The batteries we don’t need anymore

I continue on the thread I started to develop in my last update in French, titled ‘De quoi parler à la prochaine réunion de faculté’, i.e. I am using that blog, and the fact of writing, to put some order in the almost ritual mess that happens at the beginning of the academic year. New calls for tenders start in the ministerial grant programs, new syllabuses need to be prepared, new classes start. Ordinary stuff, mind you, this is just something about September, as if I were in Vivaldi’s ‘Four seasons’: the hot, tumultuous Summer slowly folds into the rich, textured, and yet implacably realistic Autumn.

My central idea is to use some of the science which I dove into during the summer holidays as an intellectual tool for putting order in that chaos. That almost new science of mine is mostly based on the theory of complex systems, and my basic claim is that technological change is an emergent phenomenon in complex social systems. We don’t know why exactly our technologies change the way they change. We can trace the current technologies back to their most immediate ancestors and sometimes we can predict their most immediate successors, but that’s about it. Futuristic visions of technologies that could be there in 50 years from now are already some kind of traditional entertainment. The concept of technological progress, when we try to find a developmental logic in the historically known technological change, is usually standing on wobbly legs, on the other hand. Yes, electricity allowed the emergence of medical technologies used in hospitals, and that saved a lot of human lives, but there is no way Thomas Edison could have known that. The most spectacular technological achievements of mankind, such as the Egyptian pyramids, the medieval cathedrals, the Dutch windmills from the 16^th century, or the automobile, seen from the historical distance, look ambiguous. Yes, it all solved some problems, but it facilitated the emergence of new problems. The truly unequivocal benefit of those technological leaps, which could have been actually experienced by the people who made them, was to learn how to develop technologies.

The studies I did during the Summer holidays 2021 focused on four essential, mathematical models of emergent technological change: cellular automata, flock of birds AKA particle swarm, ants’ nest, and imperfect Markov chains. I start with passing in review the model of cellular automata. At any given moment, the social complexity can be divided into a finite number of social entities (agents). They can be individual humans, businesses, NGOs, governments, local markets etc. Each such entity has an immediate freedom of movement, i.e. a finite number of one-step moves. The concept is related to the theory of games and corresponds to what happens in real life. When we do something social, we seldom just rush forwards. Most frequently, we make one step, observe the outcomes, adjust, then we make the next step etc. When all social agents do it, the whole social complexity can be seen as a collection of cells, or pixels. Each such cell (pixel) is a local state of being in society. A social entity can move into that available state, or not, at their pleasure and leisure. All the one-step moves a social entity can make translate into a trajectory it can follow across the social space. Collective outcomes we strive for and achieve can be studied as temporary complex states of those entities following their respective trajectories. The epistemological trick here is that individual moves and their combinations can be known for sure only ex post. All we can do ex ante is to define the possible states, and just wait where does the reality go.

As we are talking about the possible states of social complexity, I found an interesting mathematical mindf**k at quite an unexpected source, namely in the book titled ‘Aware. The Science and Practice of Presence. The Groundbreaking Meditation Practice’ by Daniel J. Siegel [Penguin Random House LLC, 2018, Identifiers: LCCN 2018016987 (print), LCCN 2018027672 (ebook), ISBN 9780143111788, ISBN 9781101993040 (hardback)]. This is a mathematical way of thinking, apparently taken from quantum physics. Here is the essence of it. Everything that happens does so as 100% probability of the given thing happening. Each phenomenon which takes place is the actualization of the same phenomenon being just likely to happen.

Actualization of probability can be seen as collision of two vehicles in traffic. When the two vehicles are at a substantial distance from each other, the likelihood of them colliding is zero, for all practical purposes. As they converge towards each other, there comes a point when they become sort of provisionally entangled, e.g. they find themselves heading towards the same crossroads. The probability of collision increases slightly, and yet it is not even the probability of collision, it is just the probability that these two might find themselves in a vicinity conducive to a possible collision. Nothing to write home about, yet, like really. It can be seen as a plateau of probability slowly emerging out of the initial soup of all the things which can possibly happen.

As the two cars drive closer and closer to the crossroads in question, the panoply of possible states narrows down. There is a very clear chunk of reality which gains in likelihood, as if it was a mountain range pushing up from the provisional plateau. There comes a point where the two cars (and their drivers) just come on collision course, and there is no way around it, and this is a peak of 100% probability. Boom! Probability is being consumed.

What do those cars have in common with meditation and with the emergence of technological change? As regards meditation, thought can be viewed as a progressively emerging actualization of something that was just a weak probability, sort of a month ago it was just weakly probable that today I would think what I think, it became much more likely yesterday, as the thoughts from yesterday have an impact on the thoughts of today, and today it all comes to fruition, i.e. to the 100% probability. As regards emergent technological change, the way technology changes today can be viewed as actualization of something that was highly probable last year, just somehow probable 10 years ago, and had been just part of the amorphous soup of probability 30 years ago. Those trajectories followed by individual agents inside social complexity, as defined in the theory of cellular automata, are entangled together precisely according to that pattern of emergent probabilities. Two businesses coming up with two mutually independent, and yet similar technologies, are like two peak actualizations of 100% probability in a plateau of probable technological change, which, in turn, has been slowly emerging for some time.

Those other theories I use explain and allow to model mathematically that entanglement. The theory of particle swarm, pertinent to flocks of birds, assumes that autonomous social agents strive for a certain level of behavioural coupling. We expect some level of predictability from others, and we can cooperate with others when we are satisfactorily predictable in our actions. The strive for social coherence is, therefore, one mechanism of entanglement between individual trajectories of cellular automata. The theory of ants’ nest focuses on a specific category of communication systems in societies, working like pheromones. Ants organize by marking, reinforcing and following paths across their environment, and their pheromones serve as markers and reinforcement agents for those paths. In human societies, there are social pheromones. Money and financial markets make probably the most obvious example, but scientific publications are another one. The more scientific articles are being published on a given topic, the more likely are other articles being written on the same topic, until the whole thing reaches a point of saturation, when some ants (pardon me, scientists) start thinking about another path to mark with intellectual pheromones.

Cool. I have (OK, we have) complex social states, made of entangled probabilities that something specific happens, and they encompass technology. Those complex states change, i.e. one complex state morphs into another. Now, how the hell can I know, as a researcher, what is happening exactly? Such as the theory of complex systems suggests it, I can never know exactly, for one, and I need to observe, for two. As I don’t know exactly what is it exactly, that thing which I label ‘technological change’, it is problematic to set too many normative assumptions as for which specific path that technological change should take. I think this is the biggest point of contention as I apply my theory, such as I have just outlined it, to my main field of empirical research, namely energy economics, and technological change in the sector of energy. The more I do that research, the more convinced I am that the so-called ‘energy policies’, ‘climate policies’ etc. are politically driven bullshit based on wishful thinking, with not much of a chance to bring the positive change we expect. I have that deep feeling that setting a strategy for future innovations in our business/country/world is very much like that Polish expression ‘sharing the skin of a bear which is still running in the woods’. First, you need to kill the bear, only then you can bicker about who takes what part of the skin. In the case of innovation, long-term strategies in that domain consist in predicting what we will do when we have something we don’t even know yet what is it exactly.

I am trying to apply this general theory in the grant applications which I am in charge of preparing now, and in my teaching. We have that idea, at the faculty, to apply for funding to study the market of electric vehicles in Europe and in Poland. This is an interesting situation as regards business models. In the US, the market of electric cars is clearly divided among three categories of players. There is Tesla, which is a category and an industry in itself, with its peculiar strategy of extreme vertical integration. Then there are the big, classical car makers, such as Toyota, General Motors etc., with their business models based on rather a short vertical chain of value added inside the business, and a massive supply chain upstream of the house. Finally, there is a rising tide of small start-ups in the making of electric vehicles. I wonder what I could be in Europe. As our European market of electric vehicles is taking off, it is dominated by the incumbent big manufacturers, the old school ones, with Tesla building a factory in Germany, and progressively building a beachhead in the market. There is some timid movement towards small start-up businesses in the field, but it is really timid. In my home country, Poland, the most significant attempt at starting up an electric vehicle made in Poland is a big consortium of state-controlled companies, running under the name of ‘Electromobility Poland’.

I have that intuition, which I provisionally express as a working hypothesis, namely that business models are an emergent property of technologies which they use. As regards the market of electric vehicles, it means that Tesla’s business model is not an accidental explosion of Elon Musk’s genius mind: it is an emergent characteristic of the technologies involved.

Good. I have some theory taking shape, nice and easy. I let it ripen a bit, and I start sniffing around for facts. What is a business model, in my mind? It is the way of operating the chain of value added, and getting paid for it, in the first place. Then, it is the way of using capital. I noticed that highly innovative environments force businesses to build up and keep large amounts of cash money, arguably to manage the diverse uncertainties emerging as technologies around morph like hell. In some cases, e.g. in biotech, the right business model for rapid innovation is a money-sucker, with apparently endless pay-ins of additional equity by the shareholders, and yet with a big value in terms of technological novelty created. I can associate that phenomenon of vacuum cleaning equity with the case of Tesla, who just recently started being profitable, and had gone through something like a decade in permanent operational loss. That is all pertinent to fixed costs, thus to the cash we need to build up and keep in place the organizational structure required for managing the value chain the way we want to manage it.

I am translating those loose remarks of mine into observable phenomena. Everything I have just mentioned is to be found in the annual financial reports. This is my first source of information. When I want to study business models in the market of electric vehicles, I need to look into financial and corporate reports of businesses active in the market. I need to look into the financial reports of Mercedes Benz, BMW, Renault, PSA, Volkswagen, Fiat, Volvo, and Opel – thus the European automotive makers – and see how it is going, and whether whatever is going on can be correlated with changes in the European market of electric vehicles. Then, it is useful to look into the financial reports of global players present in the European market, e.g. Tesla, Toyota, Honda and whatnot, just to see what changes in them as the European market of electric vehicles is changing.

If my intuition is correct, i.e. if business models are truly an emergent property of technologies used, the fact of engaging into the business of electric vehicles should be correlated with some sort of recurrent pattern in those companies.

Good. This is about the big boys in the playground. Now, I turn toward the small ones, the start-up businesses. As I already said, it is not like we have a crowd of them in the European industry of electric vehicles. The intuitive axis of research which comes to my mind is to look at start-ups active in the U.S., study their business models, and see if there is any chance of something similar emerging in Europe. Somehow tangentially to that, I think it would be interesting to check whether the plan of Polish government regarding ‘Electromobility Poland’, that is the plan to develop it with public and semi-public money, and then sell it to private investors, has any grounds and under what conditions it can be a workable plan.

Good. I have rummaged a bit in my own mind, time to do the same to other people. I mean, I am passing to reviewing the literature. I type ‘electric vehicles Europe business model’ at the https://www.sciencedirect.com/ platform, and I look at what’s popping up. Here comes the paper by Pardo-Bosch, F., Pujadas, P., Morton, C., & Cervera, C. (2021). Sustainable deployment of an electric vehicle public charging infrastructure network from a city business model perspective. Sustainable Cities and Society, 71, 102957., https://doi.org/10.1016/j.scs.2021.102957 . The abstract says: ‘The unprecedented growth of global cities together with increased population mobility and a heightened concern regarding climate change and energy independence have increased interest in electric vehicles (EVs) as one means to address these challenges. The development of a public charging infrastructure network is a key element for promoting EVs, and with them reducing greenhouse gas emissions attributable to the operation of conventional cars and improving the local environment through reductions in air pollution. This paper discusses the effectiveness, efficiency, and feasibility of city strategic plans for establishing a public charging infrastructure network to encourage the uptake and use of EVs. A holistic analysis based on the Value Creation Ecosystem (VCE) and the City Model Canvas (CMC) is used to visualise how such plans may offer public value with a long-term and sustainable approach. The charging infrastructure network implementation strategy of two major European cities, Nantes (France) and Hamburg (Germany), are analysed and the results indicate the need to involve a wide range of public and private stakeholders in the metropolitan areas. Additionally, relevant, and fundamental patterns and recommendations are provided, which may help other public managers effectively implement this service and scale-up its use and business model.’

Well, I see there is a lot of work to do, as I read that abstract. I rarely find a paper where I have so much to argue with, just after having read the abstract. First of all, ‘the unprecedented growth of global cities’ thing. Actually, if you care to have a look at the World Bank data on urban land (https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2 ), as well as that on urban population (https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS ), you will see that urbanization is an ambiguous phenomenon, strongly region-specific. The central thing is that cities become increasingly distinct from the countryside, as types of human settlements. The connection between electric vehicles and cities is partly clear, but just partly. Cities are the most obvious place to start with EVs, because of the relatively short distance to travel between charging points. Still, moving EVs outside the cities, and making them functional in rural areas, is the next big challenge.

Then comes the ‘The development of a public charging infrastructure network is a key element for promoting EVs’ part. As I studied the thing in Europe, the network of charging stations, as compared to the fleet of EVs in the streets is so dense that we have like 12 vehicles per charging station on average, across the European Union. There is no way a private investor can have it for their money, when financing a private charging station, with that average density. We face a paradox: there are so many publicly funded charging stations, in relation to the car fleet out there, that private investment gets discouraged. I agree that it could be an acceptable transitory state in the market, although it begs the question whether private charging stations are a viable business in Europe. Tesla has based a large part of its business model in the US precisely on the development of their own charging stations. Is it a viable solution in Europe?

Here comes another general remark, contingent to my hypothesis of business models being emergent on the basis of technologies. Automotive technologies in general, thus the technology of a vehicle moving by itself, regardless the method of propulsion (i.e. internal combustion vs electric) is a combination of two component technologies. Said method of propulsion is one of them, and the other one is the technology of distributing the power source across space. Electric vehicles can be viewed as cousins to tramways and electric trains, with just more pronounced a taste for independence: instead of drinking electricity from a permanent wiring, EVs carry their electricity around with them, in batteries.

As we talk about batteries, here comes another paper in my cursory rummaging across other people’s science: Albertsen, L., Richter, J. L., Peck, P., Dalhammar, C., & Plepys, A. (2021). Circular business models for electric vehicle lithium-ion batteries: An analysis of current practices of vehicle manufacturers and policies in the EU. Resources, Conservation and Recycling, 172, 105658., https://doi.org/10.1016/j.resconrec.2021.105658 . Yes, indeed, the advent of electric vehicles creates a problem to solve, namely what to do with all those batteries. I mean two categories of batteries. Those which we need, and hope to acquire easily when the time comes for changing them in our vehicles, in the first place, and those we don’t need anymore and expect someone to take care of them swiftly and elegantly.

The possible Black Swans

I am re-digesting, like a cow, some of the intellectual food I figured out recently. I return to the specific strand of my research to be found in the unpublished manuscript ‘The Puzzle of Urban Density And Energy Consumption’, and I want to rummage a bit inside one specific issue, namely the meaning which I can attach to the neural activation function in the quantitative method I use.

Just to give a quick sketch of the landscape, I work through a general hypothesis that our human civilization is based on two factories: the factory of food in the countryside, and the factory of new social roles in cities. The latter produce new social roles by creating demographic anomalies, i.e. by packing humans tightly together, in abnormally high density. Being dense together makes us interact more with each other, which, whilst not always pleasant, stimulates our social brains and makes us figure out new interesting s**t, i.e. new social roles.

I made a metric of density in population, which is a coefficient derived from the available data of the World Bank. I took the coefficient of urbanization (World Bank 1[1]), and I multiplied it by the headcount of population (World Bank 4[2]). This is how I got the number of people living in cities. I divided it by the surface of urban land (World Bank 2[3]), and I got the density of population in cities, which I further label as ‘DU’. Further, I gather that the social difference between cities and the countryside, hence the relative impact of cities as breeding ground for new social roles, is determined by the difference in the depth of demographic anomalies created by the urban density of population. Therefore, I took the just-calculated coefficient DU and I divided it by the general density of population, or ‘DG’ (World Bank 5[4]). This is how I ended up the with the coefficient ‘DU/DG’, which, mathematically, denominates the density of urban population in units of general density in population.

I simulate an artificial reality, where we, humans, optimize the coefficient ‘DU/DG’ as our chief collective orientation. We just want to get it right. Enough human density in cities to be creative, and yet enough space for each human being able to practice mindfulness when taking a #2 in the toilet. We optimize ourselves being dense together in cities on the base of 7 input characteristics of ours, namely:

Population – this is a typical scale variable. The intuition behind it is that size matters, and that’s why in most socio-economic research, when we really mean business in quantitative terms, we add such variables, pertinent to the size of the social entity studied. Urbanization occurring in a small country, like Belgium (with all my due respect for Belgians), is likely to occur differently from urbanization in India or in the U.S. In this specific case, I assume that a big population, like hundreds of millions of people, has to move more resources around to accommodate people in cities, as compared to a population counted in dozens of millions.

Urban population absolute – same tune, a scale variable, more specifically pertinent to the headcount of urban populations.

Gross Domestic Product (GDP, constant 2010 US$) – scale variable, once again, but this time it is about the real output of the economy. In my approach, the GDP is not exactly a measure of the wealth produced, but more of an appraisal of total productive activity in the humans living around. This is why I use constant prices. That shaves off the price-and-relative-wealth component, and leaves GDP as a metric pertinent to how much tradable surpluses do humans create in a given place and time.

Broad money (% of GDP) – this is essentially the opposite to the velocity of money, and it corresponds to another strand in my research. I discovered and I keep studying the fact that in the presence of quick technological change, human societies stuff themselves up with abnormally high amounts of cash (or cash equivalents, for that matter). It holds for entire countries as well as for individual businesses. You can find more on that in my article ‘Technological change as a monetary phenomenon’. I guess that when humans make more new social roles in cities, technologies change faster.

Energy use (kg of oil equivalent per capita) – this is one of the fundamental variables I frequently work with. I guess I included it in this particular piece of research just in case, in order to be able to connect with my research on the market of energy.

Agricultural land (km2) – the surface of agricultural land available is a logical correlate of urban population. A given number of people in cities need a given amount of food, which, in turn, can be provided by a given surface of agricultural land.

Cereal yield (kg per hectare) – logically complementary to the surface of agricultural land. Yield per hectare in France is different from what an average hectare can contribute in Nigeria, and that is likely to be correlated with urbanization.

You can get the raw data I used UNDER THIS LINK. It covers Australia, Brazil, Canada, China, Colombia, France, Gabon, Germany, Ghana, India, Malaysia, Mexico, Mozambique, Namibia, New Zealand, Nigeria, Norway, Poland, Russian Federation, United Kingdom, and the United States. All that lot observed over the window in time stretching from 1961 all the way to 2015.

I make that data into a neural network, which means that I make h(t_j) = x₁(t_j)*R* E[x_i(t_j-1)] + x₂(t_j)*R* E[x₂(t_j-1)] + … + x_n(t_j)*R* E[x_n(t_j-1)], as explained in my update titled ‘Representative for collective intelligence’, with x₁, x₂,…, x₇ input variables described above, grouped in 21 social entities (countries), and spread over 2015 – 1961= 54 years. After the curation of data for empty cells, I have m = 896 experimental rounds in the (alleged) collective intelligence, whose presence I guess behind the numbers. I made that lot learn how to squeeze the partly randomized input, controlled for internal coherence, into the mould of the desired output of the coefficient x_o = DU/DG. I ran the procedure of learning with 4 different methods of estimating the error of optimization. Firstly, I computed that error the way we do it in basic statistics, namely e₁ = x_o – h(t_j). The mixed-up input is simply subtracted from expected output. In the background, I assume that the locally output x_o is an expected value in statistical terms, i.e. it is the mean value of some hypothetical Gaussian distribution, local and specific to that concrete observation. With that approach to error, there is no neural activation as such. It is an autistic neural network, which does not discriminate input as for its strength. It just reacts.

As I want my collective intelligence to be smarter than your average leech, I make three more estimations of errors, with the input h(t_j) passing through a neural activation function. I start with the ReLU rectifier, AKA max[0, h(t_j)], and, correspondingly, with e₂ = x_o – ReLU[h(t_j)]. Then I warm up, and I use neural activation via hyperbolic tangent tanh[h] = (e^2h – 1) / (e^2h + 1), and I compute e₃ = x_o – tanh[h(t_j)]. The hyperbolic tangent is a transcendental number generated by periodical observation of a hyperbola, and that means that hyperbolic tangent has no functional correlation to its input. Neural activation with hyperbolic tangent creates a projection of input into a separate, non-correlated space of states, like cultural transformation of cognitive input into symbols, ideologies and whatnot. Fourthly and finally, I use the sigmoid function (AKA logistic function) sig(h) = 1 / (1 + e^-h) which can be read as smoothed likelihood that something happens, i.e. that input h(t_j) has full power. The corresponding error is e₄ = x_o – sig[h(t_j)].

From there, I go my normal way. I create 4 artificial realities out of my source dataset. Each of these realities assumes that humans strive to nail down the right social difference between cities and the countryside, as measured with the DU/DG coefficient. Each of these realities is generated with a different way of appraising how far we are from the desired DU/DG, this with four different ways of computing the error: e₁, e₂, e₃, and e₄. The expected states of both the source empirical dataset, and sets representative for those 4 alternative realities, are given by their respective vectors of mean values, i.e. mean DU/DG, mean population etc. Those vectors of means are provided in Table 1 below. The source dataset shows a mean DU/DG = 41,14, which means that cities in this dataset display, on average across countries, 41 times greater a density of population than the general density of population. Mean empirical population is 149,6 million people, with mean urban population being 67,34 million people. Yes, we have China and India in the lot, and they really pump those scale numbers up.

Table 1 – Vectors of mean values in the source empirical set and in the perceptrons simulating alternative realities, optimizing the coefficient DU/DG

		Perceptrons pegged on DU/DG
Variable	Source dataset	*error = xo – h*	*error = xo – ReLu(h)*	*error = xo – tanh(h)*	*error = xo – sigmoid(h)*
DU/DG	41,14	36,38	4,91	61,56	324,29
Population	149 625 587,07	125 596 355,00	(33 435 417,00)	252 800 741,00	1 580 356 431,00
GDP (constant 2010 US$)	1 320 025 624 972,08	1 025 700 000 000,00	(922 220 000 000,00)	2 583 780 000 000,00	18 844 500 000 000,00
Broad money (% of GDP)	57,50	54,13	31,80	71,99	258,38
Urban population absolute	67 349 480,42	54 311 459,20	(31 977 590,00)	123 331 287,00	843 649 729,00
Energy use (kg of oil equivalent per capita)	2 918,69	2 769,76	1 784,11	3 558,16	11 786,15
Agricultural land km2	1 227 301,86	1 135 064,25	524 611,51	1 623 345,71	6 719 245,69
Cereal yield (kg per hectare)	3 153,31	3 010,54	2 065,68	3 766,31	11 653,77

One of the first things which jumps to the eye in Table 1 – at least to my eye – is that one of the alternative realities, namely that based on the ReLU activation function, is an impossible reality. There are negative populations in this one, and this is not a livable state of things. I don’t know about you, my readers, but I would feel horrible knowing that I am a minus. People can’t be negative by default. By the way, in this specific dataset, the ReLU looks like almost identical to the basic difference e₁ = x_o – h(t_j). Yet, whilst making an alternative reality with no neural transformation of quasi-randomized input, thus making it with e₁ = x_o – h(t_j), creates something pretty close to the original empirics.

Another alternative reality which looks sort of sketchy is the one based on neural activation via the sigmoid function. This one transforms the initial mean expected values into their several-times-multiples. Looks like the sigmoid is equivalent, in this case, to powering the collective intelligence of societies studied with substantial doses of interesting chemicals. That particular reality is sort of a wild dream, like what it would be like to produce almost 4 times more cereal yield per hectare, having more than 4 times more agricultural land, and over 10 times more people in cities. The surface of available land being finite as it is, 4 times more agricultural land and 10 times more people in cities would mean cities tiny in terms of land surface, probably all in height, both under and above ground, with those cities being 324 times denser with humans than the general landscape. Sounds familiar, a bit like sci fi movies.

Four different ways of pitching input variables against the expected output of optimal DU/DG coefficient produce four very different alternative realities. Out of these four, one is impossible, one is hilarious, and we stay with two acceptable ones, namely that based on no proper neural activation at all, and the other one using the hyperbolic tangent for assessing the salience of things. Interestingly, errors estimated as e₁ = x_o – h(t_j) are essentially correlated with the input variables, whilst those assessed as e₃ = x_o – tanh[h(t_j)] are essentially uncorrelated. It means that in the former case one can more or less predict how satisfied the neural network will be with the local input, and that prediction can be reliably made a priori. In the latter case, with the hyperbolic tangent, there is no way to know in advance. In this case, neural activation is a true transformation of reality.

Table 2 below provides the formal calculation of standardized Euclidean distance between all the 4 alternative realities and the real world of tears we live in. By standardized Euclidean I mean: E = {[(mean_X – mean_S)²]^0,5} / mean_X. The ‘/ mean_X’ part means that divide the basic Euclidean distance by the mean value which serves me as benchmark, i.e. the empirical one. That facilitates subsequent averaging of those variable-specific Euclidean distances into one metric of mathematical similarity between entire vectors of values.

Table 2 – Vectors of standardized Euclidean distances between the source set X and the perceptrons simulating alternative realities, optimizing the coefficient DU/DG

	error = xo – h	error = xo – ReLu(h)	error = xo – tanh(h)	error = xo – sigmoid(h)
DU/DG]	0,115597874	0,88065496	0,496346621	6,882843342
Population	0,160595741	1,223460557	0,68955555	9,562073386
GDP (constant 2010 US$)	0,22296963	1,698637953	0,957371093	13,27585923
Broad money (% of GDP)	0,058672324	0,446981172	0,251923403	3,493424228
Urban population absolute	0,193587555	1,474800842	0,831213637	11,52644748
Energy use (kg of oil equivalent per capita)	0,051026181	0,388730845	0,219092892	3,038163202
Agricultural land km2	0,075154787	0,572548914	0,322694736	4,474810971
Cereal yield (kg per hectare)	0,045275	0,344916834	0,194398841	2,695730596
Average	*0,115359886*	*0,87884151*	*0,495324596*	*6,868669054*

Interestingly, whilst alternative reality based on neural activation through the ReLU function creates impossibly negative populations, its overall Euclidean similarity to the source dataset is not as big as it could seem. The impossible alternative is specific just to some variables.

Now, what does it all have to do with anything? How is that estimation of error representative for collective intelligence in human societies? Good question. I am doing my best to give some kind of answer to it. Quantitative socio-economic variables represent valuable collective outcomes, and thus are informative about alternative orientations in collective action. The process of learning how to nail those valuable outcomes down consumes said orientation in action. Assuming that figuring out the right proportion of demographic anomaly in cities, as measured with DU/DG, is a valuable collective outcome, four collective orientations thereon have been simulated. One goes a bit haywire (negative populations), and yet it shows a possible state of society which attempts to sort of smooth out the social difference between cities and the countryside, with DU/DG being ten times lower than reality. Another one goes fantasque, with huge numbers and a slightly sci-fi-ish shade. The remaining two look like realistic alternatives, one essentially predictable with e₁ = x_o – h(t_j), and another one essentially unpredictable, with e₃ = x_o – tanh[h(t_j)].

I want my method to serve as a predictive tool for sketching the possible scenarios of technological change, in particular as regards the emergence and absorption of radically new technologies. On the other hand, I want my method to be of help when it comes to identifying the possible Black Swans, i.e. the rather unlikely and yet profoundly disturbing states of nature. As I look at those 4 alternative realities my perceptron has just made up (it’s not me, its him! Well, it…), I can see two Black Swans. The one made with the sigmoid activation function shows a possible direction which, for example, African countries could follow, should they experience rapid demographic growth. This particular Black Swan is a hypothetical situation, when population grows like hell. This automatically puts enormous pressure on agriculture. More people need more food. More agriculture requires more space and there is fewer left for cities. Still, more people around need more social roles, and we need to ramp up the production thereof in very densely packed urban populations, where the sheer density of human interaction makes our social brains just race for novelty. This particular Black Swan could be actually a historical reconstruction. It could be representative for the type of social change which we know as civilisational revival: passage from the nomad life to the sedentary one, like a dozen of thousands of years ago, reconstruction of social tissue after the fall of the Western Roman Empire in Europe, that sort of stuff.

Another Black Swan is made with the ReLU activation function and simulates a society, where cities lose their function as factories of new social roles. It is the society in downsizing. It is actually a historical reconstruction, too. This is what must have happened when the Western Roman Empire was collapsing, and before the European civilization bounced back.

Well, well, well, that s**t makes sense… Amazing.

[1] World Bank 1: https://data.worldbank.org/indicator/SP.URB.TOTL.IN.ZS

[2] World Bank 4: https://data.worldbank.org/indicator/SP.POP.TOTL

[3] World Bank 2: https://data.worldbank.org/indicator/AG.LND.TOTL.UR.K2

[4] World Bank 5: https://data.worldbank.org/indicator/EN.POP.DNST

Representative for collective intelligence

I am generalizing from the article which I am currently revising, and I am taking a broader view on many specific strands of research I am running, mostly in order to move forward with my hypothesis of collective intelligence in human social structures. I want to recapitulate on my method – once more – in order to extract and understand its meaning.

I have recently realized a few things about my research. Firstly, I am using the logical structure of an artificial neural network as a simulator more than an optimizer, as digital imagination rather than functional, goal-oriented intelligence, and that seems to be the way of using AI which hardly anyone else in social sciences seems to be doing. The big question which I am (re)asking myself is to what extent are my simulations representative for the collective intelligence of human societies.

I start gently, with variables, hence with my phenomenology. I mostly use the commonly accessible and published variables, such as those published by the World Bank, the International Monetary Fund, STATISTA etc. Sometimes, I make my own coefficients out of those commonly accepted metrics, e.g. the coefficient of resident patent applications per 1 million people, the proportion between the density of population in cities and the general one, or the coefficient of fixed capital assets per 1 patent application.

My take on any variables in social sciences is very strongly phenomenological, or even hermeneutic. I follow the line of logic which you can find, for example, in “Phenomenology of Perception” by Maurice Merleau-Ponty (reprint, revised, Routledge, 2013, ISBN 1135718601, 9781135718602). I assume that any of the metrics we have in social sciences is an entanglement of our collective cognition with the actual s**t going on. As the actual s**t going on encompasses our way of forming our collective cognition, any variable used in social sciences is very much like a person’s attempt to look at themselves from a distance. Yes! This is what we use mirrors for! Variables used in social sciences are mirrors. Still, they are mirrors made largely by trial and error, with a little bit of a shaky hand, and each of them shows actual social reality in slightly disformed a manner.

Empirical research in social sciences consists, very largely, in a group of people trying to guess something about themselves on the basis of repeated looks into a set of imperfect mirrors. Those mirrors are imperfect, and yet they serve some purpose. I pass to my second big phenomenological take on social reality, namely that our entangled observations thereof are far from being haphazard. The furtive looks we catch of the phenomenal soup, out there, are purposeful. We pay attention to things which pay off. We define specific variables in social sciences because we know by experience that paying attention to those aspects of social reality brings concrete rewards, whilst not paying attention thereto can hurt, like bad.

Let’s take inflation. Way back in the day, like 300 years ago, no one really used the term of inflation because the monetary system consisted in a multitude of currencies, mixing private and public deeds of various kinds. Entire provinces in European countries could rely on bills of exchange issued by influential merchants and bankers, just to switch to other type of bills 5 years later. Fluctuations in the rates of exchange in those multiple currencies very largely cancelled each other. Each business of respectable size was like a local equivalent of the today’s Forex exchange. Inflation was a metric which did not even make sense at the time, as any professional of finance would intuitively ask back: ‘Inflation? Like… inflation in which exactly among those 27 currencies I use everyday?’.

Standardized monetary systems, which we call ‘FIAT money’ today, steadied themselves only in the 19^th century. Multiple currencies progressively fused into one, homogenized monetary mass, and mass conveys energy. Inflation is loss of monetary energy, like entropy of the monetary mass. People started paying attention to inflation when it started to matter.

We make our own social reality, which is fundamentally unobservable to us, and it makes sense because it is hard to have an objective, external look at a box when we are staying inside the box. Living in that box, we have learnt, over time, how to pay attention to the temporarily important properties of the box. We have learnt how to use maths for fine tuning in that selective perception of ours. We learnt, for example, to replace the basic distinction between people doing business and people not doing business at all with finer shades of how much business are people doing exactly in a unit of time-space.

Therefore, a set of empirical variables, e.g. from the World Bank, is a collection of imperfect observations, which represent valuable outcomes social outcomes. A set of N socio-economic variables represents N collectively valuable social outcomes, which, in turn, correspond to N collective pursuits – it is a set of collective orientations. Now, my readers have the full right to protest: ‘Man, just chill. You are getting carried away by your own ideas. Quantitative variables about society and economy are numbers, right? They are the metrics of something. Measurement is objective and dispassionate. How can you say that objectively gauged metrics are collective orientations?’. Yes, these are all valid objections, and I made up that little imaginary voice of my readers on the basis of reviews that I had for some of my papers.

Once again, then. We measure the things we care about, and we go to great lengths in creating accurate scales and methods of measurement for the things we very much care about. Collective coordination is costly and hard to achieve. If we devote decades of collective work to nail down the right way of measuring, e.g. the professional activity of people, it probably matters. If it matters, we are collectively after optimizing it. A set of quantitative, socio-economic variables represents a set of collectively pursued orientations.

In the branch of philosophy called ethics, there is a stream of thought labelled ‘contextual ethics’, whose proponents claim that whatever normatively defined values we say we stick to, the real values we stick to are to be deconstructed from our behaviour. Things we are recurrently and systematically after are our contextual ethical values. Yes, the socio-economic variables we can get from your average statistical office are informative about the contextual values of our society.

When I deal with a variable like the % of electricity in the total consumption of energy, I deal with a superimposition of two cognitive perspectives. I observe something that happens in the social reality, and that phenomenon takes the form of a spatially differentiated, complex state of things, which changes over time, i.e. one complex state transitions into another complex state etc. On the other hand, I observe a collective pursuit to optimize that % of electricity in the total consumption of energy.

The process of optimizing a socio-economic metric makes me think once again about the measurement of social phenomena. We observe and measure things which are important to us because they give us some sort of payoff. We can have collective payoffs in three basic ways. We can max out, for one. Case: Gross Domestic Product, access to sanitation. We can keep something as low as possible, for two. Case: murder, tuberculosis. Finally, we can maintain some kind of healthy dynamic balance. Case: inflation, use of smartphones. Now, let’s notice that we don’t really do fine calculations about murder or tuberculosis. Someone is healthy or sick, still alive or already murdered. Transitional states are not really of much of a collective interest. As it comes to outcomes which pay off by the absence of something, we tend to count them digitally, like ‘is there or isn’t there’. On the other hand, those other outcomes, which we max out on or keep in equilibrium, well, that’s another story. We invent and perfect subtle scales of measurement for those phenomena. That makes me think about a seminal paper titled ‘Selection by consequences’, by the founding father of behaviourism, Burrhus Frederic Skinner. Skinner introduced the distinction between positive and negative reinforcements. He claimed that negative reinforcements are generally stronger in shaping human behaviour, whilst being clumsier as well. We just run away from a tiger, we don’t really try to calibrate the right distance and the right speed of evasion. On the other hand, we tend to calibrate quite finely our reactions to positive reinforcements. We dose our food, we measure exactly the buildings we make, we learn by small successes etc.

If a set of quantitative socio-economic variables is informative about a set of collective orientations (collectively pursued outcomes), one of the ways we can study that set consists in establishing the hierarchy of orientations. Are some of those collective values more important than others? What does it even mean ‘more important’ in this context, and how can it be assessed? We can imagine that each among the many collective orientations is an individual pursuing their idiosyncratic path of payoffs from interactions with the external world. By the way, this metaphor is closer to reality than it could appear at the first sight. Each human is, in fact, a distinct orientation. Each of us is action. This perspective has been very sharply articulated by Martin Heidegger, in his “Being and Time”.

Hence, each collective orientation can be equated to an individual force, pulling the society in a specific direction. In the presence of many socio-economic variables, I assume the actual social reality is a superimposition of those forces. They can diverge or concur, as they please, I do not make any assumptions about that. Which of those forces pulls the most powerfully?

Here comes my mathematical method, in the form of an artificial neural network. I proceed step by step. What does it mean that we collectively optimize a metric? Mostly by making it coherent with our other orientations. Human social structures are based on coordination, and coordination happens both between social entities (individuals, cities, states, political parties etc.), and between different collective pursuits. Optimizing a metric representative for a collectively valuable outcome means coordinating with other collectively valuable outcomes. In that perspective, a phenomenon represented (imperfectly) with a socio-economic metric is optimized when it remains in some kind of correlation with other phenomena, represented with other metrics. The way I define correlation in that statement is a broad one: correlation is any concurrence of events displaying a repetitive, functional pattern.

Thus, when I study the force of a given variable as a collective orientation in a society, I take this variable as the hypothetical output in the process (of collective orientation, and I simulate that process as the output variable sort of dragging the remaining variables behind it, by the force of functional coherence. With a given set of empirical variables, I make as many mutations thereof as I have variables. Each mutated set represents a process, where one variable as output, and the remaining ones as input. The process consists of as many experiments as there are observational rows in my database. Most socio-economic variables come in rows of the type “country A in year X”.

Here, I do a little bit of mathematical cavalry with two different models of swarm intelligence: particle swarm and ants’ colony (see: Gupta & Srivastava 2020[1]). The model of particle swarm comes from the observation of birds, which keeps me in a state of awe about human symbolic creativity, and it models the way that flocks of birds stay collectively coherent when they fly around in the search of food. Each socio-economic variable is a collective orientation, and in practical terms it corresponds to a form of social behaviour. Each such form of social behaviour is a bird, which observes and controls its distance from other birds, i.e. from other forms of social behaviour. Societies experiment with different ways of maintaining internal coherence between different orientations. Each distinct collective orientation observes and controls its distance from other collective orientations. From the perspective of an ants’ colony, each form of social behaviour is a pheromonal trace which other forms of social behaviour can follow and reinforce, or not give a s**t about it, to their pleasure and leisure. Societies experiment with different strengths attributed to particular forms of social behaviour, which mimics an ants’ colony experimenting with different pheromonal intensities attached to different paths toward food.

Please, notice that both models – particle swarm and ants’ colony – mention food. Food is the outcome to achieve. Output variables in mutated datasets – which I create out of the empirical one – are the food to acquire. Input variables are the moves and strategies which birds (particles) or ants can perform in order to get food. Experimentation the ants’ way involves weighing each local input (i.e. the input of each variable in each experimental round) with a random weight R, 0 < R < 1. When experimenting the birds’ way, I drop into my model the average Euclidean distance E from the local input to all the other local inputs.

I want to present it all rolled nicely into an equation, and, as noblesse oblige, I introduce symbols. The local input of an input variable x_i in experimental round t_j is represented with x_i(t_j), whilst the local value of the output variable x_o is written as x_o(t_j). The compound experimental input which the society makes, both the ants’ way and the birds’ way, is written as h(t_j), and it spells h(t_j) = x₁(t_j)*R* E[x_i(t_j-1)] + x₂(t_j)*R* E[x₂(t_j-1)] + … + x_n(t_j)*R* E[x_n(t_j-1)].

Up to that point, this is not really a neural network. It mixes things up, but it does not really adapt. I mean… maybe there is a little intelligence? After all, when my variables act like a flock of birds, they observe each other’s position in the previous experimental round, through the E[x_i(t_j-1)] Euclidean thing. However, I still have no connection, at this point, between the compound experimental input h(t_j) and the pursued output x_o(t_j). I need a connection which would work like an observer, something like a cognitive meta-structure.

Here comes the very basic science of artificial neural networks. There is a function called hyperbolic tangent, which spells tanh = (e^2x – 1)/(e^2x + 1) where x can be whatever you want. This function happens to be one of those used in artificial neural networks, as neural activation, i.e. as a way to mediate between a compound input and an expected output. When I have that compound experimental input h(t_j) = x₁(t_j)*R* E[x_i(t_j-1)] + x₂(t_j)*R* E[x₂(t_j-1)] + … + x_n(t_j)*R* E[x_n(t_j-1)], I can put it in the place of x in the hyperbolic tangent, and I bet tanh = (e^2h – 1)/(e^2h + 1). In a neural network, error in optimization can be calculated, generally, as e = x_o(t_j) – tanh[h(t_j)]. That error can be fed forward into the next experimental round, and then we are talking, ‘cause the compound experimental input morphs into:

>> input h(t_j) = x₁(t_j)*R* E[x_i(t_j-1)]*e(t_j-1) + x₂(t_j)*R* E[x₂(t_j-1)] *e(t_j-1) + … + x_n(t_j)*R* E[x_n(t_j-1)] *e(t_j-1)

… and that means that each compound experimental input takes into account both the coherence of the input in question (E), and the results of previous attempts to optimize.

Here, I am a bit stuck. I need to explain, how exactly the fact of computing the error of optimization e = x_o(t_j) – tanh[h(t_j)] is representative for collective intelligence.

[1] Gupta, A., & Srivastava, S. (2020). Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Computer Science, 173, 245-253. https://doi.org/10.1016/j.procs.2020.06.029

Living next door to such small success

Just two updates ago, I was trying to combine my work on the technological concept which I labelled ‘Energy Ponds’ AKA ‘Project Aqueduct’, with more theoretical a strand of research on collective intelligence in human societies. A third component thread has come into the game, a bit as a surprise. The editor of ‘International Journal of Energy Sector Management’ has just asked me to give a final revision to the manuscript which I am about to publish with them, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’. More specifically, the editor asks me to refine the style of the paper, so as to make it more accessible to non-initiated readers.

I think I am smart. Many people think they are. I know I tend to overestimate my work capacity, though. I need an intellectual synthesis for all the three things: ‘Energy Ponds’, research on collective intelligence, and the final revision of my article. I need some kind of common denominator over which I could put and denominate all that intellectual stuff. I focus on the phenomenon of technological change. My most fundamental intuition about technological change is that it happens as a by-product of us, humans, collectively pursuing some other outcomes. I perceive technology as an emergence (not to confound with emergency) which happens when human societies reach a given level of complexity. Technologies are complex ways human interaction with the broadly spoken natural environment, i.e. with both natural resources and natural constraints.

I am rummaging in my most personal cases of technological change, namely my idea of ‘Energy Ponds’, and my investment decisions in the stock market. Non-linearity of change keeps floating to the surface. When the most obvious path of development in a technology is tight optimization through a sequence of small incremental improvements in efficiency, that technology is close to maturity in its lifecycle, and is not much of a big deal anymore. The truly promising technologies, those able to wake up the neighbours, are those with yet unclear prospects for optimization, with different alternative paths of experimentation in view.

Deep technological change occurs as non-linear path of experimentation in collective human interaction with both natural resources and natural constraints. Non-linearity means uncertainty, and uncertainty implies alternative states of nature, spread over a broad spectrum of outcomes. Them Black Swans are just waiting around the street corner. Deep technological change can play out according to different scenarios. We tend to think about scenarios as sequences, only with technological change the sequence is highly speculative, and the more uncertain the further we go from the starting point. There is another way of defining a scenario, namely as an orientation, a social force which pushes in a specific direction.

I start connecting the dots. Deep, break-through technological change practically never happens as a clearly purposeful collective strategy. It is always a disruption, and it takes us by surprise. Technological change happens as a sudden shortcut to achieve whatever collective outcomes we are after. People who invented the wheel probably didn’t want to invent the wheel as such, they were after a better way of transportation by land. Internet was invented because scientists started to work in large, dispersed networks of labs and needed a fast communication system for a lot of content.

Thus, we are after something, and, accidentally, we invent something else, which makes ripples across the social structure. We use the transformational force conveyed in those ripples to keep pursuing the same collective outcomes. It is interesting to notice that a new technology is practically never superior per se to the incumbent solutions. Social improvement happens only when human societies wrap themselves around that novel stuff and learn how to use it. Let’s suppose that a benevolent and very advanced alien race hands out to us a technology to travel between parallel universes. Looks cool, at the first sight. When we think about it longer, though, questions arise. What are the practical benefits of travelling between parallel universes? It is only when we figure out those benefits that we start absorbing that otherwise revolutionary technology.

I double back a bit on my own words. Deep technological change is essentially disruptive and surprising, and yet there is more to technological change than just the deep and disruptive kind. Periods of grinding, progressive optimization come after and between deep technological ripples. Here, I ask: why? Why the hell having all that business of technological change? It is interesting to notice that rapid technological change makes rifts in space just as it does in time. There are places on this planet where humans have been living for quite a few millennia without inventing s**t. It is even more interesting to notice that some among those no-progress lands used to be quite the opposite in the past. Amazonian jungle is a good example. Pre-Colombian people (i.e. people who used to live there before they learnt they had just been discovered) had a thriving civilization, with a lot of innovations up their sleeve, such as altitude specific agriculture in terraced fields, or written communication using pieces of string. Afghanistan (hic!) is another example. Centuries before Pythagoras figured out his angles and them square roots from sums of square powers, the place which we call ‘Afghanistan’ today used to be a huge mining hub, providing tin to all of the Bronze Age civilization in the Mediterranean and the Levant.

My point is that we, humans, need a good kick where it really hurts, plus some favourable conditions to recover when it really hurts, and then we start inventing stuff. Still, many of us can pass entire epochs (literally epochs) without figuring out anything new. As I like surfing through literature as I write, a few quotes come to my mind, out of the books I am reading now. Out of ‘The Black Swan. The impact of the highly improbable’ by Nassim Nicolas Taleb , Penguin, 2010, I have that passage from page 114: “Consider the following: of all the colorful adventurers who have lived on our planet, many were occasionally crushed, and a few did bounce back repeatedly. It is those who survive who will tend to believe that they are indestructible; they will have a long and interesting enough experience to write books about it. Until, of course … Actually, adventurers who feel singled out by destiny abound, simply because there are plenty of adventurers, and we do not hear the stories of those down on their luck”. The point is that we mostly know about technological change we know, as it were. The folds of history, which we tend to smooth out ex post, cover thousands of episodes when inventions simply didn’t work. I wonder how many people got mauled to death before someone finally nailed down the right way to make big, strong oxen pull heavy carts on wheels.

The adventure of technological change plays out favourably just sometimes, and yet we keep trying. Here come two quotes from another book: ‘The Knowledge Illusion. Why we never think alone’ by Steven Sloman and Philip Fernbach, RIVERHEAD BOOKS (An imprint of Penguin Random House LLC, Ebook ISBN: 9780399184345, Kindle Edition). On page page 133 thereof a whole new chapter starts under the provocative title: Technology as an Extension of Thought. It goes: ‘The mastery of new technology has gone hand in hand with the evolution of our species. According to Ian Tattersall, curator emeritus with the American Museum of Natural History in New York, “cognitive capacity and technology reinforced each other” as civilization developed. Genetic evolution and technological change have run in tandem throughout our evolutionary history. As brains increased in size from one hominid species to its descendants, tools became more sophisticated and more common. Our predecessors started using rocks with sharp edges. Later generations discovered fire, stone axes, and knives, followed by harpoons and spears, then nets, hooks, traps, snares, and bows and arrows, and eventually farming. Each of these technological changes was accompanied by all the other changes that led to the modern human being: cultural, behavioral, and genetic changes’. A few pages further, p. 150, the same authors write about the modern technology of crowdsourcing: ‘The power of crowdsourcing and the promise of collaborative platforms suggest that the place to look for real superintelligence is not in a futuristic machine that can outsmart human beings. The superintelligence that is changing the world is in the community of knowledge’.

It seems that we, humans, invent new things just because we can, just because we are biologically wired for it. Still, that creative interaction with our environment is full of failures, which, from time to time, produce some timid successes. The local humans, living next door to such small success, have the drive and the capacity to put a big fire up, starting from such a small spark. Once it has worked, deep technological rift happens, which transforms civilizations.

As I return to the final revision of the manuscript which I am about to publish with them, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, for the ‘International Journal of Energy Sector Management’, I wonder how to describe the kind of technological change which I write about in that paper, namely the development of renewable energies and the transition to electricity from the straightforward use of fossil thermal energy, in the European market. What I see in the empirical data is a historically short window of progress which, whilst being a bit bumpy, generally follows an upward trend. As I look at all of my so-far research on collective intelligence, it is largely the same. I have been studying historically short windows of technological change which generally looks like progress with some minor accidents on the way. On the other hand, when I refer to my ‘Energy Ponds’ concept and to the feasibility studies I am running for it, it is the deep-ripple type. I propose to implement a complex solution whose outcomes will be more environmental (water management and landscape management) more than straightforwardly financial. Yes, the whole thing has a chance to earn a living by selling electricity from hydroelectric turbines, but this is like Nicola Tesla earning a living by repairing people’s house equipment.

Is there any theoretical way I can use my toolbox of collective intelligence – tested on incremental technological change – to represent the socio-economic absorption of ‘Energy Ponds’? Good question. It is about social structures reacting to something disturbing. The general intuition I have in that respect, and which I developed through simulations described in my draft paper: ‘Behavioral absorption of Black Swans: simulation with an artificial neural network’ is that social structures tend to smooth out disturbances, for one. New things enter the game easier and faster than old things get pushed out of it, for two. I think that both cases, namely technological change in the European market of electricity and the possible development of ‘Energy Ponds’ are the kind of story, when new technologies sort of pile up on the top of old ones. Increased complexity is created. Increasing complexity means the build-up of some kind of non-equilibrium, which either gest smoothed out, and the corresponding technological change is nicely absorbed, or it doesn’t, and we have the Schumpeterian creative destruction.

I pretty much know how social structures wrap themselves around new power installations. There is one Black Swan, though, swimming surreptitiously around: the nuclear. In Europe, we have a keen interest in passing from combustion engines to electric vehicles. Combustion engines run on gasoline or on diesel, which all boils down to oil, which we don’t have and need to import. Transportation based on electricity makes us much less dependent on imported fuels, and that means more strategic security. Still, I think we will need to come back to developing nuclear power plants if we want to have enough juice for all those batteries on wheels.

As regards ‘Energy Ponds’, the big question is how will urban and peri-urban structures get along with swamp-like reservoirs of water. That is really a deep question. For centuries, cities in Europe have been developing by drying out and draining down swamps. Swamps and buildings do not really like each other. Do we have the technologies to make their mutual neighbourhood liveable?

Que je finisse de façon bien élégante

Me revoilà avec l’idée de faire un rapprochement théorique entre mon « Projet Aqueduc » et ma recherche sur le phénomène d’intelligence collective. Comme je passe en revue ce que j’ai écrit jusqu’à maintenant sur les deux sujets, des conclusions provisoires se forment dans ma tête. L’idée primordiale est que je ne suis pas sûr du tout si mon « Projet Aqueduc » n’est pas, par le plus grand des hasards, une connerie déguisée. Comme je fais de la recherche sur le business d’énergies renouvelables, j’ai pu constater maintes fois qu’à part l’écologie fonctionnelle et pragmatique il y a une écologie religieuse, quelque chose comme l’Église Généralisée de la Mère Nature. Moi je veux rester dans le fonctionnel. Je n’achète pas vraiment l’argument que toute solution « naturelle » est meilleure qu’une « artificielle ». Le retour à la nature que tellement de mes amis prêchent comme une confession de foi c’est aussi le retour à la tuberculose et à l’absence de canalisation.

Je pense donc que « Projet Aqueduc » est une idée intéressante pour expérimenter avec mais pas vraiment une solution complète à optimaliser. L’idée d’utiliser un algorithme génétique du type NSGA-II ou « Non-dominated Sorting Genetic Algorithm » (comparez : Chang et al. 2016[1]; Jain & Sachdeva 2017[2]; Assaf & Shabani 2018[3]; Zhou et al. 2019[4]), que j’avais formulée au début de juillet ( We keep going until we observe du 5 juillet) serait prématurée. Le « Projet Aqueduc » est une chaîne complexe des technologies dont certaines sont plutôt certaines – pardonnez le jeu de mots – pendant que d’autres sont très incertaines dans leur développement général ainsi que leur application exacte dans ce contexte spécifique.

Je pense que je vais me concentrer sur la mise au point d’une méthode de vérifier la faisabilité du concept en question dans la phase où il est actuellement, donc dans la phase d’expérimentation initiale, tôt dans le cycle de développement. Une telle méthode pourrait être appliquée à l’étude de faisabilité d’autres technologies en phase de développement expérimental. Le « Projet Aqueduc » présente un défi intellectuel intéressant de ce point de vue. L’une des dimensions importantes de ce concept est sa taille physique, surtout la quantité d’eau retenue dans les structures marécageuses qui agissent comme des pseudo-réservoirs hydrologiques (Harvey et al. 2009[5]; Phiri et al. 2021[6] ; Lu et al. 2021[7]; Stocks et al. 2021[8]). Plus nous expérimentons avec la taille physique des installations, plus grands sont les risques liés à cette expérimentation. Tester une installation de petite taille pour le « Projet Aqueduc » engendre des risques négligeables mais en même temps n’apporte pas vraiment de données empiriques sur ce qui se passe dans une installation de taille autrement plus substantielle. C’est donc un cas où – apparemment au moins – je dois expérimenter avec des risques de plus en plus élevés pour acquérir des données sur ce qui peut se passer si ces risques se consument en vie réelle. Voilà un casse-tête digne de ce nom.

En termes de défi intellectuel, j’en ai un autre, tombé un peu à l’improviste. Le journal « International Journal of Energy Sector Management » vient de me demander de donner un dernier coup de pinceau, avant la publication, à mon article intitulé « Climbing the right hill – an evolutionary approach to the European market of electricity ». Les recommandations du réviseur ainsi que celles du rédacteur responsable sont à peu près homogènes et se résument à donner plus de clarté à mon texte, de façon à le rendre plus facile à approcher pour des lecteurs non-initiés à la méthode que j’y utilise. Je relève le gant pour ainsi dire et je vais essayer de résumer le manuscrit en français, de façon aussi claire que possible. J’espère que ça va me donner un bon point de départ pour faire la révision finale de mon manuscrit en anglais.

Je commence par le résumé d’en-tête, donc ce qui s’appelle « abstract » en jargon scientifique anglais. L’article étudie changement socio-économique comme phénomène évolutif et plus précisément comme une marche adaptative en paysage rugueux, avec l’assomption d’intelligence collective et en vue d’optimiser la participation d’électricité dans la consommation totale de l’énergie ainsi que la participation des sources renouvelables dans la consommation d’électricité. Une méthode originale est présentée, où un réseau neuronal artificiel est utilisé pour produire des réalités alternatives à partir de l’ensemble originel de données empiriques. La distance Euclidienne entre ces réalités alternatives et la réalité empirique est utilisée comme base pour évaluer les objectifs collectifs. La variance de distance Euclidienne entre variables est utilisée comme base pour évaluer l’intensité d’interactions épistatiques entre les phénomènes représentés avec les variables. La méthode est testée dans un échantillon de 28 pays européens, entre 2008 et 2017, en présence d’imperfections du marché au niveau des prix de détail d’électricité. Les variables-clés, pertinentes à l’énergie, semblent être instrumentales par rapport à la poursuite d’autres valeurs collectives et ces dernières semblent se concentrer sur l’intensité de travail ainsi que sa rémunération.

Voilà un résumé bien scientifique. J’avoue : si je n’avais pas écrit cet article moi-même, je n’y comprendrais que dalle, à ce résumé. Pas étonnant que le réviseur et le rédacteur responsable me demandent gentiment de simplifier et de clarifier. Où commence-je donc ? Voilà une question qui mérite un peu de réflexion. Je pense qu’il faut que recule dans le temps et que je me souvienne le cheminement logique que j’avais pris, il y a un an et demi, lorsque j’avais écrit la première version de cet article. Oui, un an et demi. La science, ça traine parfois. Les idées-éclair, ça ralentit considérablement dans la phase de publication.

Je recule donc dans le temps. Il y avait deux trucs en concours, pour ainsi dire. D’une part, j’étais content après la publication d’un article chez « Energy », un journal bien respectable, sous le titre « Energy efficiency as manifestation of collective intelligence in human societies ». La méthode que j’y avais utilisée était largement la même que dans cet article chez « International Journal of Energy Sector Management », auquel je suis en train de donner une touche finale. Un réseau neuronal artificiel produit des simulations d’intelligence collective des sociétés humaines. Chaque simulation est une sorte de réalité alternative orientée sur l’optimisation d’une variable spécifique. Chaque réalité alternative demeure à une certaine distance mathématique de la réalité empirique. J’assume que celles qui sont les plus proches reflètent le mieux les rapports entre les variables empiriques. Puisque ce rapport est en fait une orientation – la poursuite d’optimisation d’une variable précise – j’interprète le tout comme une évolution collectivement intelligente avec un système de valeurs collectives.

Les résultats empiriques que j’avais obtenus dans cet article chez « Energy » étaient un peu surprenants, mais juste un peu. Les économies nationales que j’étudiais semblaient être orientés sur l’optimisation de rapport entre le flux d’invention scientifique et la capitalisation des entreprises (coefficient du nombre des demandes domestique de brevet par un million de dollars en actifs productifs fixes) plus que tout le reste. L’efficience énergétique, mesurée à l’échelle d’économies nationales, semblait être le cadet des soucis de lesdites économies nationales, pour ainsi dire. En général, ces 59 pays que j’avais pris sous ma loupe, démontraient bien une croissance d’efficience énergétique, mais cette amélioration semblait être un effet secondaire obtenu dans la poursuite d’équilibre local entre la science mûre (pour demander des brevets) et l’investissement.

Le catalogue des variables que j’avais pris en considération dans « Energy efficiency as manifestation of collective intelligence in human societies » était plutôt restreint. J’avais étudié 14 variables, dont la plupart étaient là en raison d’assomptions que j’avais prises à propos du contexte socio-économique de l’efficience énergétique. Alors, je m’étais posé la question suivante : qu’est-ce qui va se passer si je prends une poignée des variables pertinentes au secteur d’énergie, dans un contexte plus ou moins environnemental, et je les plonge dans un bain commun avec un catalogue vraiment large des variables macroéconomiques ? Côté méthode, c’est une approche classique dans la science. Un truc marche avec des assomptions bien serrées et le pas suivant est de tester le même truc avec des assomptions plus relax, genre pas trop d’idées préconçues.

En ce qui concerne le catalogue exhaustif des variables macroéconomiques, Penn Tables 9.1. (Feenstra et al. 2015[9]), avec 49 variables du type classique (Produit National, inflation, le marché d’emploi etc.) semblaient être une source convenable. J’avais déjà expérimenté avec cette base des données et ma méthode d’étudier l’intelligence collective en produisant des réalités alternatives avec un réseau neuronal et j’avais obtenu des résultats intéressants. Je les avais décrits dans un manuscrit plutôt technique intitulé « The Labour Oriented, Collective Intelligence of Ours : Penn Tables 9.1 Seen Through the Eyes of A Neural Network ». Il semble que les économies nationales de quelques 168 pays décrits dans Penn Tables 9.1 sont orientées sur l’optimalisation du marché de l’emploi plus que sur quoi que ce soit d’autre. Les variables dont l’optimalisation produit des réalités alternatives les plus proches de la réalité empirique sont, dans ce cas : le nombre moyen d’heures ouvrables par année par personne, la participation des salaires dans le Revenu National Brut et finalement le coefficient de capital humain, qui mesure le nombre moyen d’années d’éducation que les jeunes gens ont dans leur CV au moment d’entrer le marché d’emploi.

Encore une fois : lorsque la plupart d’économistes développent sur le sort horrible des travailleurs dans un monde dominé par des capitalistes rapaces sans pitié ni conscience, ma méthode suggérait le contraire, donc un monde orienté sur le travail et les travailleurs, beaucoup plus que sur l’optimalisation du retour interne sur l’investissement, par exemple. Ma méthode donnait donc des résultats surprenants avec des données empiriques tout à fait classiques. J’étais donc bien sûr que les résultats tout aussi surprenants que j’avais présenté dans « Energy efficiency as manifestation of collective intelligence in human societies » n’étaient pas le résultat de mon propre biais cognitif au niveau du matériel empirique de base mais bel et bien le résultat d’une méthode originale de recherche.

Ces résultats en main, je me demandais comment faire un rapprochement avec le secteur d’énergie. A l’époque, j’avais participé à un colloque public à propos des voitures électriques. Le colloque lui-même n’était pas vraiment excitant, mais après j’ai eu une discussion très intéressante avec mon fils. Le fiston avait dit : « En Europe, on n’a pas de notre pétrole bien à nous. Nous avons un système de transport routier très dense, presque entièrement dépendant d’une source d’énergie que nous devons importer, donc sur le pétrole. Comme risque stratégique, c’en est un gros ». Je me suis dit : il a raison, mon fiston. Encore une fois. C’est agaçant. Faut que je fasse quelque chose. Les voitures électriques, ça a besoin d’électricité et donc la participation d’électricité dans la consommation totale d’énergie serait un bon indicateur de notre préparation à passer vers les véhicules électriques, en Europe. Je peux prendre Penn Tables 9.1. (Feenstra et al. 2015 op. cit.), en extraire les données à propos des pays Européens, ajouter des variables pertinentes au secteur d’énergie et voilà : je peux tester l’hypothèse générale que ces variables énergétiques sont des orientations significative dans l’intelligence collective des pays Européens.

Il y avait un autre truc, en fait. Ça fait déjà un bout de temps que j’ai fait attention aux prix d’électricité en Europe, et plus précisément à la différence très marquée entre les prix pour petits consommateurs d’énergie, calibre ménages, d’une part, et les prix réservés aux usagers plus grands. Vous pouvez consulter, à ce sujet, ma mise à jour du 28 Juin 2018 : « Deux cerveaux, légèrement différents l’un de l’autre ». C’est une imperfection du marché en une forme classique. J’avais donc décidé d’ajouter les prix d’électricité en Europe à cet ensemble déjà bien hétéroclite et voilà que ça a commencé.

Bon, j’ai reconstitué à peu de choses près le raisonnement originel qui m’a poussé à écrire cet article « Climbing the right hill – an evolutionary approach to the European market of electricity ». Si je sais comment j’avais commencé, il y a des chances que je finisse de façon bien élégante.

[1] Chang, F. J., Wang, Y. C., & Tsai, W. P. (2016). Modelling intelligent water resources allocation for multi-users. Water resources management, 30(4), 1395-1413. https://doi.org/10.1007/s11269-016-1229-6

[2] Jain, V., & Sachdeva, G. (2017). Energy, exergy, economic (3E) analyses and multi-objective optimization of vapor absorption heat transformer using NSGA-II technique. Energy Conversion and Management, 148, 1096-1113. https://doi.org/10.1016/j.enconman.2017.06.055

[3] Assaf, J., & Shabani, B. (2018). Multi-objective sizing optimisation of a solar-thermal system integrated with a solar-hydrogen combined heat and power system, using genetic algorithm. Energy Conversion and Management, 164, 518-532. https://doi.org/10.1016/j.enconman.2018.03.026

[4] Zhou, Y., Chang, L. C., Uen, T. S., Guo, S., Xu, C. Y., & Chang, F. J. (2019). Prospect for small-hydropower installation settled upon optimal water allocation: An action to stimulate synergies of water-food-energy nexus. Applied Energy, 238, 668-682. https://doi.org/10.1016/j.apenergy.2019.01.069

[5] Harvey, J.W., Schaffranek, R.W., Noe, G.B., Larsen, L.G., Nowacki, D.J., O’Connor, B.L., 2009. Hydroecological factors governing surface water flow on a low-gradient floodplain. Water Resour. Res. 45, W03421, https://doi.org/10.1029/2008WR007129.

[6] Phiri, W. K., Vanzo, D., Banda, K., Nyirenda, E., & Nyambe, I. A. (2021). A pseudo-reservoir concept in SWAT model for the simulation of an alluvial floodplain in a complex tropical river system. Journal of Hydrology: Regional Studies, 33, 100770. https://doi.org/10.1016/j.ejrh.2020.100770.

[7] Lu, B., Blakers, A., Stocks, M., & Do, T. N. (2021). Low-cost, low-emission 100% renewable electricity in Southeast Asia supported by pumped hydro storage. Energy, 121387. https://doi.org/10.1016/j.energy.2021.121387

[8] Stocks, M., Stocks, R., Lu, B., Cheng, C., & Blakers, A. (2021). Global atlas of closed-loop pumped hydro energy storage. Joule, 5(1), 270-284. https://doi.org/10.1016/j.joule.2020.11.015

[9] Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at http://www.ggdc.net/pwt 

Tax on Bronze

I am trying to combine the line of logic which I developed in the proof-of-concept for the idea I labelled ‘Energy Ponds’ AKA ‘Project Aqueduct’ with the research on collective intelligence in human societies. I am currently doing serious review of literature as regards the theory of complex systems, as it looks like just next door to my own conceptual framework. The general idea is to use the theory of complex systems – within the general realm of which the theory of cellular automata looks the most promising, for the moment – to simulate the emergence and absorption of a new technology in the social structure.

I started to sketch the big lines of that picture in my last update in French, namely in ‘L’automate cellulaire respectable’. I assume that any new technology burgeons inside something like a social cell, i.e. a group of people connected by common goals and interests, together with some kind of institutional vehicle, e.g. a company, a foundation etc. It is interesting to notice that new technologies develop through the multiplication of such social cells rather than through linear growth of just one cell. Up to a point this is just one cell growing, something like the lone wolf of Netflix in the streaming business, and then ideas start breeding and having babies with other people.

I found an interesting quote in the book which is my roadmap through the theory of complex systems, namely in ‘What Is a Complex System?’ by James Landyman and Caroline Wiesner (Yale University Press 2020, ISBN 978-0-300-25110-4). On page 56 (Kindle Edition), Landyman and Wiesner write something interesting about the collective intelligence in colonies of ants: ‘What determines a colony’s survival is its ability to grow quickly, because individual workers need to bump into other workers often to be stimulated to carry out their tasks, and this will happen only if the colony is large. Army ants, for example, are known for their huge swarm raids in pursuit of prey. With up to 200 000 virtually blind foragers, they form trail systems that are up to 20 metres wide and 100 metres long (Franks et al. 1991). An army of this size harvests prey of 40 grams and more each day. But if a small group of a few hundred ants accidentally gets isolated, it will go round in a circle until the ants die from starvation […]’.

Interesting. Should nascent technologies have an ant-like edge to them, their survival should be linked to them reaching some sort of critical size, which allows the formation of social interactions in the amount which, in turn, an assure proper orientation in all the social cells involved. Well, looks like nascent technologies really are akin to ant colonies because this is exactly what happens. When we want to push a technology from its age of early infancy into the phase of development, a critical size of the social network is required. Customers, investors, creditors, business partners… all that lot is necessary, once again in a threshold amount, to give a new technology the salutary kick in the ass, sending it into the orbit of big business.

I like jumping quickly between ideas and readings, with conceptual coherence being an excuse just as frequently as it is a true guidance, and here comes an article on urban growth, by Yu et al. (2021[1]). The authors develop a model of urban growth, based on the empirical data on two British cities: Oxford and Swindon. The general theoretical idea here is that strictly speaking urban areas are surrounded by places which are sort of in two minds whether they like being city or countryside. These places can be represented as spatial cells, and their local communities are cellular automatons which move cautiously, step by step, into alternative states of being more urban or more rural. Each such i-th cellular automaton displays a transition potential N_i, which is a local balance between the benefits of urban agglomeration N_i(U), as opposed to the benefits N_i(N) of conserving scarce non-urban resources. The story wouldn’t be complete without the shit-happens component R_i of randomness, and the whole story can be summarized as: N_i = N_i(U) – N_i(N) + R_i.

Yu et al. (2021 op. cit.) add an interesting edge to the basic theory of cellular automata, such as presented e.g. in Bandini, Mauri & Serra (2001[2]), namely the component of different spatial scales. A spatial cell in a peri-urban area can be attracted to many spatial aspects of being definitely urban. Those people may consider the possible benefits of sharing the same budget for local schools in a perimeter of 5 kilometres, as well as the possible benefits of connecting to a big hospital 20 km away. Starting from there, it looks a bit gravitational. Each urban cell has a power of attraction for non-urban cells, however that power decays exponentially with physical distance.

I generalize. There are many technologies spreading across the social space, and each of them is like a city. I mean, it does not necessarily have a mayor, but it has dense social interactions inside, and those interactions create something like a gravitational force for external social cells. When a new technology gains new adherents, like new investors, new engineers, new business entities, it becomes sort of seen and known. I see two phases in the development of a nascent technology. Before it gains enough traction in order to exert significant gravitational force on the temporarily non-affiliated social cells, a technology grows through random interactions of the initially involved social cells. If those random interactions exceed a critical threshold, thus if there are enough forager ants in the game, their simple interactions create an emergence, which starts coagulating them into a new industry.

I return to cities and their growth, for a moment. I return to the story which Yu et al. (2021[3]) are telling. In my own story on a similar topic, namely in my draft paper ‘The Puzzle of Urban Density And Energy Consumption’, I noticed an amazing fact: whilst individual cities grow, others decay or even disappear, and the overall surface of urban areas on Earth seems to be amazingly stationary over many decades. It looks as if the total mass, and hence the total gravitational attraction of all the cities on Earth was a constant over at least one human generation (20 – 25 years). Is it the same with technologies? I mean, is there some sort of constant total mass that all technologies on Earth have, within the lifespan of one human generation, and there are just specific technologies getting sucked into that mass whilst others drop out and become moons (i.e. cold, dry places with not much to do and hardly any air to breathe).

What if a new technology spreads like Tik-Tok, i.e. like a wildfire? There is science for everything, and there is some science about fires in peri-urban areas as well. That science is based on the same theory of cellular automata. Jiang et al. (2021[4]) present a model, where territories prone to wildfires are mapped into grids of square cells. Each cell presents a potential to catch fire, through its local properties: vegetation, landscape, local climate. The spread of a wildfire from a given cell R₀ is always based on the properties of the cells surrounding the fire.

Cirillo, Nardi & Spitoni (2021[5]) present an interesting mathematical study of what happens when, in a population of cellular automata, each local automaton updates itself into a state which is a function of the preceding state in the same cell, as well as of the preceding states in the two neighbouring cells. It means, among other things, that if we add the dimension of time to any finite space Z^d where cellular automata dwell, the immediately future state of a cell is a component of the available neighbourhood for the present state of that cell. Cirillo, Nardi & Spitoni (2021) demonstrate, as well, that if we know the number and the characteristics of the possible states which one cellular automaton can take, like (-1, 0, 1), we can compute the total number of states that automaton can take in a finite number of moves. If we make many such cellular automatons move in the same space Z^d , a probabilistic chain of complex states emerge.

As I wrote in ‘L’automate cellulaire respectable’, I see a social cell built around a new technology, e.g. ‘Energy Ponds’, moving, in the first place, along two completely clear dimensions: physical size of installations and financial size of the balance sheet. Movements along these two axes are subject to the influence happening along some foggy, unclear dimensions connected to preferences and behaviour: expected return on investment, expected future value of the firm, risk aversion as opposed to risk affinity etc. That makes me think, somehow, about a theory next door to that of cellular automata, namely the theory of swarms. This is a theory which explains complex changes in complex systems through changes in strength of correlation between individual movements. According to the swarm theory, a complex set which behaves like a swarm can adapt to external stressors by making the moves of individual members more or less correlated with each other. A swarm in routine action has its members couple their individual behaviour rigidly, like marching in step. A swarm alerted by a new stressor can loosen it a little, and allow individual members some play in their behaviour, like ‘If I do A, you do B or C or D, anyway one out of these three’. A swarm in mayhem loses it completely and there is no behavioural coupling whatsoever between members.

When it comes to the development and societal absorption of a new technology, the central idea behind the swarm-theoretic approach is that in order to do something new, the social swarm has to shake it off a bit. Social entities need to loosen their mutual behavioural coupling so as to allow some of them to do something else than just ritually respond to the behaviour of others. I found an article which I can use to transition nicely from the theory of cellular automata to the swarm theory: Puzicha & Buchholz (2021[6]). The paper is essentially applicable to the behaviour of robots, yet it is about a swarm of 60 distributed autonomous mobile robots which need to coordinate through a communication network with low reliability and restricted capacity. In other words, sometimes those robots can communicate with each other, and sometimes they don’t. When some robots out of the 60 are having a chat, they can jam the restricted capacity of the network and thus bar the remaining robots from communicating. Incidentally, this is how innovative industries work. When a few companies, let’s say the calibre of unicorns, are developing a new technology. They absorb the attention of investors, governments, potential business partners and potential employees. They jam the restricted field of attention available in the markets of, respectively, labour and capital.

Another paper from the same symposium ‘Intelligent Systems’, namely Serov, Voronov & Kozlov (2021[7]), leads in a slightly different direction. Whilst directly derived from the functioning of communication systems, mostly the satellite-based ones, the paper suggests a path of learning in a network, where the capacity for communication is restricted, and the baseline method of balancing the whole thing is so burdensome for the network that it jams communication even further. You can compare it to a group of people who are all so vocal about the best way to allow each other to speak that they have no time and energy left for speaking their mind and listening to others. I have found another paper, which is closer to explaining the behaviour of those individual agents when they coordinate just sort of. It is Gupta & Srivastava (2020[8]), who compare two versions of swarm intelligence: particle swarm and ant colony. The former (particle swarm) generalises a problem applicable to birds. Simple, isn’t it? A group of birds will randomly search for food. Birds don’t know where exactly the food is, so they follow the bird which is nearest to the food. The latter emulates the use of pheromones in a colony of ants. Ants selectively spread pheromones as they move around, and they find the right way of moving by following earlier deposits of pheromones. As many ants walk many times a given path, the residual pheromones densify and become even more attractive. Ants find the optimal path by following maximum pheromone deposition.

Gupta & Srivastava (2020) demonstrate that the model of ant colony, thus systems endowed with a medium of communication which acts by simple concentration in space and time are more efficient for quick optimization than the bird-particle model, based solely on observing each other’s moves. From my point of view, i.e. from that of new technologies, those results reach deeper than it could seem at the first sight. Financial capital is like a pheromone. One investor-ant drops some financial deeds at a project, and it can hopefully attract further deposits of capital etc. Still, ant colonies need to reach a critical size in order for that whole pheromone business to work. There needs to be a sufficient number of ants per unit of available space, in order to create those pheromonal paths. Below the critical size, no path becomes salient enough to create coordination and ants starve to death fault of communicating efficiently. Incidentally, the same is true for capital markets. Some 11 years ago, right after the global financial crisis, a fashion came to create small, relatively informal stock markets, called ‘alternative capital markets’. Some of them were created by the operators of big stock markets (e.g. the AIM market organized by the London Stock Exchange), some others were completely independent ventures. Now, a decade after that fashion exploded, the conclusion is similar to ant colonies: fault of reaching a critical size, those alternative capital markets just don’t work as smoothly as the big ones.

All that science I have quoted makes my mind wander, and it starts walking down the path of hilarious and absurd. I return, just for a moment, to another book: ‘1177 B.C. THE YEAR CIVILIZATION COLLAPSED. REVISED AND UPDATED’ by Eric H. Cline (Turning Points in Ancient History, Princeton University Press, 2021, ISBN 9780691208022). The book gives in-depth an account of the painful, catastrophic end of a whole civilisation, namely that of the Late Bronze Age, in the Mediterranean and the Levant. The interesting thing is that we know that whole network of empires – Egypt, Hittites, Mycenae, Ugarit and whatnot – collapsed at approximately the same moment, around 1200 – 1150 B.C., we know they collapsed violently, and yet we don’t know exactly how they collapsed.

Alternative history comes to my mind. I imagine the transition from Bronze Age to the Iron Age similarly to what we do presently. The pharaoh-queen VanhderLeyenh comes up with the idea of iron. Well, she doesn’t, someone she pays does. The idea is so seducing that she comes, by herself this time, with another one, namely tax on bronze. ‘C’mon, Mr Brurumph, don’t tell me you can’t transition to iron within the next year. How many appliances in bronze do you have? Five? A shovel, two swords, and two knives. Yes, we checked. What about your rights? We are going through a deep technological change, Mr Brurumph, this is not a moment to talk about rights. Anyway, this is not even the new era yet, and there is no such thing as individual rights. So, Mr Brurumph, a one-year notice for passing from bronze to iron is more than enough. Later, you pay the bronze tax on each bronze appliance we find. Still, there is a workaround. If you officially identify as a non-Bronze person, and you put the corresponding sign over your door, you have a century-long prolongation on that tax’.

Mr Brurumph gets pissed off. Others do too. They feel lost in a hostile social environment. They start figuring s**t out, starting from the first principles of their logic. They become cellular automata. They focus on nailing down the next immediate move to make. Errors are costly. Swarm behaviour forms. Fights break out. Cities get destroyed. Not being liable to pay the tax on bronze becomes a thing. It gets support and gravitational attraction. It becomes tempting to join the wandering hordes of ‘Tax Free People’ who just don’t care and go. The whole idea of iron gets postponed like by three centuries.

[1] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689

[2] Bandini, S., Mauri, G., & Serra, R. (2001). Cellular automata: From a theoretical parallel computational model to its application to complex systems. Parallel Computing, 27(5), 539-553. https://doi.org/10.1016/S0167-8191(00)00076-4

[3] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689

[4] Jiang, W., Wang, F., Fang, L., Zheng, X., Qiao, X., Li, Z., & Meng, Q. (2021). Modelling of wildland-urban interface fire spread with the heterogeneous cellular automata model. Environmental Modelling & Software, 135, 104895. https://doi.org/10.1016/j.envsoft.2020.104895

[5] Cirillo, E. N., Nardi, F. R., & Spitoni, C. (2021). Phase transitions in random mixtures of elementary cellular automata. Physica A: Statistical Mechanics and its Applications, 573, 125942. https://doi.org/10.1016/j.physa.2021.125942

[6] Puzicha, A., & Buchholz, P. (2021). Decentralized model predictive control for autonomous robot swarms with restricted communication skills in unknown environments. Procedia Computer Science, 186, 555-562. https://doi.org/10.1016/j.procs.2021.04.176

[7] Serov, V. A., Voronov, E. M., & Kozlov, D. A. (2021). A neuro-evolutionary synthesis of coordinated stable-effective compromises in hierarchical systems under conflict and uncertainty. Procedia Computer Science, 186, 257-268. https://doi.org/10.1016/j.procs.2021.04.145

[8] Gupta, A., & Srivastava, S. (2020). Comparative analysis of ant colony and particle swarm optimization algorithms for distance optimization. Procedia Computer Science, 173, 245-253. https://doi.org/10.1016/j.procs.2020.06.029

L’automate cellulaire respectable

J’essaie de développer une jonction entre deux créneaux de ma recherche : l’étude de faisabilité pour mon « Projet Aqueduc » d’une part et ma recherche plus théorique sur le phénomène d’intelligence collective d’autre part. Question : comment prédire et prévoir l’absorption d’une technologie nouvelle dans une structure sociale ? En des termes plus concrets, comment puis-je prévoir l’absorption de « Projet Aqueduc » dans l’environnement socio-économique ? Pour me rendre la vie plus difficile – ce qui est toujours intéressant – je vais essayer de construire le modèle de cette absorption à partir d’une base théorique relativement nouvelle pour moi, notamment la théorie d’automates cellulaires. En termes de littérature, pour le moment, je me réfère à deux articles espacés de 20 ans l’un de l’autre : Bandini, Mauri & Serra (2001[1]) ainsi que Yu et al. (2021[2]).

Pourquoi cette théorie précise ? Pourquoi pas, en fait ? Sérieusement, la théorie d’automates cellulaires essaie d’expliquer des phénomènes très complexes – qui surviennent dans des structures qui ont l’air d’être vraiment intelligentes – à partir d’assomptions très faibles à propos du comportement individuel d’entités simples à l’intérieur de ces structures. En plus, cette théorie est déjà bien traduite en termes d’intelligence artificielle et se marie donc bien avec mon but général de développer une méthode de simuler des changements socio-économiques avec des réseaux neuronaux.

Il y a donc un groupe des gens qui s’organisent d’une façon ou d’une autre autour d’une technologie nouvelle. Les ressources économiques et la structure institutionnelle de ce groupe peuvent varier : ça peut être une société de droit, un projet public-privé, une organisation non-gouvernementale etc. Peu importe : ça commence comme une microstructure sociale. Remarquez : une technologie existe seulement lorsque et dans la mesure qu’une telle structure existe, sinon une structure plus grande et plus complexe. Une technologie existe seulement lorsqu’il y a des gens qui s’occupent d’elle.

Il y a donc ce groupe organisé autour d’une technologie naissante. Tout ce que nous savons sur l’histoire économique et l’histoire des technologies nous dit que si l’idée s’avère porteuse, d’autres groupes plus ou moins similaires vont se former. Je répète : d’autres groupes. Lorsque la technologie des voitures électriques avait finalement bien mordu dans le marché, ceci n’a pas entraîné l’expansion monopolistique de Tesla. Au contraire : d’autres entités ont commencé à bâtir de façon indépendante sur l’expérience de Tesla. Aujourd’hui, chacun des grands constructeurs automobiles vit une aventure plus ou moins poussée avec les bagnoles électriques et il y a toute une vague des startups crées dans le même créneau. En fait, la technologie du véhicule électrique a donné une deuxième jeunesse au modèle de petite entreprise automobile, un truc qui semblait avoir été renvoyé à la poubelle de l’histoire.

L’absorption d’une technologie nouvelle peut donc être représentée comme la prolifération des cellules bâties autour de cette technologie. A quoi bon, pouvez-vous demander. Pourquoi inventer un modèle théorique de plus pour le développement des technologies nouvelles ? Après tout, il y en a déjà pas mal, de tels modèles. Le défi théorique consiste à simuler le changement technologique de façon à cerner des Cygnes Noirs possibles. La différence entre un cygne noir tout simple et un Cygne Noir écrit avec des majuscules est que ce dernier se réfère au livre de Nassim Nicolas Taleb « The Black Swan. The impact of the highly improbable », Penguin, 2010. Oui, je sais, il y a plus que ça. Un Cygne Noir en majuscules peut bien être le Cygne Noir de Tchaïkovski, donc une femme (Odile) autant attirante que dangereuse par son habileté d’introduire du chaos dans la vie d’un homme. Je sais aussi que si j’arrangerai une conversation entre Tchaïkovski et Carl Gustav Jung, les deux messieurs seraient probablement d’accord qu’Odile alias Cygne Noir symbolise le chaos, en opposition à l’ordre fragile dans la vie de Siegfried, donc à Odette. Enfin, j’fais pas du ballet, moi, ici. Je blogue. Ceci implique une tenue différente, ainsi qu’un genre différent de flexibilité. Je suis plus âgé que Siegfried, aussi, comme par une génération.

De tout en tout, mon Cygne Noir à moi est celui emprunté à Nassim Nicolas Taleb et c’est donc un phénomène qui, tout en étant hors d’ordinaire et surprenant pour les gens concernés, est néanmoins fonctionnellement et logiquement dérivé d’une séquence des phénomènes passés. Un Cygne Noir se forme autour des phénomènes qui pendant un certain temps surviennent aux extrémités de la courbe Gaussienne, donc à la frange de probabilité. Les Cygnes Noirs véhiculent du danger et des opportunités nouvelles, à des doses aussi variées que le sont les Cygnes Noirs eux-mêmes. L’intérêt pratique de cerner des Cygnes Noirs qui peuvent surgir à partir de la situation présente est donc celui de prévenir des risques du type catastrophique d’une part et de capter très tôt des opportunités exceptionnelles d’autre part.

Voilà donc que, mine de rien, je viens d’enrichir la description fonctionnelle de ma méthode de simuler l’intelligence collective des sociétés humaines avec les réseaux neuronaux artificiels. Cette méthode peut servir à identifier à l’avance des développements possibles du type de Cygne Noir : significatifs, subjectivement inattendus et néanmoins fonctionnellement enracinées dans la réalité présente.

Il y a donc cette technologie nouvelle et il y a des cellules socio-économiques qui se forment autour d’elle. Il y a des espèces distinctes des cellules et chaque espèce correspond à une technologie différente. Chaque cellule peut être représentée comme un automate cellulaire A = (Z^d, S, n, Sⁿ⁺¹ -> S), dont l’explication commence avec Z^d, donc l’espace à d dimensions ou les cellules font ce qu’elles ont à faire. L’automate cellulaire ne sait rien sur cet espace, tout comme une truite n’est pas vraiment forte lorsqu’il s’agit de décrire une rivière. Un automate cellulaire prend S états différents et ces états sont composés des mouvements du type un-pas-à-la-fois, dans n emplacements cellulaires adjacents. L’automate sélectionne ces S états différents dans un catalogue plus large Sⁿ⁺¹ de tous les états possibles et la fonction Sⁿ⁺¹ -> S alias la règle locale de l’automate A décrit de façon générale le quotient de cette sélection, donc la capacité de l’automate cellulaire d’explorer toutes les possibilités de bouger son cul (cellulaire) juste d’un cran à partir de la position actuelle.

Pourquoi distinguer ces quatre variables structurelles dans l’automate cellulaire ? Pourquoi n’assumons-nous pas que le nombre possible des mouvements « n » est une fonction constante des dimensions offertes par l’espace Z^d ? Pourquoi ne pas assumer que le nombre réel d’états S est égal au total possible de Sⁿ⁺¹ ? Eh bien parce que la théorie d’automates cellulaires a des ambitions de servir à quelque chose d’utile et elle s’efforce de simuler la réalité. Il y a donc une technologie nouvelle encapsulée dans une cellule sociale A. L’espace social autour d’A est vaste, mais il peut y avoir des portes verrouillées. Des marchés oligopoles, des compétiteurs plus rapides et plus entreprenants, des obstacles légaux et mêmes des obstacles purement sociaux. Si une société à qui vous proposez de coopérer dans votre projet innovant craint d’être exposée à 10 000 tweets enragés de la part des gens qui n’aiment pas votre technologie, cette porte-là est fermée, quoi que la dimension où elle se trouve est théoriquement accessible.

Si je suis un automate cellulaire tout à fait ordinaire et j’ai la possibilité de bouger dans n emplacements sociaux adjacents à celui où je suis maintenant, je commence par choisir juste un mouvement et voir ce qui se passe. Lorsque tout se passe de façon satisfaisante, j’observe mon environnement immédiat nouveau – j’observe donc le « n » nouveau visible à partir de la cellule où je viens de bouger – je fais un autre mouvement dans un emplacement sélectionné dans ce nouveau « n » et ainsi de suite. Dans un environnement immédiat « n » moi, l’automate cellulaire moyen, j’explore plus qu’un emplacement possible de parmi n seulement lorsque je viens d’essuyer un échec dans l’emplacement précédemment choisi et j’avais décidé que la meilleure stratégie est de retourner à la case départ tout en reconsidérant les options possibles.

La cellule sociale bâtie autour d’une technologie va donc se frayer un chemin à travers l’espace social Z^d, en essayant de faire des mouvement réussis, donc en sélectionnant une option de parmi les « n » possibles. Oui, les échecs ça arrive et donc parfois la cellule sociale va expérimenter avec k > 1 mouvements immédiats. Néanmoins, la situation où k = n c’est quand les gens qui travaillent sur une technologie nouvelle ont essayé, en vain, toutes les options possibles sauf une dernière et se jettent la tête en avant dans celle-ci, qui s’avère une réussite. De telles situations arrivent, je le sais. Je crois bien que Canal+ était une aventure de ce type à ces débuts. Néanmoins, lorsqu’un truc marche, dans le lancement d’une technologie nouvelle, on juste continue dans la foulée sans regarder par-dessus l’épaule.

Le nombre réel S d’états que prend un automate cellulaire est donc largement sujet à l’hystérèse. Chaque mouvement réussi est un environnement immédiat de moins à exploiter, donc celui laissé derrière nous. En même temps, c’est un défi nouveau de faire l’autre mouvement réussi au premier essai sans s’attarder dans des emplacements alternatifs. L’automate cellulaire est donc un voyageur plus qu’un explorateur. Bref, la formulation A = (Z^d, S, n, Sⁿ⁺¹ -> S) d’un automate cellulaire exprime donc des opportunités et des contraintes à la fois.

Ma cellule sociale bâtie autour de « Projet Aqueduc » coexiste avec des cellules sociales bâties autour d’autres technologies. Comme tout automate cellulaire respectable, je regarde autour de moi et je vois des mouvements évidents en termes d’investissement. Je peux bouger ma cellule sociale en termes de capital accumulé ainsi que de l’échelle physique des installations. Je suppose que les autres cellules sociales centrées sur d’autres technologies vont faire de même : chercher du capital et des opportunités de croître physiquement. Excellent ! Voilà donc que je vois deux dimensions de Z^d : l’échelle financière et l’échelle physique. Je me demande comment faire pour y bouger et je découvre d’autres dimensions, plus comportementales et cognitives celles-là : le retour interne (profit) espéré sur l’investissement ainsi que le retour externe (croissance de valeur d’entreprise), la croissance générale du marché de capital d’investissement etc.

Trouver des dimensions nouvelles, c’est fastoche, par ailleurs. Beaucoup plus facile que c’est montré dans les films de science-fiction. Il suffit de se demander ce qui peut bien gêner nos mouvements, regarder bien autour, avoir quelques conversations et voilà ! Je peux découvrir des dimensions nouvelles même sans accès à un téléporteur inter-dimensionnel à haute énergie. Je me souviens d’avoir vu sur You Tube une série de vidéos dont les créateurs prétendaient savoir à coup sûr que le grand collisionneur de hadrons (oui, celui à Genève) a ouvert un tunnel vers l’enfer. Je passe sur des questions simplissimes du genre : « Comment savez-vous que c’est un tunnel, donc un tube avec une entrée et une sortie ? Comment savez-vous qu’il mène en enfer ? Quelqu’un est-il allé de l’autre côté et demandé les locaux où ça où ils habitent ? ». Le truc vraiment épatant est qu’il y a toujours des gens qui croient dur comme fer que vous avez besoin des centaines de milliers de dollars d’investissement et des années de recherche scientifique pour découvrir un chemin vers l’enfer. Ce chemin, chacun de nous l’a à portée de la main. Suffit d’arrêter de découvrir des dimensions nouvelles dans notre existence.

Bon, je suis donc un automate cellulaire respectable qui développe le « Projet Aqueduc » à partir d’une cellule d’enthousiastes et en présence d’autres automates cellulaires. On bouge, nous, les automates cellulaires, le long de deux dimensions bien claires d’échelle – capital accumulé et taille physique des installations – et on sait que bouger dans ces dimensions-ci exige un effort dans d’autres dimensions moins évidentes qui s’entrelacent autour d’intérêt général pour notre idée de la part des gens extra – cellulaires. Notre Z^d est en fait un Z^d eh^{ben alors !}. Le fait d’avoir deux dimensions bien visibles et un nombre discutable de dimensions plus floues fait que le nombre « n » des mouvements possibles est tout aussi discutable et on évite d’en explorer toutes les nuances. On saute sur le premier emplacement possible de parmi « n », ce qui nous transporte dans un autre « n », puis encore et encore.

Lorsque tous les automates cellulaires démontrent des règles locales Sⁿ⁺¹ -> S à peu près cohérentes, il est possible d’en faire une description instantanée Z^d -> S, connue aussi comme configuration de A ou bien son état global. Le nombre d’états possibles que mon « Projet Aqueduc » peut prendre dans un espace rempli d’automates cellulaires va dépendre du nombre d’états possibles d’autres automates cellulaires. Ces descriptions instantanées Z^d -> S sont, comme le nom l’indique, instantanées, donc temporaires et locales. Elles peuvent changer. En particulier, le nombre S d’états possibles de mon « Projet Aqueduc » change en fonction de l’environnement immédiat « n » accessible à partir de la position courante t. Une séquence de positions correspond donc à une séquence des configurations c_t = Z^d -> S (t) et cette séquence est désignée comme comportement de l’automate cellulaire A ou bien son évolution.

[1] Bandini, S., Mauri, G., & Serra, R. (2001). Cellular automata: From a theoretical parallel computational model to its application to complex systems. Parallel Computing, 27(5), 539-553. https://doi.org/10.1016/S0167-8191(00)00076-4

[2] Yu, J., Hagen-Zanker, A., Santitissadeekorn, N., & Hughes, S. (2021). Calibration of cellular automata urban growth models from urban genesis onwards-a novel application of Markov chain Monte Carlo approximate Bayesian computation. Computers, environment and urban systems, 90, 101689. https://doi.org/10.1016/j.compenvurbsys.2021.101689

The red-neck-cellular automata

I continue revising my work on collective intelligence, and I am linking it to the theory of complex systems. I return to the excellent book ‘What Is a Complex System?’ by James Landyman and Karoline Wiesner (Yale University Press, 2020, ISBN 978-0-300-25110-4, Kindle Edition). I take and quote their summary list of characteristics that complex systems display, on pages 22 – 23: “ […] which features are necessary and sufficient for which kinds of complexity and complex system. The features are as follows:

1. Numerosity: complex systems involve many interactions among many components.

2. Disorder and diversity: the interactions in a complex system are not coordinated or controlled centrally, and the components may differ.

3. Feedback: the interactions in complex systems are iterated so that there is feedback from previous interactions on a time scale relevant to the system’s emergent dynamics.

4. Non-equilibrium: complex systems are open to the environment and are often driven by something external.

5. Spontaneous order and self-organisation: complex systems exhibit structure and order that arises out of the interactions among their parts.

6. Nonlinearity: complex systems exhibit nonlinear dependence on parameters or external drivers.

7. Robustness: the structure and function of complex systems is stable under relevant perturbations.

8. Nested structure and modularity: there may be multiple scales of structure, clustering and specialisation of function in complex systems.

9. History and memory: complex systems often require a very long history to exist and often store information about history.

10. Adaptive behaviour: complex systems are often able to modify their behaviour depending on the state of the environment and the predictions they make about it”.

As I look at the list, my method of simulating collective intelligence is coherent therewith. Still, there is one point which I think I need to dig a bit more into: that whole thing with simple entities inside the complex system. In most of my simulations, I work on interactions between cognitive categories, i.e. between quantitative variables. Interaction between real social entities is most frequently implied rather than empirically nailed down. Still, there is one piece of research which sticks out a bit in that respect, and which I did last year. It is devoted to cities and their role in the human civilisation. I wrote quite a few blog updates on the topic, and I have one unpublished paper written thereon, titled ‘The Puzzle of Urban Density And Energy Consumption’. In this case, I made simulations of collective intelligence with my method, thus I studied interactions between variables. Yet, in the phenomenological background of emerging complexity in variables, real people interact in cities: there are real social entities interacting in correlation with the connections between variables. I think the collective intelligence of cities the piece of research where I have the surest empirical footing, as compared to others.

There is another thing which I almost inevitably think about. Given the depth and breadth of the complexity theory, such as I start discovering it with and through that ‘What Is a Complex System?’ book, by James Landyman and Karoline Wiesner, I ask myself: what kind of bacon can I bring to that table? Why should anyone bother about my research? What theoretical value added can I supply? A good way of testing it is talking real problems. I have just signalled my research on cities. The most general hypothesis I am exploring is that cities are factories of new social roles in the same way that the countryside is a factory of food. In the presence of demographic growth, we need more food, and we need new social roles for new humans coming around. In the absence of such new social roles, those new humans feel alienated, they identify as revolutionaries fighting for the greater good, they identify the incumbent humans as oppressive patriarchy, and the next thing you know, there is systemic, centralized, government-backed terror. Pardon my French, this is a system of social justice. Did my bit of social justice, in the communist Poland.

Anyway, cities make new social roles by making humans interact much more abundantly than they usually do in a farm. More abundant an interaction means more data to process for each human brain, more s**t to figure out, and the next thing you know, you become a craftsman, a businessperson, an artist, or an assassin. Once again, being an assassin in the countryside would not make much sense. Jumping from one roof to another looks dashing only in an urban environment. Just try it on a farm.

Now, an intellectual challenge. How can humans, who essentially don’t know what to do collectively, can interact so as to create emergent complexity which, in hindsight, looks as if they had known what to do? An interesting approach, which hopefully allows using some kind of neural network, is the paradigm of the maze. Each individual human is so lost in social reality that the latter appears as a maze, which one ignores the layout of. Before I go further, one linguistic thing is to nail down. I feel stupid using impersonal forms such as ‘one’, or ‘an individual’. I like more concreteness. I am going to start with George the Hero. George the Hero lives in a maze, and I stress it: he lives there. Social reality is like a maze to George, and, logically, George does not even want to get out of that maze, ‘cause that would mean being lonely, with no one around to gauge George’s heroism. George the Hero needs to stay in the maze.

The first thing which George the Hero needs to figure out is the dimensionality of the maze. How many axes can George move along in that social complexity? Good question. George needs to experiment in order to discover that. He makes moves in different social directions. He looks around what different kinds of education he can possibly get. He assesses his occupational options, mostly jobs and business ventures. He asks himself how he can structure his relations with family and friends. Is being an asshole compatible with fulfilling emotional bonds with people around?

Wherever George the Hero currently is in the maze, there are n neighbouring and available cells around him. In each given place of the social maze, George the Hero has n possible ways to move further, into those n accessible cells in the immediate vicinity, and that is associated with k dimensions of movement. What is k, exactly? Here, I can refer to the theory of cellular automata, which attempts to simulate interactions between really simple, cell-like entities (Bandini, Mauri & Serra 2001[1]; Yu et al. 2021[2]). There is something called ‘von Neumann neighbourhood’. It corresponds to the assumption that if George the Hero has n neighbouring social cells which he move into, he can move like ‘left-right-forward-back’. That, in turn, spells k = n/2. If George can move into 4 neighbouring cells, he moves in a 2-dimensional space. Should he be able to move into 6 adjacent cells of the social maze, he has 3 dimensions to move along etc. Trouble starts when George sees an odd number of places to move to, like 5 or 7, on the account of these giving half-dimensions, like 5/2 = 2.5, 7/2 = 3.5 etc. Half a dimension means, in practical terms, that George the Hero faces social constraints. There might be cells around, mind you, which technically are there, but there are walls between George and them, and thus, for all practical purposes, the Hero can afford not to give a f**k.

George the Hero does not like to move back. Hardly anyone does. Thus, when George has successfully moved from cell A to cell B, he will probably not like going back to A, just in order to explore another cell adjacent thereto. People behave heuristically. People build up on their previous gains. Once George the Hero has moved from A to B, B becomes his A for the next move. He will choose one among the cells adjacent to B (now A), move there etc. George is a Hero, not a scientist, and therefore he carves a path through the social maze rather than discovers the maze as such. Each cell in the maze contains some rewards and some threats. George can get food and it means getting into a dangerously complex relation with that sabre-tooth tiger. George can earn money and it means giving up some of his personal freedom. George can bond with other people and find existential meaning and it means giving up even more of what he provisionally perceives as his personal freedom.

The social maze is truly a maze because there are many Georges around. Interestingly, many Georges in English give one Georges in French, and I feel this is the point where I should drop the metaphor of George the Hero. I need to get more precise, and thus I go to a formal concept in the theory of cellular automata, namely that of a d-dimensional cellular automaton, which can be mathematically expressed as A = (Z^d, S, N, Sⁿ⁺¹ -> S). In that automaton A, Z^d stands for the architecture of the maze, thus a lattice of d – tuples of integer numbers. In plain human, Z^d is given by the number of dimensions, possibly constrained, which a human can move along in the social space. Many people carve their paths across the social maze, no one likes going back, and thus the more people are around, and the better they can communicate their respective experiences, the more exhaustive knowledge we have of the surrounding Z^d.

There is a finite set S of states in that social space Z^d, and that finitude is connected to the formally defined neighbourhood of the automaton A, namely the N. Formally, N is a finite ordered subset of Z^d, and, besides the ‘left-right-forward-back’ neighbourhood of von Neumann, there is a more complex one, namely the Moore’s neighbourhood. In the latter, we can move diagonally between cells, like to the left and forward, to the right and forward etc. Keeping in mind that neighbourhood means, in practical terms, the number n of cells which we can move into from the social cell we are currently in, the cellular automaton can be rephrased as as A = (Z^d, S, n, Sⁿ⁺¹ -> S). The transition Sⁿ⁺¹ -> S, called the local rule of A, makes more sense now. With me being in a given cell of the social maze, and there being n available cells immediately adjacent to mine, that makes n +1 cells where I can possibly be in, and I can technically visit all those cells in a finite number of Sⁿ⁺¹ combinatorial paths. The transition Sⁿ⁺¹ -> S expresses the way which I carve my finite set S of states out of the generally available Sⁿ⁺¹.

If I assume that cities are factories of new social roles, the cellular automaton of an urban homo sapiens should be more complex than the red-neck-cellular automaton in a farm folk. It might mean greater an n, thus more cells available for moving from where I am now. It might also mean more efficient a Sⁿ⁺¹ -> S local rule, i.e. a better way to explore all the possible states I can achieve starting from where I am. There is a separate formal concept for that efficiency in the local rule, and it is called configuration of the cellular automaton AKA its instantaneous description AKA its global state, and it refers to the map Z^d -> S. Hence, the configuration of my cellular automaton is the way which the overall social space Z^d mapes into the set S of states actually available to me.

Right, if I have my cellular automaton with a configuration map Z^d -> S, it is sheer fairness that you have yours too, and your cousin Eleonore has another one for herself, as well. There are many of us in the social space Z^d. We are many x’s in the Z^d. Each x of us has their own configuration map Z^d -> S. If we want to get along with each other, our individual cellular automatons need to be mutually coherent enough to have a common, global function of cellular automata, and we know there is such a global function when we can collectively produce a sequence of configurations.

According to my own definition, a social structure is a collectively intelligent structure to the extent that it can experiment with many alternative versions of itself and select the fittest one, whilst staying structurally coherent. Structural coherence, in turn, is the capacity to relax and tighten, in a sequence, behavioural coupling inside the society, so as to allow the emergence and grounding of new behavioural patterns. The theory of cellular automata provides me some insights in that respect. Collective intelligence means the capacity to experiment with ourselves, right? That means experimenting with our global function Z^d -> S, i.e. with the capacity to translate the technically available social space Z^d into a catalogue S of possible states. If we take a random sample of individuals in a society, and study their cellular automatons A, they will display local rules Sⁿ⁺¹ -> S, and these can be expressed as coefficients (S / Sⁿ⁺¹), 0 ≤ (S / Sⁿ⁺¹) ≤ 1. The latter express the capacity of individual cellular automatons to generate actual states S of being out of the generally available menu of Sⁿ⁺¹.

In a large population, we can observe the statistical distribution of individual (S / Sⁿ⁺¹) coefficients of freedom in making one’s cellular state. The properties of that statistical distribution, e.g. the average (S / Sⁿ⁺¹) across the board, are informative about how intelligent collectively the given society is. The greater the average (S / Sⁿ⁺¹), the more possible states can the given society generate in the incumbent social structure, and the more it can know about the fittest state possible. That looks like a cellular definition of functional freedom.

L’impression de respirer

J’avance avec la révision de ma recherche sur le phénomène d’intelligence collective, que je viens de documenter dans « The collective of individual humans being any good at being smart ». Je m’efforce à faire jonction entre mes idées à moi, d’une part, et deux autres créneaux de recherche : la théorie des systèmes complexes et l’approche psychologique à l’intelligence collective. La première, je la travaille sur la base du livre ‘What Is a Complex System?’ écrit par James Landyman et Karoline Wiesner, publié en 2020 chez Yale University Press (ISBN 978-0-300-25110-4, Kindle Edition). Quant à l’approche psychologique, ma lecture de référence est, pour le moment, le livre ‘The Knowledge Illusion. Why we never think alone’ écrit par Steven Sloman et Philip Fernbach, publié en 2017 chez RIVERHEAD BOOKS (originellement chez Penguin Random House LLC, Ebook ISBN: 9780399184345, Kindle Edition).

Je viens de cerner l’idée centrale de mon approche au phénomène d’intelligence collective, et c’est l’utilisation des réseaux neuronaux artificiels – donc de l’Intelligence Artificielle – comme simulateurs des phénomènes sociaux complexes. La touche originale bien à moi que je veux ajouter à ce sujet, vaste par ailleurs, est la façon d’utiliser des réseaux neuronaux très simples, possibles à programmer dans une feuille de calcul Excel. Ma méthode va donc un peu à l’encontre du stéréotype des super-nuages numériques portés par des super-ordinateurs joints eux-mêmes en réseau, tout ça pour prédire la prochaine mode vestimentaire ou la prochaine super-affaire en Bourse.

Lorsque je pense à la structure d’un livre que je pourrais écrire à ce sujet, le squelette conceptuel qui me vient à l’esprit est du scientifique classique. Ça commence avec une « Introduction » générale et peu formelle, genre montrer pourquoi faire tout ce bruit à propos de l’idée en question. Une section de « Matériel empirique et méthode » ensuit, ou je discute le type de données empiriques à travailler avec ainsi que la méthode de leur traitement. Le pas suivant est de présenter « La théorie et revue de littérature du sujet » en un chapitre séparé et enfin des « Exemples d’application », soit des calculs faits sur des données réelles avec la méthode en question.

Le noyau conceptuel formel de mon approche est – pour le moment – la fonction d’adaptation. Lorsque j’ai un ensemble de variables socio-économiques quantitatives, je peux faire des assomptions plus ou moins fortes à propos de leur signification et pertinence empirique, mais je peux assumer de manière tout à fait solide que chacune de ces variables peut représenter un résultat fonctionnel important, dont l’achèvement nous poursuivons comme société. En présence de « n » variables que peux poser « n » hypothèses du type : ces gens-là poursuivent l’optimisation de la variable « i » comme orientation collective. Une telle hypothèse veut dire que toutes les variables dans l’ensemble X = (x₁, x₂, …, x_n), observées dans une séquence de « m » occurrences locales (t₁, t₂,…, t_m), forment une chaîne d’états fonctionnels locaux f{x₁(t), x₂(t), …, x_n(t)}. La société étudiée compare chaque état fonctionnel local à une valeur espérée de résultat x_i(t) et la fonction d’adaptation produit l’erreur locale d’adaptation e(t) = x_i(t) – f{x₁(t), x₂(t), …, x_n(t)}. La variable « x_i » fait partie de l’ensemble X = (x₁, x₂, …, x_n). La chaîne d’états fonctionnels f{x₁(t), x₂(t), …, x_n(t)} est donc produite aussi bien avec la variable optimisée « x_i » elle-même qu’avec les autres variables. La logique de ceci est simple : la plupart de phénomènes sociaux que nous décrivons avec des variables quantitatives, tel le Produit National Brut (mon exemple préféré), démontrent une hystérèse significative. Le PNB d’aujourd’hui sert à produire le PNB de l’après-demain, tout comme le nombre des demandes de brevet d’aujourd’hui contribue à créer le même PNB de l’après-demain.

J’essaie de faire un rapprochement entre la théorie des systèmes complexes et ma méthode à moi. Je me réfère en particulier à ‘What Is a Complex System?’ (Landyman, Wiesner 2020). Le passage que je trouve particulièrement intéressant vu ma propre méthode est celui de la page 16, que je me permets de traduire sur le champ : « Comportement coordonné ne requiert pas de contrôleur suprême […] Il est surprenant que le mouvement collectif d’une volée d’oiseaux, d’un banc de poissons ou d’un essaim d’insectes peut être reproduit par un ensemble de robots programmés à obéir juste quelques règles simples. Chaque individu doit rester près d’une poignée des voisins et ne peut pas heurter d’autres individus. Comme l’individu avance, il contrôle régulièrement sa distance par rapport aux autres pour l’ajuster de façon correspondante. En conséquence, un mouvement de groupe se forme spontanément. Le comportement adaptatif du collectif surgit d’interactions répétées, dont chacune est relativement simple en elle-même […] ».

Le truc intéressant, là, c’est que je fais exactement la même opération logique dans les réseaux neuronaux que je fais et utilise dans ma recherche sur l’intelligence collective. A l’intérieur de chaque occurrence empirique dans mon ensemble de données (donc, de façon pratique, dans chaque vers de ma base de données), je calcule en ensuite je propage un méta-paramètre de distance Euclidienne entre chaque variable et toutes les autres. Le Produit Intérieur Brut en Suède en 2007 vérifie donc sa distance Euclidienne par rapport à l’inflation, au taux d’emploi etc., tout ça en Suède en 2007. Le PIB dans mon réseau neuronal se comporte donc comme un oiseau : ça vole de façon à contrôler sa distance par rapport aux autres phénomènes sociaux.

Chaque vers de la base de données est donc accompagné d’un vecteur-fantôme des distances Euclidiennes, qui est ensuite utilisé par le réseau comme information pertinente à la tentative d’adaptation dans l’occurrence empirique suivante, donc dans le vers suivant de la base des données. Initialement, lorsque je programmais ce truc, je ne savais pas ce que ça va donner. Je ne savais presque rien de cet aspect particulier de la théorie de complexité. Je venais juste de lire quelques articles sur la théorie d’essaim dans la programmation des robots et je voulais voir comment ça marche chez moi (Wood & Thompson 2021[1]; Li et al. 2021[2]). Je m’adaptais juste de façon (probablement) intelligente au flot de mes propres pensées. Il se fait que la propagation de ces distances Euclidiennes locales entres les variables impacte le réseau et son apprentissage de façon profonde.

Voilà donc un point certain de rapprochement entre ma méthode d’utiliser les réseaux neuronaux artificiels pour simuler l’intelligence collective et la théorie des systèmes complexes. Lorsque je crée, pour un ensemble des variables quantitatives socio-économiques, un ensemble fantôme accompagnant des distances mathématiques locales entre ces variables et je propage ces distances à travers le réseau, les nombres apprennent de façon accélérée.

Une petite explication est de rigueur, à propos de la notion de distance mathématique. Moi, j’utilise la distance Euclidienne entre les nombres simples. Dans le domaine du Data Science c’est l’équivalent de la pierre taillée. Il y a des mesures beaucoup plus sophistiquées, ou une distance Euclidienne est calculée entre des matrices entières des nombres. Moi, j’aime bien utiliser le type d’intelligence artificielle que je comprends.

Je peux donc résumer un point important de ma méthode, tout en l’enracinant dans la théorie des systèmes complexes. Nous pouvons imaginer les sociétés humaines comme des essaims des phénomènes que nous observons de façon imparfaite à travers des variables quantitatives. L’essaim des phénomènes s’auto-organise à travers les actions d’êtres humains qui contrôlent, de façon imparfaite et néanmoins cohérente, quelle est la distance (cohérence mutuelle) entre les phénomènes distincts. Le fait que chaque culture humaine s’efforce de créer et maintenir une cohérence interne est donc le mécanisme de contrôle qui facilite l’émergence des systèmes complexes.

Mon intuition à moi, lorsque j’introduisais ces mesures-fantômes de distance Euclidienne entre les variables était un peu contraire, en fait. Mon truc, depuis ma thèse de doctorat, c’est l’innovation et le changement technologique. Après avoir lu ces articles sur la théorie d’essaim je me suis dit que l’innovation survient lorsqu’une société se dit (collectivement) « Merde ! Ras le bol avec la monotonie ! Faut secouer tout ça un peu ! Eh, les gars ! Oui, vous ! On veut dire : oui, nous ! On relâche la cohérence interne ! Oui, juste pour quelques années, pas de souci ! Oui, merde, on vous (nous) promet de ne pas inventer Facebook, enfin on espère… ».

La société que je représente avec un réseau neuronal est donc capable d’innovation parce qu’elle peut relâcher sa cohérence culturelle interne juste ce qu’il faut pour laisser entrer des phénomènes nouveaux. Ce que j’observe mathématiquement dans mes simulations avec des données socio-économiques réelles : lorsque je propage la distance Euclidienne entre les variables à travers le réseau, celui-ci donne l’impression de respirer. Ça se gonfle et ça se dégonfle, en cadence rythmique.

[1] Wood, M. A., & Thompson, C. (2021). Crime prevention, swarm intelligence and stigmergy: Understanding the mechanisms of social media-facilitated community crime prevention. The British Journal of Criminology, 61(2), 414-433. https://doi.org/10.1093/bjc/azaa065

[2] Li, M., Porter, A. L., Suominen, A., Burmaoglu, S., & Carley, S. (2021). An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence. Technological Forecasting and Social Change, 166, 120621. https://doi.org/10.1016/j.techfore.2021.120621

The collective of individual humans being any good at being smart

I am working on two topics in parallel, which is sort of normal in my case. As I know myself, instead of asking “Isn’t two too much?”, I should rather say “Just two? Run out of ideas, obviously”. I keep working on a proof-of-concept article for the idea which I provisionally labelled “Energy Ponds” AKA “Project Aqueduct”, on the one hand. See my two latest updates, namely ‘I have proven myself wrong’ and ‘Plusieurs bouquins à la fois, comme d’habitude’, as regards the summary of what I have found out and written down so far. As in most research which I do, I have come to the conclusion that however wonderful the concept appears, the most important thing in my work is the method of checking the feasibility of that concept. I guess I should develop on the method more specifically.

On the other hand, I am returning to my research on collective intelligence. I have just been approached by a publisher, with a kind invitation to submit the proposal for a book on that topic. I am passing in review my research, and the available literature. I am wondering what kind of central thread I should structure the entire book around. Two threads turn up in my mind, as a matter of fact. The first one is the assumption that whatever kind of story I am telling, I am actually telling the story of my own existence. I feel I need to go back to the roots of my interest in the phenomenon of collective intelligence, and those roots are in my meddling with artificial neural networks. At some point, I came to the conclusion that artificial neural networks can be good simulators of the way that human societies figure s**t out. I need to dig again into that idea.

My second thread is the theory of complex systems AKA the theory of complexity. The thing seems to be macheting its way through the jungle of social sciences, those last years, and it looks interestingly similar to what I labelled as collective intelligence. I came by the theory of complexity in three books which I am reading now (just three?). The first one is a history book: ‘1177 B.C. The Year Civilisation Collapsed. Revised and Updated’, published by Eric H. Cline with Princeton University Press in 2021[1]. The second book is just a few light years away from the first one. It regards mindfulness. It is ‘Aware. The Science and Practice of Presence. The Groundbreaking Meditation Practice’, published by Daniel J. Siegel with TarcherPerigee in 2018[2]. The third book is already some sort of a classic; it is ‘The Black Swan. The impact of the highly improbable’ by Nassim Nicolas Taleb with Penguin, in 2010.

I think it is Daniel J. Siegel who gives the best general take on the theory of complexity, and I allow myself to quote: ‘One of the fundamental emergent properties of complex systems in this reality of ours is called self-organization. That’s a term you might think someone in psychology or even business might have created—but it is a mathematical term. The form or shape of the unfolding of a complex system is determined by this emergent property of self-organization. This unfolding can be optimized, or it can be constrained. When it’s not optimizing, it moves toward chaos or toward rigidity. When it is optimizing, it moves toward harmony and is flexible, adaptive, coherent, energized, and stable’. (Siegel, Daniel J.. Aware (p. 9). Penguin Publishing Group. Kindle Edition).

I am combining my scientific experience with using AI as social simulator with the theory of complex systems. I means I need to UNDERSTAND, like really. I need to understand my own thinking, in the first place, and then I need to combine it with whatever I can understand from other people’s thinking. It started with a simple artificial neural network, which I used to write my article ‘Energy efficiency as manifestation of collective intelligence in human societies’ (Energy, 191, 116500, https://doi.org/10.1016/j.energy.2019.116500 ). I had a collection of quantitative variables, which I had previously meddled with using classical regression. As regression did not really bring much conclusive results, I had the idea of using an artificial neural network. Of course, today, neural networks are a whole technology and science. The one I used is the equivalent of a spear with a stone tip as compared to a battle drone. Therefore, the really important thing is the fundamental logic of neural networking as compared to regression, in analyzing quantitative data.

When I do regression, I come up with a function, like y = a₁*x₁ + a₂*x₂ + …+ b, I trace that function across the cloud of empirical data points I am working with, and I measure the average distance from those points to the line of my function. That average distance is the average (standard) error of estimation with that given function. I repeat the process as many times as necessary to find a function which both makes sense logically and yields the lowest standard error of estimation. The central thing is that I observe all my data at once, as if it was all happening at the same time and as if I was observing it from outside. Here is the thing: I observe it from outside, but when that empirical data was happening, i.e. when the social phenomena expressed in my quantitative variables were taking place, everybody (me included) was inside, not outside.

How to express mathematically the fact of being inside the facts measured? One way is to take those empirical occurrences one by one, sort of Denmark in 2005, and then Denmark in 2006, and then Germany in 2005 etc. Being inside the events changes my perspective on what is the error of estimation, as compared to being outside. When I am outside, error means departure from the divine plan, i.e. from the regression function. When I am inside things that are happening, error happens as discrepancy between what I want and expect, on the one hand, and what I actually get, on the other hand. These are two different errors of estimation, measured as departures from two different functions. The regression function is the most accurate (or as accurate as you can get) mathematical explanation of the empirical data points. The function which we use when simulating the state of being inside the events is different: it is a function of adaptation.

Intelligent adaptation means that we are after something: food, sex, power, a new Ferrari, social justice, 1000 000 followers on Instagram…whatever. There is something we are after, some kind of outcome we try to optimize. When I have a collection of quantitative variables which describe a society, such as energy efficiency, headcount of population, inflation rates, incidence of Ferraris per 1 million people etc., I can make a weak assumption that any of these can express a desired outcome. Here, a digression is due. In science and philosophy, weak assumptions are assumptions which assume very little, and therefore they are bloody hard to discard. On the other hand, strong assumptions assume a lot, and that makes them pretty good targets for discarding criticism. In other words, in science and philosophy, weak assumptions are strong and strong assumptions are weak. Obvious, isn’t it? Anyway, I make that weak assumption that any phenomenon we observe and measure with a numerical scale can be a collectively desired outcome we pursue.

Another assumption I make, a weak one as well, is sort of hidden in the word ‘expresses’. Here, I relate to a whole line of philosophical and scientific heritage, going back to people like Plato, Kant, William James, Maurice Merleau-Ponty, or, quite recently, Michael Keane (1972[3]), as well as Berghout & Verbitskiy (2021[4]). Very nearly everyone who seriously thought (or keeps thinking, on the account of being still alive) about human cognition of reality agrees that we essentially don’t know s**t. We make cognitive constructs in our minds, so as to make at least a little bit of sense of the essentially chaotic reality outside our skin, and we call it empirical observation. Mind you, stuff inside our skin is not much less chaotic, but this is outside the scope of social sciences. As we focus on quantitative variables commonly used in social sciences, the notion of facts becomes really blurred. Have you ever shaken hands with energy efficiency, with Gross Domestic Product or with the mortality rate? Have you touched it? No? Neither have I. These are highly distilled cognitive structures which we use to denote something about the state of society.

Therefore, I assume that quantitative, socio-economic variables express something about the societies observed, and that something is probably important if we collectively keep record of it. If I have n empirical variables, each of them possibly represents collectively important outcomes. As these are distinct variables, I assume that, with all the imperfections and simplification of the corresponding phenomenology, each distinct variable possibly represents a distinct type of collectively important outcome. When I study a human society through the lens of many quantitative variables, I assume they are informative about a set of collectively important social outcomes in that society.

Whilst a regression function explains how many variables are connected when observed ex post and from outside, an adaptation function explains and expresses the way that a society addresses important collective outcomes in a series of trials and errors. Here come two fundamental differences between studying a society with a regression function, as opposed to using an adaptation function. Firstly, for any collection of variables, there is essentially one regression function of the type: y = a₁*x₁ + a₂*x₂ + …+ a_n*x_n + b. On the other hand, with a collection of n quantitative variables at hand, there is at least as many functions of adaptation as there are variables. We can hypothesize that each individual variable x is the collective outcome to pursue and optimize, whilst the remaining n – 1 variables are instrumental to that purpose. One remark is important to make now: the variable informative about collective outcomes pursued, that specific x, can be and usually is instrumental to itself. We can make a desired Gross Domestic Product based on the Gross Domestic Product we have now. The same applies to inflation, energy efficiency, share of electric cars in the overall transportation system etc. Therefore, the entire set of n variables can be assumed instrumental to the optimization of one variable x from among them.

Mathematically, it starts with assuming a functional input f(x₁, x₂, …, x_n) which gets pitched against one specific outcome x_i. Subtraction comes as the most logical representation of that pitching, and thus we have the mathematical expression ‘x_i – f(x₁, x₂, …, x_n)’, which informs about how close the society observed has come to the desired outcome x_i. It is technically possible that people just nail it, and x_i = f(x₁, x₂, …, x_n), whence x_i – f(x₁, x₂, …, x_n) = 0. This is a perfect world, which, however, can be dangerously perfect. We know those societies of apparently perfectly happy people, who live in harmony with nature, even if that harmony means hosting most intestinal parasites of the local ecosystem. One day other people come, with big excavators, monetary systems, structured legal norms, and the bubble bursts, and it hurts.

Thus, on the whole, it might be better to hit x_i ≠ f(x₁, x₂, …, x_n), whence x_i – f(x₁, x₂, …, x_n) ≠ 0. It helps learning new stuff. The ‘≠ 0’ part means there is an error in adaptation. The functional input f(x₁, x₂, …, x_n) hits above or below the desired x_i. As we want to learn, that error in adaptation AKA e = x_i – f(x₁, x₂, …, x_n) ≠ 0, makes any practical sense when we utilize it in subsequent rounds of collective trial and error. Sequence means order, and a timeline. We have a sequence {t₀, t₁, t₂, …, t_m} of m moments in time. Local adaptation turns into ‘x_i(t) – f_t(x₁, x₂, …, x_n)’, and error of adaptation becomes the time-specific e_t = x_i(t) – f_t(x₁, x₂, …, x_n) ≠ 0. The clever trick consists in taking e(t₀) = x_i(t0) – f_t0(x₁, x₂, …, x_n) ≠ 0 and combining it somehow with the next functional input f_t1(x₁, x₂, …, x_n). Mathematically, if we want to combine two values, we can add them up or multiply them. We keep in mind that division is a special case of multiplication, namely x * (1/z). We I add up two values, I assume they are essentially of the same kind and sort of independent from each other. When, on the other hand, I multiply them, they become entwined so that each of them reproduces the other one. Multiplication ‘x * z’ means that x gets reproduced z times and vice versa. When I have the error of adaptation e_t0 from the last experimental round and I want to combine it with the functional input of adaptation f_t1(x₁, x₂, …, x_n) in the next experimental round, that whole reproduction business looks like a strong assumption, with a lot of weak spots on it. I settle for the weak assumption then, and I assume that f_t1(x₁, x₂, …, x_n) becomes f_t0(x₁, x₂, …, x_n) + e(t₀).

The expression f_t0(x₁, x₂, …, x_n) + e(t₀) makes any functional sense only when and after we have e(t₀) = x_i(t0) – f_t0(x₁, x₂, …, x_n) ≠ 0. Consequently, the next error of adaptation, namely e(t₁) = x_i(t₁) – f_t1(x₁, x₂, …, x_n) ≠ 0 can come into being only after its predecessor e_t0 has occurred. We have a chain of m states in the functional input of the society, i.e. {f_t0(x₁, x₂, …, x_n) => f_t1(x₁, x₂, …, x_n) => … => f_tm(x₁, x₂, …, x_n)}, associated with a chain of m desired outcomes {x_i(t₀) => x_i(t₁) => … => x_i(t_m)}, and with a chain of errors in adaptation {e(t₀) => e(t₁) => …=> e(t_m)}. That triad – chain of functional inputs, chain of desired outcomes, and the chain of errors in adaptation – makes for me the closest I can get now to the mathematical expression of the adaptation function. As errors get fed along the chain of states (as I see it, they are being fed forward, but in the algorithmic version, you can backpropagate them), those errors are some sort of dynamic memory in that society, the memory from learning to adapt.

Here we can see the epistemological difference between studying a society from outside, and explaining its workings with a regression function, on the one hand, and studying those mechanisms from inside, by simulation with an adaptation function, on the other hand. Adaptation function is the closest I can get, in mathematical form, to what I understand by collective intelligence. As I have been working with that general construct, I progressively zoomed in on another concept, namely that of intelligent structure, which I define as a structure which learns by experimenting with many alternative versions of itself whilst staying structurally coherent, i.e. by maintaining basic coupling between particular components.

I feel like comparing my approach to intelligent structures and their collective intelligence with the concept of complex systems, as discussed in the literature I have just referred to. I returned, therefore, to the book entitled ‘1177 B.C. The Year Civilisation Collapsed. Revised and Updated’, by Eric H. Cline, Princeton University Press, 2021. The theory of complex systems is brought forth in that otherwise very interesting piece in order to help formulating an answer to the following question: “Why did the great empires of the Late Bronze Age, such as Egypt, the Hittites, or the Myceneans, collapse all in approximately the same time, around 1200 – 1150 B.C.?”. The basic assertion which Eric Cline develops on and questions is that the entire patchwork of those empires in the Mediterranean, the Levant and the Middle East was one big complex system, which collapsed on the account of having overkilled it slightly in the complexity department.

I am trying to reconstruct the definition of systemic complexity such as Eric Cline uses it in his flow of logic. I start with the following quote: ‘Complexity science or theory is the study of a complex system or systems, with the goal of explaining the phenomena which emerge from a collection of interacting objects’. If we study a society as a complex system, we need to assume two things. There are many interacting objects in it, for one, and their mutual interaction leads to the emergence of some specific phenomena. Sounds cool. I move on, and a few pages later I find the following statement: ‘In one aspect of complexity theory, behavior of those objects is affected by their memories and “feedback” from what has happened in the past. They are able to adapt their strategies, partly on the basis of their knowledge of previous history’. Nice. We are getting closer. Entities inside a complex system accumulate memory, and they learn on that basis. This is sort of next door to the three sequential chains: states, desired outcomes, and errors in adaptation, which I coined up.

Further, I find an assertion that a complex social system is typically “alive”, which means that it evolves in a complicated, nontrivial way, whilst being open to influences from the environment. All that leads to the complex system to generate phenomena which can be considered as surprising and extreme. Good. This is the moment to move to the next book: ‘The Black Swan. The impact of the highly improbable’ by Nassim Nicolas Taleb , Penguin, 2010. Here comes a lengthy quote, which I bring here for the sheer pleasure of savouring one more time Nassim Taleb’s delicious style: “[…] say you attribute the success of the nineteenth-century novelist Honoré de Balzac to his superior “realism,” “insights,” “sensitivity,” “treatment of characters,” “ability to keep the reader riveted,” and so on. These may be deemed “superior” qualities that lead to superior performance if, and only if, those who lack what we call talent also lack these qualities. But what if there are dozens of comparable literary masterpieces that happened to perish? And, following my logic, if there are indeed many perished manuscripts with similar attributes, then, I regret to say, your idol Balzac was just the beneficiary of disproportionate luck compared to his peers. Furthermore, you may be committing an injustice to others by favouring him. My point, I will repeat, is not that Balzac is untalented, but that he is less uniquely talented than we think. Just consider the thousands of writers now completely vanished from consciousness: their record does not enter into analyses. We do not see the tons of rejected manuscripts because these writers have never been published. The New Yorker alone rejects close to a hundred manuscripts a day, so imagine the number of geniuses that we will never hear about. In a country like France, where more people write books while, sadly, fewer people read them, respectable literary publishers accept one in ten thousand manuscripts they receive from first-time authors”.

Many people write books, few people read them, and that creates something like a flow of highly risky experiments. That coincides with something like a bottleneck of success, with possibly great positive outcomes (fame, money, posthumous fame, posthumous money for other people etc.), and a low probability of occurrence. A few salient phenomena are produced – the Balzacs – whilst the whole build-up of other writing efforts, by less successful novelists, remains in the backstage of history. That, in turn, somehow rhymes with my intuition that intelligent structures need to produce big outliers, at least from time to time. On the one hand, those outliers can be viewed as big departures from the currently expected outcomes. They are big local errors. Big errors mean a lot of information to learn from. There is an even further-going, conceptual coincidence with the theory and practice of artificial neural networks. A network can be prone to overfitting, which means that it learns too fast, sort of by jumping prematurely to conclusions, before and without having worked through the required work through local errors in adaptation.

Seen from that angle, the function of adaptation I have come up with has a new shade. The sequential chain of errors appears as necessary for the intelligent structure to be any good. Good. Let’s jump to the third book I quoted with respect to the theory of complex systems: ‘Aware. The Science and Practice of Presence. The Ground-breaking Meditation Practice’, by Daniel J. Siegel, TarcherPerigee, 2018. I return to the idea of self-organisation in complex systems, and the choice between three different states: a) the optimal state of flexibility, adaptability, coherence, energy and stability b) non-optimal rigidity and c) non-optimal chaos.

That conceptual thread concurs interestingly with my draft paper: ‘Behavioral absorption of Black Swans: simulation with an artificial neural network’ . I found out that with the chain of functional input states {f_t0(x₁, x₂, …, x_n) => f_t1(x₁, x₂, …, x_n) => … => f_tm(x₁, x₂, …, x_n)} being organized in rigorously the same way, different types of desired outcomes lead to different patterns of learning, very similar to the triad which Daniel Siegel refers to. When my neural network does its best to optimize outcomes such as Gross Domestic Product, it quickly comes to rigidity. It makes some errors in the beginning of the learning process, but then it quickly drives the local error asymptotically to zero and is like ‘We nailed it. There is no need to experiment further’. There are other outcomes, such as the terms of trade (the residual fork between the average price of exports and that of imports), or the average number of hours worked per person per year, which yield a curve of local error in the form of a graceful sinusoid, cyclically oscillating between different magnitudes of error. This is the energetic, dynamic balance. Finally, some macroeconomic outcomes, such as the index of consumer prices, can make the same neural network go nuts, and generate an ever-growing curve of local error, as if the poor thing couldn’t learn anything sensible from looking at the prices of apparel and refrigerators. The (most) puzzling thing in all that differences in pursued outcomes are the source of discrepancy in the patterns of learning, not the way of learning as such. Some outcomes, when pursued, keep the neural network I made in a state of healthy adaptability, whilst other outcomes make it overfit or go haywire.

When I write about collective intelligence and complex system, it can come as a sensible idea to read (and quote) books which have those concepts explicitly named. Here comes ‘The Knowledge Illusion. Why we never think alone’ by Steven Sloman and Philip Fernbach, RIVERHEAD BOOKS (An imprint of Penguin Random House LLC, Ebook ISBN: 9780399184345, Kindle Edition). In the introduction, titled ‘Ignorance and the Community of Knowledge’, Sloman and Fernbach write: “The human mind is not like a desktop computer, designed to hold reams of information. The mind is a flexible problem solver that evolved to extract only the most useful information to guide decisions in new situations. As a consequence, individuals store very little detailed information about the world in their heads. In that sense, people are like bees and society a beehive: Our intelligence resides not in individual brains but in the collective mind. To function, individuals rely not only on knowledge stored within our skulls but also on knowledge stored elsewhere: in our bodies, in the environment, and especially in other people. When you put it all together, human thought is incredibly impressive. But it is a product of a community, not of any individual alone”. This is a strong statement, which I somehow distance myself from. I think that collective human intelligence can be really workable when individual humans are any good at being smart. Individuals need to have practical freedom of action, based on their capacity to figure s**t out in difficult situations, and the highly fluid ensemble of individual freedoms allows the society to make and experiment with many alternative versions of themselves.

Another book is more of a textbook. It is ‘What Is a Complex System?’ by James Landyman and Karoline Wiesner, published with Yale University Press (ISBN 978-0-300-25110-4, Kindle Edition). In the introduction (p.15), Landyman and Wiesner claim: “One of the most fundamental ideas in complexity science is that the interactions of large numbers of entities may give rise to qualitatively new kinds of behaviour different from that displayed by small numbers of them, as Philip Anderson says in his hugely influential paper, ‘more is different’ (1972). When whole systems spontaneously display behaviour that their parts do not, this is called emergence”. In my world, those ‘entities’ are essentially the chained functional input states {f_t0(x₁, x₂, …, x_n) => f_t1(x₁, x₂, …, x_n) => … => f_tm(x₁, x₂, …, x_n)}. My entities are phenomenological – they are cognitive structures which fault of a better word we call ‘empirical variables’. If the neural networks I make and use for my research are any good at representing complex systems, emergence is the property of data in the first place. Interactions between those entities are expressed through the function of adaptation, mostly through the chain {e(t₀) => e(t₁) => …=> e(t_m)} of local errors, concurrent with the chain of functional input states.

I think I know what the central point and thread of my book on collective intelligence is, should I (finally) write that book for good. Artificial neural networks can be used as simulators of collective social behaviour and social change. Still, they do not need to be super-performant network. My point is that with the right intellectual method, even the simplest neural networks, those possible to program into an Excel spreadsheet, can be reliable cognitive tools for social simulation.

[1] LCCN 2020024530 (print) | LCCN 2020024531 (ebook) | ISBN 9780691208015 (paperback) | ISBN 9780691208022 (ebook) ; Cline, Eric H.. 1177 B.C.: 6 (Turning Points in Ancient History, 1) . Princeton University Press. Kindle Edition.

[2] LCCN 2018016987 (print) | LCCN 2018027672 (ebook) | ISBN 9780143111788 | ISBN 9781101993040 (hardback) ; Siegel, Daniel J.. Aware (p. viii). Penguin Publishing Group. Kindle Edition.

[3] Keane, M. (1972). Strongly mixing measures. Inventiones mathematicae, 16(4), 309-324. DOI https://doi.org/10.1007/BF01425715

[4] Berghout, S., & Verbitskiy, E. (2021). On regularity of functions of Markov chains. Stochastic Processes and their Applications, Volume 134, April 2021, Pages 29-54, https://doi.org/10.1016/j.spa.2020.12.006

Tag: collective intelligence

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