Combinatorial meaning and the cactus

My editorial on You Tube

I am back into blogging, after over two months of pausing. This winter semester I am going, probably, for record workload in terms of classes: 630 hours in total. October and November look like an immersion time, when I had to get into gear for that amount of teaching. I noticed one thing that I haven’t exactly been aware of, so far, or maybe not as distinctly as I am now: when I teach, I love freestyling about the topic at hand. Whatever hand of nice slides I prepare for a given class, you can bet on me going off the beaten tracks and into the wilderness of intellectual quest, like by the mid-class. I mean, I have nothing against Power Point, but at some point it becomes just so limiting… I remember that conference, one year ago, when the projector went dead during my panel (i.e. during the panel when I was supposed to present my research). I remember that mixed, and shared feeling of relief and enjoyment in people present in the room: ‘Good. Finally, no slides. We can like really talk science’.

See? Once again, I am going off track, and that in just one paragraph of writing. You can see what I mean when I refer to me going off track in class. Anyway, I discovered one more thing about myself: freestyling and sailing uncharted intellectual waters has a cost, and this is a very clear and tangible biological cost. After a full day of teaching this way I feel as if my brain was telling me: ‘Look, bro. I know you would like to write a little, but sorry: no way. Them synapses are just tired. You need to give me a break’.

There is a third thing I have discovered about myself: that intense experience of teaching makes me think a lot. I cannot exactly put all this in writing on the spot, fault of fresh neurotransmitter available, still all that thinking tends to crystallize over time and with some patience I can access it later. Later means now, as it seems. I feel that I have crystallized enough and I can start to pull it out into the daylight. The « it » consists, mostly, in a continuous reflection on collective intelligence. How are we (possibly) smart together?

As I have been thinking about it, three events combined and triggered in me a string of more specific questions. I watched another podcast featuring Jordan Peterson, whom I am a big fan of, and who raised the topic of the neurobiological context of meaning. How our brain makes meaning, and how does it find meaning in sensory experience? On the other hand, I have just finished writing the manuscript of an article on the energy-efficiency of national economies, which I have submitted to the ‘Energy Economics’ journal, and which, almost inevitably, made me work with numbers and statistics. As I had been doing that empirical research, I found out something surprising: the most meaningful econometric results came to the surface when I transformed my original data into local coefficients of an exponential progression that hypothetically started in 1989. Long story short, these coefficients are essentially growth rates, which behave in a peculiar way, due to their arithmetical structure: they decrease very quickly over time, whatever is the source, raw empirical observation, as if they were representing weakening shock waves sent by an explosion in 1989.

Different types of transformed data, the same data, in that research of mine, produced different statistical meanings. I am still coining up real understanding of what it exactly means, by the way. As I was putting that together with Jordan Peterson’s thoughts on meaning as a biological process, I asked myself: what is the exact meaning of the fact that we, as scientific community, assign meaning to statistics? How is it connected with collective intelligence?

I think I need to start more or less where Jordan Peterson moves, and ask ‘What is meaning?’. No, not quite. The ontological type, I mean the ‘What?’ type of question, is a mean beast. Something like a hydra: you cut the head, namely you explain the thing, you think that Bob’s your uncle, and a new head pops up, like out of nowhere, and it bites you, where you know. The ‘How?’ question is a bit more amenable. This one is like one of those husky dogs. Yes, it is semi wild, and yes, it can bite you, but once you tame it, and teach it to pull that sleigh, it will just pull. So I ask ‘How is meaning?’. How does meaning occur?

There is a particular type of being smart together, which I have been specifically interested in, for like the last two months. It is the game-based way of being collectively intelligent. The theory of games is a well-established basis for studying human behaviour, including that of whole social structures. As I was thinking about it, there is a deep reason for that. Social interactions are, well, interactions. It means that I do something and you do something, and those two somethings are supposed to make sense together. They really do at one condition: my something needs to be somehow conditioned by how your something unfolds, and vice versa. When I do something, I come to a point when it becomes important for me to see your reaction to what I do, and only when I will have seen it, I will further develop on my action.

Hence, I can study collective action (and interaction) as a sequence of moves in a game. I make my move, and I stop moving, for a moment, in order to see your move. You make yours, and it triggers a new move in me, and so the story goes further on in time. We can experience it very vividly in negotiations. With any experience in having serious talks with other people, thus when we negotiate something, we know that it is pretty counter-efficient to keep pushing our point in an unbroken stream of speech. It is much more functional to pace our strategy into separate strings of argumentation, and between them, we wait for what the other person says. I have already given a first theoretical go at the thing in « Couldn’t they have predicted that? ».

This type of social interaction, when we pace our actions into game-like moves, is a way of being smart together. We can come up with new solutions, or with the understanding of new problems – or a new understanding of old problems, as a matter of fact – and we can do it starting from positions of imperfect agreement and imperfect coordination. We try to make (apparently) divergent points, or we pursue (apparently) divergent goals, and still, if we accept to wait for each other’s reaction, we can coordinate and/or agree about those divergences, so as to actually figure out, and do, some useful s**t together.

What connection with the results of my quantitative research? Let’s imagine that we play a social game, and each of us makes their move, and then they wait for the moves of other players. The state of the game at any given moment can be represented as the outcome of past moves. The state of reality is like a brick wall, made of bricks laid one by one, and the state of that brick wall is the outcome of the past laying of bricks.  In the general theory of science, it is called hysteresis. There is a mathematical function, reputed to represent that thing quite nicely: the exponential progression. On a timeline, I define equal intervals. To each period of time, I assign a value y(t) = et*a, where ‘t’ is the ordinal of the time period, ‘e’ is a mathematical constant, the base of natural logarithm, e = 2,7188, and ‘a’ is what we call the exponential coefficient.

There is something else to that y = et*a story. If we think like in terms of a broader picture, and assume that time is essentially what we imagine it is, the ‘t’ part can be replaced by any number we imagine. Then, the Euler’s formula steps in: ei*x = cos x + i*sin x. If you paid attention in math classes, at high school, you might remember that sine and cosine, the two trigonometric functions, have a peculiar property. As they refer to angles, at the end of the day they refer to a full circle of 360°. It means they go in a circle, thus in a cycle, only they go in perfectly negative a correlation: when the sine goes one unit one way, the cosine goes one unit exactly the other way round etc. We can think about each occurrence we experience – the ‘x’ –  as a nexus of two, mutually opposing cycles, and they can be represented as, respectively, the sine, and the cosine of that occurrence ‘x’. When I grow in height (well, when I used to), my current height can be represented as the nexus of natural growth (sine), and natural depletion with age (cosine), that sort of things.

Now, let’s suppose that we, as a society, play two different games about energy. One game makes us more energy efficient, ‘cause we know we should (see Settlement by energy – can renewable energies sustain our civilisation?). The other game makes us max out on our intake of energy from the environment (see Technological Change as Intelligent, Energy-Maximizing Adaptation). At any given point in time, the incremental change in our energy efficiency is the local equilibrium between those two games. Thus, if I take the natural logarithm of our energy efficiency at a given point in space-time, thus the coefficient of GDP per kg of oil equivalent in energy consumed, that natural logarithm is the outcome of those two games, or, from a slightly different point of view, it descends from the number of consecutive moves made (the ordinal of time period we are currently in), and from a local coefficient – the equivalent of ‘i’ in the Euler’s formula – which represents the pace of building up the outcomes of past moves in the game.

I go back to that ‘meaning’ thing. The consecutive steps ‘t’ in an exponential progression y(t) = et*a progression correspond to successive rounds of moves in the games we play. There is a core structure to observe: the length of what I call ‘one move’, and which means a sequence of actions that each person involved in the interaction carries out without pausing and waiting for the reaction observable in other people in the game. When I say ‘length’, it involves a unit of measurement, and here, I am quite open. It can be a length of time, or the number of distinct actions in my sequence. The length of one move in the game determines the pace of the game, and this, in turn, sets the timeframe for the whole game to produce useful results: solutions, understandings, coordinated action etc.

Now, where the hell is any place for ‘meaning’ in all that game stuff? My view is the following: in social games, we sequence our actions into consecutive moves, with some waiting-for-reaction time in between, because we ascribe meaning to those sub-sequences that we define as ‘one move’. The way we process meaning matters for the way we play social games.

I am a scientist (well, I hope), and for me, meaning occurs very largely as I read what other people have figured out. So I stroll down the discursive avenue named ‘neurobiology of meaning’, welcomingly lit by with the lampposts of Science Direct. I am calling by an article by Lee M. Pierson, and Monroe Trout, entitled ‘What is consciousness for?[1]. The authors formulate a general hypothesis, unfortunately not supported (yet?) with direct empirical check, that consciousness had been occurring, back in the day, I mean like really back in the day, as cognitive support of volitional movement, and evolved, since then, into more elaborate applications. Volitional movement is non-automatic, i.e. decisions have to be made in order for the movement to have any point. It requires quick assemblage of data on the current situation, and consciousness, i.e. the awareness of many abstract categories in the same time, could the solution.

According to that approach, meaning occurs as a process of classification in the neurologically stored data that we need to use virtually simultaneously in order to do something as fundamental as reaching for another can of beer. Classification of data means grouping into sets. You have a random collection of data from sensory experience, like a homogenous cloud of information. You know, the kind you experience after a particularly eventful party. Some stronger experiences stick out: the touch of cold water on your naked skin, someone’s phone number written on your forearm with a lipstick etc. A question emerges: should you call this number? It might be your new girlfriend (i.e. the girlfriend whom you don’t consciously remember as your new one but whom you’d better to if you don’t want your car splashed with acid), or it might be a drug dealer whom you’d better not call back.  You need to group the remaining data in functional sets so as to take the right action.

So you group, and the challenge is to make the right grouping. You need to collect the not-quite-clear-in-their-meaning pieces of information (Whose lipstick had that phone number been written with? Can I associate a face with the lipstick? For sure, the right face?). One grouping of data can lead you to a happy life, another one can lead you into deep s**t. It could be handy to sort of quickly test many alternative groupings as for their elementary coherence, i.e. hold all that data in front of you, for a moment, and contemplate flexibly many possible connections. Volitional movement is very much about that. You want to run? Good. It would be nice not to run into something that could hurt you, so it would be good to cover a set of sensory data, combining something present (what we see), with something we remember from the past (that thing on the 2 o’clock azimuth stings like hell), and sort of quickly turn and return all that information so as to steer clear from that cactus, as we run.

Thus, as I follow the path set by Pierson and Trout, meaning occurs as the grouping of data in functional categories, and it occurs when we need to do it quickly and sort of under pressure of getting into trouble. I am going onto the level of collective intelligence in human social structures. In those structures, meaning, i.e. the emergence of meaningful distinctions communicable between human beings and possible to formalize in language, would occur as said structures need to figure something out quickly and under uncertainty, and meaning would allow putting together the types of information that are normally compartmentalized and fragmented.

From that perspective, one meaningful move in a game encompasses small pieces of action which we intuitively guess we should immediately group together. Meaningful moves in social games are sequences of actions, which we feel like putting immediately back to back, without pausing and letting the other player do their thing. There is some sort of pressing immediacy in that grouping. We guess we just need to carry out those actions smoothly one after the other, in an unbroken sequence. Wedging an interval of waiting time in between those actions could put our whole strategy at peril, or we just think so.

When I apply this logic to energy efficiency, I think about business strategies regarding innovation in products and technologies. When we launch a new product, or implement a new technology, there is something like fixed patterns to follow. When you start beta testing a new mobile app, for example, you don’t stop in the middle of testing. You carry out the tests up to their planned schedule. When you start launching a new product (reminder: more products made on the same energy base mean greater energy efficiency), you keep launching until you reach some sort of conclusive outcome, like unequivocal success or failure. Social games we play around energy efficiency could very well be paced by this sort of business-strategy-based moves.

I pick up another article, that by Friedemann Pulvermüller (2013[2]). The main thing I see right from the beginning is that apparently, neurology is progressively dropping the idea of one, clearly localised area in our brain, in charge of semantics, i.e. of associating abstract signs with sensory data. What we are discovering is that semantics engage many areas in our brain into mutual connection. You can find developments on that issue in: Patterson et al. 2007[3], Bookheimer 2002[4], Price 2000[5], and Binder & Desai 2011[6]. As we use words, thus as we pronounce, hear, write or read them, that linguistic process directly engages (i.e. is directly correlated with the activation of) sensory and motor areas of our brain. That engagement follows multiple, yet recurrent patterns. In other words, instead of having one mechanism in charge of meaning, we are handling different ones.

After reviewing a large bundle of research, Pulvermüller proposes four different patterns: referential, combinatorial, emotional-affective, and abstract semantics. Each time, the semantic pattern consists in one particular area of the brain acting as a boss who wants to be debriefed about something from many sources, and starts pulling together many synaptic strings connected to many places in the brain. Five different pieces of cortex come recurrently as those boss-hubs, hungry for differentiated data, as we process words. They are: inferior frontal cortex (iFC, so far most commonly associated with the linguistic function), superior temporal cortex (sTC), inferior parietal cortex (iPC), inferior and middle temporal cortex (m/iTC), and finally the anterior temporal cortex (aTC). The inferior frontal cortex (iFC) seems to engage in the processing of words related to action (walk, do etc.). The superior temporal cortex (sTC) looks like seriously involved when words related to sounds are being used. The inferior parietal cortex (iPC) activates as words connect to space, and spatio-temporal constructs. The inferior and middle temporal cortex (m/iTC) lights up when we process words connected to animals, tools, persons, colours, shapes, and emotions. That activation is category specific, i.e. inside m/iTC, different Christmas trees start blinking as different categories among those are being named and referred to semantically. The anterior temporal cortex (aTC), interestingly, has not been associated yet with any specific type of semantic connections, and still, when it is damaged, semantic processing in our brain is generally impaired.

All those areas of the brain have other functions, besides that semantic one, and generally speaking, the kind of meaning they process is correlated with the kind of other things they do. The interesting insight, at this point, is the polyvalence of cortical areas that we call ‘temporal’, thus involved in the perception of time. Physicists insist very strongly that time is largely a semantic construct of ours, i.e. time is what we think there is rather than what really is, out there. In physics, what exists is rather sequential a structure of reality (things happen in an order) than what we call time. That review of literature by Pulvermüller indirectly indicates that time is a piece of meaning that we attach to sounds, colours, emotions, animal and people. Sounds come as logical: they are sequences of acoustic waves. On the other hand, how is our perception of colours, or people, connected to our concept of time? This is a good one to ask, and a tough one to answer. What I would look for is recurrence. We identify persons as distinct ones as we interact with them recurrently. Autistic people have frequently that problem: when you put on a different jacket, they have hard time accepting you are the same person. Identification of animals or emotions could follow the same logic.

The article discusses another interesting issue: the more abstract the meaning is, the more different regions of the brain it engages. The really abstract ones, like ‘beauty’ or ‘freedom’, are super Christmas-trees: they provoke involvement all over the place. When we do abstraction, in our mind, for example when writing poetry (OK, just good poetry), we engage a substantial part of our brain. This is why we can be lost in our thoughts: those thoughts, when really abstract, are really energy-consuming, and they might require to shut down some other functions.

My personal understanding of the research reviewed by Pulvermüller is that at the neurological level, we process three essential types of meaning. One consists in finding our bearings in reality, thus in identifying things and people around, and in assigning emotions to them. It is something like a mapping function. Then, we need to do things, i.e. to take action, and that seems to be a different semantic function. Finally, we abstract, thus we connect distant parcels of data into something that has no direct counterpart neither in the mapped reality, nor in our actions.

I have an indirect insight, too. We have a neural wiring, right? We generate meaning with that wiring, right? Now, how is adaptation occurring, in that scheme, over time? Do we just adapt the meaning we make to the neural hardware we have, or is there a reciprocal kick, I mean from meaning to wiring? So far, neurological research has demonstrated that physical alteration in specific regions of the brain impacts semantic functions. Can it work the other way round, i.e. can recurrent change in semantics being processed alter the hardware we have between our ears? For example, as we process a lot of abstract concepts, like ‘taxes’ or ‘interest rate’, can our brains adapt from generation to generation, so as to minimize the gradient of energy expenditure as we shift between levels of abstraction? If we could, we would become more intelligent, i.e. able to handle larger and more differentiated sets of data in a shorter time.

How does all of this translate into collective intelligence? Firstly, there seem to be layers of such intelligence. We can be collectively smart sort of locally – and then we handle those more basic things, like group identity or networks of exchange – and then we can (possibly) become collectively smarter at more combinatorial a level, handling more abstract issues, like multilateral peace treaties or climate change. Moreover, the gradient of energy consumed, between the collective understanding of simple and basic things, on the one hand, and the overarching abstract issues, is a good predictor regarding the capacity of the given society to survive and thrive.

Once again, I am trying to associate this research in neurophysiology with my game-theoretical approach to energy markets. First of all, I recall the three theories of games, co-awarded the economic Nobel prize in 1994, namely those by: John Nash, John (Yan) Harsanyi, and Reinhard Selten. I start with the latter. Reinhard Selten claimed, and seems to have proven, that social games have a memory, and the presence of such memory is needed in order for us to be able to learn collectively through social games. You know those situations of tough talks, when the other person (or you) keeps bringing forth the same argumentation over and over again? This is an example of game without much memory, i.e. without much learning. In such a game we repeat the same move, like fish banging its head against the glass wall of an aquarium. Playing without memory is possible in just some games, e.g. tennis, or poker, if the opponent is not too tough. In other games, like chess, repeating the same move is not really possible. Such games force learning upon us.

Active use of memory requires combinatorial meaning. We need to know what is meaningful, in order to remember it as meaningful, and thus to consider it as valuable data for learning. The more combinatorial meaning is, inside a supposedly intelligent structure, such as our brain, the more energy-consuming that meaning is. Games played with memory and active learning could be more energy-consuming for our collective intelligence than games played without. Maybe that whole thing of electronics and digital technologies, so hungry of energy, is a way that we, collective human intelligence, put in place in order to learn more efficiently through our social games?

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?

[1] Pierson, L. M., & Trout, M. (2017). What is consciousness for?. New Ideas in Psychology, 47, 62-71.

[2] Pulvermüller, F. (2013). How neurons make meaning: brain mechanisms for embodied and abstract-symbolic semantics. Trends in cognitive sciences, 17(9), 458-470.

[3] Patterson, K. et al. (2007) Where do you know what you know? The representation of semantic knowledge in the human brain. Nat. Rev. Neurosci. 8, 976–987

[4] Bookheimer,S.(2002) FunctionalMRIoflanguage:newapproachesto understanding the cortical organization of semantic processing. Annu. Rev. Neurosci. 25, 151–188

[5] Price, C.J. (2000) The anatomy of language: contributions from functional neuroimaging. J. Anat. 197, 335–359

[6] Binder, J.R. and Desai, R.H. (2011) The neurobiology of semantic memory. Trends Cogn. Sci. 15, 527–536

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

Inside a vector

My editorial

I am returning to the issue of collective memory, and to collective memory recognizable in numbers, i.e. in the time series of variables pertinent to the state of a society (see ‘Back to blogging, trying to define what I remember’ ). And so I take my general formula xi(t) = f1[xi(t – b)] + f2[xi(t – STOCH)] + Res[xi(t)], which means that any given moment ‘t’, current information xi(t) about the social system consists in some sort of constant-loop remembering xi(t – b), with ‘b’ standing for that fixed temporal window (in an average human it seems to be like 3 weeks), coming along with more irregular, stochastic a pick of past information, like [xi(t – STOCH)], and on the top of all that is the residual Res[xi(t)] of current information, hardly attributable to any remembering of the past, and, fault of a better expression, it can be grasped as the strictly spoken present.

I am reviewing the available mathematical tools for modelling such a process with hypothetical memory. I start with something that I could label ‘perfect remembering and only remembering’, or the Gaussian process. It represents a system, which essentially does not learn much, and is predictable on the grounds of its mean and covariance. When I do linear regression, which you could have seen a lot in my writings on this blog, I more or less consciously follow the logic of a Gaussian process. That logic is simple: if I can draw a straight line that matches the empirical distribution of my real-life variable, and if I prolong this line into the future, it will make a good predictor of the future values in my variable. It doesn’t even have to be one variable. I can deal with a vector made of many variables as well. As a matter of fact, the mathematical notation used in the Gaussian process basically refers to vectors of variables. It might be the right moment for explaining what the hell is a vector in quantitative analysis. Well, I am a vector, and you, my reader, you are a vector, and my cousin is a vector as well, and his dog is a vector. My phone is a vector, and any other phone the same. Anything we encounter in life is complex. There are no simple phenomena, even in the middle of summer holidays, on some remote tropical beach. Anything we can think of has many characteristics. To the extent that those characteristics can be represented as numbers, the state of nature at a given moment is a set of numbers. These numbers can be considered as coordinates in many criss-crossing manifolds. I have an age in the manifold of ages, a height in the manifold of heights, and numerically expressible a hair colour in the manifold of hair colours etc. Many coordinates make a vector, stands to reason.

And so I have that vector X* made of n variables, stretched over m periods of time. Each point in that vector is characterized by its appurtenance to the precise variable i out of those n variables, as well as its observability at a given moment j out of the total duration. It can look more or less like that: X*= {Xt1,1, Xt2,2, …, Xtj,i, Xtm,n} , or, in a more straightforward form of a matrix, it is something like:

                                   Moments in time (or any indexed value you want)           

                                                    t1                     t2                     …                     tj                      tm

| Variables        I          Xt1,I               Xt2,I                   …                   Xtj,I                Xtm,I

X* |                          II        Xt1,II              Xt2,II                    …                   Xtj,II             Xtm,II

|                          …

|                         n         Xt1,n              Xt2,n                    …                   Xtj,n               Xtm,n

 

Right, I got myself lost a bit in that vector thing, and I kind of stepped aside the path of wisdom regarding the Gaussian process. In order to understand the logic of the Gaussian process, you’d better revise the Gaussian distribution, or, in other words, the normal distribution. If any set of observable data follows the normal distribution, the values you can encounter the most frequently in it are those infinitely close to the arithmetical average of the set. As you probably remember from your maths class at high school, one of the reasons the arithmetical average is so frequently used in all kinds of calculations (even those pretty intuitive ones) is that it doesn’t exist. If you take any set of data and compute its arithmetical average, none of your empirical observations will be exactly equal to that average. Still, and this is really funny, you have things – especially those occurring in large amounts, like foot size in a human population – which take the most frequently those numerical values, which are relatively the closest to their arithmetical average, i.e. the closest to a value that doesn’t exist, and yet is somehow expected. These things follow the Gaussian (normal) distribution and we use to assume that their expected value (i.e. the value we can rightfully expect to meet the most frequently in those things) is their arithmetical average.

Inside the set of all those Gaussian things, there is a smaller subset of things, for which time matters. These phenomena unfold in time. Foot size is a good example. Instead of asking yourself what foot size you are the most likely to satisfy with the shoes you make for the existing population, you can ask about the expected foot size in any human being to be born in the future. What you can do is to measure the average foot size in the population year after year, like over one century. That would be a lot of foot measuring, I agree, but science requires some effort. Anyway, if you measure average foot sizes, year after year during one century, you can discover that those averages follow a normal distribution over time, i.e. the greatest number of annual averages tends to be infinitely close to the general, century-long average. If this is the case, we can say that the average foot size changes over time in a Gaussian process, and this is the first characteristic of this specific process: the mean is always the expected value.

If I apply this elementary assumption to the concept of collective intelligence, it implies a special aspect of intelligence, i.e. generalisation. My eyes transmit to my brain the image of one set of colourful points, and then the image of another set of points, kind of just next to the previous one. My brain connects those dots and labels them ‘woman’, ‘red’, ‘bag’ etc. In a sense, ‘woman’, ‘red’, and ‘bag’ are averages, because they are the incidences I expect to find the most probably in the presence of those precise kinds of colourful points. Thus, collective intelligence endowed with a memory, which works according to a Gaussian process, is the kind of intelligence we use for establishing our basic distinctions. In our collective intelligence, Gaussian processes (if they happen at all), can represent, for example, the formation of cultural constructs such as law, justice, scientific laws, and, by the way, concepts like the Gaussian process itself.

Now, we go one step further, and, in order to do it, we need to go one step back, namely back to the concept of vector. If my process in time is made of vectors, instead of single points, and each vector is like a snapshot of reality at a given moment, I am interested in something called the covariance of variables inside the vector. If one variable deviates from its own mean, and I make it power 2 in order to get rid of the possibly embarrassing minus sign, I have variance. If I have two variables, and I take their respective, local deviations from their means, and I multiply those deviations by each other, I have covariance. As we are talking vectors, we have a whole matrix of covariance, between each pair of variables in the vector. Any process, unfolding in time and involving many variables, has to answer the existential question about its own matrix of covariance. Some processes have the peculiar property of keeping a pretty repetitive matrix of covariance over time. The component, simple variables of those processes change in some sort of constant-pace contredans. If variable X1 changes by one inch, the variable X2 will change by three quarters of a gallon, and so it will reproduce for a long time. This is the second basic characteristic of a Gaussian process: future covariance is predictable on the grounds of the covariance observed so far.

As I am transplanting that concept of very recurrent covariance onto my idea of collective intelligence with memory, Gaussian collective intelligence would be the kind that establishes recurrent functional connections between things of society. We call those things institutions. Language, as a matter of fact, is an institution, as well. As we have institutions in every society, and societies that do not form institutions tend to have pretty short a life expectance, we can assume that collective intelligence certainly follows, at least to some extent, the pattern of a Gaussian process.

Back to blogging, trying to define what I remember

My editorial

It is really tough to get back to regular blogging, after a break of many weeks. This is interesting. Since like mid-October, I have been absorbed by teaching and by finishing formal scientific writing connected to my research grant. I have one major failure as for that last one – I haven’t finished my book on technological change and renewable energies. I have like 70% of it and it keeps being like 70%, as if I was blocked on something. Articles flow just smoothly, but I am a bit stuck with the book. Another interesting path for self-investigation. Anyway, teaching and formal writing seem to have kind of absorbed some finite amount of mental energy I have, leaving not much for other forms of expression, blogging included. Now, as I slowly resume both the teaching scheduled for the winter semester, and, temporarily, the writing of formal publications, my brain seems to switch, gently, back into the blogging mode.

When I start a new chapter, it is a good thing to phrase out my takeaways from previous chapters. I think there are two of them. Firstly, it is the concept of intelligent loop in collective learning. I am mildly obsessed with the phenomenon of collective intelligence, and, when we claim we are intelligent, it would be good to prove we can learn something as a civilisation. Secondly, it is that odd mathematical construct that we mostly know, in economics, as production function. The longer I work with that logical structure, you know, the ‘Y = Kµ*L1-µ*A’ one (Cobb, Douglas 1928[1]), the more I am persuaded that – together with some fundamental misunderstandings, there is an enormous cognitive potential in it. The production function is a peculiar way of thinking about social structures, where one major factor – the one with the biggest exponent –  reshuffles all the cards on the table and actually makes the structure we can observe.

The loop of intelligent learning articulates into a few specific, more detailed issues. In order to learn, I have to remember what happened to me. I need to take some kind of break from experiencing reality – the capacity of abstract thinking is of great help in this respect – and I need to connect the dots, form some patterns, test them, hopefully survive the testing, and then come up with something smart, which I can label as my new skills. Collective memory is the first condition of collective learning. There is one particular issue, pertaining to both the individual memory and the collective one: what we think is our memory of past occurrences is, in fact, our present interpretation of information we collected in the past. It is bloody hard to draw a line between what we really remember, and what we think we remember. There are scientifically defined cases of mental disturbances (e.g. the Korsakoff psychosis), where the person creates its own memory on a completely free ride, without any predictable connection to what had really happened in the past. If individual people can happen to do things like that, there is absolutely no reason why whole societies shouldn’t. Yet, when it comes to learning, looping inside our own imagination simply doesn’t work: we come up with things like Holy Inquisitions or Worst Enemies, and it is not what is going to drive us into the next millennium. In order to learn truly and usefully, we have to connect the dots from our actual, past experience, as little edited as possible. The question is, how can I find out what does the society really remember? How can I distinguish it from the imaginary bullshit? Economics are very largely about numbers. The general question about memory translates into something more abstract: how can I tell that the numbers I have today somehow remember the numbers from the past? How can I tell that a particular variable remembers its own variance from the past? What about this particular variable remembering the past variance in other variables?

In order to answer those questions, I go back to my understanding of memory as a phenomenon. How do I know I have memory? I am considering a triad of distinct phenomena, which can prove I have memory: remembering, repetition, and modification of behaviour. Remembering means that I can retrieve, somehow, from the current resources of my brain, some record relative to past experience. In other words, I can find past information in the present information. My brain needs recurrent procedures for retrieving that past information. There must be some librarian-like algorithm in my brain, which can pick up bits of my past in a predictable manner. Memory in quantitative data means, thus, that I can find numbers from the past in the present ones, and I can find them in a recurrent manner, i.e. through a function. If I have a number from the past, let’s call it x(t-1), and a present number x(t), my x(t) has the memory of x(t-1) if, in a given set X = {x1, x2, …, x3} of values attributed to x I can find a regularity of the type xi(t) = f[xi(t-1)] and the function f[xi(t-1)] is a true one, i.e. it has some recurrent shape in it. Going from mathematics back to real life, remembering means that every time I contemplate my favourite landscape – an open prairie in the mountains, by a sunny day in summer – I somehow rehearse all the past times I saw the same landscape.

Going back to maths, there are many layers and tunes in remembering. I can remember in a constant time frame. It means that right now, my brain kind of retrieves sensory experience from the past three weeks, the whole of three weeks and just three weeks. That window in time is my constant frame of remembering. Yet, older memories happen to pop up in my head. Sometimes, I go, in my memories, like two years back. On other occasions, something from a moment twenty years ago suddenly visits my consciousness. Besides the constant window of three weeks back in time, my brain uses a flexible temporal filter, when some data from further a past seems to connect with my present experience. Thus, in the present information xi(t), currently processed in my mind, there are two layers of remembering: the one in the constant window of three weeks, on the one hand, and that occurring in the shifting regressive reach. Mathematically, constant is constant, for example ‘b’, whilst something changing is basically a stochastic distribution, which I provisionally call ‘STOCH’, i.e. a range of possible states, with each of them occurring with a certain probability. My mathematical formula gets the following shape: xi(t) = f1[xi(t – b)] + f2[xi(t – STOCH)].

As someone looks at these maths, they could ask: ‘Good, but where is the residual component on the right side? Is your current information just made of remembering? Is there nothing squarely present and current?’. Well, this is a good question. How does my intelligence work? Is there anything else being processed, besides the remembered information? I start with defining the present moment. In the case of a brain, one neuron fires in about 2 milliseconds, although there is research showing that each neuron can largely control that speed and make connections faster or slower (Armbruster & Ryan 2011[2]). Two milliseconds are not that long: they are two thousandths of what we commonly perceive as the shortest unit of time in real life. Right, one neuron doesn’t make me clever, I need more of them getting to do something useful together. How many? As I was attending my lessons at the driving school, some 27 years ago, I had been taught that the normal time of reaction, in a driver, is about 1,5 seconds. This is the time between my first glimpse of a dog crossing the tarmac, and me pushing the brake pedal. It makes 1 500 milliseconds. Divided by two milliseconds for one neuron, so if each individual neuron fired after another one, it gives 750. I have roughly 100 billion neurons in my brain (you the same), and each of them has, on average, 7000 connections with other neurons. It makes 7E+14 synaptic connections. In a sequence of 750 neurons firing one after the other, I have 7507000 synapses firing. In other words, something called ‘strictly current processing of information’ activates like 7,5E-09 of my brain: not much. It looks as if my present wasn’t that important, in quantitative terms, in relation to my past. Moreover, in those 7507000 synapses firing in an on-the-spot reaction of a driver, there is a lot of remembering, like ‘Which of those three things under my feet is the brake pedal?’.

Let’s wrap it up, partially. We are in a social system, and that social system is supposed to have collective intelligence equivalent to the individual intelligence of a human brain. If this is the case, numerical data describing the actions of that social system consists, for any practical purpose, exclusively in remembering. There is some residual of what can be considered as the strictly spoken current processing of information, but this is really negligible. Thus, I come with my first test for collective intelligence in a social system. The system in question is intelligent if, in a set of numerical time series describing its behaviour, the present data can be derived from past data, without significant residual, in a function combining a fixed window of remembering with a stochastic function of reprocessing old information, or xi(t) = f1[xi(t – b)] + f2[xi(t – STOCH)]. If this condition is generally met, the social system remembers enough to learn on past experience. ‘Generally’ means that nuances can be introduced into that general scheme. Firstly, if my function yields a significant residual ‘Res[xi(t)]’, thus if its empirically verified version looks like xi(t) = f1[xi(t – b)] + f2[xi(t – STOCH)] + Res[xi(t)], it just means that my (our?) social system produces some residual information, whose role in collective learning is unclear. It can be the manifestation of super-fast learning on the spot, or, conversely, it can indicate that the social system produces some information bound not to be used for future learning.

And so I come to the more behavioural proof of memory and learning. When we do something right now, there is a component of recurrent behaviour in it, and another one, that of modified behaviour. We essentially do things we know how to do, i.e. we repeat patterns of behaviour that we have formed in the past. Still, if we are really prone to learn, thus to have active a memory, there is another component in our present behaviour: that of current experimentation, and modification in behaviour, in the view of future repetition. We repeat the past, and we experiment for the future. The residual component Res[xi(t)] in my function of memory – xi(t) = f1[xi(t – b)] + f2[xi(t – STOCH)] + Res[xi(t)]if it exists at all, can be attributed to such experimentation. Should it be the case, my Res[xi(t)] should reflect in future functions of memory in my data, and it should reflect in the same basic way as the one already defined. Probably, there is some recurrent cycle of learning, taking place in a more or less constant time window, and, paired with it, is a semi-random utilisation of present experience in the future, occurring in a stochastically varying time range. Following the basic logic, which I am trying to form here, both of the time ranges in that function of modification in behaviour should be pretty.

[1] Charles W. Cobb, Paul H. Douglas, 1928, A Theory of Production, The American Economic Review, Volume 18, Issue 1, Supplement, Papers and Proceedings of the Fortieth Annual Meeting of the American Economic Association (March 1928), pp. 139 – 165

[2] Armbruster, M., Ryan, T., 2011, Synaptic vesicle retrieval time is a cell-wide rather than individual-synapse property, Nature Neuroscience 14, 824–826 (2011), doi:10.1038/nn.2828

Equilibrium in high esteem

My editorial

I am still at this point of defining my point in that article I am supposed to hatch on the topic of evolutionary modelling in studying technological change. Yes, it takes some time and some work to define my point but, man, that’s life. I think I know things, and then I think how to say what I know about things, and it brings me to thinking once again what is it that I know. If, hopefully, I come to any interesting conclusions about what I know, I start reading literature and I discover that other people know things, too, and so I start figuring out what’s so original in what I know and how to say it. You know those scenes from Jane-Austin-style movies, where people are gossiping in a party and they try to outgossip each other, just to have that momentary feeling of being the most popular gossiper in the ballroom? Well, this is the world of scientific publications. This is what I do for a living, very largely. I am lucky, mind you. I don’t have to wear one of those white wigs with a fake tress. This is a clear sign that mankind is going some interesting way forward.

Yesterday, as I was gossiping in French (see ‘Deux lions de montagne, un bison mort et moi’ ), I came to some conclusions about my point. I think I can demonstrate that the pace and intensity of technological change we have been experiencing for the last six or seven decades can be explained as a function of intelligent adaptation, in the human civilisation, to a growing population in the presence of scarce food. This is slightly different an angle of approach from those evolutionary models I have been presenting on my blog over the last few weeks, but what do you want: blog is blog, and scientific gossip is scientific gossip. This deep ontological distinction means I have to adapt my message to my audience and to my medium of communication. Anyway, why this? Well, because as I turned and returned all the data I have about technological change, I found only one absolutely unequivocal gain in all that stuff: between 1992 and 2016, the human population on the planet has doubled, but the average food deficit per person per day has been cut by half, period. This is it. Of course, other variables follow, of similar nature: longer life expectancy, better access to healthcare and sanitation etc. Still, the bottom line remains the same: technological change occurs at intensifying a pace, it costs more and more money, and it is correlated with improvements in the living conditions much more than with increased Total Factor Productivity.

There is a clan of evolutionary models, which, when prodded with the stick labelled ‘adaptation’, automatically reply with a question: ‘Adaptation to what?’. Wrong question, clan. Really. You, clan, you have to turn your kilts over, to the other side, and see that other tartan pattern. Adaptation is adaptation to anything. Otherwise, if we select just some stressors and say we want to adapt to those, it becomes optimization, not adaptation, not anymore. The right question is ‘How do we adapt?’. Oh, yes, at this point of stating my point I suddenly remember I have to do some review of literature. So I jump onto the first piece of writing about intelligent adaptation I can find. My victim’s name is Andrew W. Lo and his article about adaptive markets hypothesis (2005[1]).  Andrew W. Lo starts from the biological assumption that individuals are organisms, which, through generations of natural selection form so as to maximize the survival of their genetic material.

Moreover, Andrew Lo states that natural selection operates not only upon genetic material as such, but also upon functions this genetic heritage performs. It means that even if a genetic line gets successfully reproduced over many generations, so if it kind of goes intact and immutable through consecutive generational turns, the functions it performs can change through natural selection. In a given set of external conditions, a Borgia (ducal bloodline) with inclinations to uncontrolled homicide can get pushed off to the margin of the dynasty by a Borgia (ducal bloodline) with inclinations to peaceful manipulation and spying. If external conditions change, the vector of pushing off can change, and the peaceful sneaky kind may be replaced by the violent beast. At the end of the day, and this is a very important statement from Andrew W. Lo, social behaviour and cultural norms are also subject to natural selection. The question ‘how?’, according to Andrew Lo, is being answered mainly as ‘through trial and error’ (which is very much my own point, too). In other words, the patterns of satisfactory behaviour are being determined by experimentation, not analytically.

I found an interesting passage to quote in this article: ‘Individuals make choices based on experience and their best guesses as to what might be optimal, and they learn by receiving positive or negative reinforcement from the outcomes. If they receive no such reinforcement, they do not learn. In this fashion, individuals develop heuristics to solve various economic challenges, and as long as those challenges remain stable, the heuristics eventually will adapt to yield approximately optimal solutions’. From that, Andrew Lo derives a general thesis, which he calls ‘Adaptive Markets Hypothesis’ or AMH, which opposes the Efficient Market Hypothesis (EMH). The way it works in practice is being derived by close analogy to biology. Andrew Lo makes a parallel between the aggregate opportunities of making profit in a given market and the amount of resources available in an ecosystem: the more resources are there, the less fierce is the competition to get a grab of them. If the balance tilts unfavourably, between the population and the resources, competition becomes more ruthless, but ultimately the population gets checked at its base, and declines. Declining population makes competition milder, and the cycle either loops in a band of predictable variance, or it goes towards a corner solution, i.e. a disaster.

The economic analogy to that basic biological framework is that – according to AMH and contrarily to EMH – ‘convergence to economic equilibrium is neither guaranteed nor likely to occur at any point in time’. Andrew Lo states that economic equilibrium is rather a special case than a general one, and that any economic system can either converge towards equilibrium or loop in a cycle of adaptation, depending on the fundamental balance between resources and population. Interestingly, Andrew Lo manages to supply convincing empirical evidence to support that claim, when he assumes that profit opportunities in a market are the economic equivalent of food supplies in an ecosystem.

I find that line of thinking in Andrew Lo really interesting, and my own research, that you could have been following over the last weeks on this blog, aims at pinning down the ‘how?’ of natural selection. The concept is being used frequently: ‘The fittest survive; that’s natural selection!’. We know that, don’t we? Still, as I have that inquisitive ape inside of me, and as that ape is being backed by an austere monk equipped with the Ockham’s razor, questions abound. Natural selection? Splendid! Who selects and how? What do you mean by what do I mean by ‘who selects?’? (two question marks in one sentence is something I have never achieved before, by the way). Well, if we say ‘selection’, it is a choice. You throw a stone in the air and you let it fall on the ground, and you watch where it falls exactly. Has there been any selection? No, this is physics. Selection is a human concept and means choice. Thus, when we state something like ‘natural selection’, I temporarily leave aside the ‘natural’ part (can there be unnatural selection?) and I focus on the act of selecting, or picking up from a lot. Natural selection means that there is a lot of items, produced naturally, through biology (both the lot in its entirety and each item separately), and then an entity comes and chooses one item from the lot, and the choice has consequences regarding biological reproduction.

In other words, as long as we see that ‘natural selection’ as performed by some higher force (Mother Nature?), we are doing metaphysics. We are pumping smoke up our ass. Selection means someone choosing. This is why in my personal research I am looking for some really basic forms of selection with biological consequences. Sexual selection seems to fit the bill. Thus, when Andrew Lo states that natural selection creates some kind of economic cycle, and possibly makes the concept of economic equilibrium irrelevant, I intuitively try to identify those two types of organisms in the population – male and female – as well as a selection function between them. That could be the value I can add, with my model, to the research presented by Andrew Lo. Still, I would have to argue with him about the notion of economic equilibrium. He seems to discard it almost entirely, whilst I hold it in high esteem. I think that if we want to go biological and evolutionist in economics, the concept of equilibrium is really that elfish biscuit we should take with us on the journey. Equilibrium is deeply biological, and even physical. Sometimes, nature is in balance. This is more or less stationary a state. An atom is an equilibrium between forces. An ecosystem is an equilibrium between species and resources. Yes, equilibrium is something we more frequently crave for rather than have, and still it is a precious benchmark for modelling what we want and what kind of s*** we can possibly encounter on the way.

[1] Lo, A.,W., 2005, Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis, The Journal of Investment Consulting, Volume 7, no. 2, pp. 1 – 24

I cannot prove we’re smart

My editorial

I am preparing an article, which presents, in a more elegant and disciplined form, that evolutionary model of technological change. I am going once again through all the observation, guessing and econometric testing. My current purpose is to find simple, intelligible premises that all my thinking started from. ‘Simple and intelligible’ means sort of hard, irrefutable facts, or, foggy, unresolved questions in the available literature. This is the point, in scientific research, when I am coining up statements like: ‘I took on that issue in my research, because facts A,B, C suggest something interesting, and the available literature remains silent or undecided about it’. So now, I am trying to reconstruct my own thinking and explain, to whomever would read my article, why the hell did I adopt that evolutionary perspective. This is the point when doing science as pure research is being transformed into scientific writing and communication.

Thus, facts should come first. The Schumpeterian process of technological progress can be decomposed into three parts: the exogenous scientific input of invention, the resulting replacement of established technologies, and the ultimate growth in productivity. Empirical data provides a puzzling image of those three sub-processes in the modern economy. Data published by the World Bank regarding science, research and development allow noticing, for example, a consistently growing number of patent applications per one million people in the global economy (see http://data.worldbank.org/indicator/IP.PAT.RESD ). On the other hand, Penn Tables 9.0 (Feenstra et al. 2015[1]) make it possible to compute a steadily growing amount of aggregate amortization per capita, just as a growing share of aggregate amortization in the global GDP (see Table 1 in the Appendix). Still, the same Penn Tables 9.0, indicate unequivocally that the mean value of Total Factor Productivity across the global economy has been consistently decreasing since 1979 until 2014.

Of course, there are alternative views of measuring efficiency in economic activity. It is possible, for example, to consider energy efficiency as informative about technological progress, and the World Bank publishes the relevant statistics, such as energy use per capita, in kilograms of oil equivalent (see http://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ). Here too, the last decades do not seem to have brought any significant slowdown in the growth of energy consumption. The overall energy-efficiency of the global economy, measured with this metric, is decreasing, and there is no technological progress to observe at this level. A still different approach is possible, namely that of measuring technological progress at the very basic level of economic activity, in farming and food supply. The statistics reported by the World Bank as, respectively, the cereal yield per hectare ( see http://data.worldbank.org/indicator/AG.YLD.CREL.KG ), and the depth of food deficit per capita (see http://data.worldbank.org/indicator/SN.ITK.DFCT ), allow noticing a progressive improvement, at the scale of global economy, in those most fundamental metrics of technological performance.

Thus, the very clearly growing effort in research and development, paired with a seemingly accelerating pace of moral ageing in established technologies, occurs together with a decreasing Total Factor Productivity, decreasing energy efficiency, and just very slowly increasing efficiency in farming and food supply chains. Now, in science, there are basically three ways of apprehending facts: the why, the what, and the how. Yes, I know, there is a fourth way, the ‘nonsense!’ one, currently in fashion as ‘this is fake news! we ignore it’. Still, this fourth way is not really science. This is idiocy dressed fancily for an electoral meeting. So, we have three: the why, the what, and the how.

The why, or ‘Why are things happening the way they are?’, is probably the oldest way of starting science. ‘Why?’ is the intuitive way we have of apprehending things we don’t quite understand, like ‘Why is this piece of iron bending after I have left it close to a furnace?’. Probably, that intuitive tendency to ask for reasons reflects the way our brain works. Something happens, and some neurons fire in response. Now, they have to get social and to inform other neurons about that something having happened. Only in the world of neurons, i.e. in our nervous system, the category ‘other neurons to inform’ is quite broad. There are millions of them, in there. Besides, they need synapses to communicate, and synapses are an investment. Sort of a fixed asset. So, neurons have to invest in creating synapses, and they have a wide choice as for where exactly they should branch. As a result, neurons like fixed patterns of communication. Once they make a synaptic connection, they just use it. The ‘why?’ reflects this predilection, as in response we expect ‘Because things happen this way’, i.e. in response to this stimulus we fire that synaptic network, period.

The problem with the ‘why?’ is that it is essentially deterministic. We ask ‘why?’ and we expect ‘Because…’ in return. The ‘Because…’ is supposed to be reassuringly repetitive. Still, it usually is not. We build a ‘Because…’ in response to a ‘why?’, and suddenly something new pops up. Something, which makes the established ‘Because…’ look a little out of place. Something that requires a new ‘Because…’ in response to essentially the same ‘why?’. We end up with many becauses being attached to one why. Picking up the right because for the situation at hand becomes a real issue. Which because is the right because can be logically derived from observation, or illogically derived from our emotional stress due to cognitive dissonance. Did you know that the experience of cognitive dissonance can trigger, in a human being, stronger a stress reaction than the actual danger of death? This is probably why we do science. Anyway, choosing the right because on the illogical grounds of personal emotions leads to metaphysics, whilst an attempt to pick up the right because for the occasion by logical inference from observation leads to the next question: the ‘what?’. What exactly is happening? If we have many becauses to choose between, choosing the right one means adapting our reaction to what is actually taking place.

The ‘what?’ is slightly more modern than the ‘why?’. Probably, mathematics were historically the first attempt to harness the subtleties of the ‘what?’, so we are talking about settled populations, with a division of labour allowing some people to think about things kind of professionally. Anyway, the ‘what?’ amounts to describing reality so as the causal sequence of ‘because…’ is being decomposed as a sequence. Instead of saying ‘C happens because of B, and B happens because of A’, we state a sequence: A comes first, then comes B, and finally comes C. If we really mean business, we observe probabilities of occurrence and we can make those sequences more complex and more flexible. A happens with a probability of 20%, and then B can happen with a probability of 30%, or B’ can happen at 50% odds, and finally we have 20% of chances that B’’ happens instead. If it is B’’ than happens, it can branch into C, C’ or C’’ with the respective probabilities of X, Y, Z etc.

Statistics are basically a baby of the ‘what?’. As the ‘why?’ is stressful and embarrassingly deterministic, we dodge and duck and dive into the reassuringly cool waters of the ‘what?’. Still, I am not the only one to have a curious ape inside of me. Everyone has, and the curiosity of the curious ape is neurologically wired around the ‘why?’ pattern. So, just to make the ape calm and logical, whilst satisfying its ‘why’-based curiosity, we use the ‘how?’ question. Instead of asking ‘why are things happening the way they are?’, so instead of looking for fixed patterns, we ask ‘how are things happening?’. We are still on the hunt for links between phenomena, but instead of trying to shoot the solid, heavy becauses, we satisfy our ambition with the faster and more flexible hows. The how is the way things happen in a given context. We have all the liberty to compare the hows from different contexts and to look for their mutual similarities and differences. With enough empirical material we can even make a set of connected hows into a family, under a common ‘why?’. Still, even with such generalisations, the how is always different an answer from ‘because…’. The how is always context-specific and always allows other hows to take place in different contexts. The ‘because…’ is much more prone to elbow its way to the front of the crowd and to push the others out of the way.

Returning to my observations about technological change, I can choose, now, between the ‘why?’, the ‘what?’, and the “how?’. I can ask ‘Why is this apparent contradiction taking place between the way technological change takes place, and its outcomes in terms of productivity?’. Answering this question directly with a ‘Because…’ means building a full-fledged theory. I do not feel ready for that, yet. All these ideas in my head need more ripening, I can feel it. I have to settle for a ‘what?’, hopefully combined into context-specific hows. Hows run fast, and they change their shape, according to the situation. If you are not quick enough to run after a how, you have to satisfy yourself with the slow, respectable because. Being quick, in science, means having access to empirical data and be able to test quickly your hypotheses. I mean, you can be quick without access to empirical data, but then you just run very quickly after your own shadow. Interesting, but moderately productive.

So I am running after my hows. I have that empirical landscape, where a continuously intensifying experimentation with new technologies leads, apparently, to decreasing a productivity. There is a how, camouflaging itself in that landscape. This how assumes that we, as a civilisation, randomly experiment with new technologies, kind of which idea comes first, and then we watch the outcomes in terms of productivity. The outcomes are not really good – Total Factor Productivity keeps falling in the global economy – and we still keep experimenting at an accelerating pace. Are we stupid? That would be a tempting because, only I can invert my how. We are experimenting with new technologies at an increasing pace as we face disappointing outcomes in terms of productivity. If technology A brings, on the long run, decreasing productivity, we quickly experiment with A’, A’’, A’’’ etc. Something that we do brings unsatisfactory results. We have two options then. Firstly, we can stop doing what we do, or, in other words, in the presence of decreasing productivity we could stop experimenting with new technologies. Secondly, we can intensify experimentation in order to find efficient ways to do what we do. Facing trouble, we can be passive or try to be clever. Which option is cleverer, at the end of the day? I cast my personal vote for trying to be clever.

Thus, it would turn out that the global innovative effort is an intelligent, collective response to the unsatisfactory outcomes of previous innovative effort. Someone could say that this is irrational to go deeper and deeper into something that does not bring results. That is a rightful objection. I can formulate two answers. First of all, any results come with a delay. If something is not bringing results we want, we can assume it is not bringing them yet. Science, which allows invention, is in itself quite a recent invention. The scientific paradigm we know today has taken definitive shape in the 19th century. Earlier, we basically have been using philosophy in order to invent science. It makes some 150 years that we can use real science to invent new technologies. Maybe it has not been enough to learn how to use science properly. Secondly, there is still the question of what we want. The Schumpeterian paradigm assumes we want increased productivity but do we really? I can assume, very biologically, what I already signalled in my previous posts: any living species tends to maximize its hold on the environment by absorbing as much energy as possible. Maybe we are not that far from amoeba, after all, and, as a species, we collectively tend towards maximizing our absorption of energy from our environment. From this point of view, technological change that leads to increasing our energy use per capita and to engaging an ever growing amount of capital and labour into the process could be a perfectly rational behaviour.

All that requires assuming collective intelligence in the mankind. Proving the existence of intelligence is both hard and easy. On the one hand, culture is proof of intelligence: this is one of the foundational principles in anthropology. From that point of view, we can perfectly assume that the whole human species has collective intelligence. Still, an economist has a problem with this view. In economics, we assume individual choice. Can individual choice be congruent with collective intelligence, i.e. can individual, conscious behaviour change in step with collective decisions? Well, we did Renaissance, didn’t we? We did electricity, we did vaccines, we did religions, didn’t we? I use the expression ‘we did’ and not ‘we made’, because it wasn’t that one day in the 15th century we collectively decided that from now on, we sculpt people with no clothes on and we build cathedrals on pseudo-ancient columns. Many people made individual choices, and those individual choices turned out to be mutually congruent, and produced a coherent social change, and so we have Tesla and Barbie dolls today.

Now, this is the easy part. The difficult one consists in passing from those general intuitions, which, in the scientific world, are hypotheses, to empirical verification. Honestly, I cannot directly, empirically prove we are collectively intelligent. I reviewed thoroughly the empirical data I have access to and I found nothing that could serve as direct proof of collective intelligence in the mankind. Maybe this is because I don’t know how exactly could I formulate the null hypothesis, here. Would it be that we are collectively dumb? Milton Friedman would say that in such a case, I have to options: forget it or do just as if. In other words, I can drop entirely the hypothesis of collective intelligence, with all its ramifications, or construe a model implying its veracity, so treating this hypothesis as an actual assumption, and see how this model fits, in confrontation with facts. In economics, we have that assumption of efficient markets. Right, I agree, they are not necessarily perfectly efficient, those markets, but they arrange prices and quantities in a predictable way. We have the assumption of rational institutions. In general, we assume that a collection of individual acts can produce coherent social action. Thus, we always somehow imply the existence of collective intelligence in our doings. Dropping entirely this hypothesis would be excessive. So I stay with doing just as if.

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