An enlightened grandfather

I am going personal in my writing, or at least in the piece of writing which follows. It is because I am going through an important change in my life. I mean, I am going through another important change in my life, and, as it is just one more twist among many, I already know a few things about change. I know that when I write about it, I can handle it better as compared to a situation, when I just try to shelf it somewhere in my head and write about something else. The something else I technically should be writing about is science, and here comes the next issue: science and life. I believe science is useful, and it is useful when I have the courage to implement it in real life. I am a social scientist. Changes in my life are social changes in microscale: it is all about me being connected in a certain way to other people. I can wrap my mind around my existential changes both honestly, as a person, and scientifically, as that peculiar mix of a curious ape, a happy bulldog living in the moment, and an austere monk equipped with an Ockham’s razor to cut bullshit out.

The change I am going through is about me and my son. Junior, age (almost) 25, has just left Poland, for Nice, France, to start a new job. In Poland, he was leaving with us, his parents. High time to leave, you would say. Yes, you’re right. I think the same. Still, here is the story. Every story needs proper characters, and thus I am going to name my son. His name is Mikołaj, or Nicolas, from the point of view of non-Slavic folks. Mikołaj used to study computer science and live with us, his parents, until Summer 2019. Steam was building up. As you can easily guess, Mikołaj was 23 in 2019. When a guy in his early twenties lives with his parents, friction starts. His nervous system is already calibrated on social expansion, sex, procreation and generally on thrusting himself into life head-first. None of these things matches with a guy living with his parents. In Summer 2019, Mikołaj left home for one year, and went on an Erasmus+ academic exchange to Nice, France, where, by a strange chain of coincidences, as well as by a lot of his own wit and grit he completed a graduated, at the Sophia Antipolis University, a separate Master’s program of studies. A nice prospect for professional career in France was sketching itself, with a job with the same company where Mikołaj had been doing his internship with.

When Mikołaj was away, we spent hundreds of hours on the phone. I swear, it was him more than I. We just git that vibe on the phone which we seldom could hit when talking face to face. I had been having those distance conversations with a guy who was turning, at a turbo speed, from an over-age teenager into an adult. I was talking to a guy who learnt to cook, who was keeping his apartment clean and tidy, who was open to talk about his mistakes and calmly pointed out at my mistakes. It was cool.

The pandemic changed a lot. The nice professional prospect faded away, as the company in question is specialized in IT services for hotels, airports and airlines, which is not really a tail wind right now. Mikołaj came back home on October 1st, 2020, with the purpose of completing the Polish Master’s program – which he initially started the whole Erasmus adventure with – and another purpose of finding a job. His plan was to wrap it all up – graduation and job seeking – in about 3 months. As plans like doing, this one went sideways, and what was supposed to take three months took a bit more than six. During those 6 months which Mikołaj spent with us, in Poland, both we and him had the impression of having gone back in time, in a weirdly painful and unpleasant way. Mikołaj went back to being the overgrown teenager he had been before leaving for the Erasmus exchange. Our cohabitation was a bit tense. Still, things can change for the better. Around Christmas, they started to. As I was coaching and supporting Mikołaj with his job seeking, we sort of started working together, as if it was a project we would run as a team. It was cool.

Yesterday, on April 10th, 2021, early in the morning, Mikołaj left again, to start a job he found, once again in Nice, France. Splitting up was both painful and liberating. Me and my wife experienced – and still are experiencing – the syndrome of empty nest which, interestingly, rhymes with emptiness. This is precisely what I am trying to wrap my mind around in order to produce some useful, almost new wisdom. As Mikołaj called us from Nice, yesterday in the evening, he said openly he experienced the same. Still, things can change for the better. When I heard Mikołaj’s voice on the phone, yesterday, I knew he is an adult again, and happy again. I wonder what I will cook for lunch, tomorrow, he said. In his voice, he had that peculiar vibe I know from his last stay in Nice. That ‘I am lost as f**k and happy as f**k, and I am kicking ass’ vibe. It was cool.

I am still in the process of realizing that my son is happier and stronger when being away from me than what he used to be when being close to me. It is painful, liberating, and I think it is necessary. Here comes the science. Those last years, I almost obsessively do research about the social role of social roles. What is my social role, after I have realized that from now on, being a father for my son is going to be a whole lot different? First of all, I think that my social role is partly given by external circumstances, and partly created by myself as I respond to those external stimuli. I have the freedom of shaping some part of my social role. Which part exactly? As I look at it from inside, I guess the only way to know it is to try, over and over again. I am trying, over and over again, to be the best possible version of myself. Doesn’t everybody try the same, by the way? Basing on my own life experience, I can cautiously say: ‘No’. Not everybody, or at least not always. I know I haven’t always tried to be the best version of myself. I know I am trying now because I know it has paid me off over the last 6 years or so. This is the window in time when I really started to work purposefully on being the best human I can, and I can tell you, there was a lot to do. I was 46 at the time (now, I am 53).

A bit late for starting personal development, you could say. Well, yes and no. Yes, it is late. Still, there is science behind it. During the reproductive age bracket, i.e. roughly between the age of 20 and that of 45÷50, young men are driven mostly by their sexual instinct, because that instinct is overwhelming and we have the capacity to translate it into elaborate patterns of social behaviour. Long story short, between 20 and 50, we build a position in the social hierarchy. This is how sexual instinct civilizes itself. In their late 40ies, most males start experiencing a noticeable folding down in their levels of testosterone, and the strength of sexual drive follows in step. All the motivation based on it is sort of crumbling down, too. This is what we call mid-life crisis, or, in Polish, the Faun’s afternoon.

I remember a conversation I had with a data scientist specialized in Artificial Intelligence. She told me there are AI-based simulations of the human genome, which demonstrate that said genome is programmed to work until we are 50. Anything after that is culture-based. Our culture takes a lot of pains to raise and educate young humans during the first two decades of their lives. Someone has to take care of that secondary socialization, and the most logical way of assigning that role is to take someone who is post-reproductive as a person. This is how grandparents are made by culture.

As I am meditating about the best possible version of myself, right now, this is precisely what comes to my mind. I can be and I think I want to be an enlightened grandfather. There is a bit of a problem, here, ‘cause my son has no kids for the moment. It is hard to be an actual grandfather in the absence of grandchildren, and this is why I said I want to be an enlightened one. I mean that I take the essence of the social role that a grandfather plays in society, and I try to coin it up into a mission statement.  

A good grandfather should provide wisdom. It means I need to have wisdom, and I need to communicate it intelligibly. How do I know I have wisdom? I think there are two components to that. I need to be aware of and accountable for my own mistakes. I need to work through my personal story with as much objectivism as I can, for one. How can I be objective about myself? Here is a little trick. As I live and make mistakes, I learn to observe other people’s response to my own f**k-ups. I learn there is a different, external perspective on my own actions, and with a bit of effort I can reconstruct that external perspective. I can make good, almost new wisdom about myself by combining thorough introspection of my personal experience with that intersubjective reading of my actions.   

There is more to wisdom than just my personal story. I need to collect information about my cultural surroundings, and aggregate it into intelligible a narrative, and I need to do it in the same spirit of critical observation, with curiosity, love and cold objectivism, all in one. I need to be like a local server in a digital network, with enough content stored on my hard drive, and enough efficiency in retrieving that content to be a valuable node in the system.

A good grandfather should support others and accept to act in the backstage. This is what I have been experiencing since I got that job of fundraising and coordination of research projects in my home university. I take surprisingly great a pleasure in supporting other people’s work and research. I remember that 10 years ago I would approach things differently. I would take care, most of all, about putting myself in the centre and at the top of collective projects. Now, I take pleasure in outcomes more than in my own position within those outcomes.   

Now, by antithesis, what a good grandfather shouldn’t be? I think the kind of big existential mistake I could make now would be to become a burden for other people, especially for my son. How can I make such a mistake? It is simple, I observed it in my own father. I convince myself that all good things in life are over for me, because the falling level of testosterone leaves a gaping hole in my emotional structure. I stop taking care of myself, I let myself sink into depression and cynicism, and Bob’s my uncle: I have become a burden for others. Really simple. Don’t try it at home, even under the supervision of qualified professionals.

That brings me to still another positive aspect of being a good grandfather: grit. For me, grit is something that has the chance to supplant fear and anger, under favourable circumstances. When I was young, and even when I was a mature adult, I did not really know how to fight and stand up against existential adversities. It is mostly by observing other people – who developed that skill – that I progressively learnt some of it. Grit is the emotional counterpart of resilience, and I think that conscious, purposeful resilience requires the perspective of time. I need to know, by experience, how that really crazy kind of s**t unfolds over years, in human existence, in order to develop cognitive and emotional structures for coping with it.  

Summing up, an enlightened grandfather is a critical teller of his own story, a good source of knowledge about the general story of his culture, and a supportive mentor for other people. An enlightened grandfather takes care of his body and his mind, to stay healthy, strong and happy as long as possible. An enlightened grandfather keeps himself sharp enough to help those youngsters keep their s**together when things go south. This is my personal mission statement. This is who I want to be over the 20 – 25 years to come, which is what I reasonably expect to be the time I have left for being any good in this world. What next? Well, next, it is time to say goodbye.   

Investment, national security, and psychiatry

I need to clear my mind a bit. For the last few weeks, I have been working a lot on revising an article of mine, and I feel I need a little bit of a shake-off. I know by experience that I need a structure to break free from another structure. Yes, I am one of those guys. I like structures. When I feel I lack one, I make one.

The structure which I want to dive into, in order to shake off the thinking about my article, is the thinking about my investment in the stock market. My general strategy in that department is to take the rent, which I collect from an apartment in town, every month, and to invest it in the stock market. Economically, it is a complex process of converting the residential utility of a real asset (apartment) into a flow of cash, thus into a financial asset with quite steady a market value (inflation is still quite low), and then I convert that low-risk financial asset into a differentiated portfolio of other financial assets endowed with higher a risk (stock). I progressively move capital from markets with low risk (residential real estate, money) into a high-risk-high-reward market.

I am playing a game. I make a move (monthly cash investment), and I wait for a change in the stock market. I am wrapping my mind around the observable change, and I make my next move the next month. With each move I make, I gather information. What is that information? Let’s have a look at my portfolio such as it is now. You can see it in the table below:

StockValue in EURReal return in €Rate of return I have as of April 6ht, 2021, in the morning
CASH & CASH FUND & FTX CASH (EUR) € 25,82 €                                    –   €                                     25,82
ALLEGRO.EU SA € 48,86 €                               (2,82)-5,78%
ALTIMMUNE INC. – COMM € 1 147,22 €                            179,6515,66%
APPLE INC. – COMMON ST € 1 065,87 €                                8,210,77%
BIONTECH SE € 1 712,88 €                           (149,36)-8,72%
CUREVAC N.V. € 711,00 €                             (98,05)-13,79%
DEEPMATTER GROUP PLC € 8,57 €                               (1,99)-23,26%
FEDEX CORPORATION COMM € 238,38 €                              33,4914,05%
FIRST SOLAR INC. – CO € 140,74 €                             (11,41)-8,11%
GRITSTONE ONCOLOGY INC € 513,55 €                           (158,43)-30,85%
INPOST € 90,74 €                             (17,56)-19,35%
MODERNA INC. – COMMON € 879,85 €                             (45,75)-5,20%
NOVAVAX INC. – COMMON STOCK € 1 200,75 €                            398,5333,19%
NVIDIA CORPORATION – C € 947,35 €                              42,254,46%
ONCOLYTICS BIOTCH CM € 243,50 €                             (14,63)-6,01%
SOLAREDGE TECHNOLOGIES € 683,13 €                             (83,96)-12,29%
SOLIGENIX INC. COMMON € 518,37 €                           (169,40)-32,68%
TESLA MOTORS INC. – C € 4 680,34 €                            902,3719,28%
VITALHUB CORP.. € 136,80 €                               (3,50)-2,56%
WHIRLPOOL CORPORATION € 197,69 €                              33,1116,75%
  €       15 191,41 €                            840,745,53%

A few words of explanation are due. Whilst I have been actively investing for 13 months, I made this portfolio in November 2020, when I did some major reshuffling. My overall return on the cash invested, over the entire period of 13 months, is 30,64% as for now (April 6th, 2021), which makes 30,64% * (12/13) = 28,3% on the annual basis.

The 5,53% of return which I have on this specific portfolio makes roughly 1/6th of the total return in have on all the portfolios I had over the past 13 months. It is the outcome of my latest experimental round, and this round is very illustrative of the mistake which I know I can make as an investor: panic.

In August and September 2020, I collected some information, I did some thinking, and I made a portfolio of biotech companies involved in the COVID-vaccine story: Pfizer, Biontech, Curevac, Moderna, Novavax, Soligenix. By mid-October 2020, I was literally swimming in extasy, as I had returns on these ones like +50%. Pure madness. Then, big financial sharks, commonly called ‘investment funds’, went hunting for those stocks, and they did what sharks do: they made their target bleed before eating it. They boxed and shorted those stocks in order to make their prices affordably low for long investment positions. At the time, I lost control of my emotions, and when I saw those prices plummet, I sold out everything I had. Almost as soon as I did it, I realized what an idiot I had been. Two weeks later, the same stocks started to rise again. Sharks had had their meal. In response, I did what I still wonder whether it was wise or stupid: I bought back into those positions, only at a price higher than what I sold them for.

Selling out was stupid, for sure. Was buying back in a wise move? I don’t know, like really. My intuition tells me that biotech companies in general have a bright future ahead, and not only in connection with vaccines. I am deeply convinced that the pandemic has already built up, and will keep building up an interest for biotechnology and medical technologies, especially in highly innovative forms. This is even more probable as we realized that modern biotechnology is very largely digital technology. This is what is called ‘platforms’ in the biotech lingo. These are digital clouds which combine empirical experimental data with artificial intelligence, and the latter is supposed to experiment virtually with that data. Modern biotechnology consists in creating as many alternative combinations of molecules and lifeforms as we possibly can make and study, and then pick those which offer the best combination of biological outcomes with the probability of achieving said outcomes.

My currently achieved rates of return, in the portfolio I have now, are very illustrative of an old principle in capital investment: I will fail most of the times. Most of my investment decisions will be failures, at least in the short and medium term, because I cannot possibly outsmart the incredibly intelligent collective structure of the stock market. My overall gain, those 5,53% in the case of this specific portfolio, is the outcome of 19 experiments, where I fail in 12 of them, for now, and I am more or less successful in the remaining 7.

The very concept of ‘beating the market’, which some wannabe investment gurus present, is ridiculous. The stock market is made of dozens of thousands of human brains, operating in correlated coupling, and leveraged with increasingly powerful artificial neural networks. When I expect to beat that networked collective intelligence with that individual mind of mine, I am pumping smoke up my ass. On the other hand, what I can do is to do as many different experiments as I can possibly spread my capital between.

It is important to understand that any investment strategy, where I assume that from now on, I will not make any mistakes, is delusional. I made mistakes in the past, and I am likely to make mistakes in the future. What I can do is to make myself more predictable to myself. I can narrow down the type of mistakes I tend to make, and to create the corresponding compensatory moves in my own strategy.

Differentiation of risk is a big principle in my investment philosophy, and yet it is not the only one. Generally, with the exception of maybe 2 or 3 days in a year, I don’t really like quick, daily trade in the stock market. I am more of a financial farmer: I sow, and I wait to see plants growing out of those seeds. I invest in industries rather than individual companies. I look for some kind of strong economic undertow for my investments, and the kind of undertow I specifically look for is high potential for deep technological change. Accessorily, I look for industries which sort of logically follow human needs, e.g. the industry of express deliveries in the times of pandemic. I focus on three main fields of technology: biotech, digital, and energy.

Good. I needed to shake off, and I am. Thinking and writing about real business decisions helped me to take some perspective. Now, I am gently returning into the realm of science, without completely leaving the realm of business: I am navigating the somehow troubled and feebly charted waters of money for science. I am currently involved in launching and fundraising for two scientific projects, in two very different fields of science: national security and psychiatry. Yes, I know, they can conjunct in more points than we commonly think they can. Still, in canonical scientific terms, these two diverge.

How come I am involved, as researcher, in both national security and psychiatry? Here is the thing: my method of using a simple artificial neural network to simulate social interactions seems to be catching on. Honestly, I think it is catching on because other researchers, when they hear me talking about ‘you know, simulating alternative realities and assessing which one is the closest to the actual reality’ sense in me that peculiar mental state, close to the edge of insanity, but not quite over that edge, just enough to give some nerve and some fun to science.

In the field of national security, I teamed up with a scientist strongly involved in it, and we take on studying the way our Polish forces of Territorial Defence have been acting in and coping with the pandemic of COVID-19. First, the context. So far, the pandemic has worked as a magnifying glass for all the f**kery in public governance. We could all see a minister saying ‘A,B and C will happen because we said so’, and right after there was just A happening, with a lot of delay, and then a completely unexpected phenomenal D appeared, with B and C bitching and moaning they haven’t the right conditions for happening decently, and therefore they will not happen at all.  This is the first piece of the context. The second is the official mission and the reputation of our Territorial Defence Forces AKA TDF. This is a branch of our Polish military, created in 2017 by our right-wing government. From the beginning, these guys had the reputation to be a right-wing militia dressed in uniforms and paid with taxpayers’ money. I honestly admit I used to share that view. TDF is something like the National Guard in US. These are units made of soldiers who serve in the military, and have basic military training, but they have normal civilian lives besides. They have civilian jobs, whilst training regularly and being at the ready should the nation call.

The initial idea of TDF emerged after the Russian invasion of the Crimea, when we became acutely aware that military troops in nondescript uniforms, apparently lost, and yet strangely connected to the Russian government, could massively start looking lost by our Eastern border. The initial idea behind TDF was to significantly increase the capacity of the Polish population for mobilising military resources. Switzerland and Finland largely served as models.

When the pandemic hit, our government could barely pretend they control the situation. Hospitals designated as COVID-specific had frequently no resources to carry out that mission. Our government had the idea of mobilising TDF to help with basic stuff: logistics, triage and support in hospitals etc. Once again, the initial reaction of the general public was to put the label of ‘militarisation’ on that decision, and, once again, I was initially thinking this way. Still, some friends of mine, strongly involved as social workers supporting healthcare professionals, started telling me that working with TDF, in local communities, was nothing short of amazing. TDF had the speed, the diligence, and the capacity to keep their s**t together which many public officials lacked. They were just doing their job and helping tremendously.

I started scratching the surface. I did some research, and I found out that TDF was of invaluable help for many local communities, especially outside of big cities. Recently, I accidentally had a conversation about it with M., the scientist whom I am working with on that project. He just confirmed my initial observations.

M. has strong connections with TDF, including their top command. Our common idea is to collect abundant, interview-based data from TDF soldiers mobilised during the pandemic, as regards the way they carried out their respective missions. The purely empirical edge we want to have here is oriented on defining successes and failures, as well as their context and contributing factors. The first layer of our study is supposed to provide the command of TDF with some sort of case-studies-based manual for future interventions. At the theoretical, more scientific level, we intend to check the following hypotheses:      

>> Hypothesis #1: during the pandemic, TDF has changed its role, under the pressure of external events, from the initially assumed, properly spoken territorial defence, to civil defence and assistance to the civilian sector.

>> Hypothesis #2: the actual role played by the TDF during the pandemic was determined by the TDF’s actual capacity of reaction, i.e. speed and diligence in the mobilisation of human and material resources.

>> Hypothesis #3: collectively intelligent human social structures form mechanisms of reaction to external stressors, and the chief orientation of those mechanisms is to assure proper behavioural coupling between the action of external stressors, and the coordinated social reaction. Note: I define behavioural coupling in terms of the games’ theory, i.e. as the objectively existing need for proper pacing in action and reaction.   

The basic method of verifying those hypotheses consists, in the first place, in translating the primary empirical material into a matrix of probabilities. There is a finite catalogue of operational procedures that TDF can perform. Some of those procedures are associated with territorial military defence as such, whilst other procedures belong to the realm of civil defence. It is supposed to go like: ‘At the moment T, in the location A, procedure of type Si had a P(T,A, Si) probability of happening’. In that general spirit, Hypothesis #1 can be translated straight into a matrix of probabilities, and phrased out as ‘during the pandemic, the probability of TDF units acting as civil defence was higher than seeing them operate as strict territorial defence’.

That general probability can be split into local ones, e.g. region-specific. On the other hand, I intuitively associate Hypotheses #2 and #3 with the method which I call ‘study of orientation’. I take the matrix of probabilities defined for the purposes of Hypothesis #1, and I put it back to back with a matrix of quantitative data relative to the speed and diligence in action, as regards TDF on the one hand, and other public services on the other hand. It is about the availability of vehicles, capacity of mobilisation in people etc. In general, it is about the so-called ‘operational readiness’, which you can read more in, for example, the publications of RAND Corporation (https://www.rand.org/topics/operational-readiness.html).  

Thus, I take the matrix of variables relative to operational readiness observable in the TDF, and I use that matrix as input for a simple neural network, where the aggregate neural activation based on those metrics, e.g. through a hyperbolic tangent, is supposed to approximate a specific probability relative to TDF people endorsing, in their operational procedures, the role of civil defence, against that of military territorial defence. I hypothesise that operational readiness in TDF manifests a collective intelligence at work and doing its best to endorse specific roles and applying specific operational procedures. I make as many such neural networks as there are operational procedures observed for the purposes of Hypothesis #1. Each of these networks is supposed to represent the collective intelligence of TDF attempting to optimize, through its operational readiness, the endorsement and fulfilment of a specific role. In other words, each network represents an orientation.

Each such network transforms the input data it works with. This is what neural networks do: they experiment with many alternative versions of themselves. Each experimental round, in this case, consists in a vector of metrics informative about the operational readiness TDF, and that vector locally tries to generate an aggregate outcome – its neural activation – as close as possible to the probability of effectively playing a specific role. This is always a failure: the neural activation of operational readiness always falls short of nailing down exactly the probability it attempts to optimize. There is always a local residual error to account for, and the way a neural network (well, my neural network) accounts for errors consists in measuring them and feeding them into the next experimental round. The point is that each such distinct neural network, oriented on optimizing the probability of Territorial Defence Forces endorsing and fulfilling a specific social role, is a transformation of the original, empirical dataset informative about the TDF’s operational readiness.

Thus, in this method, I create as many transformations (AKA alternative versions) of the actual operational readiness in TDF, as there are social roles to endorse and fulfil by TDF. In the next step, I estimate two mathematical attributes of each such transformation: its Euclidean distance from the original empirical dataset, and the distribution of its residual error. The former is informative about similarity between the actual reality of TDF’s operational readiness, on the one hand, and alternative realities, where TDF orient themselves on endorsing and fulfilling just one specific role. The latter shows the process of learning which happens in each such alternative reality.

I make a few methodological hypotheses at this point. Firstly, I expect a few, like 1 ÷ 3 transformations (alternative realities) to fall particularly close from the actual empirical reality, as compared to others. Particularly close means their Euclidean distances from the original dataset will be at least one order of magnitude smaller than those observable in the remaining transformations. Secondly, I expect those transformations to display a specific pattern of learning, where the residual error swings in a predictable cycle, over a relatively wide amplitude, yet inside that amplitude. This is a cycle where the collective intelligence of Territorial Defence Forces goes like: ‘We optimize, we optimize, it goes well, we narrow down the error, f**k!, we failed, our error increased, and yet we keep trying, we optimize, we optimize, we narrow down the error once again…’ etc. Thirdly, I expect the remaining transformations, namely those much less similar to the actual reality in Euclidean terms, to display different patterns of learning, either completely dishevelled, with the residual error bouncing haphazardly all over the place, or exaggeratedly tight, with error being narrowed down very quickly and small ever since.

That’s the outline of research which I am engaging into in the field of national security. My role in this project is that of a methodologist. I am supposed to design the system of interviews with TDF people, the way of formalizing the resulting data, binding it with other sources of information, and finally carrying out the quantitative analysis. I think I can use the experience I already have with using artificial neural networks as simulators of social reality, mostly in defining said reality as a vector of probabilities attached to specific events and behavioural patterns.     

As regards psychiatry, I have just started to work with a group of psychiatrists who have abundant professional experience in two specific applications of natural language in the diagnosing and treating psychoses. The first one consists in interpreting patients’ elocutions as informative about their likelihood of being psychotic, relapsing into psychosis after therapy, or getting durably better after such therapy. In psychiatry, the durability of therapeutic outcomes is a big thing, as I have already learnt when preparing for this project. The second application is the analysis of patients’ emails. Those psychiatrists I am starting to work with use a therapeutic method which engages the patient to maintain contact with the therapist by writing emails. Patients describe, quite freely and casually, their mental state together with their general existential context (job, family, relationships, hobbies etc.). They don’t necessarily discuss those emails in subsequent therapeutic sessions; sometimes they do, sometimes they don’t. The most important therapeutic outcome seems to be derived from the very fact of writing and emailing.

In terms of empirical research, the semantic material we are supposed to work with in that project are two big sets of written elocutions: patients’ emails, on the one hand, and transcripts of standardized 5-minute therapeutic interviews, on the other hand. Each elocution is a complex grammatical structure in itself. The semantic material is supposed to be cross-checked with neurological biomarkers in the same patients. The way I intend to use neural networks in this case is slightly different from that national security thing. I am thinking about defining categories, i.e. about networks which guess similarities and classification out of crude empirical data. For now, I make two working hypotheses:

>> Hypothesis #1: the probability of occurrence in specific grammatical structures A, B, C, in the general grammatical structure of a patient’s elocutions, both written and spoken, is informative about the patient’s mental state, including the likelihood of psychosis and its specific form.

>> Hypothesis #2: the action of written self-reporting, e.g. via email, from the part of a psychotic patient, allows post-clinical treatment of psychosis, with results observable as transition from mental state A to mental state B.

The inflatable dartboard made of fine paper

My views on environmentally friendly production and consumption of energy, and especially on public policies in that field, differ radically from what seems to be currently the mainstream of scientific research and writing. I even got kicked out of a scientific conference because of my views. After my paper was accepted, I received a questionnaire to fill, which was supposed to feed the discussion on the plenary session of that conference. I answered those questions in good faith and sincerely, and: boom! I receive an email which says that my views ‘are not in line with the ideas we want to develop in the scientific community’. You could rightly argue that my views might be so incongruous that kicking me out of that conference was an act of mercy rather than enmity. Good. Let’s pass my views in review.

There is that thing of energy efficiency and climate neutrality. Energy efficiency, i.e. the capacity to derive a maximum of real output out of each unit of energy consumed, can be approached from two different angles: as a stationary value, on the one hand, or an elasticity, on the other hand. We could say: let’s consume as little energy as we possibly can and be as productive as possible with that frugal base. That’s the stationary view. Yet, we can say: let’s rock it, like really. Let’s boost our energy consumption so as to get in control of our climate. Let’s pass from roughly 30% of energy generated on the surface of the Earth, which we consume now, to like 60% or 70%. Sheer laws of thermodynamics suggest that if we manage to do that, we can really run the show. These is the summary of what in my views is not in line with ‘the ideas we want to develop in the scientific community’.

Of course, I can put forth any kind of idiocy and claim this is a valid viewpoint. Politics are full of such episodes. I was born and raised in a communist country. I know something about stupid, suicidal ideas being used as axiology for running a nation. I also think that discarding completely other people’s ‘ideas we want to develop in the scientific community’ and considering those people as pathetically lost would be preposterous from my part. We are all essentially wrong about that complex stuff we call ‘reality’. It is just that some ways of being wrong are more functional than others. I think truly correct a way to review the current literature on energy-related policies is to take its authors’ empirical findings and discuss them

under a different interpretation, namely the one sketched in the preceding paragraph.

I like looking at things with precisely that underlying assumption that I don’t know s**t about anything, and I just make up cognitive stuff which somehow pays off. I like swinging around that Ockham’s razor and cut out all the strong assumptions, staying just with the weak ones, which do not require much assuming and are at the limit of stylized observations and theoretical claims.

My basic academic background is in law (my Master’s degree), and in economics (my PhD). I look at social reality around me through the double lens of those two disciplines, which, when put in stereoscopic view, boil down to having an eye on patterns in human behaviour.

I think I observe that we, humans, are social and want to stay social, and being social means a baseline mutual predictability in our actions. We are very much about maintaining a certain level of coherence in culture, which means a certain level of behavioural coupling. We would rather die than accept the complete dissolution of that coherence. We, humans, we make behavioural coherence: this is our survival strategy, and it allows us to be highly social. Our cultures always develop along the path of differentiation in social roles. We like specializing inside the social group we belong to.

Our proclivity to endorse specific skillsets, which turn into social roles, has the peculiar property of creating local surpluses, and we tend to trade those surpluses. This is how markets form. In economics, there is that old distinction between production and consumption. I believe that one of the first social thinkers who really meant business about it was Jean Baptiste Say, in his “Treatise of Political Economy”. Here >> https://discoversocialsciences.com/wp-content/uploads/2020/03/Say_treatise_political-economy.pdf  you have it in the English translation, whilst there >>

https://discoversocialsciences.com/wp-content/uploads/2018/04/traite-deconomie-politique-jean-baptiste-say.pdf it is in its elegant French original.

In my perspective, the distinction between production and consumption is instrumental, i.e. it is useful for solving some economic problems, but just some. Saying that I am a consumer is a gross simplification. I am a consumer in some of my actions, but in others I am a producer. As I write this blog, I produce written content. I prefer assuming that production and consumption are two manifestations of the same activity, namely of markets working around tradable surpluses created by homo sapiens as individual homo sapiens endorse specific social roles.

When some scientists bring forth empirically backed claims that our patterns of consumption have the capacity to impact climate (e.g. Bjelle et al. 2021[1]), I say ‘Yes, indeed, and at the end of that specific intellectual avenue we find out that creating some specific, tradable surpluses, ergo the fact of endorsing some specific social roles, has the capacity to impact climate’. Bjelle et al. find out something which from my point of view is gobsmacking: whilst relative prevalence of particular goods in the overall patterns of demand has little effect on the emission of Greenhouse Gases (GHG) at the planetary scale, there are regional discrepancies. In developing countries and in emerging markets, changes in the baskets of goods consumed seem to have strong impact GHG-wise. On the other hand, in developed economies, however the consumers shift their preferences between different goods, it seems to be very largely climate neutral. From there, Bjelle et al. conclude into such issues as environmental taxation. My own take on those results is different. What impacts climate is social change occurring in developing economies and emerging markets, and this is relatively quick demographic growth combined with quick creation of new social roles, and a big socio-economic difference between urban environments, and the rural ones.

In the broad theoretical perspective, states of society which we label as classes of socio-economic development are far more than just income brackets. They are truly different patterns of social interactions. I had a glimpse of that when I was comparing data on the consumption of energy per capita (https://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ) with the distribution of gross national product per capita (https://data.worldbank.org/indicator/NY.GDP.PCAP.CD ). It looks as if different levels of economic development were different levels of energy in the social system. Each 100 ÷ 300 kilograms of oil equivalent per capita per year seem to be associated with specific institutions in society.

Let’s imagine that climate change goes on. New s**t comes our way, which we need to deal with. We need to learn. We form new skillsets, and we define new social roles. New social roles mean new tradable surpluses, and new markets with new goods in it. We don’t really know what kind of skillsets, markets and goods that will be. Enhanced effort of collective adaptation leads to outcomes impossible to predict in themselves. The question is: can we predict the way those otherwise unpredictable outcomes will take shape?         

My fellow scientists seem not to like unpredictable outcomes. Shigetomi et al. (2020[2]) straightforwardly find out empirically that ‘only the very low, low, and very high-income households are likely to achieve a reduction in carbon footprint due to their high level of environmental consciousness. These income brackets include the majority of elderly households who are likely to have higher consciousness about environmental protection and addressing climate change’. In my fairy-tale, it means that only a fringe of society cares about environment and climate, and this is the fringe which does not really move a lot in terms of new social role. People with low income have low income because their social roles do not allow them to trade significant surpluses, and elderly people with high income do not really shape the labour market.

This is what I infer from those empirical results. Yet, Shigetomi et al. conclude that ‘The Input-Output Analysis Sustainability Evaluation Framework (IOSEF), as proposed in this study, demonstrates how disparity in household consumption causes societal distortion via the supply chain, in terms of consumption distribution, environmental burdens and household preferences. The IOSEF has the potential to be a useful tool to aid in measuring social inequity and burden distribution allocation across time and demographics’.

Guys, like really. Just sit and think for a moment. I even pass over the claim that inequality of income is a social distortion, although I am tempted to say that no know human society has ever been free of that alleged distortion, and therefore we’d better accommodate with it and stop calling it a distortion. What I want is logic. Guys, you have just proven empirically that only low-income people, and elderly high-income people care about climate and environment. The middle-incomes and the relatively young high-incomes, thus people who truly run the show of social and technological change, do not care as much as you would like them to. You claim that inequality of income is a distortion, and you want to eliminate it. When you kick inequality out of the social equation, you get rid of the low-income folks, and of the high-income ones. Stands to reason: with enforced equality, everybody is more or less middle-income. Therefore, the majority of society is in a social position where they don’t give a f**k about climate and environment. Besides, when you remove inequality, you remove vertical social mobility along hierarchies, and therefore you give a cold shoulder to a fundamental driver of social change. Still, you want social change, you have just said it.  

Guys, the conclusions you derive from your own findings are the political equivalent of an inflatable dartboard made of fine paper. Cheap to make, might look dashing, and doomed to be extremely short-lived as soon as used in practice.   


[1] Bjelle, E. L., Wiebe, K. S., Többen, J., Tisserant, A., Ivanova, D., Vita, G., & Wood, R. (2021). Future changes in consumption: The income effect on greenhouse gas emissions. Energy Economics, 95, 105114. https://doi.org/10.1016/j.eneco.2021.105114

[2] Shigetomi, Y., Chapman, A., Nansai, K., Matsumoto, K. I., & Tohno, S. (2020). Quantifying lifestyle based social equity implications for national sustainable development policy. Environmental Research Letters, 15(8), 084044. https://doi.org/10.1088/1748-9326/ab9142