Black Swans happen all the time

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Cruel and fatalistic? Weelll, not necessarily.

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

What can be wanted only at the collective level

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The balance between intelligence and the way we look in seasoned black leather

MY EDITORIAL ON YOU TUBE

After having devoted some of my personal energy to reviewing other people’s science (see Second-hand observations), I return to my own science, i.e. to my book on the civilizational role of cities. Reviewing that manuscript in the field of energy management gave me some inspiration. I realized that the core message I wanted to convey in the book was that human societies have collective intelligence, that intelligence manifests itself in typical, recurrent patterns, and cities are one of those patterns, where creating a demographic anomaly allows creating new social roles for a growing population, and assuring functional partition between two types of settlements: agricultural land for producing food, on the one hand, and urban land for producing new social roles and new technologies, on the other hand. Moreover, cities are the base of markets, and of the market-based economy. The whole social system based on the development of skills and technologies, so as to produce tradable surpluses, that whole system is precisely based on the role of cities. Maybe there are other social structures to obtain the same result, but we haven’t figured them out yet. With the creation of cities, we developed a pattern of further development, where apparently fundamental markets interweave with the apparently futile ones, and the whole system facilitates technological change and the creation of new social roles. The social system based on cities is like a social vortex, largely powering its own momentum and sucking new people into it.

That overview of my thinking brings me one more time to the issue of collective intelligence and to the big methodological question: to what extent can neural networks be used to simulate collective intelligence in human societies? I know, I know, this is some n-th time I return to that thing. Still, it is important, both methodologically and fundamentally. There is a whole big stream of research, including my own, where neural networks are used as mathematical tool for validating theoretical model. I can see that neural networks tend to replace the classical methods, such as GARCH, ARIMA or the good old Ordinary Least Squares. All that stuff works with the same basic data, i.e. with residual errors which inevitably appear as we try to apply our grand ideas to the brutal reality of life. Still, the way that neural networks process those errors is fundamentally different from stochastic models. The latter just cut through the entire set of data with one line (or curve, for that matter), which minimizes the sum total of residual errors, all in one go. Neural networks are more patient, and they minimize error by error, case by case. Neural networks learn.

The point is that when I use a neural network to validate a theoretical model in social sciences, I should substantiate the claim that the network represents the way of learning in the given society. The best theory of learning which I have found so far is the Interface Theory of Perception (Hoffman et al. 2015 [1]; Fields et al. 2018 [2]; see also I followed my suspects home). I rephrase it shortly and I try to put it against (or next to) my own methodology.

When an aggregate socio-economic variable, such as e.g. GDP per capita or energy consumption per capita, changes over time, it allows assuming a society doing something differently as time passes. In other words, those aggregate variables are manifestations of collective decisions and collective action. Question: how are those collective decisions being taken and how are they being turned into action? Some sort of a null assumption is that we have no way to guess anything about that process. Still, I think I can make a slightly stronger assumption, namely that we collectively know what we are doing, we just know it imperfectly. Therefore, when I observe a variable such as GDP per capita, or the average number of hours worked per person per year, change over years, I can assume it manifests a collectively intelligent adaptation: we do something together, we contemplate the outcomes, we say ‘Blast! It is not exactly what we meant. Folks! Get ready! We adapt! That rate of secondary education has to change! We are running a civilisation here, aren’t we?’, and we engage into another set of decisions and actions.

Collective decisions and collective action mean that people argue and diverge in what they say they intend to do, in what they really do, and in what they claim they have just done. We diverge from each other and we lie to each other on the top of it, and we lie to ourselves, and yet that whole business of civilisation seems to be working. We have a set N = {se₁, se₂, …, se_n} of n social entities (people, basically, or various agglomerations thereof), and they all cheat, lie, and egoistically get after it, in the presence of a set R = {r₁, r₂, …, r_m} of m external stressors (viruses, presidents, wars, bad crops etc.). Mind you, as long as n > 1, i.e. as long as there are many social entities, probably at least one of them is doing things sufficiently well to survive in the presence of m stressors.

We have those n social entities trying to get by in the presence of m external stressors, and one could wonder how that lot can learn anything? I subtly change the shade of the question ‘how?’ into ‘how can we know that?’. How can we know that a social entity has learnt anything in the presence of external stressors? Learning can be understood from two perspectives: subjective internal impression of having learnt something, on the one hand, and objective, externally observable fact of having acquired new skills. When I prepare the same sauce 30 times, 20 times it is completely spoilt, 9 times it sort of approaches the ideal, and 1 time, the final one, I have the impression I nailed it. I have the impression I have learnt something, however it does not mean other people think the same.

I need a gauge to assess learning. One possible method, more specifically the one used in artificial neural networks, consists in checking how close my social entities are to a pre-defined desired outcome. In more elaborate artificial neural networks, the pre-defined component might be just the method of setting the desired outcome. That outcome can be simply survival or something more, such as achieving a certain amount of something valuable.

Good, so I have those n social entities, and the stressor they act under is the pressure to achieve a desired outcome, i.e. to obtain a certain payoff. The social entity se_i which gets the closest to that outcome, or which, in other words, collects the greatest payoff, can be considered as the most successful. Life is reproduction. People die, and new people are born. Governments change. On the long run our set N = {se₁, se₂, …, se_n} of n social entities is interesting to the extent that it reproduces into another set N_k of n(k) social entities. Social change can be viewed as an (almost) ever-lasting chain of sets, each with social entities inside: N₁ transforms into N₂, which turns into N₃ etc.

I think I have just nailed an important point (involuntarily, to be clear). When I study any kind of social change, I can make one of the two alternative assumptions: continuity of social entities versus their generational reproduction. Social structures can be seen such as I have just described it: as changing sets of social entities. Under that angle, the 38 million people in my native Poland today are a different set of people from the roughly 36 million who were around when I was 10, i.e. in 1978. It does not necessarily mean that each person present in 1978 died and has been replaced by someone else; I am pretty sure I didn’t die. However, some people died, some new people have come to the fore, some people changed significantly etc. On the whole, the set N₂₀₂₀ is different from the set N₁₉₇₈. There is a different angle for looking at the same reality: people in Poland, 2020, are the same big social entity as the one in Poland, 1978, and it is just the internal structure of that entity that has changed.

What is the practical (well, theoretical) difference between those two angles of approach to the same theatre of social change, i.e. consecutive sets of small entities as opposed to consecutive states of one big entity? When I simulate social change as a sequence of sets, where individual components can change their properties, a long sequence of that type is like a journey of discovery. Each consecutive set N_k comes out of learning that occurred in its predecessor N_k-1. The transformation of one set into another certainly follows some constraints, yet a long sequence of such transformations is like a long hike up a hill: we have to take turns around boulders and ravines, we have to choose between paths of different difficulty, and usually an easier path is a less steep one, thus a longer and slower one. This type of change, social or biological, is known as adaptive walk in rugged landscape in Kaufman & Levin 1987 [3]. Mathematically, it is a Markov chain, i.e. a chain of states, where the properties of each consecutive state are determined just by the properties of the previous state as well as by the algebra of transformation from one state to another (the so-called σ-αλγεβρα, oops! Excuse me, I wanted to say σ-algebra).

When I take the other approach to a social structure, i.e. when I study it as one big, perennial social entity which undergoes structural change inside, that change is something like different shapes of the same thing. I noticed strong marks of such an approach in that scientific paper entitled ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’, which I was reviewing recently on the request of the International Journal of Energy Sector Management (ISSN1750-6220). In that paper, a complex system of relations between economy, energy and society is represented as four gradients of change in, respectively, volume of pollution x, real economic output y, environmental quality z and energy reduction constraints w. I know it is a bit abstract, at this point, yet I want to make an effort and explain it. Imagine an irregular quadrilateral, i.e. a rectangle with broad intellectual horizons. Four angles, four edges, yet the angles do not have to be right and the edges do not have to be parallel in pairs. Just four of each lot. The length of each edge corresponds to the gradient of change in one of those 4 variables: x, y, z, and w. Any substantial change in that system is a change in lengths of those 4 edges, and, as it is a closed polygon, it entails a change in angles between edges.

As I am trying to grasp fundamental differences between those two views upon social change, namely sequence of sets as opposed to an internally changing perennial entity, I think the difference is mostly epistemological. As a matter of fact, I don’t know s**t about reality as it is, and, let’s be honest, neither do you, my dear readers. We just make many possible Matrixes out of the real stuff and settle for the one that offers the greatest rewards. This is the stance adopted in the Interface Theory of Perception (Hoffman et al. 2015 [4]; Fields et al. 2018 [5]), as well as in classical Western empiricism (see William James’s ‘Essays in Radical Empiricism’, 1912). This holds for social reality as well as for anything else. When I see social change, I see most of all change in itself, and only secondarily, in my spare moments, I can try to figure out what exactly is that thing that changes. This is science, or philosophy, depends on the exact method I adopt, and this is hard, and time-consuming. Most of the times, I just use a ready-made explanation, conveyed in my culture, that what is changing is, for example, the market or the constitutional order, or the set of cultural stereotypes. Still, at the bottom line, those labels are just labels. What I am really experiencing, is change in itself.

When I assume that social change is a Markov chain of sets made of small social entities, I study social change as change in itself, i.e. as the say σ-algebra of that chain. I do not pretend to know exactly what is happening, I just observe and give the account of the passage from one state to another. Conversely, when I assume that social change is structural recombination inside a big, perennial social structure, I pretend to know the limits and the shape of that big structure. This is a strong assumption, probably an overstated one.

Now, I connect the dots. I am linking my thinking about cities with my thinking about collective intelligence, and all that I serve in a sauce peppered with the possibility to use artificial neural networks for studying the whole business. I can study the civilizational role of cities under those two angles, i.e. as a Markov chain of states which I barely understand, yet which I can observe, on the one hand, or as internal reshuffling inside a finite, big social entity called ‘civilisation’, with nicely outlined contours. I am honest: I am not even going to pretend I can outline the precise contours of the civilisation we live in. With all the s**t going out there, i.e. leftist extremists in Germany erecting an illegal statue of Lenine, in response to #BlackLivesMatter in United States, and neo-Nazi extremists from The Base organization receiving orders from a leader who is an American with an Italian name, currently living in Russia: man, I do not feel up to trace the external contours of that thing.

I know that I can and want study the phenomenon of cities as change in itself, and I assume that the change I see is an aggregation of local changes in small social entities se_i. As those small se_i’s change locally, their local transformations translate and manifest as the passage from the aggregate set N_k = {se₁, se₂, …, se_n} into another set N_k+1 = {se₁, se₂, …, se_n}. The next hurdle to jump over is the connection between sets of the type N_k = {se₁, se₂, …, se_n} and aggregate socio-economic variables commonly used as so-called statistics. Those ‘statistics’ tend to have one of the 4 possible, mathematical forms: averages, totals, frequencies, or rates of change. When they are averages, e.g. GDP per capita, they are expected values of something. When the come as aggregate totals, e.g. aggregate headcount of population, they stand for the size of something. As they take the form of frequencies, e.g. the percentage of people with secondary education, they are simple probabilities. Finally, as rates of change, they are local first derivatives over time in some functions, e.g. the function of economic growth.

Each of those mathematical forms can be deemed representative for a heterogenous set of small phenomena, like small social entities se_i. I assume that each set N_k = {se₁, se₂, …, se_n} of n social entities manifests its current state in the form of a complex vector of variables: expected mean values, total sizes, simple probabilities of specific phenomena, and first derivatives over time in the underlying functions of change. Any set of socio-economic variables is an imperfect, epistemic representation of current local states in the individual social entities se_i included in the set N_k = {se₁, se₂, …, se_n}.

As I go through my notes and blog updates from the last 2 months, something emerges. The social entities I focus on, thus my se_i‘s, are individual people endorsing individual social roles. set N_k = {se₁, se₂, …, se_n} is essentially a set of people, i.e. a population. Each of those people has at least two coordinates: their place of residency (mostly defined as city vs countryside), and their social role. I messed around with a set like that in a neural network (see The perfectly dumb, smart social structure). The current state of the whole set N_k manifests itself as a vector V_se of socio-economic variables.

So far and by far, the most important variable I have identified is the density of population in cities, denominated over (i.e. divided by) the general density of population. I named this variable [DU/DG] and I assume it shows the relative social difference between cities and the countryside (see Demographic anomalies – the puzzle of urban density). The coefficient [DU/DG] enters into interesting correlations with such variables as: consumption of energy per capita, income per capita, surface of agricultural land, cereal yield in kg per hectare of arable land, number of patent applications per 1 million people, and finally the supply of money as % of the GDP. On the other hand, by studying the way that urban land is distinguished from the rural one and from wildlife, I know there is a correlation between density of urban population and the density of man-made structures, as well as the density of night-time lights.

Good. I have a set N_k = {se₁, se₂, …, se_n} of n social entities, which changes all the time, and a vector V_se = {DU/DG; energy per capita; income per capita; surface of agricultural land; cereal yield; patent applications; supply of money} of variables pertinent regarding cities and their role. Between the two I insert my mild obsession, i.e. the set SR = {sr₁, sr₂, …, sr_m} of ‘m’ social roles.

Now, I go pictographic. I use pictures to make myself spit out the words I have in mind. I mean, I know I have words in mind, only I don’t know what exact words are these. Pictures help. In Figure 1 I am trying to convey the idea of proportion between the headcount of population and the range of social roles available to endorse. My basic assumption is that we, humans, are fully socialized when we endorse social roles that suit our basic personal traits, such as intelligence, extroversion vs introversion, neuroticism, conscientiousness, the way we look in seasoned black leather etc. The state of society can be explained as a balance between the incremental headcount of humans, on the one hand, and the incremental range of social roles to take. If the headcount of humans is n, and the number of social roles available is m, we are talking about ∆n/∆m.

When both sets, i.e. N_k and SR change at the same pace, i.e. ∆n/∆m (t₀) = ∆n/∆m (t₁), the society is in some sort of dynamic equilibrium, like a constant number of humans per one social role available. When the set SR of social roles burgeons faster than the pace of demographic growth, I mean when ∆n/∆m (t₀) > ∆n/∆m (t₁), logically there is less and less humans per one social role. This is social change by differentiation. New, idiosyncratic skillsets and behavioural patterns emerge. This is like an absorptive state, which can suck new humans in like easy.

On the other hand, when demographic growth in the set N_k races far ahead, and the set SR of social roles lags behind, i.e. ∆n/∆m (t₀) < ∆n/∆m (t₁), there is consistently more and more humans per one social role. That favours standardization and institutional definition of those roles, in the form of professions, public offices, ritualized social statuses etc. Society settles down into a nice order. Still, each such institutionalized social role grows barriers to entry around itself. You need to pass some exams, you need to be appointed or elected, you need to invest some capital… New humans coming to the world encounter those barriers, and some of them end up by saying: ‘F**k it! I stay outside of everything’. This is the tipping point, when social change is needed, so as to make social room for new humans.

Figure 1

Now, I transition into the role of cities in that social pattern. I am trying to picture the idea in Figure 2. If the state of social differentiation, we need some pattern for generating diversity. We need social interaction. Cities can be seen as a social contrivance which facilitates such interaction. Still, it comes to my mind sort of right now, we don’t really know (yet), to what extent digital interaction between humans can replace the real one in that specific respect, i.e. as a mechanism of creating new social roles. My gut feeling is that digital technologies can be at least imperfect substitutes of real social interaction. You Tube or Instagram may replace cities in their civilizational role of creating new social room for new homo sapiens. We might just be evolving from a civilization of city slickers living next to rednecks, into a civilisation of city slickers, rednecks and homo onlinus.

Figure 2

In the next step, I am wrapping my mind around the math side of the problem, which I try to picture in Figure 3. I guess that what I have in terms of empirical data to put in a neural network is mostly the vector V_se of social outcomes, which I can enrich with the headcount of population, and that would be the real-life material that a neural network could learn from. What that network could try and optimize could be the gradient ∆n/∆m or some variation thereof, as the exact number of social roles is technically unobservable with the current state of technology. When I think about the practical way of doing it, I can imagine a network pegged on optimizing some sort of hard-nailed output variable, such as the average number of hours worked per person per year (solid stuff, as it comes out of my so-far research). I drop the gradient ∆n/∆m among the input variables, and I try to discover what value thereof the network would yield after a few thousands laborious trials to produce artificial history.

Another angle of approach that comes to my mind is to take all the known empirical variables as input, and the gradient ∆n/∆m as the output. Then I make different clones of the network, with ∆n/∆m going various ways, like gently up, steep up, down a bit etc. I can check which of the clones displays the closest Euclidean distance to the source empirical dataset.

Figure 3

Now, the final step: I connect the concept of social role with that of conscious agent, as represented in the Interface Theory of Perception (Hoffman et al. 2015 [1]; Fields et al. 2018 [2]). Figure 4 represents my pictographic thinking about it. Social roles are temporary outcomes of learning and social interaction between Conscious Agents (CA). In other words, social roles form as humans exchange information about their conscious experience, which serves to translate objectively existing states of the world into material possible to process by our brain, so as to decide whether to run away from the tiger or maybe rather kill the tiger and take the antelope. We take action consequently, we contemplate its consequences, and we talk about it, and we show to each other how we can learn new stuff.

Figure 4

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

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

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

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

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

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

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

Second-hand observations

MY EDITORIAL ON YOU TUBE

I keep reviewing, upon the request of the International Journal of Energy Sector Management (ISSN1750-6220), a manuscript entitled ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’. I have already formulated my first observations on that paper in the last update: I followed my suspects home, where I mostly focused on studying the theoretical premises of the model used in the paper under review, or rather of a model used in another article, which the paper under review heavily refers to.

As I go through that initial attempt to review this manuscript, I see I was bitching a lot, and this is not nice. I deeply believe in the power of eristic dialogue, and I think that being artful in verbal dispute is different from being destructive. I want to step into the shoes of those authors, technically anonymous to me (although I can guess who they are by their bibliographical references), who wrote ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’. When I write a scientific paper, my conclusion is essentially what I wanted to say from the very beginning, I just didn’t know how to phrase that s**t out. All the rest, i.e. introduction, mathematical modelling, empirical research – it all serves as a set of strings (duct tape?), which help me attach my thinking to other people’s thinking.

I assume that people who wrote ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ are like me. Risky but sensible, as assumptions come. I start from the conclusion of their paper, and I am going to follow upstream. When I follow upstream, I mean business. It is not just about going upstream the chain of paragraphs: it is going upstream the flow of language itself. I am going to present you a technique I use frequently when I really want to extract meaning and inspiration from a piece of writing. I split that writing into short paragraphs, like no more than 10 lines each. I rewrite each such paragraph in inverted syntax, i.e. I rewrite from the last word back to the first one. It gives something like Master Yoda speaking: bullshit avoid shall you. I discovered by myself, and this is backed by the science of generative grammar, that good writing, when read backwards, uncovers something like a second layer of meaning, and that second layer is just as important as the superficial one.

I remember having applied this method to a piece of writing by Deepak Chopra. It was almost wonderful how completely meaningless that text was when read backwards. There was no second layer. Amazing.

Anyway, now I am going to demonstrate the application of that method to the conclusion of ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’. I present paragraphs of the original text in Times New Roman Italic. I rewrite the same paragraphs in inverted syntax with Times New Roman Bold. Under each such pair ‘original paragraph + inverted syntax’ I write my second-hand observations inspired by those two layers of meaning, and those observations of mine come in plain Times New Roman.

Let’s dance.

Original text: Although increasing the investment in energy reduction can effectively improve the environmental quality; in a relatively short period of time, the improvement of environmental quality is also very obvious; but in the long run, the the influence of the system (1). In this study, the energy, economic and environmental (3E) four-dimensional system model of energy conservation constraints was first established. The Bayesian estimation method is used to correct the environmental quality variables to obtain the environmental quality data needed for the research.

Inverted syntax: Research the for needed data quality environmental the obtain to variables quality environmental the correct to used is method estimation Bayesian the established first was constraints conservation energy of model system dimensional four 3E environmental and economic energy the study this in system the of influence run long the in but obvious very also is quality environmental of improvement the time of period short relatively a in quality environmental the improve effectively can reduction energy in investment the increasing although.

Second-hand observations: The essential logic of using Bayesian methodology is to reduce uncertainty in an otherwise weakly studied field, and to set essential points for orientation. A Bayesian function essentially divides reality into parts, which correspond to, respectively, success and failure.

It is interesting that traits of reality which we see as important – energy, economy and environmental quality – can be interpreted as dimensions of said reality. It corresponds to the Interface Theory of Perception (ITP): it pays off to build a representation of reality based on what we want rather than on what we perceive as absolute truth.

Original text: In addition, based on the Chinese statistical yearbook data, the Levenberg-Marquardt BP neural network method optimized by genetic algorithm is used to energy, economy and environment under energy conservation constraints. The parameters in the four-dimensional system model are effectively identified. Finally, the system science analysis theory and environment is still deteriorating with the decline in the discount rate of energy reduction inputs.

Inverted syntax: Inputs reduction energy of rate discount the in decline the with deteriorating still is environment and theory analysis science system the finally identified effectively are model system dimensional four the in parameters the constraints conservation energy under environment and economy energy to used is algorithm genetic by optimized method network neural Levenberg-Marquardt Backpropagation the data yearbook statistical Chinese the on based addition in.

Second-hand observations: The strictly empirical part of the article is relatively the least meaningful. The Levenberg-Marquardt BP neural network is made for quick optimization. It is essentially the method of Ordinary Least Squares transformed into a heuristic algorithm, and it can optimize very nearly anything. When using the Levenberg-Marquardt BP neural network we risk overfitting (i.e. hasty conclusions based on a sequence of successes) rather than the inability to optimize. It is almost obvious that – when trained and optimized with a genetic algorithm – the network can set such values in the model which allow stability. It simply means that the model has a set of values that virtually eliminate the need for adjustment between particular arguments, i.e. that the model is mathematically sound. On the other hand, it would be intellectually risky to attach too much importance to the specific values generated by the network. Remark: under the concept of ‘argument’ in the model I mean mathematical expressions of the type: [coefficient]*[parameter]*[local value in variable].

The article conveys an important thesis, namely that the rate of return on investment in environmental improvement is important for sustaining long-term commitment to such improvement.

Original text: It can be better explained that effective control of the peak arrival time of pollution emissions can be used as an important decision for pollution emission control and energy intensity reduction; Therefore, how to effectively and reasonably control the peak of pollution emissions is of great significance for controlling the stability of Energy, Economy and Environment system under the constraint of energy reduction, regulating energy intensity, improving environmental quality and sustainable development.

Inverted syntax: Development sustainable and quality environmental improving intensity energy regulating reduction energy of constraint the under system environment and economy energy of stability the controlling for significance great of is emissions pollution of peak the control reasonably and effectively to how therefore reduction intensity energy and control emission pollution for decision important an as used be can emissions pollution of time arrival peak the of control effective that explained better be can.

Second-hand observations: This is an interesting logic: we can control the stability of a system by controlling the occurrence of peak states. Incidentally, it is the same logic as that used during the COVID-19 pandemic. If we can control the way that s**t unfolds up to its climax, and if we can make that climax somewhat less steep, we have an overall better control over the whole system.

Original text: As the environmental capacity decreases, over time, the evolution of environmental quality shows an upward trend of fluctuations and fluctuations around a certain central value; when the capacity of the ecosystem falls to the limit, the system collapses. In order to improve the quality of the ecological environment and promote the rapid development of the economy, we need more measures to use more means and technologies to promote stable economic growth and management of the ecological environment.

Inverted syntax: Environment ecological the of management and growth economic stable promote to technologies and means more use to measures more need we economy the of development rapid the promote and environment ecological the of quality the improve to order in collapse system the limit the to falls ecosystem the of capacity the when value central a around fluctuations and fluctuations of trend upward an shows quality environmental of evolution the time over decreases capacity environmental the as.

Second-hand observations: We can see more of the same logic: controlling a system means avoiding extreme states and staying in a zone of proximal development. As the system reaches the frontier of its capacity, fluctuations amplify and start drawing an upward drift. We don’t want such a drift. The system is the most steerable when it stays in a somewhat mean-reverted state.

I am summing up that little exercise of style. The authors of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’ claim that relations between economy, energy and environment are a complex, self-regulating system, yet the capacity of that system to self-regulate is relatively the most pronounced in some sort of central expected states thereof, and fades as the system moves towards peak states. According to this logic, our relations with ecosystems are always somewhere between homeostasis and critical disaster, and those relations are the most manageable when closer to homeostasis. A predictable, and economically attractive rate of return in investments that contribute to energy savings seems to be an important component of that homeostasis.

The claim in itself is interesting and very largely common-sense, although it goes against some views, that e.g. in order to take care of the ecosystem we should forego economics. Rephrased in business terms, the main thesis of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’ is that we can manage that dynamic system as long as it involves project management much more than crisis-management. When the latter prevails, things get out of hand. The real intellectual rabbit hole starts when one considers the method of proving the veracity of that general thesis. The authors build a model of non-linear connections between volume of pollution, real economic output, environmental quality, and constraint on energy reduction. Non-linear connections mean that output variables of the model – on the left side of each equation – are rates of change over time in each of the four variables. Output variables in the model are strongly linked, via attractor-like mathematical arguments on the input side, i.e. arguments which combine coefficients strictly speaking with standardized distance from pre-defined peak values in pollution, real economic output, environmental quality, and constraint on energy reduction. In simpler words, the theoretical model built by the authors of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’ resembles a spiderweb. It has distant points of attachment, i.e. the peak values, and oscillates between them.

It is interesting how this model demonstrates the cognitive limitations of mathematics. If we are interested in controlling relations between energy, economy, and environment, our first, intuitive approach is to consider these desired outcomes as dimensions of our reality. Yet, those dimensions could be different. If I want to become a top-level basketball player, I does not necessarily mean that social reality is truly pegged on a vector of becoming-a-top-level-basketball-player. Social mechanics might be structured around completely different variables. Still, with strong commitment, this particular strategy might work. Truth is not the same as payoffs from our actions. A model of relations energy-economy-environment pegged on desired outcomes in these fields might be essentially false in ontological terms, yet workable as a tool for policy-making. This approach is validated by the Interface Theory of Perception (see, e.g. Hoffman et al. 2015 [1] and Fields et al. 2018 [2]).

From the formal-mathematical point of view, the model is construed as a square matrix of complex arguments, i.e. the number of arguments on the left, input side of each equation is the same as the number of equations, whence the possibility to make a Jacobian matrix thereof, and to calculate its eigenvalues. The authors preset the coefficients of the model, and the peak-values so as to test for stability. Testing the model with those preset values demonstrates an essential lack of stability in the such-represented system. Stability is further tested by studying the evolution trajectory of the system. The method of evolution trajectory, in this context, seems referring to life sciences and the concept of phenotypic trajectory (see e.g. Michael & Dean 2013 [3]), and shows that the system, such as modelled, is unstable. Its evolution trajectory can change in an irregular and chaotic way.

In a next step, the authors test their model with empirical data regarding China between 2000 and 2017. They use a Levenberg–Marquardt Backpropagation Network in order to find the parameters of the system. With thus-computed parameters, and the initial state of the system set on data from 1980, evolution trajectory of the system proves stable, in a multi cycle mode.

Now, as I have passed in review the logic of ‘Evolutionary Analysis of a Four-dimensional Energy-Economy-Environment Dynamic System’, I start bitching again, i.e. I point at what I perceive as, respectively, strengths and weaknesses of the manuscript. After reading and rereading the paper, I come to the conclusion that the most valuable part thereof is precisely the use of evolution trajectory as theoretical vessel. The value added I can see here consists in representing something complex that we want – we want our ecosystem not to kill us (immediately) and we want our smartphones and our power plants working as well – in a mathematical form, which can be further processed along the lines of evolution trajectory.

That inventive, intellectually far-reaching approach is accompanied, however, by several weaknesses. Firstly, it is an elaborate path leading to common-sense conclusions, namely that managing our relations with the ecosystem is functional as long as it takes the form of economically sound project management, rather than crisis management. The manuscript seems to be more of a spectacular demonstration of method rather than discovery in substance.

Secondly, the model, such as is presented in the manuscript, is practically impossible to decipher without referring to the article Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019, https://doi.org/10.1155/2019/3941920 . When I say ‘impossible’, it means that the four equations of the model under review are completely cryptic, as the authors do not explain their mathematical notation at all, and one has to look into this Zhao, L., & Otoo, C. O. A. (2019) paper in order to make any sense of it.

After cross-referencing those two papers and the two models, I obtain quite a blurry picture. In this Zhao, L., & Otoo, C. O. A. (2019) we have a complex, self-regulating system made of 3 variables: volume of pollution x(t), real economic output y(t), and environmental quality z(t). The system goes through an economic cycle of k periods, and inside the cycle those three variables reach their respective local maxima and combine into complex apex states. These states are: F = max_k[x(t)], E = max_k[y(t)], H = max_k(∆z/∆y) – or the peak value of the impact of economic growth 𝑦(𝑡) on environmental quality 𝑧(𝑡) – and P stands for absolute maximum of pollution possible to absorb by the ecosystem, thus something like P = max(F). With those assumptions in mind, the original model by Zhao, L., & Otoo, C. O. A. (2019), which, for the sake of presentational convenience I will further designate as Model #1, goes like:

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

d(y)/d(t) = -b₁*x – b₂*y – b₃*z

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

The authors of ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ present a different model, which they introduce as an extension of that by Zhao, L., & Otoo, C. O. A. (2019). They introduce a 4^th variable, namely energy reduction constraints designated as w(t). There is no single word as for what does it exactly mean. The first moments over time of, respectively, x(t), y(t), z(t), and w(t) play out as in Model #2:

d(x)/d(t)= a₁*x*[(y/M) – 1] – a₂*y + a₃*z + a₄w

d(y)/d(t) = -b₁*x + b₂*y*[1 – (y/F)] + b₃*z*[1 – (z/E)] – b₄*w

d(z)/d(t) = c₁*x*[(x/N) – 1] – c₂*y – c₃*z – c₄*w

d(w)/d(t) = d₁*x – d₂*y + d₃*z*[1 – (z/H)] + d₄*w*[(y/P) – 1]

No, I have a problem. When the authors of ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ present their Model #2 as a simple extension of Model #1, this is simply not true. First of all, Model #2 contains two additional parameters, namely M and N, which are not explained at all in the paper. They are supposed to be normal numbers and that’s it. If I follow bona fide the logic of Model #1, M and N should be some kind of maxima, simple or compound. It is getting interesting. I have a model with two unknown maxima and 4 known ones, and I am supposed to understand the theory behind. Cool. I like puzzles.

The parameter N is used in the expression [(x/N) – 1]. We divide the local volume of pollution x by N, thus we do x/N, and this is supposed to mean something. If we keep any logic at all, N should be a maximum of pollution, yet we already have two maxima of pollution, namely F and P. As for M, it is used in the expression [(y/M) – 1] and therefore I assume M is a maximum state of real economic output ‘y’. Once again, we already have one maximum in that category, namely ‘E’. Apparently, the authors of Model #2 assume that the volume of pollution x(t) can have three different, meaningful maxima, whilst real economic output y(t) has two of them. I will go back to those maxima further below, when I discuss the so-called ‘attractor expressions’ contained in Model #2.

Second of all, Model #2 presents a very different logic than Model #1. Arbitrary signs of coefficients a_i, b_i, c_i and d_i are different, i.e. minuses replace pluses and vice versa. Attractor expressions of the type [(a/b) – 1] or [1 – (a/b)] are different, too. I am going to stop by these ones a bit longer, as it is important regarding the methodology of science in general. When I dress a hypothesis like y = a*x₁ + b*x₂, coefficients ‘a’ and ‘b’ are neutral in the sense that if x₁ > 0, then a*x₁ > 0 as well etc. In other words, positive coefficients ‘a’ and ‘b’ do not imply anything about the relation between y, x₁, and x₂.

On the other hand, when I say y = -a*x₁ + b*x₂, it is different. Instead of having a coefficient ‘a’, I have a coefficient ‘-a’, thus opposite to ‘y’. If x₁ > 0, then a*x₁ < 0 and vice versa. By assigning a negative coefficient to phenomenon x, I assume it works as a contrary force to phenomenon y. A negative coefficient is already a strong assumption. As I go through all the arbitrarily negative coefficients in Model #2, I can see the following strong claims:

>>> Assumption 1: the rate of change in the volume of pollution d(x)/d(t) is inversely proportional to the real economic output y.

>>> Assumption 2: the rate of change in real economic output d(y)/d(t) is inversely proportional to the volume of pollution x

>>> Assumption 3: the rate of change in real economic output d(y)/d(t) is inversely proportional to energy reduction constraints w.

>>> Assumption 4: the rate of change in environmental quality d(z)/d(t) is inversely proportional to environmental quality z.

>>> Assumption 5: the rate of change in environmental quality d(z)/d(t) is inversely proportional to real economic output y.

>>> Assumption 6: the rate of change in environmental quality d(z)/d(t) is inversely proportional to the volume of pollution x.

>>> Assumption 7: the rate of change in energy reduction constraints d(w)/d(t) is inversely proportional to real economic output y.

These assumptions would greatly benefit from theoretical discussion, as some of them, e.g. Assumption 1 and 2, seem counterintuitive.

Empirical data presented in ‘Evolutionary Analysis of a Four-dimensional Energy- Economy- Environment Dynamic System’ is probably the true soft belly of the whole logical structure unfolding in the manuscript. Authors present that data in the standardized form, as constant-base indexes, where values from 1999 are equal to 1. In Table 1 below, I present those standardized values:

Table 1

Year	X – volume of pollution	Y – real economic output	Z – environmental quality	W – energy reduction constraints
2000	1,9626	1,0455	1,1085	0,9837
2001	3,6786	1,1066	1,2228	0,9595
2002	2,2791	1,2064	1,3482	0,9347
2003	1,2699	1,402	1,5283	0,8747
2004	2,3033	1,6382	1,8062	0,7741
2005	1,9352	1,8594	2,0813	0,7242
2006	2,0778	2,038	2,4509	0,6403
2007	3,9437	2,2156	3,0307	0,5455
2008	6,5583	2,2808	3,5976	0,4752
2009	3,283	2,3912	3,8997	0,4061
2010	3,3307	2,5656	4,602	0,3693
2011	3,6871	2,7534	5,4243	0,3565
2012	4,013	2,8608	6,0326	0,321
2013	3,9274	2,9659	6,6068	0,2817
2014	4,2167	3,0292	7,2151	0,2575
2015	4,2893	3,0583	7,6813	0,2322
2016	4,5925	3,1004	8,2872	0,2159
2017	4,6121	3,1942	9,2297	0,2147

I found several problems with that data, and they sum up to one conclusion: it is practically impossible to check its veracity. The time series of real economic output seem to correspond to some kind of constant-price measurement of aggregate GDP of China, yet it does not fit the corresponding time series published by the World Bank (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD ). Metrics such as ‘environmental quality’ (x) or energy reduction constraints (w) are completely cryptic. Probably, they are some sort of compound indices, and their structure in itself requires explanation.

There seems to be a logical loop between the theoretical model presented in the beginning of the manuscript, and the way that data is presented. The model presents an important weakness as regards functional relations inside arguments based on peak values, such as ‘y/M’ or ‘y/P’. The authors very freely put metric tons of pollution in fractional relation with units of real output etc. This is theoretical freestyle, which might be justified, yet requires thorough explanation and references to literature. Given the form that data is presented under, a suspicion arises, namely that standardization, i.e. having driven all data to the same denomination, opened the door to those strange, cross-functional arguments. It is to remember that even standardized through common denomination, distinct phenomena remain distinct. A mathematical trick is not the same as ontological identity.

Validation of the model with a Levenberg–Marquardt Backpropagation Network raises doubts, as well. This specific network, prone to overfitting, is essentially a tool for quick optimization in a system which we otherwise thoroughly understand. This is the good old method of Ordinary Least Squares translated into a sequence of heuristic steps. The LM-BN network does not discover anything about the system at hand, it just optimizes it as quickly as possible.

In a larger perspective, using a neural network to validate a model implies an important assumption, namely that consecutive states of the system form a Markov chain, i.e. each consecutive state is exclusively the outcome of the preceding state. It is to remember that neural networks in general are artificial intelligence, and intelligence, in short, means that we figure out what to do when we have no clue as for what to do, and we do it without any providential, external guidelines. The model presented by the authors clearly pegs the system on hypothetical peak values. These are exogenous to all but one possible state of the system, whence a logical contradiction between the model and the method of its validation.

Good. After some praising and some bitching, I can assess the manuscript by answering standard questions asked by the editor of the International Journal of Energy Sector Management (ISSN1750-6220).

Originality: Does the paper contain new and significant information adequate to justify publication?

The paper presents a methodological novelty, i.e. the use of evolution trajectory as a method to study complex social-environmental systems, and this novelty deserves being put in the spotlight even more than it is in the present version of the paper. Still, substantive conclusions of the paper do not seem original at all.

Relationship to Literature: Does the paper demonstrate an adequate understanding of the relevant literature in the field and cite an appropriate range of literature sources? Is any significant work ignored?

The paper presents important weaknesses as for bibliographical referencing. First of all, there are clear theoretical gaps as regards the macroeconomic aspect of the model presented, and as regards the nature and proper interpretation of the empirical data used for validating the model. More abundant references in these two fields would be welcome, if not necessary.

Second of all, the model presented by the authors is practically impossible to understand formally without reading another paper, referenced is a case of exaggerate referencing. The paper should present its theory in a complete way.

Methodology: Is the paper’s argument built on an appropriate base of theory, concepts, or other ideas? Has the research or equivalent intellectual work on which the paper is based been well designed? Are the methods employed appropriate?

The paper combines a very interesting methodological approach, i.e. the formulation of complex systems in a way that makes them treatable with the method of evolution trajectory, with clear methodological weaknesses. As for the latter, three main questions emerge. Firstly, it seems to be methodologically incorrect to construe the cross-functional attractor arguments, where distinct phenomena are denominated one over the other. Secondly, the use of LM-BN network as a tool for validating the model is highly dubious. This specific network is made for quick optimization of something we understand and not for discovery inside something we barely understand.

Thirdly, the use of a neural network of any kind implies assuming that consecutive states of the system form a Markov chain, which is logically impossible with exogenous peak-values preset in the model.

Results: Are results presented clearly and analysed appropriately? Do the conclusions adequately tie together the other elements of the paper?

The results are clear, yet their meaning seems not to be fully understood. Coefficients calculated via a neural network represent a plausibly possible state of the system. When the authors conclude that the results so-obtained, combined with the state of the system from the year 1980, it seems really stretched in terms of empirical inference.

Implications for research, practice and/or society: Does the paper identify clearly any implications for research, practice and/or society? Does the paper bridge the gap between theory and practice? How can the research be used in practice (economic and commercial impact), in teaching, to influence public policy, in research (contributing to the body of knowledge)? What is the impact upon society (influencing public attitudes, affecting quality of life)? Are these implications consistent with the findings and conclusions of the paper?

The paper presents more implications for research than for society. As stated before, substantive conclusions of the paper boil down to common-sense claims, i.e. that it is better to keep the system stable rather than unstable. On the other hand, some aspects of the method used, i.e. the application of evolutionary trajectory, seem being very promising for the future. The paper seems to create abundant, interesting openings for future research rather than practical applications for now.

Quality of Communication: Does the paper clearly express its case, measured against the technical language of the field and the expected knowledge of the journal’s readership? Has attention been paid to the clarity of expression and readability, such as sentence structure, jargon use, acronyms, etc.

The quality of communication is a serious weakness in this case. The above-mentioned exaggerate reference to Zhao, L., & Otoo, C. O. A. (2019). Stability and Complexity of a Novel Three-Dimensional Environmental Quality Dynamic Evolution System. Complexity, 2019, https://doi.org/10.1155/2019/3941920 is one point. The flow of logic is another. When, for example, the authors suddenly claim (page 8, top): ‘In this study we set …’, there is no explanation why and on what theoretical grounds they do so.

Clarity and correct phrasing clearly lack as regards all the macroeconomic aspect of the paper. It is truly hard to understand what the authors mean by ‘economic growth’.

Finally, some sentences are clearly ungrammatical, e.g. (page 6, bottom): ‘By the system (1) can be launched energy intensity […].

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

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

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

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

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

[3] Michael, L. C., & Dean, C. A. (2013). Phenotypic trajectory analysis: comparison of shape change patterns in evolution and ecology. https://doi.org/10.4404/hystrix-24.1-6298

I followed my suspects home

MY EDITORIAL ON YOU TUBE

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

d[y(t)]/d(t) = -b₁*x – b₂*y – b₃*z

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

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

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

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

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

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

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

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

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

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

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

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

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

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

A civilisation of droplets

I am getting into the groove of a new form of expression: the rubber duck. I explained more specifically the theory of the rubber duck in the update entitled A test pitch of my ‘Energy Ponds’ business concept. I use mostly videos, where, as I am talking to an imaginary audience, I sharpen (hopefully) my own ideas and the way of getting them across. In this update, I am cutting out some slack from my thinking about the phenomenon of collective intelligence, and my use of neural networks to simulate the way that human, collective intelligence works (yes, it works).

The structure of my updates on this blog changes as my form is changing. Instead of placing the link to my video in like the first subheading of the update, I place it further blow, sort of in conclusion. I prepare my updates with an extensive use of Power Point, in order both to practice a different way of formulating my ideas, and in order to have slides for my video presentation. Together with the link to You Tube, you will find another one, to the Power Point document.

Ad rem, i.e. get the hell to the point, man. I am trying to understand better my own thinking about collective intelligence and the bridging towards artificial intelligence. As I meditate about it, I find an essential phenomenological notion: the droplet of information. With the development of digital technologies, we communicate more and more with some sort of pre-packaged, whoever-is-interested-can-pick-it-up information. Videos on you tube, blogging updates, books, articles are excellent examples thereof. When I talk to the camera of my computer, I am both creating a logical structure for myself, and a droplet of information for other people.

Communication by droplets is fundamentally different from other forms, like meetings, conversations, letters etc. Until recently, and by ‘recently’ I mean like 1990, most organized human structures worked with precisely addressed information. Since we started to grow the online part of our civilization, we have been coordinating more and more with droplet information. It is as if information was working like a hormone. As You Tube swells, we have more and more of that logical hormone accumulated in our civilization.

That’s precisely my point of connection with artificial intelligence. When I observe the way a neural network works (yes, I observe them working step by step, iteration by iteration, as strange as it might seem), I see a structure which uses error as food for learning. Residual local error is to a neural network what, once again, a hormone is to a living organism.

Under the two links below, you will find:

The Power Point Presentation with slides that accompany the YT video

The video on You Tube corresponding to this update

That would be all for now. If you want to contact me directly, you can mail at: goodscience@discoversocialsciences.com .

Sketching quickly alternative states of nature

My editorial on You Tube

I am thinking about a few things, as usually, and, as usually, it is a laborious process. The first one is a big one: what the hell am I doing what I am doing for? I mean, what’s the purpose and the point of applying artificial intelligence to simulating collective intelligence? There is one particular issue that I am entertaining in this regard: the experimental check. A neural network can help me in formulating very precise hypotheses as for how a given social structure can behave. Yet, these are hypotheses. How can I have them checked?

Here is an example. Together with a friend, we are doing some research about the socio-economic development of big cities in Poland, in the perspective of seeing them turning into so-called ‘smart cities’. We came to an interesting set of hypotheses generated by a neural network, but we have a tiny little problem: we propose, in the article, a financial scheme for cities but we don’t quite understand why we propose this exact scheme. I know it sounds idiotic, but well: it is what it is. We have an idea, and we don’t know exactly where that idea came from.

I have already discussed the idea in itself on my blog, in « Locally smart. Case study in finance.» : a local investment fund, created by the local government, to finance local startup businesses. Business means investment, especially at the aggregate scale and in the long run. This is how business works: I invest, and I have (hopefully) a return on my investment. If there is more and more private business popping up in those big Polish cities, and, in the same time, local governments are backing off from investment in fixed assets, let’s make those business people channel capital towards the same type of investment that local governments are withdrawing from. What we need is an institutional scheme where local governments financially fuel local startup businesses, and those businesses implement investment projects.

I am going to try and deconstruct the concept, sort of backwards. I am sketching the landscape, i.e. the piece of empirical research that brought us to formulating the whole idea of investment fund paired with crowdfunding. Big Polish cities show an interesting pattern of change: local populations, whilst largely stagnating demographically, are becoming more and more entrepreneurial, which is observable as an increasing number of startup businesses per 10 000 inhabitants. On the other hand, local governments (city councils) are spending a consistently decreasing share of their budgets on infrastructural investment. There is more and more business going on per capita, and, in the same time, local councils seem to be slowly backing off from investment in infrastructure. The cities we studied as for this phenomenon are: Wroclaw, Lodz, Krakow, Gdansk, Kielce, Poznan, Warsaw.

More specifically, the concept tested through the neural network consists in selecting, each year, 5% of the most promising local startups, and funds each of them with €80 000. The logic behind this concept is that when a phenomenon becomes more and more frequent – and this is the case of startups in big Polish cities – an interesting strategy is to fish out, consistently, the ‘crème de la crème’ from among those frequent occurrences. It is as if we were soccer promotors in a country, where more and more young people start playing at a competitive level. A viable strategy consists, in such a case, in selecting, over and over again, the most promising players from the top of the heap and promote them further.

Thus, in that hypothetical scheme, the local investment fund selects and supports the most promising from amongst the local startups. Mind you, that 5% rate of selection is just an idea. It could be 7% or 3% just as well. A number had to be picked, in order to simulate the whole thing with a neural network, which I present further. The 5% rate can be seen as an intuitive transference from the s-Student significance test in statistics. When you test a correlation for its significance, with the t-Student test, you commonly assume that at least 95% of all the observations under scrutiny is covered by that correlation, and you can tolerate a 5% outlier of fringe cases. I suppose this is why we picked, intuitively, that 5% rate of selection among the local startups: 5% sounds just about right to delineate the subset of most original ideas.

Anyway, the basic idea consists in creating a local investment fund controlled by the local government, and this fund would provide a standard capital injection of €80 000 to 5% of most promising local startups. The absolute number ST_F (i.e. financed startups) those 5% translate into can be calculated as: ST_F = 5% * (N/10 000) * ST_{10 000}, where N is the population of the given city, and ST_{10 000} is the coefficient of startup businesses per 10 000 inhabitants. Just to give you an idea what it looks like empirically, I am presenting data for Krakow (KR, my hometown) and Warsaw (WA, Polish capital), in 2008 and 2017, which I designate, respectively, as ST_F(city_acronym; 2008) and ST_F(city_acronym; 2017). It goes like:

ST_F(KR; 2008) = 5% * (754 624/ 10 000) * 200 = 755

ST_F(KR; 2017) = 5* * (767 348/ 10 000) * 257 = 986

ST_F(WA; 2008) = 5% * (1709781/ 10 000) * 200 = 1 710

ST_F(WA; 2017) = 5% * (1764615/ 10 000) * 345 = 3 044

That glimpse of empirics allows guessing why we applied a neural network to that whole thing: the two core variables, namely population and the coefficient of startups per 10 000 people, can change with a lot of autonomy vis a vis each other. In the whole sample that we used for basic stochastic analysis, thus 7 cities from 2008 through 2017 equals 70 observations, those two variables are Pearson-correlated at r = 0,6267. There is some significant correlation, and yet some 38% of observable variance in each of those variables doesn’t give a f**k about the variance of the other variable. The covariance of these two seems to be dominated by the variability in population rather than by uncertainty as for the average number of startups per 10 000 people.

What we have is quite predictable a trend of growing propensity to entrepreneurship, combined with a bit of randomness in demographics. Those two can come in various duos, and their duos tend to be actually trios, ‘cause we have that other thing, which I already mentioned: investment outlays of local governments and the share of those outlays in the overall local budgets. Our (my friend’s and mine) intuitive take on that picture was that it is really interesting to know the different ways those Polish cities can go in the future, rather that setting one central model. I mean, the central stochastic model is interesting too. It says, for example, that the natural logarithm of the number of startups per 10 000 inhabitants, whilst being negatively correlated with the share of investment outlays in the local government’s budget, it is positively correlated with the absolute amount of those outlays. The more a local government spends on fixed assets, the more startups it can expect per 10 000 inhabitants. That latter variable is subject to some kind of scale effects from the part of the former. Interesting. I like scale effects. They are intriguing. They show phenomena, which change in a way akin to what happens when I heat up a pot full of water: the more heat have I supplied to water, the more different kinds of stuff can happen. We call it increase in the number of degrees of freedom.

The stochastically approached degrees of freedom in the coefficient of startups per 10 000 inhabitants, you can see them in Table 1, below. The ‘Ln’ prefix means, of course, natural logarithms. Further below, I return to the topic of collective intelligence in this specific context, and to using artificial intelligence to simulate the thing.

Table 1

Explained variable: Ln(number of startups per 10 000 inhabitants) R² = 0,608 N = 70
Explanatory variable	Coefficient of regression	Standard error	Significance level
Ln(investment outlays of the local government)	-0,093	0,048	p = 0,054
Ln(total budget of the local government)	0,565	0,083	p < 0,001
Ln(population)	-0,328	0,09	p < 0,001
Constant	-0,741	0,631	p = 0,245

I take the correlations from Table 1, thus the coefficients of regression from the first numerical column, and I check their credentials with the significance level from the last numerical column. As I want to understand them as real, actual things that happen in the cities studied, I recreate the real values. We are talking about coefficients of startups per 10 000 people, comprised somewhere the observable minimum ST_10 000 = 140, and the maximum equal to ST_10 000 = 345, with a mean at ST_10 000 = 223. It terms of natural logarithms, that world folds into something between ln(140) = 4,941642423 and ln(345) = 5,843544417, with the expected mean at ln(223) = 5,407171771. Standard deviation Ω from that mean can be reconstructed from the standard error, which is calculated as s = Ω/√N, and, consequently, Ω = s*√N. In this case, with N = 70, standard deviation Ω = 0,631*√70 = 5,279324767.

That regression is interesting to the extent that it leads to an absurd prediction. If the population of a city shrinks asymptotically down to zero, and if, in the same time, the budget of the local government swells up to infinity, the occurrence of entrepreneurial behaviour (number of startups per 10 000 inhabitants) will tend towards infinity as well. There is that nagging question, how the hell can the budget of a local government expand when its tax base – the population – is collapsing. I am an economist and I am supposed to answer questions like that.

Before being an economist, I am a scientist. I ask embarrassing questions and then I have to invent a way to give an answer. Those stochastic results I have just presented make me think of somehow haphazard a set of correlations. Such correlations can be called dynamic, and this, in turn, makes me think about the swarm theory and collective intelligence (see Yang et al. 2013 [1] or What are the practical outcomes of those hypotheses being true or false?). A social structure, for example that of a city, can be seen as a community of agents reactive to some systemic factors, similarly to ants or bees being reactive to pheromones they produce and dump into their social space. Ants and bees are amazingly intelligent collectively, whilst, let’s face it, they are bloody stupid singlehandedly. Ever seen a bee trying to figure things out in the presence of a window? Well, not only can a swarm of bees get that s**t down easily, but also, they can invent a way of nesting in and exploiting the whereabouts of the window. The thing is that a bee has its nervous system programmed to behave smartly mostly in social interactions with other bees.

I have already developed on the topic of money and capital being a systemic factor akin to a pheromone (see Technological change as monetary a phenomenon). Now, I am walking down this avenue again. What if city dwellers react, through entrepreneurial behaviour – or the lack thereof – to a certain concentration of budgetary spending from the local government? What if the budgetary money has two chemical hooks on it – one hook observable as ‘current spending’ and the other signalling ‘investment’ – and what if the reaction of inhabitants depends on the kind of hook switched on, in the given million of euros (or rather Polish zlotys, or PLN, as we are talking about Polish cities)?

I am returning, for a moment, to the negative correlation between the headcount of population, on the one hand, and the occurrence of new businesses per 10 000 inhabitants. Cities – at least those 7 Polish cities that me and my friend did our research on – are finite spaces. Less people in the city means less people per 1 km² and vice versa. Hence, the occurrence of entrepreneurial behaviour is negatively correlated with the density of population. A behavioural pattern emerges. The residents of big cities in Poland develop entrepreneurial behaviour in response to greater a concentration of current budgetary spending by local governments, and to lower a density of population. On the other hand, greater a density of population or less money spent as current payments from the local budget act as inhibitors of entrepreneurship. Mind you, greater a density of population means greater a need for infrastructure – yes, those humans tend to crap and charge their smartphones all over the place – whence greater a pressure on the local governments to spend money in the form of investment in fixed assets, whence the secondary in its force, negative correlation between entrepreneurial behaviour and investment outlays from local budgets.

This is a general, behavioural hypothesis. Now, the cognitive challenge consists in translating the general idea into as precise empirical hypotheses as possible. What precise states of nature can happen in those cities? This is when artificial intelligence – a neural network – can serve, and this is when I finally understand where that idea of investment fund had come from. A neural network is good at producing plausible combinations of values in a pre-defined set of variables, and this is what we need if we want to formulate precise hypotheses. Still, a neural network is made for learning. If I want the thing to make those hypotheses for me, I need to give it a purpose, i.e. a variable to optimize, and learn as it is optimizing.

In social sciences, entrepreneurial behaviour is assumed to be a good thing. When people recurrently start new businesses, they are in a generally go-getting frame of mind, and this carries over into social activism, into the formation of institutions etc. In an initial outburst of neophyte enthusiasm, I might program my neural network so as to optimize the coefficient of startups per 10 000 inhabitants. There is a catch, though. When I tell a neural network to optimize a variable, it takes the most likely value of that variable, thus, stochastically, its arithmetical average, and it keeps recombining all the other variables so as to have this one nailed down, as close to that most likely value as possible. Therefore, if I want a neural network to imagine relatively high occurrences of entrepreneurial behaviour, I shouldn’t set said behaviour as the outcome variable. I should mix it with others, as an input variable. It is very human, by the way. You brace for achieving a goal, you struggle the s**t out of yourself, and you discover, with negative amazement, that instead of moving forward, you are actually repeating the same existential pattern over and over again. You can set your personal compass, though, on just doing a good job and having fun with it, and then, something strange happens. Things get done sort of you haven’t even noticed when and how. Goals get nailed down even without being phrased explicitly as goals. And you are having fun with the whole thing, i.e. with life.

Same for artificial intelligence, as it is, as a matter of fact, an artful expression of our own, human intelligence: it produces the most interesting combinations of variables as a by-product of optimizing something boring. Thus, I want my neural network to optimize on something not-necessarily-fascinating and see what it can do in terms of people and their behaviour. Here comes the idea of an investment fund. As I have been racking my brains in the search of place where that idea had come from, I finally understood: an investment fund is both an institutional scheme, and a metaphor. As a metaphor, it allows decomposing an aggregate stream of investment into a set of more or less autonomous projects, and decisions attached thereto. An investment fund is a set of decisions coordinated in a dynamically correlated manner: yes, there are ways and patterns to those decisions, but there is a lot of autonomous figuring-out-the-thing in each individual case.

Thus, if I want to put functionally together those two social phenomena – investment channelled by local governments and entrepreneurial behaviour in local population – an investment fund is a good institutional vessel to that purpose. Local government invests in some assets, and local homo sapiens do the same in the form of startups. What if we mix them together? What if the institutional scheme known as public-private partnership becomes something practiced serially, as a local market for ideas and projects?

When we were designing that financial scheme for local governments, me and my friend had the idea of dropping a bit of crowdfunding into the cooking pot, and, as strange as it could seem, we are bit confused as for where this idea came from. Why did we think about crowdfunding? If I want to understand how a piece of artificial intelligence simulates collective intelligence in a social structure, I need to understand what kind of logical connections had I projected into the neural network. Crowdfunding is sort of spontaneous. When I am having a look at the typical conditions proposed by businesses crowdfunded at Kickstarter or at StartEngine, these are shitty contracts, with all the due respect. Having a Master’s in law, when I look at the contracts offered to investors in those schemes, I wouldn’t sign such a contract if I had any room for negotiation. I wouldn’t even sign a contract the way I am supposed to sign it via a crowdfunding platform.

There is quite a strong piece of legal and business science to claim that crowdfunding contracts are a serious disruption to the established contractual patterns (Savelyev 2017 [2]). Crowdfunding largely rests on the so-called smart contracts, i.e. agreements written and signed as software on Blockchain-based platforms. Those contracts are unusually flexible, as each amendment, would it be general or specific, can be hash-coded into the history of the individual contractual relation. That puts a large part of legal science on its head. The basic intuition of any trained lawyer is that we negotiate the s**t of ourselves before the signature of the contract, thus before the formulation of general principles, and anything that happens later is just secondary. With smart contracts, we are pretty relaxed when it comes to setting the basic skeleton of the contract. We just put the big bones in, and expect we gonna make up the more sophisticated stuff as we go along.

With the abundant usage of smart contracts, crowdfunding platforms have peculiar legal flexibility. Today you sign up for having a discount of 10% on one Flower Turbine, in exchange of £400 in capital crowdfunded via a smart contract. Next week, you learn that you can turn your 10% discount on one turbine into 7% on two turbines if you drop just £100 more into that pig coin. Already the first step (£400 against the discount of 10%) would be a bit hard to squeeze into classical contractual arrangements as for investing into the equity of a business, let alone the subsequent amendment (Armour, Enriques 2018 [3]).

Yet, with a smart contract on a crowdfunding platform, anything is just a few clicks away, and, as astonishing as it could seem, the whole thing works. The click-based smart contracts are actually enforced and respected. People do sign those contracts, and moreover, when I mentally step out of my academic lawyer’s shoes, I admit being tempted to sign such a contract too. There is a specific behavioural pattern attached to crowdfunding, something like the Russian ‘Davaj, riebiata!’ (‘Давай, ребята!’ in the original spelling). ‘Let’s do it together! Now!’, that sort of thing. It is almost as I were giving someone the power of attorney to be entrepreneurial on my behalf. If people in big Polish cities found more and more startups, per 10 000 residents, it is a more and more recurrent manifestation of entrepreneurial behaviour, and crowdfunding touches the very heart of entrepreneurial behaviour (Agrawal et al. 2014 [4]). It is entrepreneurship broken into small, tradable units. The whole concept we invented is generally placed in the European context, and in Europe crowdfunding is way below the popularity it has reached in North America (Rupeika-Aboga, Danovi 2015 [5]). As a matter of fact, European entrepreneurs seem to consider crowdfunding as really a secondary source of financing.

Time to sum up a bit all those loose thoughts. Using a neural network to simulate collective behaviour of human societies involves a few deep principles, and a few tricks. When I study a social structure with classical stochastic tools and I encounter strange, apparently paradoxical correlations between phenomena, artificial intelligence may serve. My intuitive guess is that a neural network can help in clarifying what is sometimes called ‘background correlations’ or ‘transitive correlations’: variable A is correlated with variable C through the intermediary of variable B, i.e. A is significantly correlated with B, and B is significantly correlated with C, but the correlation between A and C remains insignificant.

When I started to use a neural network in my research, I realized how important it is to formulate very precise and complex hypotheses rather than definitive answers. Artificial intelligence allows to sketch quickly alternative states of nature, by gazillions. For a moment, I am leaving the topic of those financial solutions for cities, and I return to my research on energy, more specifically on energy efficiency. In a draft article I wrote last autumn, I started to study the relative impact of the velocity of money, as well as that of the speed of technological change, upon the energy efficiency of national economies. Initially, I approached the thing in the nicely and classically stochastic a way. I came up with conclusions of the type: ‘variance in the supply of money makes 7% of the observable variance in energy efficiency, and the correlation is robust’. Good, this is a step forward. Still, in practical terms, what does it give? Does it mean that we need to add money to the system in order to have greater an energy efficiency? Might well be the case, only you don’t add money to the system just like that, ‘cause most of said money is account money on current bank accounts, and the current balances of those accounts reflect the settlement of obligations resulting from complex private contracts. There is no government that could possibly add more complex contracts to the system.

Thus, stochastic results, whilst looking and sounding serious and scientific, have remote connexion to practical applications. On the other hand, if I take the same empirical data and feed it into a neural network, I get alternative states of nature, and those states are bloody interesting. Artificial intelligence can show me, for example, what happens to energy efficiency if a social system is more or less conservative in its experimenting with itself. In short, artificial intelligence allows super-fast simulation of social experiments, and that simulation is theoretically robust.

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] Yang, X. S., Cui, Z., Xiao, R., Gandomi, A. H., & Karamanoglu, M. (2013). Swarm intelligence and bio-inspired computation: theory and applications.

[2] Savelyev, A. (2017). Contract law 2.0:‘Smart’contracts as the beginning of the end of classic contract law. Information & Communications Technology Law, 26(2), 116-134.

[3] Armour, J., & Enriques, L. (2018). The promise and perils of crowdfunding: Between corporate finance and consumer contracts. The Modern Law Review, 81(1), 51-84.

[4] Agrawal, A., Catalini, C., & Goldfarb, A. (2014). Some simple economics of crowdfunding. Innovation Policy and the Economy, 14(1), 63-97

[5] Rupeika-Apoga, R., & Danovi, A. (2015). Availability of alternative financial resources for SMEs as a critical part of the entrepreneurial eco-system: Latvia and Italy. Procedia Economics and Finance, 33, 200-210.

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.

[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 17^th, 2019

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

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

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

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

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

[8] https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE?type=points last access May 17^th, 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, …, a_z} possible actions. These, in turn, can combine into _zP_k = 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 _zP_k 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?

Tag: neural networks

A civilisation of droplets

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