Lean, climbing trends

My editorial on You Tube

Our artificial intelligence: the working title of my research, for now. Volume 1: Energy and technological change. I am doing a little bit of rummaging in available data, just to make sure I keep contact with reality. Here comes a metric: access to electricity in the world, measured as the % of total human population[1]. The trend line looks proudly ascending. In 2016, 87,38% of mankind had at least one electric socket in their place. Ten years earlier, by the end of 2006, they were 81,2%. Optimistic. Looks like something growing almost linearly. Another one: « Electric power transmission and distribution losses »[2]. This one looks different: instead of a clear trend, I observe something shaking and oscillating, with the width of variance narrowing gently down, as time passes. By the end of 2014 (last data point in this dataset), we were globally at 8,25% of electricity lost in transmission. The lowest coefficient of loss occurred in 1998: 7,13%.

I move from distribution to production of electricity, and to its percentage supplied from nuclear power plants[3]. Still another shape, that of a steep bell with surprisingly lean edges. Initially, it was around 2% of global electricity supplied by the nuclear. At the peak of fascination, it was 17,6%, and at the end of 2014, we went down to 10,6%. The thing seems to be temporarily stable at this level. As I move to water, and to the percentage of electricity derived from the hydro[4], I see another type of change: a deeply serrated, generally descending trend. In 1971, we had 20,2% of our total global electricity from the hydro, and by the end of 2014, we were at 16,24%. In the meantime, it looked like a rollercoaster. Yet, as I am having a look at other renewables (i.e. other than hydroelectricity) and their share in the total supply of electricity[5], the shape of the corresponding curve looks like a snake, trying to figure something out about a vertical wall. Between 1971 and 1988, the share of those other renewables in the total electricity supplied moved from 0,25% to 0,6%. Starting from 1989, it is an almost perfectly exponential growth, to reach 6,77% in 2015. 

Just to have a complete picture, I shift slightly, from electricity to energy consumption as a whole, and I check the global share of renewables therein[6]. Surprise! This curve does not behave at all as it is expected to behave, after having seen the previously cited share of renewables in electricity. Instead of a snake sniffing a wall, we can see a snake like from above, or something like e meandering river. This seems to be a cycle over some 25 years (could it be Kondratiev’s?), with a peak around 18% of renewables in the total consumption of energy, and a trough somewhere by 16,9%. Right now, we seem to be close to the peak. 

I am having a look at the big, ugly brother of hydro: the oil, gas and coal sources of electricity and their share in the total amount of electricity produced[7]. Here, I observe a different shape of change. Between 1971 and 1986, the fossils dropped their share from 62% to 51,47%. Then, it rockets up back to 62% in 1990. Later, a slowly ascending trend starts, just to reach a peak, and oscillate for a while around some 65 ÷ 67% between 2007 and 2011. Since then, the fossils are dropping again: the short-term trend is descending.  

Finally, one of the basic metrics I have been using frequently in my research on energy: the final consumption thereof, per capita, measured in kilograms of oil equivalent[8]. Here, we are back in the world of relatively clear trends. This one is ascending, with some bumps on the way, though. In 1971, we were at 1336,2 koe per person per year. In 2014, it was 1920,655 koe.

Thus, what are all those curves telling me? I can see three clearly different patterns. The first is the ascending trend, observable in the access to electricity, in the consumption of energy per capita, and, since the late 1980ies, in the share of electricity derived from renewable sources. The second is a cyclical variation: share of renewables in the overall consumption of energy, to some extent the relative importance of hydroelectricity, as well as that of the nuclear. Finally, I can observe a descending trend in the relative importance of the nuclear since 1988, as well as in some episodes from the life of hydroelectricity, coal and oil.

On the top of that, I can distinguish different patterns in, respectively, the production of energy, on the one hand, and its consumption, on the other hand. The former seems to change along relatively predictable, long-term paths. The latter looks like a set of parallel, and partly independent experiments with different sources of energy. We are collectively intelligent: I deeply believe that. I mean, I hope. If bees and ants can be collectively smarter than singlehandedly, there is some potential in us as well.

Thus, I am progressively designing a collective intelligence, which experiments with various sources of energy, just to produce those two, relatively lean, climbing trends: more energy per capita and ever growing a percentage of capitae with access to electricity. Which combinations of variables can produce a rationally desired energy efficiency? How is the supply of money changing as we reach different levels of energy efficiency? Can artificial intelligence make energy policies? Empirical check: take a real energy policy and build a neural network which reflects the logical structure of that policy. Then add a method of learning and see, what it produces as hypothetical outcome.

What is the cognitive value of hypotheses made with a neural network? The answer to this question starts with another question: how do hypotheses made with a neural network differ from any other set of hypotheses? The hypothetical states of nature produced by a neural network reflect the outcomes of logically structured learning. The process of learning should represent real social change and real collective intelligence. There are four most important distinctions I have observed so far, in this respect: a) awareness of internal cohesion b) internal competition c) relative resistance to new information and d) perceptual selection (different ways of standardizing input data).

The awareness of internal cohesion, in a neural network, is a function that feeds into the consecutive experimental rounds of learning the information on relative cohesion (Euclidean distance) between variables. We assume that each variable used in the neural network reflects a sequence of collective decisions in the corresponding social structure. Cohesion between variables represents the functional connection between sequences of collective decisions. Awareness of internal cohesion, as a logical attribute of a neural network, corresponds to situations when societies are aware of how mutually coherent their different collective decisions are. The lack of logical feedback on internal cohesion represents situation when societies do not have that internal awareness.

As I metaphorically look around and ask myself, what awareness do I have about important collective decisions in my local society. I can observe and pattern people’s behaviour, for one. Next thing: I can read (very literally) the formalized, official information regarding legal issues. On the top of that, I can study (read, mostly) quantitatively formalized information on measurable attributes of the society, such as GDP per capita, supply of money, or emissions of CO2. Finally, I can have that semi-formalized information from what we call “media”, whatever prefix they come with: mainstream media, social media, rebel media, the-only-true-media etc.

As I look back upon my own life and the changes which I have observed on those four levels of social awareness, the fourth one, namely the media, has been, and still is the biggest game changer. I remember the cultural earthquake in 1990 and later, when, after decades of state-controlled media in the communist Poland, we suddenly had free press and complete freedom of publishing. Man! It was like one of those moments when you step out of a calm, dark alleyway right into the middle of heavy traffic in the street. Information, it just wheezed past.         

There is something about media, both those called ‘mainstream’, and the modern platforms like Twitter or You Tube: they adapt to their audience, and the pace of that adaptation is accelerating. With Twitter, it is obvious: when I log into my account, I can see the Tweets only from people and organizations whom I specifically subscribed to observe. With You Tube, on my starting page, I can see the subscribed channels, for one, and a ton of videos suggested by artificial intelligence on the grounds of what I watched in the past. Still, the mainstream media go down the same avenue. When I go bbc.com, the types of news presented are very largely what the editorial team hopes will max out on clicks per hour, which, in turn, is based on the types of news that totalled the most clicks in the past. The same was true for printed newspapers, 20 years ago: the stuff that got to headlines was the kind of stuff that made sales.

Thus, when I simulate collective intelligence of a society with a neural network, the function allowing the network to observe its own, internal cohesion seems to be akin the presence of media platforms. Actually, I have already observed, many times, that adding this specific function to a multi-layer perceptron (type of neural network) makes that perceptron less cohesive. Looks like a paradox: observing the relative cohesion between its own decisions makes a piece of AI less cohesive. Still, real life confirms that observation. Social media favour the phenomenon known as « echo chamber »: if I want, I can expose myself only to the information that minimizes my cognitive dissonance and cut myself from anything that pumps my adrenaline up. On a large scale, this behavioural pattern produces a galaxy of relatively small groups encapsulated in highly distilled, mutually incoherent worldviews. Have you ever wondered what it would be to use GPS navigation to find your way, in the company of a hardcore flat-Earther?   

When I run my perceptron over samples of data regarding the energy – efficiency of national economies – including the function of feedback on the so-called fitness function is largely equivalent to simulating a society with abundant mediatic activity. The absence of such feedback is, on the other hand, like a society without much of a media sector.

Internal competition, in a neural network, is the deep underlying principle for structuring a multi-layer perceptron into separate layers, and manipulating the number of neurons in each layer. Let’s suppose I have two neural layers in a perceptron: A, and B, in this exact order. If I put three neurons in the layer A, and one neuron in the layer B, the one in B will be able to choose between the 3 signals sent from the layer A. Seen from the A perspective, each neuron in A has to compete against the two others for the attention of the single neuron in B. Choice on one end of a synapse equals competition on the other end.

When I want to introduce choice in a neural network, I need to introduce internal competition as well. If any neuron is to have a choice between processing input A and its rival, input B, there must be at least two distinct neurons – A and B – in a functionally distinct, preceding neural layer. In a collective intelligence, choice requires competition, and there seems to be no way around it.  In a real brain, neurons form synaptic sequences, which means that the great majority of our neurons fire because other neurons have fired beforehand. We very largely think because we think, not because something really happens out there. Neurons in charge of early-stage collection in sensory data compete for the attention of our brain stem, which, in turn, proposes its pre-selected information to the limbic system, and the emotional exultation of the latter incites he cortical areas to think about the whole thing. From there, further cortical activity happens just because other cortical activity has been happening so far.

I propose you a quick self-check: think about what you are thinking right now, and ask yourself, how much of what you are thinking about is really connected to what is happening around you. Are you thinking a lot about the gradient of temperature close to your skin? No, not really? Really? Are you giving a lot of conscious attention to the chemical composition of the surface you are touching right now with your fingertips? Not really a lot of conscious thinking about this one either? Now, how much conscious attention are you devoting to what [fill in the blank] said about [fill in the blank], yesterday? Quite a lot of attention, isn’t it?

The point is that some ideas die out, in us, quickly and sort of silently, whilst others are tough survivors and keep popping up to the surface of our awareness. Why? How does it happen? What if there is some kind of competition between synaptic paths? Thoughts, or components thereof, that win one stage of the competition pass to the next, where they compete again.           

Internal competition requires complexity. There needs to be something to compete for, a next step in the chain of thinking. A neural network with internal competition reflects a collective intelligence with internal hierarchies that offer rewards. Interestingly, there is research showing that greater complexity gives more optimizing accuracy to a neural network, but just as long as we are talking about really low complexity, like 3 layers of neurons instead of two. As complexity is further developed, accuracy decreases noticeably. Complexity is not the best solution for optimization: see Olawoyin and Chen (2018[9]).

Relative resistance to new information corresponds to the way that an intelligent structure deals with cognitive dissonance. In order to have any cognitive dissonance whatsoever, we need at least two pieces of information: one that we have already appropriated as our knowledge, and the new stuff, which could possibly disturb the placid self-satisfaction of the I-already-know-how-things-work. Cognitive dissonance is a potent factor of stress in human beings as individuals, and in whole societies. Galileo would have a few words to say about it. Question: how to represent in a mathematical form the stress connected to cognitive dissonance? My provisional answer is: by division. Cognitive dissonance means that I consider my acquired knowledge as more valuable than new information. If I want to decrease the importance of B in relation to A, I divide B by a factor greater than 1, whilst leaving A as it is. The denominator of new information is supposed to grow over time: I am more resistant to the really new stuff than I am to the already slightly processed information, which was new yesterday. In a more elaborate form, I can use the exponential progression (see The really textbook-textbook exponential growth).

I noticed an interesting property of the neural network I use for studying energy efficiency. When I introduce choice, internal competition and hierarchy between neurons, the perceptron gets sort of wild: it produces increasing error instead of decreasing error, so it basically learns how to swing more between possible states, rather than how to narrow its own trial and error down to one recurrent state. When I add a pinchful of resistance to new information, i.e. when I purposefully create stress in the presence of cognitive dissonance, the perceptron calms down a bit, and can produce a decreasing error.   

Selection of information can occur already at the level of primary perception. I developed on this one in « Thinking Poisson, or ‘WTF are the other folks doing?’ ». Let’s suppose that new science comes as for how to use particular sources of energy. We can imagine two scenarios of reaction to that new science. On the one hand, the society can react in a perfectly flexible way, i.e. each new piece of scientific research gets evaluated as for its real utility for energy management, and gest smoothly included into the existing body of technologies. On the other hand, the same society (well, not quite the same, an alternative one) can sharply distinguish those new pieces of science into ‘useful stuff’ and ‘crap’, with little nuance in between.

What do we know about collective learning and collective intelligence? Three essential traits come to my mind. Firstly, we make social structures, i.e. recurrent combinations of social relations, and those structures tend to be quite stable. We like having stable social structures. We almost instinctively create rituals, rules of conduct, enforceable contracts etc., thus we make stuff that is supposed to make the existing stuff last. An unstable social structure is prone to wars, coups etc. Our collective intelligence values stability. Still, stability is not the same as perfect conservatism: our societies have imperfect recall. This is the second important trait. Over (long periods of) time we collectively shake off, and replace old rules of social games with new rules, and we do it without disturbing the fundamental social structure. In other words: stable as they are, our social structures have mechanisms of adaptation to new conditions, and yet those mechanisms require to forget something about our past. OK, not just forget something: we collectively forget a shitload of something. Thirdly, there had been many local human civilisations, and each of them had eventually collapsed, i.e. their fundamental social structures had disintegrated. The civilisations we have made so far had a limited capacity to learn. Sooner or later, they would bump against a challenge which they were unable to adapt to. The mechanism of collective forgetting and shaking off, in every known historically documented case, had a limited efficiency.

I intuitively guess that simulating collective intelligence with artificial intelligence is likely to be the most fruitful when we simulate various capacities to learn. I think we can model something like a perfectly adaptable collective intelligence, i.e. the one which has no cognitive dissonance and processes information uniformly over time, whilst having a broad range of choice and internal competition. Such a neural network behaves in the opposite way to what we tend to associate with AI: instead of optimizing and narrowing down the margin of error, it creates new alternative states, possibly in a broadening range. This is a collective intelligence with lots of capacity to learn, but little capacity to steady itself as a social structure. From there, I can muzzle the collective intelligence with various types of stabilizing devices, making it progressively more and more structure-making, and less flexible. Down that avenue, the solver-type of artificial intelligence lies, thus a neural network that just solves a problem, with one, temporarily optimal solution.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. You can communicate with me directly, via the mailbox of this blog: goodscience@discoversocialsciences.com. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

[1] https://data.worldbank.org/indicator/EG.ELC.ACCS.ZS last access May 17th, 2019

[2] https://data.worldbank.org/indicator/EG.ELC.LOSS.ZS?end=2016&start=1990&type=points&view=chart last access May 17th, 2019

[3] https://data.worldbank.org/indicator/EG.ELC.NUCL.ZS?end=2014&start=1960&type=points&view=chart last access May 17th, 2019

[4] https://data.worldbank.org/indicator/EG.ELC.HYRO.ZS?end=2014&start=1960&type=points&view=chart last access May 17th, 2019

[5] https://data.worldbank.org/indicator/EG.ELC.RNWX.ZS?type=points last access May 17th, 2019

[6] https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS?type=points last access May 17th, 2019

[7] https://data.worldbank.org/indicator/EG.ELC.FOSL.ZS?end=2014&start=1960&type=points&view=chart last access May 17th, 2019

[8] https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE?type=points last access May 17th, 2019

[9] Olawoyin, A., & Chen, Y. (2018). Predicting the Future with Artificial Neural Network. Procedia Computer Science, 140, 383-392.

Thinking Poisson, or ‘WTF are the other folks doing?’

My editorial on You Tube

I think I have just put a nice label on all those ideas I have been rummaging in for the last 2 years. The last 4 months, when I have been progressively initiating myself at artificial intelligence, have helped me to put it all in a nice frame. Here is the idea for a book, or rather for THE book, which I have been drafting for some time. « Our artificial intelligence »: this is the general title. The first big chapter, which might very well turn into the first book out of a whole series, will be devoted to energy and technological change. After that, I want to have a go at two other big topics: food and agriculture, then laws and institutions.

I explain. What does it mean « Our artificial intelligence »? As I have been working with an initially simple algorithm of a neural network, and I have been progressively developing it, I understood a few things about the link between what we call, fault of a better word, artificial intelligence, and the way my own brain works. No, not my brain. That would be an overstatement to say that I understand fully my own brain. My mind, this is the right expression. What I call « mind » is an idealized, i.e. linguistic description of what happens in my nervous system. As I have been working with a neural network, I have discovered that artificial intelligence that I make, and use, is a mathematical expression of my mind. I project my way of thinking into a set of mathematical expressions, made into an algorithmic sequence. When I run the sequence, I have the impression of dealing with something clever, yet slightly alien: an artificial intelligence. Still, when I stop staring at the thing, and start thinking about it scientifically (you know: initial observation, assumptions, hypotheses, empirical check, new assumptions and new hypotheses etc.), I become aware that the alien thing in front of me is just a projection of my own way of thinking.

This is important about artificial intelligence: this is our own, human intelligence, just seen from outside and projected into electronics. This particular point is an important piece of theory I want to develop in my book. I want to compile research in neurophysiology, especially in the neurophysiology of meaning, language, and social interactions, in order to give scientific clothes to that idea. When we sometimes ask ourselves whether artificial intelligence can eliminate humans, it boils down to asking: ‘Can human intelligence eliminate humans?’. Well, where I come from, i.e. Central Europe, the answer is certainly ‘yes, it can’. As a matter of fact, when I raise my head and look around, the same answer is true for any part of the world. Human intelligence can eliminate humans, and it can do so because it is human, not because it is ‘artificial’.

When I think about the meaning of the word ‘artificial’, it comes from the Latin ‘artificium’, which, in turn, designates something made with skill and demonstrable craft. Artificium means seasoned skills made into something durable so as to express those skills. Artificial intelligence is a crafty piece of work made with one of the big human inventions: mathematics. Artificial intelligence is mathematics at work. Really at work, i.e. not just as another idealization of reality, but as an actual tool. When I study the working of algorithms in neural networks, I have a vision of an architect in Ancient Greece, where the first mathematics we know seem to be coming from. I have a wall and a roof, and I want them both to hold in balance, so what is the proportion between their respective lengths? I need to learn it by trial and error, as I haven’t any architectural knowledge yet. Although devoid of science, I have common sense, and I make small models of the building I want (have?) to erect, and I test various proportions. Some of those maquettes are more successful than others. I observe, I make my synthesis about the proportions which give the least error, and so I come up with something like the Pythagorean z2 = x2 + y2, something like π = 3,14 etc., or something like the discovery that, for a given angle, the tangent proportion y/x makes always the same number, whatever the empirical lengths of y and x.

This is exactly what artificial intelligence does. It makes small models of itself, tests the error resulting from comparison between those models and something real, and generalizes the observation of those errors. Really: this is what a face recognition piece of software does at an airport, or what Google Ads does. This is human intelligence, just unloaded into a mathematical vessel. This is the first discovery that I have made about AI. Artificial intelligence is actually our own intelligence. Studying the way AI behaves allows seeing, like under a microscope, the workings of human intelligence.

The second discovery is that when I put a neural network to work with empirical data of social sciences, it produces strange, intriguing patterns, something like neighbourhoods of the actual reality. In my root field of research – namely economics – there is a basic concept that we, economists, use a lot and still wonder what it actually means: equilibrium. It is an old observation that networks of exchange in human societies tend to find balance in some precise proportions, for example proportions between demand, supply, price and quantity, or those between labour and capital.

Half of economic sciences is about explaining the equilibriums we can empirically observe. The other half employs itself at discarding what that first half comes up with. Economic equilibriums are something we know that exists, and constantly try to understand its mechanics, but those states of society remain obscure to a large extent. What we know is that networks of exchange are like machines: some designs just work, some others just don’t. One of the most important arguments in economic sciences is whether a given society can find many alternative equilibriums, i.e. whether it can use optimally its resources at many alternative proportions between economic variables, or, conversely, is there just one point of balance in a given place and time. From there on, it is a rabbit hole. What does it mean ‘using our resources optimally’? Is it when we have the lowest unemployment, or when we have just some healthy amount of unemployment? Theories are welcome.

When trying to make predictions about the future, using the apparatus of what can now be called classical statistics, social sciences always face the same dilemma: rigor vs cognitive depth. The most interesting correlations are usually somehow wobbly, and mathematical functions we derive from regression always leave a lot of residual errors.    

This is when AI can step in. Neural networks can be used as tools for optimization in digital systems. Still, they have another useful property: observing a neural network at work allows having an insight into how intelligent structures optimize. If I want to understand how economic equilibriums take shape, I can observe a piece of AI producing many alternative combinations of the relevant variables. Here comes my third fundamental discovery about neural networks: with a few, otherwise quite simple assumptions built into the algorithm, AI can produce very different mechanisms of learning, and, consequently, a broad range of those weird, yet intellectually appealing, alternative states of reality. Here is an example: when I make a neural network observe its own numerical properties, such as its own kernel or its own fitness function, its way of learning changes dramatically. Sounds familiar? When you make a human being performing tasks, and you allow them to see the MRI of their own brain when performing those tasks, the actual performance changes.

When I want to talk about applying artificial intelligence, it is a good thing to return to the sources of my own experience with AI, and explain it works. Some sequences of mathematical equations, when run recurrently many times, behave like intelligent entities: they experiment, they make errors, and after many repeated attempts they come up with a logical structure that minimizes the error. I am looking for a good, simple example from real life; a situation which I experienced personally, and which forced me to learn something new. Recently, I went to Marrakech, Morocco, and I had the kind of experience that most European first-timers have there: the Jemaa El Fna market place, its surrounding souks, and its merchants. The experience consists in finding your way out of the maze-like structure of the alleys adjacent to the Jemaa El Fna. You walk down an alley, you turn into another one, then into still another one, and what you notice only after quite a few such turns is that the whole architectural structure doesn’t follow AT ALL the European concept of urban geometry.  

Thus, you face the length of an alley. You notice five lateral openings and you see a range of lateral passages. In a European town, most of those lateral passages would lead somewhere. A dead end is an exception, and passages between buildings are passages in the strict sense of the term: from one open space to another open space. At Jemaa El Fna, its different: most of the lateral ways lead into deep, dead-end niches, with more shops and stalls inside, yet some other open up into other alleys, possibly leading to the main square, or at least to a main street.

You pin down a goal: get back to the main square in less than… what? One full day? Just kidding. Let’s peg that goal down at 15 minutes. Fault of having a good-quality drone, equipped with thermovision, flying over the whole structure of the souk, and guiding you, you need to experiment. You need to test various routes out of the maze and to trace those, which allow the x ≤ 15 minutes time. If all the possible routes allowed you to get out to the main square in exactly 15 minutes, experimenting would be useless. There is any point in experimenting only if some from among the possible routes yield a suboptimal outcome. You are facing a paradox: in order not to make (too much) errors in your future strolls across Jemaa El Fna, you need to make some errors when you learn how to stroll through.

Now, imagine a fancy app in your smartphone, simulating the possible errors you can make when trying to find your way through the souk. You could watch an imaginary you, on the screen, wandering through the maze of alleys and dead-ends, learning by trial and error to drive the time of passage down to no more than 15 minutes. That would be interesting, wouldn’t it? You could see your possible errors from outside, and you could study the way you can possibly learn from them. Of course, you could always say: ‘it is not the real me, it is just a digital representation of what I could possibly do’. True. Still, I can guarantee you: whatever you say, whatever strong the grip you would try to keep on the actual, here-and-now you, you just couldn’t help being fascinated.

Is there anything more, beyond fascination, in observing ourselves making many possible future mistakes? Let’s think for a moment. I can see, somehow from outside, how a copy of me deals with the things of life. Question: how does the fact of seeing a copy of me trying to find a way through the souk differ from just watching a digital map of said souk, with GPS, such as Google Maps? I tried the latter, and I have two observations. Firstly, in some structures, such as that of maze-like alleys adjacent to Jemaa El Fna, seeing my own position on Google Maps is of very little help. I cannot put my finger on the exact reason, but my impression is that when the environment becomes just too bizarre for my cognitive capacities, having a bird’s eye view of it is virtually no good. Secondly, when I use Google Maps with GPS, I learn very little about my route. I just follow directions on the screen, and ultimately, I get out into the main square, but I know that I couldn’t reproduce that route without the device. Apparently, there is no way around learning stuff by myself: if I really want to learn how to move through the souk, I need to mess around with different possible routes. A device that allows me to see how exactly I can mess around looks like having some potential.

Question: how do I know that what I see, in that imaginary app, is a functional copy of me, and how can I assess the accuracy of that copy? This is, very largely, the rabbit hole I have been diving into for the last 5 months or so. The first path to follow is to look at the variables used. Artificial intelligence works with numerical data, i.e. with local instances of abstract variables. Similarity between the real me, and the me reproduced as artificial intelligence is to find in the variables used. In real life, variables are the kinds of things, which: a) are correlated with my actions, both as outcomes and as determinants b) I care about, and yet I am not bound to be conscious of caring about.

Here comes another discovery I made on my journey through the realm of artificial intelligence: even if, in the simplest possible case, I just make the equations of my neural network so as they represent what I think is the way I think, and I drop some completely random values of the relevant variables into the first round of experimentation, the neural network produces something disquietingly logical and coherent. In other words, if I am even moderately honest in describing, in the form of equations, my way of apprehending reality, the AI I thus created really processes information in the way I would.  

Another way of assessing the similarity between a piece of AI and myself is to compare the empirical data we use: I can make a neural network think more or less like me if I feed it with an accurate description of my so-far experience. In this respect, I discovered something that looks like a keystone in my intellectual structure: as I feed my neural network with more and more empirical data, the scope of the possible ways to learning something meaningful narrows down. When I minimise the amount of empirical data fed into the network, the latter can produce interesting, meaningful results via many alternative sequences of equations. As the volume of real-life information swells, some sequences of equations just naturally drop off the game: they drive the neural network into a state of structural error, when it stops performing calculations.

At this point, I can see some similarity between AI and quantum physics. Quantum mechanics have grown as a methodology, as they proved to be exceptionally accurate in predicting the outcomes of experiments in physics. That accuracy was based on the capacity to formulate very precise hypotheses regarding empirical reality, and the capacity to increase the precision of those hypotheses through the addition of empirical data from past experiments.  

Those fundamental observations I made about the workings of artificial intelligence have progressively brought me to use AI in social sciences. An analytical tool has become a topic of research for me. Happens all the time in science, mind you. Geometry, way back in the day, was a thoroughly practical set of tools, which served to make good boats, ships and buildings. With time, geometry has become a branch of science on its own rights. In my case, it is artificial intelligence. It is a tool, essentially, invented back in the 1960ies and 1970ies, and developed over the last 20 years, and it serves practical purposes: facial identification, financial investment etc. Still, as I have been working with a very simple neural network for the last 4 months, and as I have been developing the logical structure of that network, I am discovering a completely new opening in my research in social sciences.

I am mildly obsessed with the topic of collective human intelligence. I have that deeply rooted intuition that collective human behaviour is always functional regarding some purpose. I perceive social structures such as financial markets or political institutions as something akin to endocrine systems in a body: complex set of signals with a random component in their distribution, and yet a very coherent outcome. I follow up on that intuition by assuming that we, humans, are most fundamentally, collectively intelligent regarding our food and energy base. We shape our social structures according to the quantity and quality of available food and non-edible energy. For quite a while, I was struggling with the methodological issue of precise hypothesis-making. What states of human society can be posited as coherent hypotheses, possible to check or, fault of checking, to speculate about in an informed way?

The neural network I am experimenting with does precisely this: it produces strange, puzzling, complex states, defined by the quantitative variables I use. As I am working with that network, I have come to redefining the concept of artificial intelligence. A movie-based approach to AI is that it is fundamentally non-human. As I think about it sort of step by step, AI is human, as it has been developed on the grounds of human logic. It is human meaning, and therefore an expression of human neural wiring. It is just selective in its scope. Natural human intelligence has no other way of comprehending but comprehending IT ALL, i.e. the whole of perceived existence. Artificial intelligence is limited in scope: it works just with the data we assign it to work with. AI can really afford not to give a f**k about something otherwise important. AI is focused in the strict sense of the term.

During that recent stay in Marrakech, Morocco, I had been observing people around me and their ways of doing things. As it is my habit, I am patterning human behaviour. I am connecting the dots about the ways of using energy (for the moment I haven’t seen any making of energy, yet) and food. I am patterning the urban structure around me and the way people live in it.

Superbly kept gardens and buildings marked by a sense of instability. Human generosity combined with somehow erratic behaviour in the same humans. Of course, women are fully dressed, from head to toes, but surprisingly enough, men too. With close to 30 degrees Celsius outside, most local dudes are dressed like a Polish guy would dress by 10 degrees Celsius. They dress for the heat as I would dress for noticeable cold. Exquisitely fresh and firm fruit and vegetables are a surprise. After having visited Croatia, on the Southern coast of Europe, I would rather expect those tomatoes to be soft and somehow past due. Still, they are excellent. Loads of sugar in very nearly everything. Meat is scarce and tough. All that has been already described and explained by many a researcher, wannabe researchers included. I think about those things around me as about local instances of a complex logical structure: a collective intelligence able to experiment with itself. I wonder what other, hypothetical forms could this collective intelligence take, close to the actually observable reality, as well as some distance from it.

The idea I can see burgeoning in my mind is that I can understand better the actual reality around me if I use some analytical tool to represent slight hypothetical variations in said reality. Human behaviour first. What exactly makes me perceive Moroccans as erratic in their behaviour, and how can I represent it in the form of artificial intelligence? Subjectively perceived erraticism is a perceived dissonance between sequences. I expect a certain sequence to happen in other people’s behaviour. The sequence that really happens is different, and possibly more differentiated than what I expect to happen. When I perceive the behaviour of Moroccans as erratic, does it connect functionally with their ways of making and using food and energy?  

A behavioural sequence is marked by a certain order of actions, and a timing. In a given situation, humans can pick their behaviour from a total basket of Z = {a1, a2, …, az} possible actions. These, in turn, can combine into zPk = z!/(z – k)! = (1*2*…*z) / [1*2*…*(z – k)] possible permutations of k component actions. Each such permutation happens with a certain frequency. The way a human society works can be described as a set of frequencies in the happening of those zPk permutations. Well, that’s exactly what a neural network such as mine can do. It operates with values standardized between 0 and 1, and these can be very easily interpreted as frequencies of happening. I have a variable named ‘energy consumption per capita’. When I use it in the neural network, I routinely standardize each empirical value over the maximum of this variable in the entire empirical dataset. Still, standardization can convey a bit more of a mathematical twist and can be seen as the density of probability under the curve of a statistical distribution.

When I feel like giving such a twist, I can make my neural network stroll down different avenues of intelligence. I can assume that all kinds of things happen, and all those things are sort of densely packed one next to the other, and some of those things are sort of more expected than others, and thus I can standardize my variables under the curve of the normal distribution. Alternatively, I can see each empirical instance of each variable in my database as a rare event in an interval of time, and then I standardize under the curve of the Poisson distribution. A quick check with the database I am using right now brings an important observation: the same empirical data standardized with a Poisson distribution becomes much more disparate as compared to the same data standardized with the normal distribution. When I use Poisson, I lead my empirical network to divide sharply empirical data into important stuff on the one hand, and all the rest, not even worth to bother about, on the other hand.

I am giving an example. Here comes energy consumption per capita in Ecuador (1992) = 629,221 kg of oil equivalent (koe), Slovak Republic (2000) = 3 292,609 koe, and Portugal (2003) = 2 400,766 koe. These are three different states of human society, characterized by a certain level of energy consumption per person per year. They are different. I can choose between three different ways of making sense out of their disparity. I can see them quite simply as ordinals on a scale of magnitude, i.e. I can standardize them as fractions of the greatest energy consumption in the whole sample. When I do so, they become: Ecuador (1992) =  0,066733839, Slovak Republic (2000) =  0,349207223, and Portugal (2003) =  0,254620211.

In an alternative worldview, I can perceive those three different situations as neighbourhoods of an expected average energy consumption, in the presence of an average, standard deviation from that expected value. In other words, I assume that it is normal that countries differ in their energy consumption per capita, as well as it is normal that years of observation differ in that respect. I am thinking normal distribution, and then my three situations come as: Ecuador (1992) = 0,118803134, Slovak Republic (2000) = 0,556341893, and Portugal (2003) = 0,381628627.

I can adopt an even more convoluted approach. I can assume that energy consumption in each given country is the outcome of a unique, hardly reproducible process of local adjustment. Each country, with its energy consumption per capita, is a rare event. Seen from this angle, my three empirical states of energy consumed per capita could occur with the probability of the Poisson distribution, estimated with the whole sample of data. With this specific take on the thing, my three empirical values become: Ecuador (1992) = 0, Slovak Republic (2000) = 0,999999851, and Portugal (2003) = 9,4384E-31.

I come back to Morocco. I perceive some behaviours in Moroccans as erratic. I think I tend to think Poisson distribution. I expect some very tightly defined, rare event of behaviour, and when I see none around, I discard everything else as completely not fitting the bill. As I think about it, I guess most of our human intelligence is Poisson-based. We think ‘good vs bad’, ‘edible vs not food’, ‘friend vs foe’ etc.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Shut up and keep thinking

This time, something went wrong with the uploading of media on the Word Press server, and so I am publishing my video editorial on You Tube only. Click here to see and hear me saying a few introductory words.

I am trying to put some order in all the updates I have written for my research blog. Right now, I am identifying the main strands of my writing. Still, I want to explain I am doing that sorting of my past thought. I had the idea that, as the academic year is about to start, I could use those past updates as material for teaching. After all, I am writing this blog in sort of a quasi-didactic style, and a thoughtful compilation of such content can be of help for my students.

Right, so I am disentangling those strands of writing. As for the main ideas, I have been writing mostly about three things: a) the market of renewable energies b) monetary systems and cryptocurrencies, as well as the FinTech sector, c) political systems, law and institutions, and d) behavioural research. As I am reviewing what I wrote along these three lines, a few distinct patterns of writing emerge. The first are case studies, focused on interpreting the financial statements of selected companies. I went into four distinct avenues with that form of expression: a) companies operating in the market of renewable energies b) investment funds c) FinTech companies and, lately, d) film and TV companies. Then, as a different form of my writing, come quantitative studies, where I use large databases to run correlations and linear regressions. Finally, there are whole series of updates, which, fault of a better term, I call ‘concept development’. They give account of my personal work on business or scientific concepts, and look very much like daily reports of creative thinking.

Funny, by the way, how I write a lot about behavioural patterns and their importance in social structures, and I have fallen, myself, into recurrent behavioural patterns in my writing. Good, so what I am going to do is to use my readings and findings about behavioural patterns in order to figure out, and make the best possible use of my own behavioural patterns.

How can I use my past writing for educational purposes? I guess that my essential mission, as an educator, consists in communicating an experience in a teachable form, i.e. in a form possible to reproduce, and that reproduction of my experience should be somehow beneficial to other people. Logically, if I want to be an efficient educator in social sciences, what I should do now, is to distillate some sort of essence from my past experience, and formalize it in a teachable form.

My experience is that of looking for recurrent patterns in the most basic phenomena around me. As I am supposed to be clever as a social scientist, let’s settle for social phenomena. Those three distinct forms of my expression correspond to three distinct experiences: focus on one case, search for quantitative data on a s**tload of cases grouped together, and, finally, progressive coining up of complex ideas. This is what I can communicate, as a teacher.

Yet, another idea germinates in my mind. I am a being in time, and I thrust myself into the time to come, as Martin Heidegger would say (if he was alive). I define my social role very largely as that of a scientist and a teacher, and I wonder what am I thrusting, of myself as a scientist and a teacher, into this time that is about to advance towards me. I was tempted to answer grandly that it is my passion to discover that I project into current existence. Yet, precisely, I noticed it is grand talk, and I want to go to the core of things, like to the flesh of my being in time.

As I take off the pompous, out of that ‘passion to discover’ thing, something scientific emerges: a sequence. It all always starts when I see something interesting, and sort of vaguely useful. I intuitively want to know more about that interesting and possibly useful thing, and so I touch, I explore, I turn it under different angles, and yes, my initial intuition was right: it is interesting and useful. Years ago, even before having my PhD, I was strongly involved in preparing new material for management training. I was part of a team lead by a respectable professor from the University of Warsaw, and we were in scientific charge of training for the middle management of a few Polish banks. At the time, I started to read financial reports of companies listed in the stock market. I progressively figured out that large, publicly listed companies published periodical reports, which are like made of two completely different, semantic substances.

In those financial reports, there was the corporate small talk, about ‘exciting new opportunities’, ‘controlled growth’, ‘value for our shareholders’, which, honestly, I find interesting for the sake of its peculiar style, seemingly detached from real life. Yet, there is another semantic substance in those reports: the numbers. Numbers tell a different story. Even if the management of a company do their best to disguise some facts so as they look fancier, the numbers tell the truth. They tell the truth about product markets, about doubtful mergers and acquisitions, about the capacity of a business to accumulate capital etc.

As I started to work seriously on my PhD, and I started to sort out the broadly spoken microeconomic theories, including those of the new institutional school, I suddenly realised the connection between those theories and the sense that numbers make in those financial reports. I discovered that financial statements, i.e. the bare numbers, backed with some technical, explanatory notes, tend to show the true face of any business. They make of those Ockham’s razors, which cut out the b*****it and leave only the really meaningful.

Here comes the underlying, scientifically defined phenomenon. Financial markets have been ever present in human societies. In this respect, I could never recommend enough the monumental work by Fernand Braudel (Braudel 1992a[1]; Braudel 1992b[2]; Braudel 1995[3]). Financial markets have their little ways, and one of them is the charming veil of indefiniteness, put on the facts that laymen should-not-exactly-excite-themselves-about-for-their-own-good. Big business likes to dress into those fancy clothes, made of fancy and foggy language. Still, as soon as numbers have to be published, they start telling the true story. However elusive the management of a company would be in their verbal statements, the financials tell the truth. It is fascinating, how the introduction of precise measurements and accounts, into a realm of social life where plenty of b*****it floats, instantaneously makes things straight and clear.

I know what you can think now, ‘cause I used to think the same when I was (much) younger and listened to lectures at the university: here is that guy, who can be elegantly labelled as more than mature, and he gets excited about his own fascinations, financial reports in the occurrence. Still, I invite you to explore the thing. Financial markets are crucial for the current functioning of our civilisation. We need to shift towards renewable energies, we need to figure out how to make more food in sustainable ways, we need to remove plastic from the oceans, we need to go and see if Mars is an interesting place to hang around: we have a lot of challenges to face. Financial markets are crucial to that end, because they can greatly help in mobilising collective effort, and if we want them to work the way they should work, we need to assure that money goes where it is really needed. Bringing clarity and transparency to finance, over and over again, is really important. Being able to cut through the veil of corporate propaganda and go to the core of business is just as important. Careful reading of financial reports matters. It just matters.

So here is how one of my scientific fascinations formed. More or less at the same epoch, i.e. when I was working on my PhD, I started to work seriously with large datasets, mostly regarding innovation. Patents, patent applications, indicators of R&D effort: I started to go really quantitative about that stuff. I still remember that strange feeling, when synthetic measures of those large datasets started to make sense. I would run some correlations, just because you just need a lot of correlations in a PhD in economics, and vlam!: things would start to be meaningful. Those of you who work with Big Data probably know that feeling well, but I was experiencing it in the 1990ies, when digital technologies were like the grand-parents of the current ones, and even things like Panel Data Analysis, an analytical routine today, were seen as the impressionism of economic research.

I had progressively developed a strongly exploratory manner of working with quantitative data. A friend of mine, the same professor whom I used to work for in those management training projects, called it ‘the bulldog’ approach. He said: ‘Krzysztof, when you find some interesting data, you are like one of those anecdotal bulldogs: you bite into it so strongly, that sometimes you don’t even know how to let go, and you need someone who comes with a crowbar at forces your jaws open’.  Yes, indeed, this is the very same that I have just noticed as I am reviewing the past updates in that research blog of mine. What I do with data can be best described as sniffing, rummaging, playing with, digging and biting into – anything but serious scientific approach.

This is how two of my typical forms of scientific expression – case studies and quantitative studies – formed out of my fascination with the sense coming out of numbers. There is that third form of expression, which I have provisionally labelled ‘concept forming’, and which I developed the most recently, like over the last 18 months, precisely as I started to blog.

I am thinking about the best way to describe my experience in that respect. Here it comes. You have probably experienced those episodes of going outdoors, hiking or running, and then you or someone else starts moaning: ‘These backpack straps are just killing my shoulders! I am thirsty! I am exhausted! My knees are about to explode!’ etc. When I was a kid, I joined the boy scouts, and it was all about hiking. I used to be a fat kid, and that hiking was really killing me, but I liked company, too, and so I went for it. I used to moan exactly the way I have just portrayed. The team leader would just reply in the lines of ‘Just shut up and keep walking! You will adapt!’. Now, I know he was bloody right. There are times in life, when we take on something new and challenging, and then it seems just so hard to carry on, and the best way to deal with it is to shut up and carry on. You will adapt.

This is very much what I experienced as regards thinking and writing. When I started to keep this blog, I had a lot of ideas to express (hopefully, I still have), but I was really struggling with giving an intelligible form to those ideas. This is how I discovered the deep truth of that sentence, attributed to Pablo Picasso (although it could be anyone): ‘When a stroke of genius comes, it finds me at work’. As strange as it could seem, I experienced, and I am still experiencing, over and over again, the fundamental veracity of that principle. When I start working on an idea, the initial enthusiasm sooner or later yields to some moaning function in my brain: ‘F*ck, it is to hard! That thinking about one thing is killing me! And it is sooo complex! I will never sort it out! There is no point!’. Then, hopefully, another part of my brain barks: ‘Just shut up, and think, write, repeat! You will adapt’.

And you know what? It works. When, in the presence of a complex concept to figure out I just shut up (metaphorically, I mean I stop moaning), and keep thinking and writing, it takes shape. Step by step, I am sketching the contours of what’s simmering in the depths of my mind. The process is a bit painful, but rewarding.

Thus, here is the pattern of myself, which I am thrusting into the future, as it comes to science and teaching, and which, hopefully, I can teach. People around me, voluntarily or involuntarily, attract my attention to some sort of scientific and/or teaching work I should do. This is important, and I have just realized it: I take on goals and targets that other people somehow suggest. I need that social prod to wake me up. As I take on that work, I almost instinctively start flipping my Ockham’s razor between and around my intellectual fingers (some people do it with cards, phones, or even knives, you might have spotted it), and I causally give a shave here and there, and I slice observable reality into layers: there is the foam of common narrative about the thing, and there are those factual anchors I can attach to. Usually they are numbers, and, at a deeper philosophical level, they are proportions between things of reality.

As I observe those proportions, I progressively attach them to facts of life, and I start seeing patterns. Those patterns provide me something more or less interesting to say, and so I maintain my intellectual interaction with other people, and sooner or later they attract my attention to another interesting thing to focus on. And so it goes on. And one day, I die. And what will really matter will be made of things that I do but which outlive me. The ethically valuable things.

Good. I return to that metaphor I coined up a like 10 weeks ago, that of social sciences used as a social GPS system, i.e. serving to find one’s location in the social space, and then figure out a sensible route to follow. My personal experience, the one I have just given the account of, can serve to that purpose. My experience tells me that finding my place in the social space always involves interaction with other people. Understanding, and sort of embracing my social role, i.e. the way I can be really useful to other people, is the equivalent of finding my location on the social map. Another important thing I discovered as I deconstructed my experience: my social role is largely made of goals I pursue, not just of labels and rituals. It is sort of dynamic, it is very much my Heideggerian being-in-time, thrusting myself into my own immediate future.

I feel like getting it across really precisely: that thrusting-myself-into-the-future thing is not just pure phenomenology. It is hard science as well. We are defined by what we do. By ‘we’ I mean both individuals and whole societies. What we do involves something we are trying to achieve, i.e. some ethical values we seek to maximise, and to balance with other values. Understanding my social role means tracing the path I am moving along.

Now, whatever goal I am to achieve, according to my social role, around me I can see the foam of common narrative, and the factual anchors. The practical use of social sciences consists in finding those anchors, and figuring out the way to use them so as to thrive in the social role we have now, or change that role efficiently. Here comes the outcome from another piece of my personal experience: forming a valuable understanding requires just shutting up and thinking, and discovering things. Valuable discovery goes beyond and involves more than just amazement: it is intimately connected to purposeful work on discovering things.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Support this blog


[1] Braudel, F. (1992). Civilization and capitalism, 15th-18th Century, Vol. I: The structure of everyday life (Vol. 1). Univ of California Press.

[2] Braudel, F. (1992). Civilization and capitalism, 15th-18th century, vol. II: The wheels of commerce (Vol. 2). Univ of California Press.

[3] Braudel, F. (1995). A history of civilizations (p. 178). New York: Penguin Books

My own zone of proximal development


Let’s face it: I am freestyling intellectually. I have those syllabuses to prepare for the next academic year, and so I decided to let my brain crystallize a little bit, subconsciously, without being disturbed, around the business plan for the EneFin concept. Crystallization occurs subconsciously, and I can do plenty of other thinking in the meantime, and so I started doing that other thinking, and I am skating happily on the thin ice of fundamental questions concerning my mission as a scientist and a teacher. The ice those questions make is really thin, and if it cracks under my weight, I will dive into the cold depth of imperative necessity for answers.

You probably know that saying about economics, one of my fundamental disciplines, besides law, namely that economics are the art of making forecasts which do not hold. Nasty, but largely true. I want to devise a method of teaching social sciences, and possibly a contingent method of research, which can be directly useful to the individual, without said individual having to become the president of something big in order to find real utility in social sciences.

I am starting to form that central principle of my teaching and research: social sciences can be used and developed similarly to geography, i.e. they can be used to find one’s bearings in a complex environment, to trace a route towards valuable and attainable goals, and to plan for a realistic pace as for covering this route. Kind of a fundamental thought comes to me, from the realm of hermeneutic philosophy , which I am really fond of, and the thought goes as follows: whatever kind of story I am telling, at the bottom line I am telling the story of my own existence. Question (I mean, a real question, which I am asking right now, not some fake, rhetorical stuff): this view of social sciences, as a quasi-cartographic pathway towards orienting oneself in the social context, is it the story of my own existence? Answer: hell, yes. As I look back at my adult life, it is indeed a long story of wandering, and I perceive a substantial part of that wandering as having been pretty pointless. I could have done much of the same faster, simpler, and with more ethical value achieved on the way. Mind you, here, I am largely sailing the uncharted waters of ‘what could have happened if’. Anyway, what happened, stays happened.

OK, this is the what. Now, I want to phrase out the how. Teaching means essentially two things. Firstly, the student gets to know the skills he or she should master. In educational language it is described as the phase of conscious incompetence: the student gets to know what they don’t know and should develop a skill in. Secondly, teaching should lead them through at least a portion of the path from that conscious incompetence to conscious competence, i.e. to the phase of actually having developed those skills they became aware of in the phase of conscious incompetence.

Logically, I assume there is a set of skills that a person – especially a young one – needs to find and pursue their personal route through the expanse of social structure, once they have been dropped, by the helicopter of adolescence and early adulthood, in some remote spot of said structure. My mission is to use social sciences in order to show them the type of skill they’d better develop, and, possibly, to train them at those skills.

My strictly personal experience of learning is strongly derived from the practice of sport, and there is a piece of wisdom that anyone can have as their takeaway from athletic training: it is called ‘mesocycle’. A mesocycle of training is a period of about 3 months, which is the minimum time our body needs to develop a complex and durable response to training. In any type of learning, a mesocycle can be observed. It is the interval of time that our nervous system needs to get all the core processes, involved in a given pattern of behaviour being under development, well aligned and acceptably optimized.

My academic teaching is structured into semesters. In the curriculum of each particular subject, the realistic cycle of my interaction with students is like 4 months, which gives room to one full mesocycle of training, from conscious incompetence towards conscious competence, plus a little extra time for outlining that conscious incompetence. Logically, I need to structure my teaching into 25% of developing the awareness of skills to form, and 75% of training in those skills.

One of the first syllabuses I am supposed to prepare for the next academic year is ‘Introduction to Management’ for the undergraduate major of film and TV production. It is part of those students’ curriculum for the first year, when, essentially, every subject is an introduction to something. I follow the logic I have just outlined. First of all, what is the initial point of social start, in the world of film and TV production? Someone joins a project, most frequently: the production of a movie, an advertising campaign, the creation of a You Tube channel etc. The route to follow from there? The challenge consists in demonstrably proving one’s value in that project in order to be selected for further projects, rather than maxing out on the profits from this single venture. The next level consists in passing from projects to organisation, i.e. in joining or creating a relatively stable organisation, combining networks and hierarchies, which, in turn, can allow the sprouting of new projects.

Such a path of social movement involves skills centred around the following core episodes: a) quickly and efficiently finding one’s place in a project typical for the world of film and TV production b) starting and managing new projects c) finding one’s place in networks and hierarchies typical for film and TV production and d) possibly developing such an organisation.

Such defined, the introduction to management involves the ability to define social roles and social values, peculiar to the given project and/or organisation, as well as elementary skills in teamwork. As I think of it, the most essential competences in dealing with adversity, like getting one’s s**t together under pressure and forming a realistic plan B, could be helpful.

Good. Roles and values in a project of film and TV production. What comes to my mind in the first place, as I am thinking of it, is once again the teaching of Hans Georg Gadamer, the heavyweight champion of hermeneutic philosophy: historically, art at its best has been a fully commercial enterprise, based on business rules. Concepts such as ‘art for the sake of art’ or ‘pure art’ are relatively new – they emerged by the end of the 19th century – and they are the by-product of another emergence, that of the so-called leisure class, made of people rich enough to afford not to worry about their daily subsistence, and, in the same time, not seriously involved into killing someone in order to stay this way.

One of the first social patterns to teach my students regarding the values of film and TV production is something which, fault of a better word, I call ‘economic base’. It is a value, in this business, to have a relatively predictable stream of income, which is enough for keeping people working on creative projects. The understanding I want my students to form, thus, is precisely this economic base. How much do I need to earn, and how, if I want to keep working on that YT channel long enough for turning it into a business? What kind of job can I do whilst running such a project? How much capital do I need to raise in order to make 50 people work on a movie for 6 months? I think that studying the cases of real businesses in the film and TV production, and building simple business plans on the grounds of those cases can be a good, skill-forming practice.

Once this value identified, it is important to understand how people are most likely to behave whilst striving to achieve it. In other words, it is about the fundamentals of social competition and cooperation. A simple version of the theory of games seems the most workable, in terms of teaching tools.

The economic base for creative work makes one important value, still not the only one. Creation itself is another one. Managing creative teams is tricky. You have a bunch of strong personalities, and you want them to stay this way, and yet you want them to reach some kind of compromise. I think that simple role playing in class, paired with collective projects (i.e. projects carried out by teams of students) can be instructive.

I am summing up. I am a big fan of long-term tasks as educational tools. Preparing a simple business plan, specific to this precise industry (i.e. film and TV production), paired with training in teamwork, should do the job. Now, the easy path is just to tell students ‘Listen, guys! You have those projects to complete until the end of the semester. Just get on with it. We will be having those strange gatherings called “lectures”, but you don’t have to pay too much attention to it. Just have those projects done’. I have already experimented with this approach, and my conclusion is that it generally allows those clever ones to prove they are clever, but not much more. It is a pity to watch those less clever students struggling with a task they have to carry out over the length of one semester.

I want to devise come kind of path in my students’ zone of proximal development : a series of measured, feasible lessons, leading to tangible improvement. Each lesson covers 6 steps: i) define the project to carry out, as well as its goals and constraints, make a plan, make a team, and make them work on the thing ii) purposefully lead to a crisis iii) draw conclusions from the crisis iv) define the improvement needed v) carry out the improvement and vi) check the results.

As I see my usual schedule over one semester, I can arrange like 5 such sequences of 6 steps, thus 5 big lessons. Now, I am thinking about the kind of core task to carry out in each lesson, so as the task is both representative for film and TV production, and feasible in class. Pitching the concept of a movie is a must, and the concept of a YT platform seems to be a sensible idea as well. I have two types of business concepts, and I feel like repeating each of them twice. That gives 4 sequences of training, and leaves one more in reserve. That one more could be, for example, a content store, in the lines of the early Netflix.

Good. One thing to tick off. As I am having a look at it, the same pattern can be transferred, almost as it is, into the curriculum of Principles of Management, which I teach to the 1st year undergraduates in the major of International Relations. In this particular case, the same path is applicable, just the factual scope needs a bit of broadening. Each of those complex, sequenced lessons should be focused on a different type of business. Typical industrial, for one, something in the IT sector, for two, then something really scientific, like biotech, followed by typical service business, and finally something financial.

Now, I jump. It happens all the time in my mind. Something in those synaptic connexions of mine makes them bored with one topic, and willing to embrace the diversity of being. I am asking myself what I can possibly teach to my students, in terms of finding one’s way across the social jungle, on the grounds of the economic theory which either I fully embrace or I have developed by myself. Here come a few ideas.

However inventive and original you think you are, you are as inventive and original as quite a bunch of other people’. This one comes mostly from my reading of Joseph Schumpeter’s theory of creative destruction and neighbourhood of equilibrium. How can it be useful? If you want to do something important, like starting a business or a social action, going for a job connected to expatriation etc.? Well, look for patterns in what other people do. Someone is bound to have the kind of experience you can learn from.

This is deeper than some people could think. As I work with my students on the general issue of business planning, this particular approach proves really useful. There are many instances of complex business planning – the ‘what if?’ sequences, for example – when emulating some existing businesses is the only sensible approach.

The next one spells: ‘Recurrent bargaining leads to figuring out sensible, workable compromises that minimize waste and that nobody is quite satisfied with’. This principle refers to the theoretical concept of local Marshallian equilibrium, but it is also strongly connected to the theory of games. Frequently, you have the impression of being forced into some kind of local custom or ritual, like the average wage you can expect for a given job, or the average rent you have to pay for your apartment, or the habitual way of settling a dispute. It chafes, and it hurts what you perceive as your own originality, but people around you are strangely attached to this particular way of doing things. This is a local equilibrium.

If you want to understand a given local equilibrium, try and figure out the way this equilibrium is being achieved. Who? What? When? How? Under what conditions does the process work, and in which cases it doesn’t? In other words, if you want to figure out the way to influence and change those uncomfortable rituals around you, you need to find a way of making people bargain and get a compromise around a new ritual.

Comes my own research, now, and the fundamental principles of social path-finding I can phrase out of that research. I begin with stating that population matters, in the most numerical sense. The rate of demographic growth, together with the rate of migration, are probably the most powerful social changes we can imagine. Whatever those changing populations do, they adapt to the available supply of food and energy. At the individual level, people express that adaptation by maximizing their personal intake of energy, within socially accepted boundaries, by maintaining a certain portfolio of technologies. Social structures we live in act as regulators of the technological repertoire we have access to, and they change as this repertoire changes.

Practical implications? You want to experience creative social change, with a lot of new types of jobs emerging every year, and a lot of new products? You need a society with vivid demographic growth and a lot of migration going in and/or out. You want security, stability and predictability? You want people around you to be always calm and nice to each other? Then you need a society with slow or null demographic growth, not much of a migration, and plenty of food and energy to tap into. You want to have both, i.e. plenty of creative change, and people being always nice? Sorry, pal, not with this genotype. It just wouldn’t work with humans.

I am consistently delivering good, almost new science to my readers, and love doing it, and I am working on crowdfunding this activity of mine. As we talk business plans, I remind you that you can download, from the library of my blog, the business plan I prepared for my semi-scientific project Befund  (and you can access the French version as well). You can also get a free e-copy of my book ‘Capitalism and Political Power’ You can support my research by donating directly, any amount you consider appropriate, to my PayPal account. You can also consider going to my Patreon page and become my patron. If you decide so, I will be grateful for suggesting me two things that Patreon suggests me to suggest you. Firstly, what kind of reward would you expect in exchange of supporting me? Secondly, what kind of phases would you like to see in the development of my research, and of the corresponding educational tools?

Support this blog


The other cheek of business

My editorial

I am turning towards my educational project. I want to create a step-by-step teaching method, where I guide a student in their learning of social sciences, and this learning is by doing research in social sciences. I have a choice between imposing some predefined topics for research, or invite each student to propose their own. The latter seems certainly more exciting. As a teacher, I know what a brain storm is, and believe: a dozen dedicated and bright individuals, giving their ideas about what they think it is important to do research about, can completely uproot your (my own?) ideas as what it is important to do research about. Still, I can hardly imagine me, individually, handling efficiently all that bloody blissful diversity of ideas. Thus, the first option, namely imposing some predefined topics for research, seems just workable, whilst still being interesting. People can get creative about methods of research, after all, not just about topics for it. Most of the great scientific inventions was actually methodology, and what was really breakthrough about it consisted in the universal applicability of those newly invented methods.

Thus, what I want to put together is a step-by-step path of research, communicable and teachable, regarding my own topics for research. Whilst I still admit the possibility of student-generated topics coming my way, I will consider them as a luxurious delicacy I can indulge in under the condition I can cope with those main topics. Anyway, my research topics for 2018 are:

  1. Smart cities, their emergence, development, and the practical ways of actually doing business there
  2. Fintech, and mostly cryptocurrencies, and even more mostly those hybrid structures, where cryptocurrencies are combined with the “traditional” financial assets
  • Renewable energies
  1. Social and technological change as a manifestation of collective intelligence

Intuitively, I can wrap (I), (II), and (III) into a fancy parcel, decorated with (IV). The first three items actually coincide in time and space. The fourth one is that kind of decorative cherry you can put on a cake to make it look really scientific.

As I start doing research about anything, hypotheses come handy. If you investigate a criminal case, assuming that anyone could have done anything anyhow gives you certainly the biggest possible picture, but the picture is blurred. Contours fade and dance in front on your eyes, idiocies pop up, and it is really hard to stay reasonable. On the other hand, if you make some hypotheses as for who did what and how, your investigation gathers both speed and sense. This is what I strongly advocate for: make some hypotheses at the starting point of your research. Before I go further with hypothesising on my topics for research, a few preliminary remarks can be useful. First of all, we always hypothesise about anything we experience and think. Yes, I am claiming this very strongly: anything we think is a hypothesis or contains a hypothesis. How come? Well, we always generalise, i.e. we simplify and hope the simplification will hold. We very nearly always have less data than we actually need to make the judgments we make with absolute certainty. Actually, everything we pretend to claim with certainty is an approximation.

Thus, we hypothesise intuitively, all the time. Now, I summon the spirit of Milton Friedman from the abyss of pre-Facebook history, and he reminds us the four basic levels of hypothesising. Level one: regarding any given state of nature, we can formulate an indefinitely great number of hypotheses. In practice, there is infinitely many of them. Level two: just some of those infinitely many hypotheses are checkable at all, with the actual access to data I have. Level three: among all the checkable hypotheses, with the data at hand, there are just some, regarding which I can say with reasonable certainty whether they are true or false. Level four: it is much easier to falsify a hypothesis, i.e. to say under what conditions it does not hold at all, than to verify it, i.e. claiming under what conditions it is true. This comes from level one: each hypothesis has cousins, who sound almost exactly the same, but just almost, so under given conditions many mutually non-exclusive hypotheses can be true.

Now, some of you could legitimately ask ‘Good, so I need to start with formulating infinitely many hypotheses, then check which of them are checkable, then identify those allowing more or less rigorous scientific proof? Great. It means that at the very start I get entangled for eternity into checking how checkable is each of the infinitely many hypotheses I can think of. Not very promising as for results’. This is legit to say that, and this is the reason why, in science, we use that tool known as the Ockham’s razor. It serves to give a cognitive shave to badly kept realities. In its traditional form it consists in assuming that the most obvious answer is usually the correct one. Still, as you have a closer look at this precise phrasing, you can see a lot of hidden assumptions. It assumes you can distinguish the obvious from the dubious, which, in turn, means that you have already applied the razor beforehand. Bit of a loop. The practical way of wielding that razor is to assume that the most obvious thing is observable reality. I start with finding my bearings in reality. Recently, I gave an example of that: check ‘My individual square of land, 9 meters on 9’  . I look around and I assess what kind of phenomena, which, at this stage of research, I can intuitively connect to the general topic of my research, and which I can observe, measure, and communicate intelligibly about. These are my anchors in reality.

I look at those things, I measure them, and I do my best to communicate by observations to other people. This is when the Ockham’s razor is put to an ex post test: if the shave has been really neat, other people can easily understand what I am communicating. If I and a bunch of other looneys (oops! sorry, I wanted to say ‘scientists’) can agree on the current reading of the density of population, and not really on the reading of unemployment (‘those people could very well get a job! they are just lazy!), then the density of population is our Ockham’s razor, and unemployment not really (I love the ‘not really’ expression: it can cover any amount of error and bullshit). This is the right moment for distinguishing the obvious from the dubious, and to formulate my first hypotheses, and then I move backwards the long of the Milton Friedman’s four levels of hypothesising. The empirical application of the Ockham’s razor has allowed me to define what I can actually check in real life, and this, in turn, allows distinguishing between two big bags, each with hypotheses inside. One bag contains the verifiable hypotheses, the other one is for the speculative ones, i.e. those non-verifiable.

Anyway, I want my students to follow a path of research together with me. My first step is to organize the first step on this path, namely to find the fundamental, empirical bearings as for those four topics: smart cities, Fintech, renewable energies and collective intelligence. The topic of smart cities certainly can find its empirical anchors in the prices of real estate, and in the density of population, as well as in the local rate of demographic growth. When these three dance together – once again, you can check ‘My individual square of land, 9 meters on 9’ – the business of building smart cities suddenly gets some nice, healthy, reddish glow on its cheeks. Businesses have cheeks, didn’t you know? Well, to be quite precise, businesses have other cheeks. The other cheek, in a business, is what you don’t want to expose when you already get hit somewhere else. Yes, you could call it crown jewels as well, but other cheek sounds just more elegantly.

As for Fintech, the first and most obvious observation, from my point of view, is diversity. The development of Fintech calls into existence many different frameworks for financial transactions in times and places when and where, just recently, we had just one such framework. Observing Fintech means, in the first place, observing diversity in alternative financial frameworks – such as official currencies, cryptocurrencies, securities, corporations, payment platforms – in the given country or industry. In terms of formal analytical tools, diversity refers to a cross-sectional distribution and its general shape. I start with I taking a convenient denominator. The Gross Domestic Product seems a good one, yet you can choose something else, like the aggregate value of intellectual property embodied in selfies posted on Instagram. Once you have chosen your denominator, you measure the outstanding balances, and the current flows, in each of those alternative, financial frameworks, in the units of your denominator. You get things like market capitalization of Ethereum as % of GDP vs. the supply of US dollar as % of its national GDP etc.

I pass to renewable energies, now. When I think about what is the most obviously observable in renewable energies, it is a dual pattern of development. We can have renewable sources of energy supplanting fossil fuels: this is the case in the developed countries. On the other hand, there are places on Earth where electricity from renewable sources is the first source of electricity ever: those people simply didn’t have juice to power their freezer before that wind farm started up in the whereabouts. This is the pattern observable in the developing countries. In the zone of overlapping, between those two patterns, we have emerging markets: there is a bit of shifting from fossils to green, and there is another bit of renewables popping up where nothing had dared to pop up in the past. Those patterns are observable as, essentially, two metrics, which can possibly be combined: the final consumption of energy per capita, and the share of renewable sources in the final consumption of energy. Crude as they are, they allow observing a lot, especially when combined with other variables.

And so I come to collective intelligence. This is seemingly the hardest part. How can I say that any social entity is kind of smart? It is even hard to say in humans. I mean, virtually everybody claims they are smart, and I claim I’m smart, but when it comes to actual choices in real life, I sometimes feel so bloody stupid… Good, I am getting a grip. Anyway, intelligence for me is the capacity to figure out new, useful things on the grounds of memory about old things. There is one aspect of that figuring out, which is really intriguing my internal curious ape: the phenomenon called ultra-socialisation, or supersocialisation. I am inspired, as for this one, by the work of a group of historians: see ‘War, space, and the evolution of Old World complex societies’ (Turchin et al. 2013[1]). As a matter of fact, Jean Jacques Rousseau, in his “Social Contract”, was chasing very much the same rabbit. The general point is that any group of dumb assholes can get social on the level of immediate gains. This is how small, local societies emerge: I am better at running after woolly mammoths, you are better at making spears, which come handy when the mammoth stops running and starts arguing, and he is better at healing wounds. Together, we can gang up and each of us can experience immediate benefits of such socialisation. Still, what makes societies, according to Jean Jacques Rousseau, as well as according to Turchin et al., is the capacity to form institutions of large geographical scope, which require getting over the obsession of immediate gains and provide long-term, developmental a kick. What is observable, then, are precisely those institutions: law, state, money, universally enforceable contracts etc.

Institutions – and this is the really nourishing takeaway from that research by Turchin et al. (2013[2]) – are observable as a genetic code. I can decompose institutions into a finite number of observable characteristics, and each of them can be observable as switched on, or switched off. Complex institutional frameworks can be denoted as sequences of 1’s and 0’s, depending on whether the given characteristics is, respectively, present or absent. Somewhere between the Fintech, and collective intelligence, I have that metric, which I found really meaningful in my research: the share of aggregate depreciation in the GDP. This is the relative burden, imposed on the current economic activity, due to the phenomenon of technologies getting old and replaced by younger ones. When technologies get old, accountants accounts for that fact by depreciating them, i.e. by writing off the book a fraction of their initial value. All that writing off, done by accountants active in a given place and time, makes aggregate depreciation. When denominated in the units of current output (GDP), it tends to get into interesting correlations (the way variables can socialize) with other phenomena.

And so I come with my observables: density of population, demographic growth, prices of real estate, balances and flows of alternative financial platforms expressed as percentages of the GDP, final consumption of energy per capita, share of renewable energies in said final consumption, aggregate depreciation as % of the GDP, and the genetic code of institutions. What I can do with those observables, is to measure their levels, growth rates, cross-sectional distributions, and, at a more elaborate level, their correlations, cointegrations, and their memory. The latter can be observed, among other methods, as their Gaussian vector autoregression, as well as their geometric Brownian motion. This is the first big part of my educational product. This is what I want to teach my students: collecting that data, observing and analysing it, and finally to hypothesise on the grounds of basic observation.

[1] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

[2] Turchin P., Currie, T.E.,  Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389

The dashing drip of Ketonal, or my fundamental questions for the New Year

My editorial

Recently, I have been working on a few parallel projects, which all orient me towards business and management rather than economics. Of course, the three are related: you cannot expect to draft a decent business plan without understanding markets around you, just as you have meagre chances to understand markets if you ignore the way organisations work. Anyway, the business projects that I am currently working on orient me towards those most fundamental questions of social theory. It starts with a very practical issue, like ‘How can I know this precise project is going to be successful?’, and then, as I am articulating the answer, I feel as if I were ploughing through the entire field of social sciences. I have just finished preparing a business plan for an investment in real estate. At the first sight, it seemed simple as the blueprint of flail: you buy a residential building, you renovate it, in the course of renovation you consider rearranging the division of the building into apartments, then you sell the apartments one by one, and you rent the shops at the ground floor. A textbook of doing business, someone could say. Yet, as I started to estimate the profitability of the thing, questions popped up, initially very simple: will the Internal Rate of Return (IRR) be more suitable to estimate the return on investment, or should I rather consider the Net Present Value (NPV) of the project? Whatever I choose, what window of time should I take to gauge the profits: 5 years, 7 years, or maybe 12? Simple, almost stupid questions, and still they matter. For the mildly initiated: the IRR tends to be used mostly for low-risk, long-run projects, whilst the NPV looks better for short-termist, hit-and-run ventures.

By textbook rules, real estate is definitely the first kind: no technological life-cycle, not really much to handle in terms of manufacturing or logistics, sniper-like customer relations. The IRR should be better. Yet, as I did my homework about the local market of residential real estate, the sheer distribution of prices showed me a dilemma in the design of apartments in the course of renovation: you go into structure and design A, and you gain a modest return, not much above the average yield of sovereign bonds, but if you go into structure and design B, you can achieve way higher. The fork of disparity between the two scenarios was such that I decided to settle for NPV as my main metric of profitability. Absolutely against the textbook, but I intuitively guess this is a better measure. As I applied it, an interesting result appeared: whatever the design chosen, the project has any chance to bring a positive cash flow to the investor if and only if he leverages the thing with a mortgage, like 60% of the assets planned. You could ask: how can borrowing, with the obvious necessity to share your profits with the bank, at a fixed rate, enhance the net cash-flow? Well, yes, it can.

There is that other project I am working on, namely the practical implementation of CSR (Corporate Social Responsibility) in a real company. The company’s boss, who happens to be my friend, asked me to assess objectively whether it would pay him off to implement a code of CSR in his organisation. As a social scientist, I almost intuitively assume that what people think is one thing, what they do is another, what they think they do is still different story, and what they say they think they do is the ultimate smokescreen of human behaviour. For me, the current fashion for Corporate Social Responsibility is very much the Bullshido of management. Still, that friend of mine does not really care about theory. He is about to participate in a few serious tenders in his market, and in each of them he can score additional points if his company has a code of CSR effectively in place. Of course, as you can easily guess, the ‘effectively in place’ part is the trickiest one. There are pennies to collect, actually implementing CSR is the way to collect them, and the practical questions are: is it feasible at all, how to do it, and is it any good outside the formal conditions of those tenders? Just if you haven’t spotted it yet: it is about predicting complex human behaviour, in an organisation, and about assessing the economic outcomes of that behaviour. Tough s**t, actually.

There is still another venture I am progressively becoming involved into, in the Fintech. A group of business people is considering the issuance of cryptocurrencies in the stock-market fashion, i.e. they would like to issue cryptocurrencies in the way financial institutions issue various financial instruments: with an Initial Public Offering, and some respectable balance sheet to back up that IPO, with decent underwriting, with book building etc. The only difference in comparison to regular securities is that what will be issued are not securities as such but numerical codes corresponding to the units of cryptocurrency, and there has to be a technological platform to sustain the whole thing. The question those business people ask is very simple, once again: at the end of the day, after we account for the discount at issuance, and for the cost of maintaining the technology, what will be the return on investment? Intuitively, I am thinking about taking the history of Bitcoin (the best documented so far) as a case study, and try to build any kind of bridging to the world of classical financial instruments. Still, this is just an intuition. I am trying to find out the right scientific questions to ask, in order to answer the general business question.

I am nurturing my own business project on a small scale: an educational website in the field of social sciences. That scientific blog I keep, progressively more and more nested in the WordPress environment at https://discoversocialsciences.com , and still mirrored at http://researchsocialsci.blogspot.com makes some sort of core, which I want to build that project around. The blog serves me to make my hand as for the style, and the general drill of producing content. I am applying that principle, anecdotally attributed to Pablo Picasso, namely that when inspiration comes, it is likely to find me at work. For the moment, I have very loose ideas as for this project. I haven’t even formalized my paid content yet. I suppose I will start with some sort of crowdfunding, Patreon or other beast, and then move towards something more organized.

The questions I have in my mind, regarding this project, sum up to a loop between ‘who?’, ‘what?’, and ‘how?’. How do I want to interact with my customers? What exactly will be my product? Who will be my customer? Initially, I am thinking about providing additional, educational resources for students. By ‘additional’ I mean that what I will be offering will be a step further beyond textbooks, rather than the typical textbook content. Probably it is because I have always been bored to death with textbooks. I have always preferred to discover things by myself. Yes, it involves getting some dirt on my hands, but it is a lot more fun, and this is the kind of fun I want to provide my customers with: the fun with doing research in social sciences, where doing research is training, in the same time. Thus, the kind of interaction I want to have with my customers consists in involving them in doing research, where I will mentor them. Here, I come to the ‘what?’, and, honestly, I still don’t know what the ‘what?’ is going to look like, in details. I know, for sure, that I want to go for real stuff, i.e. the actual, important, social issues, not the distilled type of problems you can find in textbooks. If I want to learn how to hunt grizzly bears, I have to learn with someone who actually hunts them, not with a rat catcher.

Finally, there is that last one, and in the same time the biggest. I have been working, all the 2017-long, on technological change, and, more particularly, on renewable energies. I have been progressively becoming aware that there is an actual, huge business building up around the topic: smart cities. In Europe, the idea is really gathering speed. Recently, I did some research about the ‘Confluence’ project in Lyon, France. This is another smart city – or rather smart district in a city – being built in parallel with similar ones popping up in Vienna and Munich. As I had a provisional glimpse of the thing, the speed and magnitude of business and investment projects going on there is just appalling. I think I am going to devote much of my time until this summer to preparing and promoting a business plan for entering the business of smart cities. Here, once again, I am at the stage of figuring out what are the right questions to ask.

Those last months, I tend to think that fundamental questions are the best ones to ask. Additionally, I had that funny kind of New Year’s Night, at an emergency ward, in my district hospital, under an intravenous drip, with symptoms of acute food poisoning. The main suspect is a piece of herring with unclear curriculum. This is the thing with us, Polish people, and the herring: this is really tough love. Anyway, that night spent with a cannula stuck in my left hand’s veins, it made me really consider life under a different angle. The angle was really different. I asked my friends around: no one was seeing their drinks from underneath, only I did. Ketanol was the dashiest one. Such situations tend to push you back to fundamental questions, like: what is the point of being here and now? Now, you could legitimately wonder about the connection between the doings of a vicious herring, and social sciences. Well, I am wondering what are the most fundamental questions of social sciences, when said sciences are being applied to real-life, practical situations. This is the kind of questions I would like my students to know how to ask.

I ask ‘What kinds of things are happening around me and how are they happening?’. In each of the situations I have just described, I am kind of tuning my brain on some specific distinctions: prices of real estate and their distribution over time and across space, cash flows, patterns in human behaviour regarding ethical norms, creation and evolution of monetary systems, ways of entering the general field of smart cities, and, finally, the number of drops that an intravenous drip produces per minute. When I am wrapping my mind around something new, the wrapping means finding the right categories to distinguish in the given situation. How can I know my distinctions are the right ones? There is only one way to know it, baby: by trial and error. Make some distinctions, apply them in the situation at hand, figure out whether they make you advance any further, draw a bottom line, repeat the whole sequence until you reach a subjective state of intellectual satisfaction. There is a useful scientific method of checking if my distinctions are accurate for the piece of real life I am currently dealing with. Just check if you can deconstruct, out of that piece of real life, meaningful sequences of things you have distinguished. If you can build a sequence of events, and that sequence is kind of relevant for the situation, the way you describe those events is probably accurate. Not necessarily accurate at 100% – it never is – but accurate just enough to set a path for further action. The scientific bottom line of this question is to transform experience into data, which, in turn, can be subject to mathematical modelling. Yes, I like maths. They are logic expressed without bullshit.

Then, in a second step, I ask ‘How is the way of happening in the thing A connected to the way of happening in the thing B?’. In terms of formal scientific tools, there is a cartload of statistics to apply here, and this is the right track. Still, my own path of thinking, and the path of thinking I would like my students to follow, is even more fundamental. When I study the connection between happenings, I want to know, for example, whether the thing A kind of pulls the thing B out of inexistence, or does it rather push the thing B out of the world? If reality had a DNA, and if that DNA reproduced itself by recombination (which requires female realities and male realities, and this is really interesting), my two things, A and B, would be like two genes, which can be switched on or off. In one possible scenario, when the gene A gets activated, the gene B follows, and it is like 1 -> 1. In another scenario, the activation of A automatically switches B off, or 1 -> 0. On the long run, the kind of gene B that gets deactivated frequently, gets kicked out of reality, into a dormant state, as there is no respectable, female reality who would like to give birth to baby realities with B in their genome. This is how some phenomena, like burning the alleged witches at the stake, get kicked into a blissfully dormant state, at least until some idiot has the idea of waking them up. Easier than you think, to wake those monsters up, by the way.

If, on the other hand, phenomenon B gets activated in noticeably many cases, it kind of gets knighted into the reality, and it acquires the privilege of activating or deactivating other genes in reality. This is how small, functional interactions between measurable occurrences can lead to structural changes in the social system (see, for example: Krugman 1991[1];  Krugman 1998[2]). That approach to functional connection between phenomena is slightly different from what you can learn in you class of statistics, yet there is a lot on common between the two ways of apprehending coincidental change. Statistically, we can talk about correlation between phenomena, when they swing away from their statistically expected states by a similar distance in the same time, or about their mutual cointegration, if they swing at the same rhythm, i.e. with the same length, amplitude, and frequency in their respective cycles. What I am the most interested in, in my research, are those specific cases of correlation and/or cointegration, when the functional connection at hand leads to one of the connected phenomena suddenly shifting into a qualitatively different state (into a different equilibrium, as we could say in economics).

Now, I ask my third BIG question: ‘So what? Does it matter at all? How does it matter?’. Things matter to me, when they change I do things. Things matter to the society, when they change the way people do things. Thus, in all the multitude of coincidentally changing things, in their correlations and cointegrations, in the way they affect the genetic code of reality, there are changes that involve our behaviour. These ones matter. If the given piece of reality switches its genes into the ON position, what will I do? How can some patterns of behaviour become dormant? How can they be revived (and, bloody hell, is it a good idea to revive them)? What should I do? What will other people do? What do I expect them to do? This is when social science becomes the real fun. I look for that tiny little correlation, which makes my behaviour wise or aberrant in the given situation. I look for those little signals, predictors of wonderful progress, or, in more complex a situation, of deplorable recess.

[1] Krugman, P., 1991, Increasing Returns and Economic Geography, The Journal of Political Economy, Volume 99, Issue 3 (Jun. 1991), pp. 483 – 499

[2] Krugman, P., 1998, What’s New About The New Economic Geography?, Oxford Review of Economic Policy, vol. 14, no. 2, pp. 7 – 17