below:

Intelligent Cloud, i.e. Microsoft Azure (https://azure.microsoft.com/en-us/ ), seems to be the most dynamic segment in their business. In other words, a lot of data combined with a lot of server power, and with artificial neural networks to extract patterns and optimize. If I consider the case of Microsoft as representative for the technological race taking place in the IT industry, cloud computing seems to be the main track in that race.

Before I forget: IBM has just confirmed that intuition of mine. If you go and call by https://www.ibm.com/investor , you can pick up their half-year results (https://www.ibm.com/investor/att/pdf/IBM-2Q20-Earnings-Press-Release.pdf ) and their latest strategic update (https://www.ibm.com/investor/att/pdf/IBM-Strategic-Update-2020-Press-Release.pdf ). One fact comes out of it: cloud computing at IBM brings the most gross margin and the most growth in business. It goes to the point of IBM splitting their business in two, with cloud computing spinning out of all the rest, as a separate business.

I would suggest my students to think about digital innovations in the domain of cloud computing. Microsoft Azure (https://azure.microsoft.com/en-us/ ) and cloud computing provided by Okta (https://investor.okta.com/ ), seen a bit more in focus in their latest annual report (https://discoversocialsciences.com/wp-content/uploads/2020/10/Okta-10K-2019.pdf ), serve me as quick benchmarks. Well, as I think about benchmarks, there are others, more obvious or less obvious, depending on the point of view. You Tube, when you think about it, does cloud computing. It stores data – yes, videos are data – and it adapts the list of videos presented to each user according to the preferences of said used, guessed by algorithms of artificial intelligence. Netflix – same thing: a lot of data, in the form of movies, shows and documentaries, and a lot of server power to support the whole thing.     

My internal curious ape has grabbed this interesting object – innovations in the domain of cloud computing – and now my internal happy bulldog starts playing with it, sniffing around and digging holes, haphazardly, in the search for more stuff like that. My internal austere monk watches the ape and the bulldog, holding his razor ready, I mean the Ockham’s razor to cut bullshit out, should such need arise.

What’s cloud computing from the point of view of a team made of an ape and a bulldog? This is essentially a f**king big amount of data, permeated with artificial neural networks, run on and through f**king big servers, consuming a lot of computational power and a lot of energy. As cloud computing is becoming a separate IT business on its own right, I try to decompose it into key factors of value added. The technology of servers as such is one such factor. Energy efficiency, resilience to factors of operational risk, probably fiberoptics as regards connectivity, sheer computational power per 1 cubic meter of space, negotiably low price of electricity – all those things are sort of related to servers.

Access to big, useful datasets is another component of that business. I see two openings here. Acquiring now intellectual property rights to datasets which are cheap today, but likely to be expensive tomorrow is certainly important. People tend to say that data has become a commodity, and it is partly true. Still, I see that data is becoming an asset, too. As I look at the financials of Netflix (see, for example, The hopefully crazy semester), thus at cloud computing for entertainment, I realize that cloud-stored (clouded?) data can be both a fixed asset and a circulating one. It all depends on its lifecycle. There is data with relatively short shelf life, which works as a circulating asset, akin to inventories. It earns money when it flows: some parcels of data flow into my server, some flow out, and I need that flow to stay in the flow of business. There is other data, which holds value for a longer time, similarly to a fixed asset, and yet is subject to depreciation and amortization.

Here is that emerging skillset: data trader. Being a data trader means that you: a) know where to look for interesting datasets b) have business contacts with people who own it c) can intuitively gauge its market value and its shelf life d) can effectively negotiate its acquisition and e) can do the same on the selling side. I think one more specific skill is to add: intuitive ability to associate the data I am trading with proper algorithms of artificial intelligence, just to blow some life into the otherwise soulless databases. One more comes to my mind: the skill to write and enforce contracts which effectively protect the acquired data from infringement and theft.

Cool. There are the servers, and there is the data. Now, we need to market it somehow. The capacity to invent and market digital products based on cloud computing, i.e. on lots of server power combined with lots of data and with agile artificial neural networks, are another aspect of the business model. As I think of it, it comes to my mind that the whole fashion for Blockchain technology and its emergent products – cryptocurrencies and smart contracts – arose when the technology of servers passed a critical threshold, allowing to play with computational power as a fixed asset.

I am very much Schumpeterian, i.e. I am quite convinced that Joseph Schumpeter’s theory of business cycles was and still is a bloody deep vision, which states, among other things, that with the advent of new technologies and new assets, some incumbent technologies and assets will inevitably disappear. Before inevitability consumes itself, a transitory period happens, when old assets coexist with the new ones and choosing the right cocktail thereof is an art and a craft, requiring piles of cash on the bank account, just to keep the business agile and navigable.     

Another thing strikes me: the type of emergent programming languages. The Python, the R, the Pragma Solidity: all that stuff is primarily about managing data. Twenty years ago, programming was mostly about… well, about programming, i.e. about creating algorithms to make those electronics do what we want. Today, programming is more and more about data management. When we invent new languages for a new type of business, we really mean business, as a collective intelligence.

It had to come. I mean, in me. That mild obsession of mine about collective intelligence just had to poke its head from around the corner. Whatever. Let’s go down that rabbit hole. Collective intelligence consists in an intelligent structure experimenting with many alternative versions of itself whilst staying coherent. The whole business of cloud computing, as it is on the rise and before maturity, consists very largely in experimenting with many alternative versions of claims on data, claims on server power, as well as with many alternative digital products sourced therefrom. Some combinations are fitter than others. What are the criteria of fitness? At the business scale, it would be return on investment, I guess. Still, at the collective level of whole societies, it would be about the capacity to assure high employment and low average workload per person. Yes, Sir Keynes, it still holds.

As I indulge in obsessions, I go to another one of mine: the role of cities in our civilization. In my research, I have noticed strange regularities as for the density of urban population. When I compute a compound indicator which goes as density of urban population divided by the general density of population, or [DU/DG], that coefficient enters into strange correlations with other socio-economic variables. One of the most important observations I made about it is that the overall DU/DG for the whole planet is consistently growing. There is a growing difference in social density between cities and the countryside. See Demographic anomalies – the puzzle of urban density, from May 14th, 2020, in order to make yourself an idea. I think that we, humans, invented cities as complex technologies which consist in stacking a large number of homo sapiens (for some humans, it is just allegedly sapiens, let’s face it) on a relatively small surface, with a twofold purpose: that of preserving and developing agricultural land as a food base, and that of fabricating new social roles for new humans, through intense social interaction in cities. My question regarding the rise of technologies in cloud computing is whether it is concurrent with growing urban density, or, conversely, is it a countering force to that growth. In other words, are those big clouds of data on big servers a by-product of citification or is it rather something completely new, possibly able to supplant cities in their role of factories making new social roles?

When I think about cloud computing in terms of collective intelligence, I perceive it as a civilization-wide mechanism which helps making sense of growing information generated by growing mankind. It is a bit like an internal control system inside a growing company. Cloud computing is essentially a pattern of maintaining internal cohesion inside the civilization. Funny how it plays on words. Clouds form in the atmosphere when the density of water vapour passes a critical threshold. As the density of vaporized water per 1 cubic meter of air grows, other thresholds get passed. The joyful, creamy clouds morph into rain clouds, i.e. clouds able to re-condensate water from vapour back to liquid. I think that technologies of cloud computing do precisely that. They collect sparse, vaporized data and condensate it into effective action in and upon the social environment.

Now comes the funny part. Rain clouds turn into storm clouds when they get really thick, i.e. when wet and warm air – thus air with a lot of water vaporized in it and a lot of kinetic energy in its particles – collides with much colder and drier air. Rain clouds pile up and start polarizing their electric charges. The next thing we know, lightning starts hitting, winds become scary etc. Can a cloud of data pile up to the point of becoming a storm cloud of data, when it enters in contact with a piece of civilisation poor in data and low on energy? Well, this is something I observe with social media and their impact. Any social medium, I mean Twitter, Facebook, Instagram, whatever pleases, essentially, is a computed cloud of data. When it collides with population poor in data (i.e. poor in connection with real life and real world), and low on energy (not much of a job, not much of adversity confronted, not really a pile of business being done), data polarizes in the cloud. Some of it flows to the upper layers of the cloud, whilst another part, the heavier one, flows down to the bottom layer and starts attracting haphazard discharges of lighter data, more sophisticated data from the land underneath. The land underneath is the non-digital realm of social life. The so-polarized cloud of data becomes sort of aggressive and scary. It teaches humans to seek shelter and protection from it.           

Metaphors have various power. This one, namely equating a cloud of data to an atmospheric cloud, seems pretty kickass. It leads me to concluding that cloud computing arises as a new, big digital business because there are good reasons for it to do so. There is more and more of us, humans, on the planet. More and more of us live in cities, in a growing social density, i.e. with more and more social interactions. Those interactions inevitably produce data (e.g. #howcouldtheyhavedoneittome), whence growing information wealth of our civilisation, whence the computed clouds of data.

Metaphors have practical power, too, namely that of making me shoot educational videos. I made two of them, sort of in the stride of writing. Here they are, to your pleasure and leisure (in brackets, you have links to You Tube): International Economics #3 The rise of cloud computing [ https://youtu.be/FerCBcsGyq0], for one, and Managerial Economics and Economic Policy #4 The growth of cloud computing and what can governments do about it [ https://youtu.be/J-T4QQDEdlU], for two.

Neighbourhoods of Cineworld

As I write about cities and their social function, I want to mess around a bit with a business model known as Real Estate Investment Trust, or REIT. You can consult my video on REITs in general, namely the one titled ‘In ‘Urban Economics and City Management #2 Case study of REIT: Urban Edge and Atrium [https://youtu.be/BURimdfpxcY ]’. I study there the cases of two REITs, i.e. Real Estate Investment Trusts, namely Urban Edge (U.S.) and Atrium (Central Europe).

I am pursuing the idea of investment as fundamental social activity. I intuitively guess that cities will be developing along the lines of what we will be collectively investing in. By investment I mean a compound process which loops between two specific activities: the accumulation of resources, and the allocation thereof. Since the dawn of human civilization, we have been putting things in reserve. First, it was food. Then, we discovered that putting some of our current resources into building durable architectural structures paid off: warmer in winter, cooler in summer, plenty of room for storing food, some protection against anyone or anything willing to take that food from us etc. Yes, architectural construction is investment. I put my resources – capital, labour, natural resources – into something that will pay me back in the future, over a prolonged period of time.

Investment is an interesting component of our collective intelligence. Our society changes in directions and at paces very much determined by the things we willingly invest in. We organize those things according to the principle of delayed gratification, as controlled today’s deprivation oriented on having some durable outcomes in the future. I deliberately use the term ‘things’, so general and plain. We invest in railroads, and we invest in feeling safe from natural disasters. We invest in businesses, and we invest in the expectation of having the most luxurious car/house/dress/holiday in the entire neighbourhood. We invest in collections of physical things and we invest in ideas.

We have governments and political systems because we have that pattern in our collective intelligence. Governments are in place because and as long as they have legitimation, i.e. because and as long as at least some part of the population accepts being governed, without being coerced into obedience. People give legitimation to governments because they accept sacrificing some of the presently available resources (taxes) and freedoms (compliance with the law) in order to have delayed gratification in the form of security, territorial stability, enforceable contracts etc.

Thus, we go in the direction we invest into. That direction is set by the exact kind of delayed gratification we expect to have in the future, and by the exact type of resources and freedoms we give away today in order to have that delayed thing. Cities evolve exactly according to that pattern. Cities look what they look today because at some point in the past, citizens (yes, the term ‘citizen’ comes from the status of being officially acknowledged and accepted as permanent resident of a city) collectively invested in a given type of urban structures. It is important to understand the way I use words such as ‘collective’ and ‘collectively’. People do things collectively even when they say they completely disagree about doing those things together. This is called ‘tacit coordination’. Let’s consider an example. We disagree, in a city, about the way of organizing a piece of urban space. Some people want to build residential structures there, essentially made for rent. Some others want to see a green space in exactly the same spot, like a park. What you can see emerging out of that disagreement on the long run is a patchwork of residential buildings and green spaces, all over the neighbourhood.

Disagreement is a pattern of tacit coordination, thus a pattern of collective intelligence. We disagree about things which we judge important. Openly expressed disagreement is, in the first place, tacit agreement as for what we really care for (object of disagreement) and who really cares for it (protagonists of disagreement). In my personal experience, if a collective, e.g. a business organization, follows a strategy with unanimous enthusiasm, without any voices of dissent, I am like ‘Ooooh, f**k! That thing is heading towards the edge of the cliff…’.

Good. We invest, i.e. we are collectively intelligent about what kind of present satisfaction we sacrifice for the sake of future delayed gratification. The most important investments we collectively make are subject to disagreement, which is more or less ritualized with legal norms and/or political institutions. Here comes an interesting case, disquietingly connected to real life. Cineworld, a chain of cinema theatres (https://www.cineworldplc.com/en/investors) has just announced that ‘In response to an increasingly challenging theatrical landscape and sustained key market closures due to the COVID-19 pandemic, Cineworld confirms that it will be temporarily suspending operations at all of its 536 Regal theatres in the US and its 127 Cineworld and Picturehouse theatres in the UK from Thursday, 8 October 2020’ (look up https://otp.tools.investis.com/clients/uk/cineworldplc1/rns/regulatory-story.aspx?cid=655&newsid=1420306). That provokes a question: what will happen to those theatres as physical places? Will the pandemic force a rethinking and reengineering of their functions in the surrounding urban space and of the way they should be managed? Is that closure of cinema theatres a durable, irreversible change or is it just temporary?

You can see the entire map of Cineworld’s cinemas under this link: https://www.cineworldplc.com/en/our-cinemas . A bit of digital zoom, i.e. at https://www.picturehouses.com/cinema?search=London, and you can make yourself an opinion about the Cineworld cinemas located in London under the brand of ‘PictureHouse’. Look at the Clapham PictureHouse (https://www.picturehouses.com/cinema/clapham-picturehouse ).  and at its location: 76 Venn St, Clapham Town, London SW4 0AT, United Kingdom. The neighbourhood looks more or less like that:

What can be done there? What will the locals collectively invest in? What will be the key features of that investment which they will be disagreeing about? These are low buildings; the neighbourhood looks like a combination of residential structures and small utility ones. Whatever can that cinema theatre be turned into, that thing will make sense for the immediate neighbourhood, like 5 kilometres around.

I turn that cursory reflection on the closure of Cineworld’s theatres into three pieces of teaching, namely as a case of Urban Development sensu stricte (https://youtu.be/B6fFnStK-eA ),  for one, then as a case of Economic Policy ( https://youtu.be/lTDqGG0tVpU), for two, and finally as a case of International Economics (https://youtu.be/5mx47eInQbI), because as cinemas close, folks are bound to spend more time in front of their private screens, and that means growth in the global market of digital entertainment.

Strangely accommodative of problems

I am returning to the strictly speaking written blogging, after a long break, which I devoted to preparing educational material for the upcoming winter semester 2020/2021. I am outlining a line of research which I can build my teaching around, in the same time. Something looms, and that something is my old obsession: collective intelligence of our human societies and its connection to artificial intelligence. Well, when I say ‘old’, it means ‘slightly seasoned’. I mean, I have been nurturing that obsession for a total of like 4 years, with having it walking around and talking like for the last 18 months or so. It is not truly old, even if ideas were red wine. Anyway, the current shade I paint into that obsession of mine is that human societies have a built-in mechanism of creating new social roles for new humans coming in, in the presence of demographic growth. Cities are very largely factories of social roles, in my view. Close, intense social interactions in a limited space are a mechanism of accelerated collective learning, whence accelerated formation of new skillsets, and those new skillsets, all they need is an opportunity to earn a living with and they turn into social roles.

I have a deep feeling that digital platforms, ranging from the early-hominid-style things like Twitter, all the way up to working and studying via MS Teams or Zoom, have developed as another accelerator of social roles. This accelerator works differently. It is essentially spaceless, although, on the large scale, it is very energy consuming at the level of server power. Still, early cities used to shape new social roles through the skilled labour they required to be built and expanded. A substantial part of whatever we think we know about mathematics and physics comes from geometry, which, in turn, comes from architecture and early machine-building. Similarly, digital platforms make new social roles by stimulating the formation of new skillsets required to develop those platforms, and then to keep them running.

Crazy thoughts come to my mind. What if we, humans, are truly able to think ahead, like really ahead, many generations ahead? What if by the mid-20th century we collectively told ourselves: ‘Look, guys. We mean, us. Cities are great, but there is more and more of us around, all that lot needs food, and food needs agricultural land to be grown and bred on. We need to keep the surface of agricultural land intact at the least, or slightly growing at best, whence the necessity to keep the total surface of urban land under control. Still, we need that space of intense social interactions to make new social roles. Tough nut to crack, this one. Cool, so here is the deal: we start by shrinking transistors to a size below the perceptual capacity of human sight, which is going to open up on a whole range of electronic technologies, which, in turn, will make it worthwhile to create a whole new family of languages just for giving them orders, to those electronics. Hopefully, after 2 or 3 human generations, that is going to create a new plane of social interactions, sort of merging with cities and yet sort of supplanting them’.

And so I follow that trail of collective human intelligence configuring itself in the view of making enough social roles for new humans coming. I am looking for parallels with the human brain. I know, I know, this is a bit far-fetched as parallel, still it is better than nothing. Anyway, in the brain, there is the cortex, i.e. the fancy intellectual, then we have the limbic system, i.e. the romantic Lord Byron, and finally there is the hypothalamus, i.e. the primitive stuff in charge of vegetative impulses. Do we have such distinct functional realms in our collective intelligence? I mean, do we have a subsystem that generates elementary energies (i.e. capacities to perform basic types of action), another one which finds complex cognitive bearings in the world, and something in between, which mediates between objective data and fundamental drives, forming something like preferences, proclivities, values etc. ?

Cool. Enough philosophy. Let’s get into science. As I am writing about digital platforms, I can do something useful just as well, i.e. I can do some review of literature and use it both in my own science and in my teaching. Here comes an interesting paper by Beeres et al. (2020[1]) regarding the correlation between the use of social media, and the prevalence of mental health problems among adolescents in Sweden. The results are strangely similar to the correlation between unemployment and criminality, something I know well from my baseline field of science, i.e. economics. It is a strong correlation across space and a weak, if not a non-existent one over time. The intensity of using social media by Swedish adolescents seems to be correlated positively with the incidence of mental disorders, i.e. adolescents with higher a probability of such disorders tend to use social media more heavily than those mentally more robust adolescents. Still, when an adolescent person increases their starting-point intensity of using social media, that change is not correlated longitudinally with an increased incidence of mental disorders. In other words, whoever is solid in the beginning, stays this way, and whoever is f**ked up, stays that way, too.

The method of research presented in that paper looks robust. The sample is made of 3959 willing participants, fished out from among an initial sample of 12 512 people. This is respectable, as social science comes. The gauge of mental health was Strength and Difficulties Questionnaire (SDQ), which is practically 100% standardized (Goodman & Goodman 2009[2]) and allows distinguishing between internalized, emotional and peer problems on the one hand, and those externalized ones, connected to conduct and hyperactivity. If you are interested in the exact way this questionnaire looks, you can go and consult: https://www.sdqinfo.org/a0.html . The use of social media was self-reported, as answer to the question on the number of hours spent on social media, writing or reading blogs, and chatting online, separately for weekdays and weekends. That answer was standardized, on a scale ranging from 30 minutes a day up to 7 hours a day. Average daily time spent on social media was calculated on the basis of answers given.

The results reported by Beeres et al. (2020) are interesting in a few different ways. Firstly, they seem to discard very largely the common claim that increased use of social media contributes to increased prevalence of mental disorders in adolescents. Intensive use of social media is rather symptomatic of such disorders. That would reverse the whole discourse about this specific phenomenon. Instead of saying ‘Social media make kids go insane’, we should be rather saying ‘Social media facilitate the detection of mental disorders’. Still, one problem remains: if the most intense use of social media among adolescents is observable in those most prone to mental disorders, we have a possible scenario where either the whole culture forming on and through social media, or some specific manifestations thereof, are specifically adapted to people with mental disorders.

Secondly, we have a general case of a digital technology serving a specific social function, i.e. that of mediating social relations of a specific social group (adolescents in developed countries) in a specific context (propensity to mental disorders). Digital technologies are used as surrogate of other social interactions, in people who most likely have hard times going through such interactions.

Another paper, still warm, straight from bakery, by Lin et al. (2020[3]), is entitled ‘Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia’. The first significant phenomena it is informative about is the difficulty to make a simple, catchy title for a scientific paper. Secondly, the authors start from the same hypothesis which Beeres et al. (2020) seem to have discarded, namely that social media use (especially problematic social media use) may give rise to psychological distress. Moreover, Lin et al. (2020) come to the conclusion that it is true. Same science, same hypothesis, different results. I f**king love science. You just need to look into the small print.

The small print here starts with the broad social context. Empirical research by Lin et al. (2020) was conducted in Iran, on participants over 18 years old, whose participation was acquired via Google Forms. The sample consisted of 1506 persons, with an average age of 26 years, and a visible prevalence of women, who made over 58% of the sample. The tool used for detecting mental disorders was the Hospital Anxiety and Depression Scale (HADS). The follow up period was of two weeks, against two years in the case of research by Beeres et al. (2020). Another thing is that whilst Beeres et al. (2020) explicitly the longitudinal within-person variance from the lateral inter-person one, Lin et al. (2020) compute their results without such distinction. Consequently, they come to the conclusion that problematic use of social media is significantly correlated with mental disorders.

I try to connect those two papers to my concept of collective intelligence, and with the use of artificial intelligence. We have an intelligent structure, i.e. humans hanging around together. How do we know we are collectively intelligent? Well, we can make many alternative versions of us being together, each version being like one-mutation neighbour to others, and we can learn new ways of doing things by choosing the best fitting version among those alternatives. On the top of that, we can do the whole stunt whilst staying acceptably cohesive as society. Among many alternative versions of us being together there is a subset, grouping different manners of using social media. Social media are based on artificial intelligence. Each platform runs an algorithm which adapts the content you see to your previously observed online behaviour: the number of times you click on an add, the number of times you share and repost somebody else’s posts, the number of times you publish your own content etc. At the bottom line, the AI in action here adapts so as you max out on the time spent on the platform, and on the clicks you make whilst hanging around there.

The papers I have just quoted suggest that artificial intelligence at work in social media is somehow accommodative of people with mental disorders. This is truly interesting, because the great majority of social institutions we have had so far, i.e. since however we started as intelligent hominids, has been actually the opposite. One of the main ways to detect serious mental problems in a person consists in observing their social relations. If they have even a mild issue with mental health, they are bound to have something seriously off either with their emotional bonds to the immediate social environment (family and friends, mostly) or with their social role in the broader environment (work, school etc.).   I made an educational video out of that quick review of literature, and I placed it on You Tube as: Behavioural modelling and content marketing #3 Social media and mental health


[1] Beeres, D. T., Andersson, F., Vossen, H. G., & Galanti, M. R. (2020). Social media and mental health among early adolescents in Sweden: a longitudinal study with 2-year follow-up (KUPOL Study). Journal of Adolescent Health, https://doi.org/10.1016/j.jadohealth.2020.07.042

[2] Goodman, A., Goodman, R. (2009) Strengths and Difficulties Questionnaire as a Dimensional Measure of Child Mental Health, Journal of the American Academy of Child & Adolescent Psychiatry, Volume 48, Issue 4,

2009, Pages 400-403, ISSN 0890-8567, https://doi.org/10.1097/CHI.0b013e3181985068

[3] Lin, C. Y., Broström, A., Griffiths, M. D., & Pakpour, A. H. (2020). Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia. Internet interventions, 21, 100345, https://doi.org/10.1016/j.invent.2020.100345

New, complete course of Business Planning

I have just finished putting together a complete course of Business Planning. You can find the link on the sidebar. In a series of video lectures combined with Power Point presentations, you will go through all the basic skills of business planning: pitching and modelling your business concept, market research and its translation into financials, assessment of the optimal capital base, and thorough reflection on the soft side of the business plan, i.e. your goals, your risks, your people etc.

Click, dive into, dig through and enjoy.