The social brain

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I am thinking about my opening lectures in the coming semester. I am trying to phrase out sort of a baseline philosophy of mine, underlying all or most of what I teach, i.e. microeconomics, management, political systems, international economic relations, and economic policy. Certainly, my most fundamental message to my students is: watch reality in a scientific way. Get the hell above clichés, first impressions and tribal thinking. Reach for the information that most other people don’t, and process it rigorously. You will see that once you really mean it, scientific method is anything but boring. When you really swing that Ockham’s razor with dexterity, and cut out the bullshit, you can come to important existential realizations.

Science starts with observation. Social sciences start with the observation of what people do, and what people do consists very largely in doing something with other people. We are social beings, we do things in recurrent sequences of particular actions, sequences that we have learnt and that we keep on learning. Here I come to an interesting point, namely to what I call the « action and reaction paradigm » and what is a slightly simplistic application of the Newtonian principle labelled with the same expression. It goes more or less like: what people do is a reaction to what happens. There is a ‘yes-but’ involved. Yes, people do things in reaction to what happens, but you need to add the component of temporal sequence. People do things in reaction to everything relevant that has happened within their span of memory connected to the particular phenomenon in question.

This is a fundamental distinction. If I say ‘I do what I do in reaction to what is happening now’, my claim is essentially different from saying that ‘I do what I do as a learnt response to all the things which I know to have happened so far and which my brain considers as relevant for the case’. Two examples come to my mind: social conflicts, and technological change. When a social conflict unfolds, would it be a war between countries, a civil war, or a sharp rise in political tension, the first, superficial interpretation is that someone has just done something annoying, and the other someone just couldn’t refrain themselves from reacting, and it all ramped up to the point of being out of control. In this approach, patterns of behaviour observable in social conflicts are not really patterns, in the sense that they are not really predictable. There is a strong temptation to label conflictual behaviour as more or less random and chaotic, devoid of rationality.

Still, here, social sciences come with a firm claim: anything we do is a learnt, recurrent pattern of doing things. Actions that we take in a situation of conflict are just as much a learnt, repetitive strategy as any other piece of behaviour. Some could argue: ‘But how is it possible that people who have very seldom been aggressive in the past suddenly develop whole patterns of aggressive behaviour? And in the case of whole social groups? How can they learn being aggressive if there has not been conflict before?’. Well, this is one of the wonders observable in human culture. Culture is truly like a big virtual server. There are things stored in our culture – and by ‘things’ I mean, precisely, patterns of behaviour – which we could have hardly imagined to be there. We accumulate information over weeks, months, and years, and, all of a sudden, a radical shift in our behaviour occurs. We have tendency to consider such a brusque shift as insanity, but this usually not the case. As long as the newly manifested set of actions is coherent around an expected outcome, this is a new, subjectively rational strategy that we have just picked up from the cultural toolbox.

Cultural memory is usually much longer in its backwards reach than individual memory. If the right set of new information is being input into the life of a social group, or of an individual, centuries-old strategies can suddenly pop up. It works like a protocol: ‘OK, we have now enough information accumulated in this file so as to trigger the strategy AAA’. Different cultures have different toolboxes stored in them, and yet, the simple tools of social conflict are almost omnipresent. Wherever any tribe has ever had to fight for its hunting grounds, the corresponding patterns of whacking-the-other-over-the-head-with-that-piece-of-rock are stored in the depths of culture, most fundamentally in language.

Yes, the language we use is a store of information about how to do things. Never have looked at the thing like that? Just think: the words and expressions we use describe something that happens in our brain in response to accumulated sensory experience. Usually we have less words at hand than different things to designate. In all the abundance of our experience just some among its pieces become dignified enough to have their own words.  For a word or expression to form as part of a language, generations need to recapitulate their things of life. This is how language becomes an archive of strategies. The information it conveys is like a ZIP file in a computer: it is tightly packed, and requires some kind of semantic crowbar in order to become fully accessible and operational. The crowbar is precisely the currently absorbed experience.

Right, so we can get to fighting each other even without special training, as we have the basic strategies stored in the language we speak. And technological change? How do we innovate? When we shift towards some new technology, do we also use old patterns of behaviour conveyed in our cultural heritage? Let’s see… Here is a little intellectual experiment I use to run with my students, when we talk about innovation and technological change. Look around you. Look at all those things that surround you and which, fault of a better word, you call ‘civilisation’. Which of those things would you change, like improve or replace with something else, possibly better?

Now comes an interesting, stylized fact that I can observe in that experiment. Sometimes, I hold my classes in a big conference room, furnished in a 19th – centurish style, and equipped with a modern overhead projector attached to the ceiling. When I ask my students whether they would like to innovate with that respectable, sort of traditional furniture, they give me one of those looks, as if I were out of my mind. ‘What? Change these? But this is traditional, this is chic, this is… I don’t know, it has style!’. On the other hand, virtually each student is eager to change the overhead projector for a new(er) one.

Got it? In that experiment, people would rather change things that are already changing at an observably quick pace. The old and steady things are being left out of the scope of innovation. The 100% rational approach to innovation suggests something else: if you want to innovate, start with the oldest stuff, because it seems to be the most in need of some shake-off. Yet, the actual innovation, such as we can observe it in the culture around us, goes the other way round: it focuses on innovating in things which are already being innovated with.

Got it? Most of what we call innovation is based on a millennia-old pattern of behaviour called ‘joining the fun’. We innovate because we join an observable trend towards innovating. Yes, there are some minds, like Edison or Musk, who start innovating apparently from scratch, when there is no passing wagon to jump on. Thus, we have two patterns of innovation: joining a massively observable trend of change, or starting a new trend. The former is clear in its cultural roots. It has always been fun to join parties, festivities and public executions. The latter is more interesting in its apparent obscurity. What is the culturally rooted pattern of doing something completely new?

Easy, man, easy. Let’s do it step by step. When we perceive something as ‘completely new’, it means there are two sets of phenomena: one made of things that look old, and the other looking new. In other words, we experience cognitive dissonance. Certain things look out of date when, after having been experiencing them as functional, we start experiencing them as no more up to facing the situation at hand. We experience their dissonance as compared to other things of life. This is called perceived obsolescence.

Anything is perceived as completely new only if there is something obsolete to compare with. Let’s generalise it mathematically. There are two sets of phenomena, which I can probably define as two strings of data. I say ‘strings’, and not ‘lists’, on the account of that data being complex. Well, yes: data about real life is complex. In terms of digital technology, our experience is made of strings (not to confound with that special type of beachwear).

And so I have those two strings, and I keep using and reusing them. With time, I notice that I need to add new data, from my ongoing experience, to one of the strings, whilst the other one stays the same. With even more time, as my first string of data gets new pieces of information, i.e. new memory, that other string slowly turns from ‘the same’ into ‘old school’, then into ‘retro’, and finally into ‘that old piece of junk’. This is learning by experiencing cognitive dissonance.

We have, then, two cultural patterns of technological change. The more commonly practiced one consists in the good old ‘let’s join the fun’ sequence of actions. Willing to do things together with other people is simple, universal, and essentially belongs to the very basis of each culture. The much rarer pattern consists in becoming aware of a cognitive dissonance and figuring out something new. This is interesting. Some cultural patterns are like screwdrivers or duck-tape. Sooner or later most people use it. Other models of behaviour, whilst still rooted in our culture, are sort of harder to dig out of that abyssal toolbox. Just some people do it.

I am coming back to that « action and reaction paradigm ». Yes, we act in reaction to what happens, but what happens, happens over time, and the ‘time’ part is vital here. We act in reaction to the information that our brain collects, and when enough information has been collected, it triggers a pre-learnt, culturally rooted pattern of behaviour, and this is our action. In response to basically the same set of data available in the environment, different human beings pull different patterns of action out of the cultural toolbox. This is interesting: how exactly is it happening? I mean, how exactly this differentiation of response to environment occurs?

There is that article I have just found on Science Direct, by Porcelli et al. (2018[1]). The paper puts together quite a cartload of literature concerning the link between major mental disorders – schizophrenia (SCZ), Alzheimer’s disease (AD) and major depressive disorder (MDD) – and their corresponding impairments in social behaviour. More specifically, the authors focus on the correlation between the so-called social withdrawal (i.e. abnormal passivity in social relations), and the neurological pathways observable in these three mental disorders. One of the theoretical conclusions they draw regards what they call ‘the social brain’. The social brain is a set of neurological pathways recurrently correlated with particular patterns of social behaviour.

Yes, ladies and gentlemen, it means that what is observable outside, has its counterpart inside. There is a hypothetical way that human brains can work – a hypothetical set of sequences synaptic activations observable in our neurons – to make the best of social relations, something like a neurological general equilibrium. I have just coined up that term by analogy to general economic equilibrium. Anything outside that sort of perfect model is less efficient in terms of social relations, and so it goes all the way down to pathological behaviour connected with pathological neural pathways. Porcelli et al. go even as far as quantifying the economic value of pathological behaviour grounded in pathological mental impairment. By analogy, there is a hypothetical economic value attached to any recurrent, neural pathway.

Going reeeaally far this speculative avenue, our society can look completely different if we change the way our brain works.

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?

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[1] Porcelli, S., Van Der Wee, N., van der Werff, S., Aghajani, M., Glennon, J. C., van Heukelum, S., … & Posadas, M. (2018). Social brain, social dysfunction and social withdrawal. Neuroscience & Biobehavioral Reviews

The stubbornly recurrent LCOE


I am thinking about those results I got in my last two research updates, namely in “The expected amount of what can happen”, and in “Contagion étonnement cohérente”. Each time, I found something intriguingly coherent in mathematical terms. In “The expected amount of what can happen”, I have probably nailed down some kind of cycle in business development, some 3 – 4 years, as regards the FinTech industry. In “Contagion étonnement cohérente”, on the other hand, I have seemingly identified a cycle of behavioural change in customers, like around 2 months, which allows to interpolate two distinct, predictive models as for the development of a market: the epidemic model based on a geometric-exponential function, and the classical model of absorption based on the normal distribution. That cycle of behavioural change looks like the time lap to put into an equation, where the number of customers is a function of time elapsed, like n(t) = e0,69*t.  Why ‘0,69’ in n(t) = e0,69*t? Well, the 0,69 fits nicely, when the exponential function n(t) = eß*tneeds to match a geometric process that duplicates the number of customers at every ‘t’ elapsed, like n(t) = 2*n(t-1) + 1.

I have identified those two cycles of change, thus, and they both look like cycles of behavioural change. It takes a FinTech business like 3+ years to pass from launching a product to stabilizing it, and it apparently takes the customers some 2 months to modify significantly their behaviour – or to take a distinctive, noticeable step in such behavioural change – regarding a new technology. I am trying to wrap my mind around each of those cycles separately, as well as around their mutual connection. It seems important for continuing to write that business plan of mine for the EneFinproject, that FinTech concept for the market of energy, where households and small businesses would buy their energy through futures contracts combined with participatory deeds in the balance sheet of the energy provider.

Now, before I go further, a little explanation for those of you, who might not quite grasp the way I run this blog. This is a research log in the most literal sense of the term. I write and publish as I think about things and as I channel my energy into the thinking. This blog is the living account of what I do, not a planned presentation. As for what I do the latter category, you can find it under the heading of “Your takeaways / Vos plats à emporter“. The approach I use, the one from the side of raw science on the make, is the reason why you can see me coming and going about ideas, and this is why I write in two languages: English and French. I found out that my thinking goes just sort of better when I alternate those two.

Anyway, I am trying to understand what I have discovered, I mean those two intriguing cycles of behavioural change, and I want to incorporate that understanding in the writing of my business plan for the EneFinproject. Cycle of change spells process: there is any point of talking about a cycle if it happens like recurrently, with one cycle following a previous cycle.

So, I do what I need to do, namely I am sketching the landscape. I am visualising urban networks composed of wind turbines with vertical axis, such as I started visualising in « Something to exploit subsequently». Each network has a different operator, who maintains a certain number of turbines scattered across the city. Let this city be Lisbon, Portugal, one of my favourite places in Europe, which, on the top of all its beauty, allows experiencing that shortest interval of time in the universe, i.e. the time elapsing between the traffic lights turning greed, for vehicles, and someone from among said vehicles hooting impatiently.

We are in Lisbon, and there are local operators of urban wind turbines, and with the wind speed being 4,47 m/s on average, each turbine, such as described in the patent application no. EP 3 214 303 A1, generates an electric power averaging 47,81 kilowatts. That makes 47,81 kilowatts * 8760 hours in the normal calendar year = 418 815,60 kilowatt hoursof energy a year. At €0,23 for each kWh at the basic price for households, in Portugal, the output of one turbine is worth like € 96 327,59. According to the basic scheme of EneFin, those € 96 327,59 further split themselves in two, and make:

€ 50 257,87in Futures contracts on energy, sold to households at the more advantageous rate of €0,12, normally reserved for the big institutional end users
€ 46 069,72in Participatory deeds in the balance sheet of the operator who currently owns the turbine

Thus, each local operator of those specific wind turbines has a basic business unit – one turbine – and the growth of business is measured at the pace of developing such consecutive units. Now, the transactional platform « EneFin» implants itself in this market, as a FinTech utility for managing financial flows between the local operators of those turbines, on the one hand, and the households willing to buy energy from those turbines and invest in their balance sheet. I assume, for the moment, that EneFin takes 5% of commissionon the trading of each complex contract. One turbine generates 5%*€ 96 327,59 =  € 4 816,38 of commission to EneFin.

I am progressively make the above converge with those cycles I have identified. In the first place, I take those two cycles I have identified, i.e. the ≈ 2 months of behavioural change in customers, and the ≈ 3+ years of business maturation. On the top of that, I take the simulations of absorption, as you can see in « Safely narrow down the apparent chaos». That means I take into account still another cycle, that of 7 years = 84 months for the absorption of innovation in the market of renewable energies. As I am having a look at the thing, I am going to start the checking with the last one. Thus, I take the percentages of the market, calculated « Safely narrow down the apparent chaos», and I apply them to the population of Lisbon, Portugal, i.e. 2 943 000 peopleas for the end of 2017.

The results of this particular step in my calculations are shown in Table 1 below. Before I go interpreting and transforming those numbers, further below the table, a few words of reminder and explanation for those among the readers, who might now have quite followed my previous updates on this blog. Variability of the population is the coefficient of proportion, calculated as the standard deviation divided by the mean, said mean being the average time an average customer needs in order to switch to a new technology. This average time, in the calculations I have made so far, is assumed to be 7 years = 84 months. The coefficient of variability reflects the relative heterogeneity of the population. The greater its value, the more differentiated are the observable patterns of behaviour. At v = 0,2it is like a beach, in summer, on the Mediterranean coast, or like North Korea, i.e. people behaving in very predictable, and very recurrent ways. At v = 2, it is more like a Halloween party: everybody tries to be original.

Table 1

Number of customers acquired in Lisbon
[a] [b] [c] [d]
Variability of the population 12th month 24th month 36th month
0,1 0 0 0
0,2 30 583 6 896
0,3 5 336 25 445 86 087
0,4 29 997 93 632 212 617
0,5 61 627 161 533 310 881
0,6 85 978 206 314 365 497
0,7 100 653 229 546 387 893
0,8 107 866 238 238 390 878
0,9 110 200 238 211 383 217
1 109 574 233 290 370 157
1,1 107 240 225 801 354 689
1,2 103 981 217 113 338 471
1,3 100 272 208 016 322 402
1,4 96 397 198 958 306 948
1,5 92 525 190 184 292 331
1,6 88 753 181 821 278 638
1,7 85 134 173 925 265 878
1,8 81 695 166 513 254 020
1,9 78 446 159 577 243 014
2 75 386 153 098 232 799

Now, I do two things to those numbers. Firstly, I try to make them kind of relative to incidences of epidemic contagion. Mathematically, it means referring to that geometric process, which duplicates the number of customers at every ‘t’ elapsed, like n(t) = 2*n(t-1) + 1, which is nicely (almost) matched by the exponential function n(t) = e0,69*t. So what I do now is to take the natural logarithm out of each number in columns [b] – [d]in Table 1, and I divide it by 0,69. This is how I get the ‘t’, or the number of temporal cycles in the exponential function n(t) = e0,69*tso as to obtain the same number as shown in Table 1. Then, I divide the time frames in the headings of those columns, thus, respectively, 12, 24, and 36, by the that number of temporal cycles. As a result, I get the length of one period of epidemic contagion between customers, expressed in months.

Good, let’s diagnose this epidemic contagion. Herr Doktor Wasniewski (this is me) has pinned down the numbers shown in Table 2 below. Something starts emerging, and I am telling you, I don’t really like it. I have enough emergent things, which I have no clue what they mean, on my hands. One more emergent phenomenon is one more pain in my intellectual ass. Anyway, what is emerging, is a pattern of decreasing velocity. When I take the numbers from Table 1, obtained with a classical model of absorption, and based on the normal distribution, those numbers require various paces of epidemic contagion in the behaviour of customers. In the beginning, the contagion need to be f***ing fast, like 0,7 ÷ 0,8 of a month, so some 21 – 24 days. Only in very homogenous populations, with variability sort of v = 0,2, it is a bit longer.

One thing: do not really pay attention to the row labelled ‘Variability of the population 0,1’. This is very homogenous a population, and I placed it here mostly for the sake of contrast. The values in brackets in this particular row of Table 2 are negative, which essentially suggests that if I want that few customers, I need going back in time.

So, I start with quite vivacious a contagion, something to put in the scenario of an American thriller, like ‘World War Z no. 23’. Subsequently, the velocity of contagion is supposed to curb down, to like 1,3 ÷ 1,4 months in the second year, and almost 2 months in the 3rdyear. It correlates surprisingly with that 3+ years cycle of getting some stance in the business, which I have very intuitively identified, using Euclidean distances, in «The expected amount of what can happen». I understand that as the pace of contagion between clients is to slow down, my marketing needs to be less and less aggressive, ergo my business gains in gravitas and respectability.

Table 2

The length of one temporal period « t » in the epidemic contagion n(t) = 2*n(t-1) + 1 ≈ e0,69*t, in the local market of Lisbon, Portugal
[a] [b] [c] [d]
Variability of the population 12th month 24th month 36th month
0,1  (0,34)  (1,26)  (6,55)
0,2  2,44  2,60  2,81
0,3  0,96  1,63  2,19
0,4  0,80  1,45  2,02
0,5  0,75  1,38  1,96
0,6  0,73  1,35  1,94
0,7  0,72  1,34  1,93
0,8  0,71  1,34  1,93
0,9  0,71  1,34  1,93
1  0,71  1,34  1,94
1,1  0,71  1,34  1,94
1,2  0,72  1,35  1,95
1,3  0,72  1,35  1,96
1,4  0,72  1,36  1,97
1,5  0,72  1,36  1,97
1,6  0,73  1,37  1,98
1,7  0,73  1,37  1,99
1,8  0,73  1,38  2,00
1,9  0,73  1,38  2,00
2  0,74  1,39  2,01

The second thing I do to numbers in Table 1 is to convert them into money, and more specifically into: a) the amount of transaction-based fee of 5%, collected by the EneFin platform, when b) the amount of capital collected by the suppliers of energy via the EneFin platform. I start by assuming that my customers are not really single people, but households. The numbers in Table 1, referring to single persons, are being divided by 2,6, which is the average size of one household in Portugal.

In the next step, I convert households into energy. Easy. One person in Portugal consumes, for the strictly spoken household use, some 4 288,92 kWh a year. That makes 11 151,20 kWh per household per year. Now, I convert energy into money, which, in financial terms, means €1 338,14a year in futures contracts on energy, at €0,12 per kWh, and €1 226,63in terms of capital invested in the supplier of energy via those complex contracts in the EneFin way. The commission taken by EneFin is 5%*(€1 338,14+ €1 226,63) =  €128,24. Those are the basic steps that both the operator of urban wind turbines, and the EneFin platform will be taking, in this scenario, as they will attract new customers.

People converted into money are shown in Tables 3 and 4, below, respectively as the amount of transaction-based fee collected by EneFin, and as the capital collected by the suppliers of energy via those complex contracts traded at EneFin. As I connect the dots, more specifically tables 2 – 4, I can see that time matters. Each year, out of the three, makes a very distinct phase. During the 1styear, I need to work my ass off, in terms of marketing, to acquire customers very quickly. Still, it does not make much difference, in financial terms, which exact variability of population is the context of me working my ass off. On the other hand, in the 3rdyear, I can be much more respectable in my marketing, I can afford to go easy on customers, and, in the same time, the variability of the local population starts mattering in financial terms.

Table 3

Transaction-based fee collected by EneFin in Lisbon
Variability of the population 1st year 2nd year 3rd year
0,1 € 0,00 € 0,00 € 1,11
0,2 € 1 458,22 € 28 752,43 € 340 124,01
0,3 € 263 195,64 € 1 255 033,65 € 4 246 097,13
0,4 € 1 479 526,18 € 4 618 201,31 € 10 486 926,46
0,5 € 3 039 639,48 € 7 967 324,44 € 15 333 595,20
0,6 € 4 240 693,13 € 10 176 019,80 € 18 027 422,81
0,7 € 4 964 515,36 € 11 321 936,93 € 19 132 083,67
0,8 € 5 320 300,96 € 11 750 639,54 € 19 279 326,77
0,9 € 5 435 424,51 € 11 749 281,67 € 18 901 432,22
1 € 5 404 510,95 € 11 506 577,11 € 18 257 283,50
1,1 € 5 289 424,10 € 11 137 214,92 € 17 494 337,16
1,2 € 5 128 672,87 € 10 708 687,77 € 16 694 429,35
1,3 € 4 945 700,41 € 10 259 985,98 € 15 901 851,61
1,4 € 4 754 575,54 € 9 813 197,53 € 15 139 607,38
1,5 € 4 563 606,09 € 9 380 437,89 € 14 418 674,83
1,6 € 4 377 570,97 € 8 967 947,88 € 13 743 280,35
1,7 € 4 199 088,86 € 8 578 519,11 € 13 113 914,13
1,8 € 4 029 458,58 € 8 212 936,36 € 12 529 062,43
1,9 € 3 869 177,26 € 7 870 840,04 € 11 986 204,76
2 € 3 718 261,64 € 7 551 243,62 € 11 482 385,83

Table 4

Capital collected by the suppliers of energy via EneFin, in Lisbon
Variability of the population 1st year 2nd year 3rd year
0,1  € 0,00  € 0,00  € 10,63
0,2  € 13 948,06  € 275 020,26  € 3 253 324,36
0,3  € 2 517 495,89  € 12 004 537,77  € 40 614 395,82
0,4  € 14 151 834,00  € 44 173 614,09  € 100 308 629,20
0,5  € 29 074 492,97  € 76 208 352,96  € 146 667 559,88
0,6  € 40 562 705,95  € 97 334 772,00  € 172 434 323,50
0,7  € 47 486 146,88  € 108 295 598,06  € 183 000 528,68
0,8  € 50 889 276,10  € 112 396 186,64  € 184 408 925,42
0,9  € 51 990 445,74  € 112 383 198,48  € 180 794 321,60
1  € 51 694 754,11  € 110 061 702,11  € 174 632 966,74
1,1  € 50 593 935,49  € 106 528 711,32  € 167 335 299,39
1,2  € 49 056 331,91  € 102 429 800,96  € 159 684 091,36
1,3  € 47 306 179,81  € 98 137 917,98  € 152 102 996,27
1,4  € 45 478 048,96  € 93 864 336,33  € 144 812 044,65
1,5  € 43 651 404,71  € 89 724 941,73  € 137 916 243,78
1,6  € 41 871 957,84  € 85 779 428,52  € 131 456 019,80
1,7  € 40 164 756,47  € 82 054 498,57  € 125 436 061,23
1,8  € 38 542 223,78  € 78 557 658,50  € 119 841 889,05
1,9  € 37 009 114,98  € 75 285 468,80  € 114 649 394,41
2  € 35 565 590,09  € 72 228 493,11  € 109 830 309,84

 Now, I do one final check. I take the formula of LCOE, or the levelized cost of energy, as shown in the formula below:


Symbols in the equation have the following meaning: a) Itis the capital invested in period t b) Mtstands for the cost of maintenance in period t c) Ftsymbolizes the cost of fuel in period t and d) Etis the output of energy in period t. I assume that wind is for free, so my Ftis zero. I further assume that It+ Mtmake a lump sum of capital, acquired by the supplier of energy, and equal to the amounts of capital calculated in Table 4. Thus I take those amounts from Table 4, and I divide each of them by the energy consumed in the corresponding headcount of households. Now, it becomes really strange: whatever the phase in time, and whatever the variability of behaviour assumed in the local population, the thus-computed LCOE is always equal to €0,11. Always! Can you understand? Well, if you do, you are smarter than me, because I don’t. How can so differentiated an array of numbers, in Tables 1 – 4, yield one and the same cost of energy, those €0,11? Honestly, I don’t know.

Calm down, Herr Doktor Wasniewski. This is probably how those Greeks hit their π. Maybe I am hitting another one. I am trying to take another path. I take the number(s) of people from Table 1, I take their average consumption of energy, as official for Portugal – 4 288,92 kWh a year per person – and, finally, I take the 47,81 kilowattsof capacity in one single wind turbine, as described in the patent application no. EP 3 214 303 A1, in Lisbon, with the wind speed 4,47 m/s on average. Yes, you guessed right: I want to calculate the number of such wind turbines needed to supply energy to the given number of people, as shown in Table 1. The numerical result of this particular path of thinking is shown in Table 5 below.

The Devil never sleeps, as we say in Poland. Bloody right. He has just tempted me to take the capital amounts from Table 4 (above) and divide them by the number of turbines from Table 5. Guess what. Another constant. Whatever the exact variability in behaviour, and whatever the year, it is always €46 069,64. I can’t help it, I continue. I take that constant €46 069,64 of capital invested per one turbine, and I divide it by the constant LCOE €0,11 per kWh, and it yields  418 815,60 kWh, or 37,56 households (2,6 person per household) per turbine, in order to make it sort of smooth in numbers.

Table 5

Number of wind turbines needed for the number of customers as in Table 1
Variability of the population 1st year 2nd year 3rd year
0,1 0 0 0
0,2 0 6 71
0,3 55 261 882
0,4 307 959 2 177
0,5 631 1 654 3 184
0,6 880 2 113 3 743
0,7 1 031 2 351 3 972
0,8 1 105 2 440 4 003
0,9 1 129 2 439 3 924
1 1 122 2 389 3 791
1,1 1 098 2 312 3 632
1,2 1 065 2 223 3 466
1,3 1 027 2 130 3 302
1,4 987 2 037 3 143
1,5 948 1 948 2 994
1,6 909 1 862 2 853
1,7 872 1 781 2 723
1,8 837 1 705 2 601
1,9 803 1 634 2 489
2 772 1 568 2 384

Another thing to wrap my mind around. My brain needs some rest. Enough science for today. 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 versionas 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 pageand 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?


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The business plan for the BeFund project – ready for reading

My editorial

It’s been like 10 days since I last updated my research blog and someone could think I had some time out. Well, yes and no. Yes, I stayed away from blogging. No, I did not remain idle. I have been writing that business plan for my BeFund project. I have finished by now, and I am back to blogging. You can find, in the library of my blog, both the English version of the business plan, and the French one. I am currently rewriting the whole thing in Polish, still the concept is mature enough to be disseminated.

I am sharing with you some observations as for the very process of business planning that I have been, and still am going through. This is the educational side of my blog. I teach my students how to prepare a business plan, and it can be useful to describe my actual experience in this respect.

First, the timeline, which has followed some kind of accelerating pace of work. I started with some six weeks of more or less informal a sniffing around the topic, kind of making myself familiar with my own idea. It was just about putting together any business concept proper for the environment of smart cities. Then, by the end of February, I finally knew what I want: a behavioural, experimental lab coupled with an investment fund for startups. The next two weeks or so had been devoted to coining up like pieces of the whole concept. Here some benchmarking for the lab, there some benchmarking for the investment fund, here some research about the market of startups, there some methodological planning for the fund etc. Finally, last Monday, I felt so full of separate ideas that it was either going full power and finally writing that business plan, or writing poems and joining the cavalry, as Lord Byron recommended, in the same time.

I went for the business plan. I can’t ride on horseback (I mean, never have learnt the thing, maybe I could come to like it), and my rhyme tends to be sort of awkward. One week, and it is basically done. Now, I am progressively disseminating the business plan, and expect to engage into some kind of productive negotiations about it. A really wild idea comes to my mind: “Could I come up with a business plan for any concept in a similar time frame, i.e. around 3 months?”. Interesting. Maybe I will try. Any suggestions of any business ideas?

In the process of writing the business plan itself, so over the last 10 days, the part which made me sweat the most was probably the financial plan. There is that strange little something in finance: when I need to translate my ideas into a sequence of events measurable with financial aggregates, my thinking changes. I have to form equations, either fully consciously or somewhere in the backstage of what I call thinking. Structures form, step by step, and this is really a living proof that algebra reflects some deep patterns in our brain. Structures form, and it kicks my ass to hatch them.

The interesting introspection about this step is that it is made of two steps, as a matter of fact. At first, I have like one possible, financial path in my mind. “It has to go the way I think it has to go”, sort of. Then, whatifs start popping up. “What if I take like this fixed cost and I upgrade it by €100 000? What if some actions fail, just sort of statistically?”. They are tenacious those whatifs. They keep drilling my mind until I satisfy them, i.e. until I use financial values to express a coherent logic, not a uniform vision. My whatifs are precious, too. They force me to review each major point of my business concept. Stands to reason: if there is to be any logic in my numbers, it must the same logic all across the business plan.

Anyway, the business plan is ready, still warm. I invite you to read it, possibly to comment on it.

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 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?

And so I ventured myself into the realm of what people think they can do

My editorial

I continue reviewing the literature pertaining to behavioural economics, and, even more broadly, to the behavioural approach in social sciences. I started in my last update in French (see Parfois j’ai du pot et parfois pas tout à fait ), with Herbert Simon and Thomas Bayes. Reviewing the work of those two thinkers helped me to somehow nail down the concept of behavioural learning. Now, I turn towards someone who, most probably, had never considered himself as an economist, let alone a behavioural economist: Charles Darwin. I take on one of his works, maybe less known than his evolutionary writings: ‘The Expression of The Emotions in Man and Animals’, published in London, in 1873, with John Murray. I picked up this one for two reasons. Firstly, it is a good example of early theoretical approaches to the empirical observation of behaviour and to experimental methods in this respect. Secondly, there are those three inside me: the happy bulldog, the curious ape and the austere monk. Those three, taken together, make out of me the intellectual equivalent of those baboons that people observe in the outskirts of big cities in Africa: I keep rummaging in those piles of things that people throw away, as apparently useless, and when I find something interesting, I am just fascinated with it.

Charles Darwin approached emotions in quite a behaviourist way, i.e. by observing their external expression rather than the verbal description of the emotional content. In the introduction to his work (pages 1 – 15), he states something really interesting: if we can express emotions with our facial muscles (e.g. smile or frown), those muscles have a function of communicating our emotions, i.e. we developed these muscles in order to communicate with other people. If a dog can express its emotions by the movements (or by the rigid immobility) of its tail, it means that the muscles responsible for these movements have a social function, i.e. they evolved in order to express the canine emotions to other dogs. That, in turn, means that we need to have the right muscles, trained the right way, in order to communicate correctly with other people (dogs?). Strolling further down this avenue, I call by a casual remark that a muscle – any muscle – is a resource: I use those precious gram-molecules of neurotransmitter to give it orders, I use those gram-molecules of glycogen to give it power for carrying out the orders I give etc. A muscle is costly. Still, it gives work in exchange. Basically, my muscles are like a workforce employed to move me around. My facial muscles are a specific workforce, focused on making my emotions intelligible to other people. Partial conclusion: we develop, generation after generation, specific and individually appropriated resources, which, in turn, serve to create and maintain social cohesion, possibly contribute to our position in the social hierarchy etc.

When you talk (right, when I talk) about resources appropriated mostly for socializing, ‘The Theory of the Leisure Class’ by Thorstein Veblen [1] is really hard to ignore. This another social thinker, which we do not necessarily associate with behavioural analysis, and yet there is a lot of behaviourism in what he wrote. Studying the middle class of the Western society, and especially the peculiar form it took during the second half of the 19th century, Thorstein Veblen stated something quite interesting: there are instances of societies, where most of the available resources are directed on creating internal cohesion, and that cohesion is being built as a subtle interplay of overlapping, informal hierarchies. People acquire valuable things solely in order to fit into the desired bracket of the social structure. Veblen used to derive the source of property rights, as social institution, precisely from that phenomenon. When we acquire things in order to show something to someone, possibly to many someones, and those other someones do the same, we suddenly have a lot of things to show to each other, and it becomes important to delineate the claims that people have on each particular thing.

Good. Now I am trying to connect those four behavioural theories by Thomas Bayes, Herbert Simon, Charles Darwin and Thorstein Veblen, with my own sketch of behavioural approach (see Any given piece of my behaviour (yours too, by the way) ), and with my progressively emerging business concept for investing in smart cities (see Smart cities, or rummaging in the waste heap of culture ). Smart cities make a peculiar social environment, where we are surrounded by technologies, whose main function is to learn faster than we do. Living in smart cities makes a fundamental change, as compared to other environments. We, humans, are used to figure out things about things around us. In a smart city, the smart technologies incorporated in virtually any piece of manufactured goods around us figure out things about us. When you design smart cities, you are the active subject of innovation. Still, when you live in a smart city, you become the object of innovation.

In business terms, the behavioural analysis I am developing serves me to formulate testable hypotheses as for how will people behave when transitioning to and into the peculiar environment of smart cities. In a next step, I want to design an experimental environment to test those hypotheses, and this is, as I am figuring it right now, my most immediate operational goal in that business plan for smart cities. Yes, now I can phrase out clearly that the business I want to start consists in gathering investors with capital around the development of such an experimental environment.

Anyway, starting with the late reverend Thomas Bayes, may he be resting in peace despite my recurrent attempts to talk him into my science, as people plunge into the environment of a smart city, they learn. They learn by repeated experiments, and the sequence of experiments leads them to a given sequence of successes and failures. Charles Darwin whispers in my ear that in the process of learning, people develop resources functionally oriented on communicating and building social cohesion. Thorstein Veblen adds that with any luck, most of the social effort is going to be oriented on developing whole markets of resources valuable in developing and maintaining social cohesion, and social hierarchy. Herbert Simon claims that if we want to ascribe any kind of economic rationality to human behaviour, we should assume there is some broad and not quite explored range of possible ways to do things, and we should assume that our decision making consists in exploring that universe of opportunities and picking, imperfectly, just some of those ways as our personal strategies. I claim that in smart cities, the key factors shaping the social structures in place are: super-fast technological change and quick development of new energy sources. These two factors are likely to create very high monetization of local economic systems in smart cities (a cartload of monetary mass for each unit of real output, as compared to the world outside), and very steep social hierarchies, as well as a new geography of settlement. In a smart city, actively participating in the local technological change builds one’s position in the social hierarchy. Active participation in technological change requires the capacity to mobilize quickly significant amounts of capital, thus it requires liquid capital, monetary or similar in liquidity.  On the behavioural level, I assume that all those changes are likely to get depth and sort of sink in on the condition that people develop the corresponding patterns of recurrent, ritualized behaviour accompanied by socially recognized rules of conduct.

That was fast. I think I need to go back to Thomas Bayes, and go through the whole sequence once again, more sort of step by step. Sequence is the key concept here. I make a weak assumption that in any given experiment the probability PS of me succeeding (p) is exactly the same as the probability PF of experiencing a failure (q). Thus, PS = PF = 0,5. I consider a sequence of 10 attempts, where my score is fifty-fifty, i.e. 5 successes and 5 failures, or p = q = 5, and n = p + q = 10. This is the crest separating negative behavioural learning from the positive one, as with 5 successes and 5 failures I am, basically, equally likely to discourage myself, or, conversely, to develop valuable new skills. According to the classical Bayesian development, there are (105) / 5! = 833,3333 etc. ways of having 5 successes out of 10 attempts, and the probability of each of those 833,3333 ways actually taking place is equal, with those odds being fifty-fifty, to 0,55*0,55 = 0,000976563. Thus, in terms of actual probability, that crest between the positive behavioural learning and the negative one is P = 0,813802083 wide. As probabilities come, P = 0,813802083 is quite a lot, it leaves only 1 – P = 0,093098958 of room available for other types of occurrences.

The point of this little mathematical mindf*ck is that in the absence of any strong assumptions as for our ways to learn things, the most probable scenario is the one which gives us equal odds to give up or to form new, valuable skills. Still, as I have a closer look at that P = 0,813802083, I sort of figure out there are some of those 833,3333 ways, which are much more likely to develop valuable learning than others. If I experience a sequence like ‘q – q – q – q – q – p – p – p – p – p’, thus five failures followed by five successes, I am highly likely to drop off, or to cling desperately to the outcomes of my first success, and to develop very narrow a range of skills.

Similarly, should I experience ‘p – p – p – p – p – q – q – q – q – q ’, I will have that ‘punch-in-the-face’ syndrome, when all of a sudden, everything I had previously learnt, in a sequence of successes, gets mauled by the sequence of failures. I drop off, discouraged, or I develop a narrow span of skills with a lot of emotional drive to perpetrate them. Those two sequences, namely ‘5 failures and then 5 successes’ as well as ‘5 successes followed by 5 failures’, are likely to eliminate some people from the process of collective learning (those who drop off), and to produce, in those who manage to stay in the game, the type of behaviour we can observe in outstanding, highly driven individuals with very sharp views and little flexibility.

If, on the other hand, I experience something like ‘p – q – p – q – p – q – p – q – p – q’ or ‘q – p – q – p – q – p – q – p – q – p’, this is sort of smooth learning, when I quickly reinterpret each success in the context of the subsequent failure and vice versa. There is little adversity and lots of positive learning. The odds of dropping off are low, and the probability of developing a broad, flexible range of skills is high. This is the kind of learning that makes a skilful business person, an efficient salesman etc. Both intuition, and maths suggest that, other factors held constant, the chances of going through something like a like ‘p – q – p – q – p – q – p – q – p – q’ sequence are generally (i.e. without additional assumptions) higher than experiencing something in the lines of ‘q – q – q – q – q – p – p – p – p – p’. Any group of people, who learn something by trial and error, are likely to develop a central path of social change based on the development of social flexibility, and two fringes, one made of the temporary losers, and another one consisting of highly driven, outstanding role models. All that learning can lead to deep social change if it translates into behavioural rituals and rules of conduct, and thus we have like three streams of collective learning: rules and rituals for quick adaptation in the central path (e.g. rules and rituals for changing one’s job or for making a sale), those for developing an outstanding position in the social hierarchy (rules for becoming a mayor, for example), and, finally, those for dropping off any aspirations (rules for getting jailed etc.).

Now, I go down the Darwinian path and I assume that each type of learning leads to developing, in each person, a set of resources functionally oriented on communication and social cohesion, i.e. the equivalent of facial muscles. In smart cities, if they evolve the way I think they will evolve, inhabitants will be learning and developing personal resources for getting money, which, in turn, will allow them participating in the local, super-fast technological change. The issue of personal resources makes me bounce back to that initial idea of mine, namely to gather capital for developing a centre for experimental research regarding the technologies of smart cities. If, as my personal hypothesis states it, smart cities of the future will host steeper social hierarchies than we presently have in an average city, personal resources functionally connected to social skills are likely to be even more valuable. Hence, an experimental environment could expose people to various paths of learning with new, smart technologies. For example, I invite a group of participants to follow a path of smooth learning, sort of ‘p – q – p – q – p – q – p – q – p – q’, and then, suddenly, I expose them to learning the hard way, like ‘q – q – q – q – q – p – p – p – p – p’. How many of them will drop off? How exactly will they be dropping off? Same for the tough and successful ones: how exactly will they be becoming tough and successful? How will the exposure to tough learning narrow down, or, conversely, broaden their personal horizons?

And so I ventured myself into the realm of what people think they can do, and I see a wise man sitting by a tree. I assume he is wise, ‘cause if you see a lonely man sitting by a tree and you assume he is stupid, logically you should take another path of enlightenment, just in case the stupid man has a gun. Anyway, I assume that the man I see is wise, I introduce myself, and he does the same, and so I learn his name’s Gigerenzer, and he eagerly introduces me into the Brunswikian theory of confidence [2]. It states two important things, regarding my line of research. Firstly, and contrarily to what Adam Smith used to claim, people who know they have to make an important decision will make very fine probabilistic distinctions, as in that reasoning I have just developed a few paragraphs ago. In other words, the Brunswikian theory of confidence claims strongly, with experimental results to support the claim, that overconfidence in one’s knowledge about the situation at hand is an exception rather than the rule. Secondly, the knowledge we use to make decisions is a combination of long-term memory about the environment, on the one hand, and short-term judgment regarding the structure of the task at hand. Logically, what can introduce a lot of bias in our decisions is quick change in the environment, too quick for us to internalize it into our long-term memory. Here comes that interesting thing about smart cities: they are supposed to make really a fluid environment, with technologies figuring things out about us even faster than we do about them. What kind of cognitive bias can it induce in the decisions made by people living and doing business in smart cities?

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 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] Veblen, T. (1899). The theory of the leisure class: An economic study in the evolution of institutions. Macmillan.

[2] Gigerenzer, G., Hoffrage, U., & Kleinbölting, H. (1991). Probabilistic mental models: a Brunswikian theory of confidence. Psychological review, 98(4), 506