# The mathematics of whatever you want: some educational content regarding political systems

### My editorial on You Tube

This time, I go educational, and I go educational about political systems, and more specifically about electoral regimes. I generally avoid talking politics with my friends, as I want them to keep being my friends. Really, politics have become so divisive a topic, those last years. I remember, like 20 years ago, talking politics was like talking about the way to organize a business, or to design a machine. Now, it has become more like an ideological choice. Personally, I find it deplorable. There are always people who have more power than other people. Democracy allows us to have some control over those people in power, and if we want to exercise effective control, we need to get your own s**t together, emotionally too. If we become so emotional about politics that we stop thinking rationally, there is something wrong with us.

OK, enough ranting and moaning. Let’s get into facts and method. So, I start as I frequently do: I make a structure, and I drop numbers casually into it, just like that. Later on, I will work through the meaning of those numbers. My structure is a simple political system made of a juxtaposition of threes. There are 3 constituencies, equal in terms of incumbent voters: each constituency has 200 000 of them incumbent voters. Three political parties – Party A, Party B, and Party C – rival for votes in those 3 constituencies. Each political party presents three candidates in the electoral race. Party A presents its candidate A.1. in Constituency 1, candidate A.2. runs in Constituency 2, and Candidate A.3 in Constituency 3. Party B goes sort of the opposite way, and makes its candidates run like: B.1. in Constituency 3, B.2. in Constituency 2, and B.3. in Constituency 1. Party C wants to be original and makes like a triangle: its candidate C.1. runs in Constituency 2, C.2. tries their luck in Constituency 3, and C.3. is in the race in Constituency 1.

Just to recapitulate that distribution of candidates as a choice presented to voters, those in Constituency 1 choose between candidates A.1., B.3., and C.3., voters in Constituency 2 split their votes among A.2., B.2., and C.1.; finally, voters in Constituency 3 have a choice between A.3., B.1., and C.2. It all looks a bit complicated, I know, and, in a moment, you will read a table with the electoral scores, as number of votes obtained. I am just signalling the assumption I made when I was making those scores up: as we have 3 candidates in each constituency, voters do not give, under any circumstance, more than 50% of their votes (or more than 100 000 in absolute numbers) to one candidate. Implicitly, I assume that candidates already represent, somehow, their local populations. It can be achieved through some kind of de facto primary elections, e.g. when you need a certain number of officially collected voters’ signatures in order to register a candidate as running in a given constituency. Anyway, you have those imaginary electoral scores in Table 1, below. Save for the assumption about ‘≤ 50%’, those numbers are random.

Table 1 – Example of electoral score in the case studied (numbers are fictional)

 Number of votes obtained Party Candidate Constituency 1 Constituency 2 Constituency 3 Party A Candidate A.1 23 000 total score [votes]               174 101 Candidate A.2 99 274 Candidate A.3 51 827 Party B Candidate B.1 6 389 total score [votes]               111 118 Candidate B.2 40 762 Candidate B.3 63 967 Party C Candidate C.1 13 580 total score [votes]               134 691 Candidate C.2 33 287 Candidate C.3 87 824 Total 174 791 153 616 91 503

On the whole, those random numbers had given quite a nice electoral attendance. In a total population of 600 000 voters, 419 910 had gone to the ballot, which makes 70%. In that general landscape, the three constituencies present different shades. People in the 1 and the 2 are nicely dutiful, they turned up to that ballot at the respective rates of 87,4%, and 76,8%. On the other hand, people in Constituency 3 seem to be somehow disenchanted: their electoral attendance was 45,8%. Bad citizens. Or maybe just bloody pissed.

Now, I apply various electoral regimes to that same distribution of votes. Scenario 1 is a simple one. It is a strictly proportional electoral regime, where votes from all three constituencies are pooled together, to allocate 5 seats among parties. The number of seats going to each party are calculated as: “Total score of the party/ Total number of votes cast”. Inside each party, seats go specific candidates according to their individual scores. The result is a bit messy. Party A gets 2 seats, for its candidates A.2. and A.3., party B passes its B.3. man, and Party C gets C.3. into the Parliament. The first, tiny, little problem is that we had 5 seats to assign, and just 4 got assigned. Why? Simple: the parties acquired fractions of seats. In the strictly proportional count, Party A got 2,073075183 seats, Party B had 1,323116858, and Party’s C score was 1,603807959. I agree that we could conceivably give 0,32 of one seat to a party. People can share, after all. Still, I can barely conceive assigning like 0,000000058 of one seat. Could be tricky for sharing. That is a typical problem with strictly proportional regimes: they look nice and fair at the first sight, but in real life they have the practical utility of an inflatable dartboard.

Scenario 2 is once again a strictly proportional regime, with 6 seats to distribute, only this time,  in each constituency, 2 seats are to be distributed among the candidates with the best scores. The result is a bit of an opposite of Scenario 1: it looks suspiciously neat. Each party gets an equal number of seats, i.e. 2. Candidates A.2., A.3., B2., B.3., C.2., and C.3. are unfolding their political wings. I mean, I have nothing against wings, but it was supposed to be proportional, wasn’t it? Each party got a different electoral score, and each gets the same number of seats. Looks a bit too neat, doesn’t it? Once again, that’s the thing with proportional: growing your proportions does not always translate into actual outcomes.

Good. I go for the 3rd scenario: a strictly plural regime, 3 seats to allocate, in each constituency just one candidate, the one with the best score, gets the seat. This is what the British people call ‘one past the post’, in their political jargon. Down this avenue, Party A pushes it’s A.2. and A.3. people through the gate, and Party C does so with C.3. That looks sort of fair, still there is something… In Constituency 1, 87 000 of votes, with a small change, got the voters one representative in the legislative body. In constituencies 2 and 3, the same representation – 1 person in the probably right place – has been acquired with, respectively, 99 274, and 33 287 votes. Those guys from constituencies 1 and 2 could feel a bit disappointed. If they were voting in constituency 3, they would need much less mobilisation to get their man past the post.

Scenario 4 unfolds as a mixed, plural-proportional regime, with 5 seats to allocate; 3 seats go to the single best candidate in each constituency, as in Scenario 3, and 2 seats go to the party with the greatest overall score across all the 3 constituencies. Inside that party, the 2 seats in question go to candidates with the highest electoral scores. The results leave me a bit perplex: they are identical to those in Scenario 3. The same people got elected, namely A.2., A.3., and C.3., only this time we are left with 2 vacancies. Only 3 seats have been allocated, out of the 5 available. How could it have happened? Well, we had a bit of a loop, here. The party with the highest overall score is Party A, and they should get the 2 seats in the proportional part of the regime. Yet, their two best horses, A.2. and A.3. are already past the post, and the only remaining is A.1. with the worst score inside their party. Can a parliamentary seat, reserved for the best runner in the winning party, be attributed to actually the worst one? Problematic. Makes bad publicity.

Scenarios 5 and 6 are both variations on the d’Hondt system. This is a special approach to mixing plural with proportional, and more specifically, to avoiding those fractional seats as in Scenario 1. Generally, the total number of votes cast for each party is divided by consecutive denominators in the range from 1 up to the number of seats to allocate. We get a grid, out of which we pick up as many greatest values as there are seats to allocate. In Scenario 5, I apply the d’Hondt logic to votes from all the 3 constituencies pooled together, and I allocate 6 seats. Scenario 6 goes with the d’Hondt logic down to the level of each constituency separately, 2 seats to allocate in each constituency. The total number of votes casted for each party is divided by consecutive denominators in the range from 1 up to the number of seats to allocate (2 in this case). The two greatest values in such a grid get the seats. Inside each party, the attribution of seats to candidates is proportional to their individual scores.

Scenario 5 seems to work almost perfectly. Party A gets 3 seats, thus they get all their three candidates past the post, Party C acquires 2 seats for C.2. and C.3., whilst Party B has one seat for candidate B.3. In a sense, this particular mix of plural and proportional seems even more fairly proportional that Scenario 1. The detailed results, which explain the attribution of seats, are given in Table 2, below.

Table 2 – Example of application of the d’Hondt system, Scenario 5

 Number of votes obtained divided by consecutive denominators Denominator of seats Party A Party B Party C 1 174 101 111 118 134 691 2 87 051 55 559 67 346 3 58 034 37 039 44 897 4 43 525 27 780 33 673 5 34 820 22 224 26 938 6 29 017 18 520 22 449

On the other hand, Scenario 6 seems to be losing the proportional component. Table 3, below, shows how exactly it is dysfunctional. As there are 2 seats to assign in each constituency, electoral scores of each party are being divided by, respectively, 1 and 2. In Constituency 1, the two best denominated scores befall to parties C and B, thus to their candidates C.3. and B.3. In Constituency 2, both of the two best denominated scores are attributed to Party A. The trouble is that Party A has just one candidate in this constituency, the A.2. guy, and he (she?) gets the seat. The second seat in this constituency must logically befall to the next best party with any people in the game, and it happens to be Party B and its candidate B.2. Constituency 3, in this particular scenario, gives two best denominated scores to parties A and C, thus to candidates A.3. and C.2. All in all, each party gets 2 seats out of the 6. Uneven scores, even distribution of rewards.

Table 3 – Application of the d’Hondt logic at the level of separate constituencies: Scenario 6.

 Party A Party B Party C Denominator of seats Constituency 1 1 23 000 63 967 87 824 2 11 500 31 984 43 912 Constituency 2 1 99 274 40 762  (?) 13 580 2 49 637 20 381 6 790 Constituency 3 1 51 827 6 389 33 287 2 25 914 3 195 16 644

Any mechanism can be observed under two angles: how it works, and how it doesn’t. It applies to electoral regimes, too. An electoral regime doesn’t work in two respects. First of all, it does not work if it does not lead to electing anyone. Second of all, it does not work if it fails to represent the votes cast in the people actually elected. There is a term, in the science of electoral systems: the wasted votes. They are votes, which do not elect anyone. They have been cast on candidates who lost the elections. Maybe some of you know that unpleasant feeling, when you learn that the person you voted for has not been elected. This is something like frustration, and yet, in my own experience, there is a shade of relief, as well. The person I voted for lost their electoral race, hence they will not do anything stupid, once in charge. If they were in charge, and did something stupid, I could be kind of held accountable. ‘Look, you voted for those idiots. You are indirectly responsible for the bad things they did’, someone could say. If they don’t get elected, I cannot be possibly held accountable for anything they do, ‘cause they are not in a position to do anything.

Wasted votes happen in all elections. Still, an efficient electoral regime should minimize their amount. Let’s compare those six alternative electoral regimes regarding their leakiness, i.e. their tendency to waste people’s voting power. You can see the corresponding analysis in Table 4 below. The method is simple. Numbers in the table correspond to votes from Table 1, cast on candidates who did not get elected in the given constituency, under the given electoral regime. You can see that the range of waste is quite broad, from 4,8% of votes cast, all the way up to 43% with a small change. It is exactly how real electoral regimes work, and this is, in the long run, the soft spot of any representative democracy. In whatever possible way you turn those numbers, you bump on a dilemma: either the race is fair for the candidates, or the ballot is fair for voters. A fair race means that essentially the best wins. There is no point in making an electoral regime, where inefficient contenders have big chances to get elected. On the other hand, those who lose the race represent people who voted for them. If we want all the voters to be accurately represented in the government, no candidate should be eliminated from the electoral contest, only then it would not be a contest.

Table 4

 Number of votes, which do not elect any candidate Constituency 1 Constituency 2 Constituency 3 Total elections Scenario 1 23 000 40 762 33 287 97 049 Scenario 2 0 13 580 6 389 19 969 Scenario 3 23 000 54 342 33 287 110 629 Scenario 4 63 967 54 342 39 676 157 985 Scenario 5 (d’Hondt method, pooled) 0 54 342 6 389 60 731 Scenario 6 (d’Hondt method, separately by constituency) 23 000 54 342 39 676 117 018 Percentage of votes cast, which do not elect any candidate Constituency 1 Constituency 2 Constituency 3 Total elections Scenario 1 13,2% 26,5% 36,4% 23,1% Scenario 2 0,0% 8,8% 7,0% 4,8% Scenario 3 13,2% 35,4% 36,4% 26,3% Scenario 4 36,6% 35,4% 43,4% 37,6% Scenario 5 (d’Hondt method, pooled) 0,0% 35,4% 7,0% 14,5% Scenario 6 (d’Hondt method, separately by constituency) 13,2% 35,4% 43,4% 27,9% Average 12,7% 29,5% 28,9% 22,4%

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?

# The art of pulling the right lever

I dig into the idea of revising my manuscript ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, in order to resubmit it to the journal Applied Energy , by somehow fusing it with two other, unpublished pieces of my writing, namely: ‘Behavioural absorption of Black Swans: simulation with an artificial neural network’, and ‘The labour-oriented, collective intelligence of ours: Penn Tables 9.1 seen through the eyes of a neural network’.

I am focusing on one particular aspect of that revision by recombination, namely on comparing the empirical datasets which I used for each research in question. This is an empiricist approach to scientific writing: I assume that points of overlapping, as well as possible synergies, are based, at the end of the day, on overlapping and synergies between the respective empirical bases of my different papers.

In ‘Climbing the right hill […]’, my basic dataset consisted in m = 300 ‘country-year’ observations, in the timeframe from 2008 through 2017, and covering the following countries: Belgium, Bulgaria, Czechia, Denmark, Germany, Estonia, Ireland, Greece, Spain, France, Croatia, Italy, Cyprus, Latvia, Lithuania, Luxembourg, Hungary, Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, United Kingdom, Norway, and Turkey. The scope of variables covered is essentially that of Penn Tables 9.1, plus some variables from other sources, pertinent to the market of electricity, to the energy sector in general, and to technological change, namely:

>> The price fork, in € between the retail price of electricity, paid by households and really small institutional entities, on the one hand, and the prices paid by big institutional consumers

>> The capital value of that price fork, in € mln, thus the difference in prices multiplied by the quantity of electricity consumed

>> Total consumption of energy in the country (thousands of tonnes of oil equivalent)

>> The percentage share of electricity in the total consumption of energy

>> The percentage share of renewable sources in the total output of electricity

>> The number of resident patent applications per country per year

>> The coefficient of fixed assets per 1 resident patent application

>> The coefficient of resident patent applications per 1 million people

The full set, in Excel format, is accessible via the following link: https://discoversocialsciences.com/wp-content/uploads/2019/11/Database-300-prices-of-electricity-in-context.xlsx . I also used a recombination of that database, made of m = 3000 randomly stacked records from the m = 300 set, just in order to check the influence of order in ‘country-year’ observations upon the results I obtained

In the two other manuscripts, namely in ‘The behavioural absorption of Black Swans […]’ and in ‘The labour-oriented, collective intelligence of ours […]’, I used one and the same empirical database, made of m = 3006 ‘country-year’ records, all selected from Penn Tables 9.1 , with the criteria of selection being the fullness of information. In other words, I kicked out of Penn Tables 9.1. all the rows with empty cells, and what remains is the m = 3006 set.

As I attempt to make some sort of cross analysis between my results from those three papers, one crossing is obvious. Variables pertinent to the market of labour, i.e. the average number of hours worked per person per year (AVH), the percentage of labour compensation in the gross national income (LABSH), and the indicator of human capital (HC), informative about the average length of educational path in the professionally active people, seem to play a special role as collectively pursued outcomes. The special role of those three – AVH, LABSH, and HC – seems to be impervious to, respectively, the presence or the absence of the variables I added from other sources in ‘Climbing the right hill […]’. It also seems impervious to the geographical scope and the temporal window of observation.

The most interesting direction for a further exploration seems to be in the crossing of ‘Black Swans […]’ with ‘Climbing the right hill […]. I take the structure from ‘Black Swans […]’ – namely the model where the optimization of an empirical variable impacts a range of social roles – and I put in that model the dataset from  ‘Climbing the right hill […]’. I observe the patterns of learning occurring in the perceptron, as I take different empirical variables.

Variables which are strong collective orientations – AVH, LABSH, and HC – display a special pattern of learning, different from other variables. Their local residual error (i.e. the arithmetical difference between the value of neural activation function and the local empirical value at hand), swings in a wide amplitude, yet in a predictable cycle. It is a pattern of learning in the lines of ‘we make a lot of mistakes, then we minimize them, and then we repeat: a lot of mistakes followed by a period of accuracy’. Other variables, run through the same model, display something different: a general tendency to minimal error, with occasional, pretty random bumps. Not much error, and not much of a visible cycle in learning.

The national societies which I study, seem to orient themselves on outcomes which associate with strong and predictably cyclical amplitude of error, this with abundant learning in a predictable cycle. There is one more thing. When optimizing variables relative to the market of labour – AVH, LABSH, and HC – the model from ‘Black Swans […]’ shows relatively the highest resilience in the incumbent social roles, i.e. those in place before social disruption starts.

Good. Something takes shape. I am reframing the method and the material I want to introduce in the revised version of ‘Climbing the right hill […]’, for the journal Applied Energy, and I add some results and provisional conclusions.

When I take the empirical material from Penn Tables 9.1, thus when I observe the otherwise bloody chaotic thing called ‘society’ through the lens of quantitative variables pertinent to the broadly spoken real of macroeconomics, that material shows some repetitive, robust properties. When I run in through a learning procedure, expressed in the form of a simple neural network, the learning centred on optimizing variables pertinent to the labour market (AVH, LABSH, HC), as well as on the index of prices in export (PL_X), – yields artificial datasets more similar to the original one, in terms of Euclidean similarity, than any other such artificial dataset, optimizing other variables. That phenomenological hierarchy seems to be robust both to the modifications of scope, and those of spatial-temporal range. When I add variables pertinent to technological change and to the market of electricity, they obediently take their place in the rank, and don’t step forward. When I extend the geographical scope of observation from Europe to the whole world, and when I extend the window of observation from the initial {2008 ÷ 2017} to the longer {1954 ÷ 2017}, the same still holds.

As I try to explain why is it so, and I try to find an empirical explanation, I make another neural network, where each empirical variable from the original dataset is the optimized output, and optimization takes place by experimenting with a vector of probabilities assigned to a set of social roles, and a random factor of disturbance. The pattern of learning is observed as the distribution of residual errors over the entire experimental sequence of phenomenal instances. In that different perspective, the same variables which seem to be privileged collective outcomes – PL_X, AVH, LABSH, and HC – display a specific pattern of learning: they swing broadly in their error, and yet they swing in a predictable cycle. When my experimental neural network learns on other variables, the pattern is different, with the curve of error being much calmer, less bumpy, and yet much less cyclical.

I return to my method and to my theoretical assumptions. I recapitulate. I start by assuming that social reality is essentially chaotic and unobservable directly, yet I can make epistemological approximations of that thing and see how they work. In this specific piece of research, I make two such types of approximation, based on different assumptions. On the one hand, I assume that quantitative, commonly measured, socio-economic variables, such as those in Penn Tables 9.1 are partial expressions of change in that otherwise chaotic social reality, and we collect those values because they represent change in the collective outcomes which we value. On the other hand, I assume that social reality can be represented as a collection of social roles, in two distinct categories: the already existing, active social roles, accompanied by temporarily dormant, ready-to-be triggered roles. Those social roles are observable as the relative frequency of occurrence, thus as the probability that any given individual endorses them.

I further assume that human societies are collectively intelligent structures, which, in turn, means that we collectively learn by experimenting with many alternative versions of ourselves. By the way, I have been wondering whether this is a hypothesis or an assumption, and I settled for assumption, because I do not really bring any direct proof thereof, and yet I make the claim. Anyway, with the assumption of collective intelligence, I can simulate two mutually correlated processes of learning through experimentation. On the one hand, among all the collective outcomes represented with quantitative socio-economic variables, we learn hierarchically, i.e. we optimize some of those outcomes in the first place, whilst treating the other ones as instrumental to that chief goal. On the other hand, we optimize each of those outcomes, represented with quantitative variables, by experimenting with the relative prevalence (i.e. probability of endorsement) in distinct social roles.

That general theoretical perspective is the foundation which I use to both make an empirical method of research, and to substantiate the claim that public policies and business strategies which stimulate technological race with clear prime for winners and clear penalty for losers are likely to bring better results, especially on the long run, than policies and strategies aiming at erasing local idiosyncrasies and at creating uniformly distributed outcomes. My point is that the latter, i.e. policies oriented on nullifying local idiosyncrasies, lead either to the absence of idiosyncrasies, and, consequently, to the absence of different versions in ourselves to experiment with and learn, or they simply prove inefficient, as they try to move the wrong lever in the machine.

Now, looking through another door inside my head, I am presenting below the structure of semestral projects I assign to my students, in the Summer semester 2021, in two different, and yet somehow concurrent courses: International Trade Policy in the major International Relations, and International Management in the major Management. You will see how I teach, and how I get a bit obsessive about digging into the same ideas, over and over again.

The complex project to graduate the International Management course, Summer semester 2021

Our common goal: develop your understanding of the transition from the domestically based business structure to an international one.

Your goal: prepare a developed, well-informed business plan, for the development of a business, from the level of one national market, to the international level. That business plan is your semestral project, which you graduate the course of International Management with.

You can see this course as an opportunity to put together and utilize the partial learning you have from all the individual subject courses you have had so far.

Your deadline is June 25th, 2021.

Definition – international scale of a business means that it becomes an economically significant choice to branch the operations into or move them completely to foreign markets. In other words, the essential difference between domestic management and international management – at least the difference we will focus on in this course – is that in domestic management the initial place of incorporation determines the strategy, whilst in international management the geographical location of operations and incorporation(s) is determined by strategic choices.

You work with a business concept of your own, or you take one of the pre-prepared business plans available at the digital platform. These are graduation business plans prepared by students from other groups, in the Winter semester 2020/2021. In other words, you develop either on your own idea, or on someone else’s idea. One of the things you will find out is that different business concepts have different potential, and follow very different paths for going to the international level.

Below, you will find the list of those pre-prepared business plans. They are coupled with links to the archives of my blog, where you can download them from. Still, you can find them as well in the ‘Files’ section of the group ‘International Management’, folder ‘Class materials’.

>> 3D Virtual Fitting Room >> https://discoversocialsciences.com/wp-content/uploads/2021/03/3D-Virtual-Fitting-Room-Business-Plan.docx

>> Chess Manufacturing (semi-finished, interesting to develop from that form) >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Chess-Business-Plan-Semi-Done.docx

>> Second-hand market for luxury goods >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Business-Plan-second-hand-market-for-luxury-fashion.docx

We will abundantly use real-life cases of big, internationally branched businesses as our business models. Some of them are those which you already know from past semesters, whilst other might be new to you:

>> Tesla >> https://ir.tesla.com/

>> PayPal >> https://investor.pypl.com/home/default.aspx

>> Solar Edge >> https://investors.solaredge.com/investor-overview

>> Novavax >> https://ir.novavax.com/investor-relations

>> Starbucks >> https://investor.starbucks.com/ir-home/default.aspx

>> Amazon >> https://ir.aboutamazon.com/overview/default.aspx

That orientation on real business cases means that the course of International Management is, from your point of view, a course of market research, business planning, and basic empirical science, more than a theoretical course. This is precisely what we are going to be doing in our classes: market research, business planning, and basic empirical science.

You can benefit from running yourself through my online course of business planning, to be found at https://discoversocialsciences.com/the-course-of-business-planning/ .

The basic structure of the business plan which you will prepare is the following:

• Section 1: Executive summary. This is a summary of the essentials, developed in further sections of the business plan. Particular focus on why and how going international with that business concept.
• Section 2: Description of the business concept. How do we create, and capture value added in that thing? What kind of value added is that? What are the goods we market? Who are our target customers? What kind of really existing, operational business models, observable in actually operational companies, do we emulate in that business?
• Section 3: Market research. We focus on collecting and presenting information on our customers, and our competitors.
• Section 4: Organization. How are we going to structure human work in that business? How many people do we need, and what kind of organizational structure should we make them work in? What is the estimate, total payroll per month and per year, in that organization?
• Section 5: The strategy for going international. Can we develop an original, proprietary technology, and apply it in different national markets? Can we benefit from the economies of scale, or those of scope, as we go international? Can we optimize and standardize our business concept into a franchise, attractive for smaller partners in foreign markets? << this is the ‘INTERNATIONAL MANAGEMENT’ part of that business plan. Now, you demonstrate your understanding of what international management is.
• Section 6: The corporate business structure. Do you see that business as one compact business entity, which operates internationally via digital platforms and contracts with external partners, or, conversely, would you rather create a network of affiliated companies in separate national (regional?) markets, all tied to and controlled by one mother company? Develop on those options and justify your choice.
• Section 7: The financial plan. Plan of revenues, costs, and of the resulting profit/loss for 3 years ahead. The balance sheet we need to start with, and its prospective changes over the next 3 years. The prospective cash-flow.

Guidelines for the graduation project in International Trade Policy Summer semester 2021

You graduate the course of ‘International Trade Policy’ by preparing a project. Your project will be a business report, the kind you could have to prepare if you are assistant to the CEO of a big firm, or to a prime minister. You are supposed to prepare a report on the impact of trade on individual businesses and national economies, in a sort of controlled economic experiment, limited in scope and in space. Your goal in the preparation of that project is to develop active understanding of international trade.

You can access the files provided as additional materials for this assignment in two ways. Below in this document, I provide links to the archives of my blog, ‘Discover social sciences’. On the other hand, all those files are to find in the ‘Files’ section of the ‘International Trade Policy’ group, in the folder ‘Class Materials’.

Your report will have two sections. In Section A, you study the impact of international trade on a set of businesses. Your business cases encompass real companies, some of which you already know from the course of microeconomics – Tesla, Netflix, Amazon, H&M – as well as new business entities which can emerge as per the business plans introduced below (these are real business plans made by students in other groups in the Winter semester 2020/2021).

In the Section B of your report, imagine that you are the government of, respectively, Poland, Ukraine, and France. Imagine that businesses from Part A grow in your country. Given the macroeconomic characteristics of your national economy, which types of those businesses are likely to grow the most, and which are not really fit? As a country, as those businesses grow, would you see your exports grow, or would it be rather an increase in your imports? How would it affect your overall balance on trade? What would you do as a government and why?

Additional guidelines and materials for the Section A of your report:

You can make a simplifying assumption that businesses can develop with and through trade along two different, although not exactly exclusive paths:

• Case A: there is a technology with potential for growth, which can be developed through expanding its target market, with exports or with franchise
• Case B: the gives business can develop significant economies of scale and scope, and trade, i.e. exports or/and imports, are a way to achieve that

You can benefit from studying the model contract of sales in international trade: https://discoversocialsciences.com/wp-content/uploads/2020/02/sale_of_perishables_model_contract.pdf

… as well as studying the so-called Incoterms >> https://discoversocialsciences.com/wp-content/uploads/2020/03/Incoterms.pdf , which are standard conditions of delivery in international trade.

The early business concepts developed by students from other groups, which you are supposed to assess as for their capacity to grow through trade, are:

The investor relations sites of the real, big companies, whose development with trade you are supposed to study as well:

Additional guidelines and materials for the Section B of your report:

The so-called trade profiles of countries, accessible with the World Trade Organization: https://www.wto.org/english/res_e/publications_e/trade_profiles20_e.htm

Example of an international trade agreement, namely than between South Korea and Australia: https://discoversocialsciences.com/wp-content/uploads/2021/03/korea-australia-free-trade-agreement.pdf

Macroeconomic profiles of Poland, Ukraine, and France >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Macroeconomic-Profiles.xlsx

# Phases of abundant experimentation

I am working, in parallel, on revising my manuscript, titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, on the one hand, and on preparing catchy, interesting paths of teaching for the summer semester, at the university, on the other hand. As for the former, you can read more in my last two updates, namely in ‘Still some juice in facts’, and in ‘As it is ripe, I can harvest’. In this update, I will develop on that path of work, but first, I am sharing a piece of educational structure I came up with for my workshops in Macroeconomics, with the students of 1st year, Bachelor, major International Relations, at my home university, namely the Andrzej Frycz-Modrzewski Krakow University, Krakow, Poland. Below, I am copying the description of training assignment such as it is being presented to my students.

For graduating workshops in Macroeconomics, Summer semester 2021, you will prepare just one, structured assignment. You can consider it as a follow up on the business plan you prepared in the course of Microeconomics.

You can take your business plan from the course of Microeconomics, or you can choose one of the business plans specifically provided as case studies for this assignment, namely:

Pick ONE business plan, once again: your own or one of the three provided as library. Review the customers’ profile in that particular business concept. Who are the customers? Are they individuals (households) or are they institutional (firms, public institutions etc.)?

Now, imagine the whole market of businesses such as the one described.

Those customers have a budget to finance the purchase of goods named in that business plan.

What other goods do they finance with the same budget?

What stream of cash does that budget come from?  Do they pay for those goods with their current income, or do they pay out of their capital base (i.e. from their assets)?

Now, take the entire population of those customers. Their AGGREGATE budgets represent aggregate demand, and that demand is derived from a stream of income, or from a capital base. In your analysis, at this point, phrase it out explicitly: ‘The market for this business concept is based on aggregate demand coming from the group of customers ABCD, and the value of that aggregate demand depends on the aggregate stream of income Y, or on the aggregate amount of assets X.’

Place that business plan in the context of the national economies whose macroeconomic profiles are provided in the file attached to this assignment (https://discoversocialsciences.com/wp-content/uploads/2021/03/Data-for-work-with-business-plans.xlsx). Those national economies are: Bulgaria, Croatia, Poland, Russia, Turkey, Ukraine, France, Italy, Latvia.

Use exhaustively, in an informed, articulate manner, the data provided in the attached file, to develop an analysis and answer the following question: ‘Which of these countries makes the best macroeconomic environment for the implementation of this specific business plan? Which of the countries is the worst macroeconomic environment in that respect? Provide, using the data at hand, informed argumentation for your choice’.

Provide your answer in the form of a business report, something like an extended, macroeconomic analysis for the business plan you took on studying the macroeconomic environment for. As you will be working with the data supplied to assists your answer, you will go through the following macroeconomic variables:

Workshops will largely consist in explaining those macroeconomic concepts, and I strongly encourage you to study their meaning in a textbook, and in online resources. The simplest way is to type each of these categories into a Google search and study the results of that search.

Your assignment largely consists in developing credible statements of the type: ‘Country A seems to make the best macroeconomic environment for this business, because its macroeconomic variables X, Y and Z take values x, y and z’.

Now, teaching content shared, I am returning to revising my manuscript. I think I pretty much nailed down, in  the last update (‘As it is ripe, I can harvest’), the core of the reproducible method of research which I want to present. As I am working on phrasing out the finer details of that reproducible method, and position it vis a vis the corresponding theory, whilst instrumenting it with a computational model, I feel like returning to questions, which the journal Applied Energy requires to address in my cover letter. I remind those questions below.

>> (1) what is the novelty of this work?

>> (2) is the paper appealing to a popular or scientific audience?

>> (3) why the author thinks the paper is important and why the journal should publish it?

I start with a tentative answer to the last one, about the importance of that research, as well as about the usefulness of publishing it. When my research gets published, two things happen. Firstly, it is being peer-reviewed, and is published only after a specific ritual is accomplished. The ritual starts with editor of the journal judging the paper ripe for asking other scientists to review it, usually 2 or 3 of them. That release from the editor to the reviewers results in the reviewers having a go at the paper, and assessing whether it is acceptable at all, and what kind of critical remarks they have. Generally, the reviewers are not expected to be indiscriminately enthusiastic about the paper. The type of answer to expect from them is the ‘yes, but…’ type. Once they provide their reviews of my manuscript in that form, I am expected to revise once again, whilst explicitly addressing the critical remarks from reviewers in a separate statement. At this stage, I revise in a ‘yes, but…’ style. I am like: ‘Yes, at this point, you are right, prof. YUTOONJJK, and thus I am changing my stance accordingly, but at this other point, with all the due respect, I am holding my ground and here is why I am doing so: …’. This phase of revision is tricky. Technically, I could change everything in response to critical remarks, but it wouldn’t be the same paper anymore. In order to remain in the same scientific territory, I need, first of all, to study the same facts. Thus, my empirical base remains the same. The essential points of my method should stay in place as well, I just might need to support it with more convincing an argumentation. What I can really change in response to reviewers’ criticism, are some details in my calculations, and the interpretation I give to the results of my empirical investigation.

The first aspect of having my paper published is precisely my readiness, and my ability, to go gracefully and convincingly through that ritual of peer-review, and my response thereto. If I think that my paper deserves publishing, I indirectly suggest that when it passes the ritualised dialogue of peer-review, everybody involved will be better off, i.e. the scientific community will benefit from other scientists criticising me, and me responding to their criticism through a polite, informed statement that I am holding my ground, with maybe some tiny concessions. Another aspect of publication is the capacity, for me, to cite that publication of mine in the future. Why would I do it? Mostly when I will be applying for funding, it is frequently welcome to prove that the research I will intend to conduct is relevant, important, and I am not (entirely) mad in my methods of running that research. In other words, when my paper gets published, it gives me scientific firepower to develop on the same stream of research. That, in turn, requires me to define an acceptably coherent stream of research, for one, and that stream should have potential for development.

All in all, when I claim that the journal which I am submitting to should publish my paper, I should convincingly prove that my research can enrich the scientific community, and it has strong potential for future development. Those general remarks phrased out, I can apply that line of thinking to my manuscript.

Policies pertinent to energy systems, especially in the environmental perspective, frequently assume that significant idiosyncrasies in individual agents or in political entities (countries, regions etc.) are bad for progress, and they should be equalized. In other words, public policies should be equalizers, or redistributors of gains from the technological race. I could notice that theoretical stance in one of the articles I have recently quoted, namely in ‘The energy metabolism of countries: Energy efficiency and use in the period that followed the global financial crisis’. Energy Policy, 139, 111304. https://doi.org/10.1016/j.enpol.2020.111304 (2020),  byprofessor Valeria Andreoni. Still, from the management point of view, or from the perspective of the new institutional school in economics, this is not necessarily true. If we want quick, deeply transformative technological change, we need a true technological race, with true winners and true losers. Equality does not really serve efficient adaptation.

I think that public policies supposed to drive rapid technological change should stimulate technological race, and stimulate inequality of outcomes in that race. In order to adapt to serious s**t, we need to experiment with many alternative ways of action. The question is: how exactly can we do it? How can governments experiment? In order to address that question, there is another one to answer: how exactly does that experimentation occur? What exactly is happening when we collectively experiment with ourselves, as a society? I think that the methodology I present in my paper creates a small opening up and into that realm of research: simulating social and technological change as a process of learning by trial and error.

Summing partly up that intellectual meandering of mine, I think that my paper deserves publishing because my method of studying social and technological change – as a manifestation of learning in collectively intelligent social structures, which adapt to stressors by creating many alternative versions of themselves and assessing their fitness to cope with said stressors – allows conceptualizing public policies and business strategies, in the sector of energy, as a process of heuristic, adaptive experimentation rather than as a linear path towards a determined end-state.

As I have spat this one out, I think that I need to combine that manuscript, namely ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, such as it is now, with two others, unpublished as well: ‘Behavioural absorption of Black Swans: simulation with an artificial neural network’, for one, and ‘The labour-oriented, collective intelligence of ours: Penn Tables 9.1 seen through the eyes of a neural network’, for two. They all operate on overlapping datasets, and they show different aspects of the same essential method.

The next question to address in my cover letter is the target audience of my paper. Is my article made for the popular audience, or rather for the scientific one? I am tempted to say: ‘for both’. Yet, I know this is a tricky question. It really means asking ‘Is my article refined enough, in terms of scientific method, to impress and influence my fellow scientists, or is it rather an interesting piece, detached from the main body of science, and served to non-scientific people in a tasty sauce?’. At the end of the day, I want to write it both ways, but the latter one will go down better as a book, later on. The form it has now, i.e. that of an article, my idea is addressed to a scientific audience, as a slightly provocative opening on an interesting perspective. Precisely, the deep intuition that I am opening a path of research rather than closing one, makes me stay at the level of short scientific form.

As I have provisionally walked myself through the cover letter which I should address to the editor of the journal Applied Energy , I come back to the structure I should give to the revised paper: ‘Introduction’, ‘Material and Methods’, ‘Theory’, ‘Calculation’, ‘Results’, ‘Discussion’, ‘Conclusion’, ‘Data availability’, ‘Glossary’, ‘Appendices’, Highlights, and Graphical Abstract.

As I intend to combine three manuscripts into one, the combined highlights of those three would be:

>> Public policies and business strategies can be studied as adaptive change in a collectively intelligent structure.

>> Markov chains of states are the general mathematical foundation of such an approach.

>> A simple perceptron can be used as computational tool for simulating social and technological change in real world.

>> The method presented allows discovering distinct, collectively pursued orientations of whole societies, and distinct types of collective learning.

>> Empirical findings suggest collective orientation on optimizing the labour market, rather than direct orientation on transforming the energy base of societies.

>> That collective orientation seems being pursued through an almost perfectly cyclical process of learning, where phases of abundant experimentation are interspersed with periods of relative homeostasis.

# Practical takeaways

I am trying to develop a coherent line of logic for the most basic courses I teach in the winter semester, namely ‘Microeconomics’ and ‘Management’. This is the hell of an unusual semester. The pandemic makes us pass largely to online teaching, for one. The pandemic itself is fascinating as social phenomenon and I want to include its study into my teaching, for two. Thirdly and finally, over the last 12 months, I developed an acceptably solid hypothesis of collective intelligence in human social structures, together with a method of studying said structures with the use of artificial neural networks.

I teach ‘Microeconomics’ and ‘Management’ to essentially the same group of students, 1st year undergraduate. There might be minor difference between those two subjects as regards the Erasmus students asking to enrol, yet it is really minor. Thus, I decided to combine my teaching in microeconomics and management into one thread, which consists, for my students, in graduating those two courses (i.e. ‘Microeconomics’ and ‘Management’) by preparing business plans as graduation projects. Why do I adopt such a didactic stance? First of all, I have been putting a lot of emphasis on the skill of business planning over the last 5 years or so. I like believing my students have some real takeaways from my classes, i.e. true practical skills, useful in daily life. Being able to put together an acceptably bullet-proof business plan is a skill which is both practical and logically connected to Microeconomics and Management. Yes, management too. In real life, i.e. when a young person starts a corporate career and as soon as he or she stops dreaming about instantly becoming a CEO, they will be climbing the steps of hierarchical ladder in some kind of corporate structure. The first remotely managerial assignment he or she is likely to have will be to manage a project, thus, to build a small team, negotiate a result-based budget, interface with other parts of the organization in a client-supplier manner etc. Once you can prepare a good business plan, you can plan for an intrapreneurial project as well.

Secondly, when you want to understand how something works, try to build it. Want understand microeconomics? Cool. Build the microeconomics of something: a digital start-up, a food store, a construction business, whatever practical and workable comes to your mind. As soon as you start building up your business concept, you will quickly grasp distinctions such as, for example, that between assets and equity, that between monopolistic pricing and competitive pricing, or, last but not least, your short-term cash-flow, in, respectively, the presence or the absence of amortization. Building a business plan can even help understanding those cherries on the cake of microeconomics, such as the new institutional theory. As soon as you ask yourself the practical question ‘Will it be better for my start up to invest in our own server, or maybe it is more workable to outsource server power?’, you will grasp, lightning fast, the fine niceties of transactional costs.

Long story short, I combine the teaching of microeconomics with that of management, in the courses I have with 1st year undergraduate students, and I make them graduate both with a project, which, in turn, consists in preparing a business plan. Thus, in the structure of the online course on MS Teams, I give both groups access to the basic course of business planning, on the website of my blog (https://discoversocialsciences.com/the-course-of-business-planning/ ).

From there on, I lead two parallel and concurrent lines of teaching. As regards Microeconomics, I focus on something like a spritzer. What? What is a spritzer? Oh, the youth of today… A sprizter, my dear children, is a drink made of wine, white or rosé, mixed with water and lemon juice, and a zest of ice cubes. Looks innocent, is enormously tempting during the summertime, and, comparatively to its alcohol content, kicks like a mule. My sprizter is made of classics, mostly Adam Smith (https://discoversocialsciences.com/wp-content/uploads/2018/02/adam_smith_wealth-nations1.pdf ) and Carl Menger (https://discoversocialsciences.com/wp-content/uploads/2019/02/Menger_principles_of_economics.pdf ), who come as the gentle and innocent mixture of water and orange juice, combined with wine, in the form of a strong grasp on the present-day crazy ride of digital economy based on cloud computing, the pandemic and the resulting sudden shift towards medical technologies, and all that against the background of a major shift in our energy base, from fossil fuels to renewables as well as towards a possible new generation of nuclear.

I plan to present my teaching of Microeconomics as a combination of quotes from those two big classics, and references to what is happening right now. As for Management, I stick to the spritzer philosophy. The wine is the same, i.e. all the things that are happening around, whilst just one classical name comes as lemon juice and water in one: Nicolo Machiavelli (https://discoversocialsciences.com/wp-content/uploads/2020/10/Machiavelli-the-prince.pdf ).

So far, when I am writing those words, I have prepared 5 video lectures along the lines I laid out in the preceding paragraphs. In Microeconomics & Management. Opening lecture [https://youtu.be/N7u8Hs_KATc ], I introduce the course of ‘Microeconomics’, as well as that of ‘Principles of Organization and Management’, which I will be holding with the Andrzej Frycz – Modrzewski Krakow University (Krakow, Poland). You can download the corresponding Power Point presentation from:  https://discoversocialsciences.com/wp-content/uploads/2020/09/Microeconomics_Management_Opening-Lecture.pptx

In ‘Fundamentals of Economics #1’ (https://youtu.be/OTGjJGfpdoc) I open up with the first, more or less formalized lecture in the fundamentals of economics. I use five essential readings – Netflix Annual Report 2019, Discovery Annual Report 2019, Adam Smith’s ‘Wealth of Nations’, David Ricardo’s ‘Principles of Political Economy and Taxation’, and Carl Menger’s ‘Principles of Economics’ – in order to show the basis axes of approach to economic sciences. Firstly, it is the special social tension between the diversity of skills and social roles, on the one hand, and the fact of them all summing up to one big body of labour (Smith). Secondly, I introduce the distinction between capital and labour, and the importance of capital resources (Ricardo, example Netflix). Thirdly, and finally, I present the concept of economic good (Carl Menger) and the importance of translating technology into products. Finally, in Fundamentals of Economics #2 The basic theory of markets [https://youtu.be/1nObCUBWi4E], I present the behavioural essence of markets as structure of tacit coordination between humans.

As regards Management, I have shot two video lectures so far. In Fundamentals of Management #1 [https://youtu.be/j5RmYViqcT4  ], I present the main lines of teaching and study in the path of Management. More specifically addressed to my students in the majors of Management and International Relations. The link to power point: https://discoversocialsciences.com/wp-content/uploads/2020/10/Fundamentals-Management_1.pptx . In Fundamentals of Management #2 Team building [https://youtu.be/1Ho1ZW-9GXY  ], I describe the 4 fundamental tools of team building: recruitment, alignment of values and goals, their proper communication, and the assessment of performance. The link to power point: https://discoversocialsciences.com/wp-content/uploads/2020/10/Fundamentals-Management-2-Team-building.pptx

# Neighbourhoods of Cineworld

As I write about cities and their social function, I want to mess around a bit with a business model known as Real Estate Investment Trust, or REIT. You can consult my video on REITs in general, namely the one titled ‘In ‘Urban Economics and City Management #2 Case study of REIT: Urban Edge and Atrium [https://youtu.be/BURimdfpxcY ]’. I study there the cases of two REITs, i.e. Real Estate Investment Trusts, namely Urban Edge (U.S.) and Atrium (Central Europe).

I am pursuing the idea of investment as fundamental social activity. I intuitively guess that cities will be developing along the lines of what we will be collectively investing in. By investment I mean a compound process which loops between two specific activities: the accumulation of resources, and the allocation thereof. Since the dawn of human civilization, we have been putting things in reserve. First, it was food. Then, we discovered that putting some of our current resources into building durable architectural structures paid off: warmer in winter, cooler in summer, plenty of room for storing food, some protection against anyone or anything willing to take that food from us etc. Yes, architectural construction is investment. I put my resources – capital, labour, natural resources – into something that will pay me back in the future, over a prolonged period of time.

Investment is an interesting component of our collective intelligence. Our society changes in directions and at paces very much determined by the things we willingly invest in. We organize those things according to the principle of delayed gratification, as controlled today’s deprivation oriented on having some durable outcomes in the future. I deliberately use the term ‘things’, so general and plain. We invest in railroads, and we invest in feeling safe from natural disasters. We invest in businesses, and we invest in the expectation of having the most luxurious car/house/dress/holiday in the entire neighbourhood. We invest in collections of physical things and we invest in ideas.

We have governments and political systems because we have that pattern in our collective intelligence. Governments are in place because and as long as they have legitimation, i.e. because and as long as at least some part of the population accepts being governed, without being coerced into obedience. People give legitimation to governments because they accept sacrificing some of the presently available resources (taxes) and freedoms (compliance with the law) in order to have delayed gratification in the form of security, territorial stability, enforceable contracts etc.

Thus, we go in the direction we invest into. That direction is set by the exact kind of delayed gratification we expect to have in the future, and by the exact type of resources and freedoms we give away today in order to have that delayed thing. Cities evolve exactly according to that pattern. Cities look what they look today because at some point in the past, citizens (yes, the term ‘citizen’ comes from the status of being officially acknowledged and accepted as permanent resident of a city) collectively invested in a given type of urban structures. It is important to understand the way I use words such as ‘collective’ and ‘collectively’. People do things collectively even when they say they completely disagree about doing those things together. This is called ‘tacit coordination’. Let’s consider an example. We disagree, in a city, about the way of organizing a piece of urban space. Some people want to build residential structures there, essentially made for rent. Some others want to see a green space in exactly the same spot, like a park. What you can see emerging out of that disagreement on the long run is a patchwork of residential buildings and green spaces, all over the neighbourhood.

Disagreement is a pattern of tacit coordination, thus a pattern of collective intelligence. We disagree about things which we judge important. Openly expressed disagreement is, in the first place, tacit agreement as for what we really care for (object of disagreement) and who really cares for it (protagonists of disagreement). In my personal experience, if a collective, e.g. a business organization, follows a strategy with unanimous enthusiasm, without any voices of dissent, I am like ‘Ooooh, f**k! That thing is heading towards the edge of the cliff…’.

Good. We invest, i.e. we are collectively intelligent about what kind of present satisfaction we sacrifice for the sake of future delayed gratification. The most important investments we collectively make are subject to disagreement, which is more or less ritualized with legal norms and/or political institutions. Here comes an interesting case, disquietingly connected to real life. Cineworld, a chain of cinema theatres (https://www.cineworldplc.com/en/investors) has just announced that ‘In response to an increasingly challenging theatrical landscape and sustained key market closures due to the COVID-19 pandemic, Cineworld confirms that it will be temporarily suspending operations at all of its 536 Regal theatres in the US and its 127 Cineworld and Picturehouse theatres in the UK from Thursday, 8 October 2020’ (look up https://otp.tools.investis.com/clients/uk/cineworldplc1/rns/regulatory-story.aspx?cid=655&newsid=1420306). That provokes a question: what will happen to those theatres as physical places? Will the pandemic force a rethinking and reengineering of their functions in the surrounding urban space and of the way they should be managed? Is that closure of cinema theatres a durable, irreversible change or is it just temporary?

You can see the entire map of Cineworld’s cinemas under this link: https://www.cineworldplc.com/en/our-cinemas . A bit of digital zoom, i.e. at https://www.picturehouses.com/cinema?search=London, and you can make yourself an opinion about the Cineworld cinemas located in London under the brand of ‘PictureHouse’. Look at the Clapham PictureHouse (https://www.picturehouses.com/cinema/clapham-picturehouse ).  and at its location: 76 Venn St, Clapham Town, London SW4 0AT, United Kingdom. The neighbourhood looks more or less like that:

What can be done there? What will the locals collectively invest in? What will be the key features of that investment which they will be disagreeing about? These are low buildings; the neighbourhood looks like a combination of residential structures and small utility ones. Whatever can that cinema theatre be turned into, that thing will make sense for the immediate neighbourhood, like 5 kilometres around.

I turn that cursory reflection on the closure of Cineworld’s theatres into three pieces of teaching, namely as a case of Urban Development sensu stricte (https://youtu.be/B6fFnStK-eA ),  for one, then as a case of Economic Policy ( https://youtu.be/lTDqGG0tVpU), for two, and finally as a case of International Economics (https://youtu.be/5mx47eInQbI), because as cinemas close, folks are bound to spend more time in front of their private screens, and that means growth in the global market of digital entertainment.

# Strangely accommodative of problems

I am returning to the strictly speaking written blogging, after a long break, which I devoted to preparing educational material for the upcoming winter semester 2020/2021. I am outlining a line of research which I can build my teaching around, in the same time. Something looms, and that something is my old obsession: collective intelligence of our human societies and its connection to artificial intelligence. Well, when I say ‘old’, it means ‘slightly seasoned’. I mean, I have been nurturing that obsession for a total of like 4 years, with having it walking around and talking like for the last 18 months or so. It is not truly old, even if ideas were red wine. Anyway, the current shade I paint into that obsession of mine is that human societies have a built-in mechanism of creating new social roles for new humans coming in, in the presence of demographic growth. Cities are very largely factories of social roles, in my view. Close, intense social interactions in a limited space are a mechanism of accelerated collective learning, whence accelerated formation of new skillsets, and those new skillsets, all they need is an opportunity to earn a living with and they turn into social roles.

I have a deep feeling that digital platforms, ranging from the early-hominid-style things like Twitter, all the way up to working and studying via MS Teams or Zoom, have developed as another accelerator of social roles. This accelerator works differently. It is essentially spaceless, although, on the large scale, it is very energy consuming at the level of server power. Still, early cities used to shape new social roles through the skilled labour they required to be built and expanded. A substantial part of whatever we think we know about mathematics and physics comes from geometry, which, in turn, comes from architecture and early machine-building. Similarly, digital platforms make new social roles by stimulating the formation of new skillsets required to develop those platforms, and then to keep them running.

Crazy thoughts come to my mind. What if we, humans, are truly able to think ahead, like really ahead, many generations ahead? What if by the mid-20th century we collectively told ourselves: ‘Look, guys. We mean, us. Cities are great, but there is more and more of us around, all that lot needs food, and food needs agricultural land to be grown and bred on. We need to keep the surface of agricultural land intact at the least, or slightly growing at best, whence the necessity to keep the total surface of urban land under control. Still, we need that space of intense social interactions to make new social roles. Tough nut to crack, this one. Cool, so here is the deal: we start by shrinking transistors to a size below the perceptual capacity of human sight, which is going to open up on a whole range of electronic technologies, which, in turn, will make it worthwhile to create a whole new family of languages just for giving them orders, to those electronics. Hopefully, after 2 or 3 human generations, that is going to create a new plane of social interactions, sort of merging with cities and yet sort of supplanting them’.

And so I follow that trail of collective human intelligence configuring itself in the view of making enough social roles for new humans coming. I am looking for parallels with the human brain. I know, I know, this is a bit far-fetched as parallel, still it is better than nothing. Anyway, in the brain, there is the cortex, i.e. the fancy intellectual, then we have the limbic system, i.e. the romantic Lord Byron, and finally there is the hypothalamus, i.e. the primitive stuff in charge of vegetative impulses. Do we have such distinct functional realms in our collective intelligence? I mean, do we have a subsystem that generates elementary energies (i.e. capacities to perform basic types of action), another one which finds complex cognitive bearings in the world, and something in between, which mediates between objective data and fundamental drives, forming something like preferences, proclivities, values etc. ?

Cool. Enough philosophy. Let’s get into science. As I am writing about digital platforms, I can do something useful just as well, i.e. I can do some review of literature and use it both in my own science and in my teaching. Here comes an interesting paper by Beeres et al. (2020[1]) regarding the correlation between the use of social media, and the prevalence of mental health problems among adolescents in Sweden. The results are strangely similar to the correlation between unemployment and criminality, something I know well from my baseline field of science, i.e. economics. It is a strong correlation across space and a weak, if not a non-existent one over time. The intensity of using social media by Swedish adolescents seems to be correlated positively with the incidence of mental disorders, i.e. adolescents with higher a probability of such disorders tend to use social media more heavily than those mentally more robust adolescents. Still, when an adolescent person increases their starting-point intensity of using social media, that change is not correlated longitudinally with an increased incidence of mental disorders. In other words, whoever is solid in the beginning, stays this way, and whoever is f**ked up, stays that way, too.

The method of research presented in that paper looks robust. The sample is made of 3959 willing participants, fished out from among an initial sample of 12 512 people. This is respectable, as social science comes. The gauge of mental health was Strength and Difficulties Questionnaire (SDQ), which is practically 100% standardized (Goodman & Goodman 2009[2]) and allows distinguishing between internalized, emotional and peer problems on the one hand, and those externalized ones, connected to conduct and hyperactivity. If you are interested in the exact way this questionnaire looks, you can go and consult: https://www.sdqinfo.org/a0.html . The use of social media was self-reported, as answer to the question on the number of hours spent on social media, writing or reading blogs, and chatting online, separately for weekdays and weekends. That answer was standardized, on a scale ranging from 30 minutes a day up to 7 hours a day. Average daily time spent on social media was calculated on the basis of answers given.

The results reported by Beeres et al. (2020) are interesting in a few different ways. Firstly, they seem to discard very largely the common claim that increased use of social media contributes to increased prevalence of mental disorders in adolescents. Intensive use of social media is rather symptomatic of such disorders. That would reverse the whole discourse about this specific phenomenon. Instead of saying ‘Social media make kids go insane’, we should be rather saying ‘Social media facilitate the detection of mental disorders’. Still, one problem remains: if the most intense use of social media among adolescents is observable in those most prone to mental disorders, we have a possible scenario where either the whole culture forming on and through social media, or some specific manifestations thereof, are specifically adapted to people with mental disorders.

Secondly, we have a general case of a digital technology serving a specific social function, i.e. that of mediating social relations of a specific social group (adolescents in developed countries) in a specific context (propensity to mental disorders). Digital technologies are used as surrogate of other social interactions, in people who most likely have hard times going through such interactions.

Another paper, still warm, straight from bakery, by Lin et al. (2020[3]), is entitled ‘Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia’. The first significant phenomena it is informative about is the difficulty to make a simple, catchy title for a scientific paper. Secondly, the authors start from the same hypothesis which Beeres et al. (2020) seem to have discarded, namely that social media use (especially problematic social media use) may give rise to psychological distress. Moreover, Lin et al. (2020) come to the conclusion that it is true. Same science, same hypothesis, different results. I f**king love science. You just need to look into the small print.

The small print here starts with the broad social context. Empirical research by Lin et al. (2020) was conducted in Iran, on participants over 18 years old, whose participation was acquired via Google Forms. The sample consisted of 1506 persons, with an average age of 26 years, and a visible prevalence of women, who made over 58% of the sample. The tool used for detecting mental disorders was the Hospital Anxiety and Depression Scale (HADS). The follow up period was of two weeks, against two years in the case of research by Beeres et al. (2020). Another thing is that whilst Beeres et al. (2020) explicitly the longitudinal within-person variance from the lateral inter-person one, Lin et al. (2020) compute their results without such distinction. Consequently, they come to the conclusion that problematic use of social media is significantly correlated with mental disorders.

I try to connect those two papers to my concept of collective intelligence, and with the use of artificial intelligence. We have an intelligent structure, i.e. humans hanging around together. How do we know we are collectively intelligent? Well, we can make many alternative versions of us being together, each version being like one-mutation neighbour to others, and we can learn new ways of doing things by choosing the best fitting version among those alternatives. On the top of that, we can do the whole stunt whilst staying acceptably cohesive as society. Among many alternative versions of us being together there is a subset, grouping different manners of using social media. Social media are based on artificial intelligence. Each platform runs an algorithm which adapts the content you see to your previously observed online behaviour: the number of times you click on an add, the number of times you share and repost somebody else’s posts, the number of times you publish your own content etc. At the bottom line, the AI in action here adapts so as you max out on the time spent on the platform, and on the clicks you make whilst hanging around there.

The papers I have just quoted suggest that artificial intelligence at work in social media is somehow accommodative of people with mental disorders. This is truly interesting, because the great majority of social institutions we have had so far, i.e. since however we started as intelligent hominids, has been actually the opposite. One of the main ways to detect serious mental problems in a person consists in observing their social relations. If they have even a mild issue with mental health, they are bound to have something seriously off either with their emotional bonds to the immediate social environment (family and friends, mostly) or with their social role in the broader environment (work, school etc.).   I made an educational video out of that quick review of literature, and I placed it on You Tube as: Behavioural modelling and content marketing #3 Social media and mental health

[1] Beeres, D. T., Andersson, F., Vossen, H. G., & Galanti, M. R. (2020). Social media and mental health among early adolescents in Sweden: a longitudinal study with 2-year follow-up (KUPOL Study). Journal of Adolescent Health, https://doi.org/10.1016/j.jadohealth.2020.07.042

[2] Goodman, A., Goodman, R. (2009) Strengths and Difficulties Questionnaire as a Dimensional Measure of Child Mental Health, Journal of the American Academy of Child & Adolescent Psychiatry, Volume 48, Issue 4,

2009, Pages 400-403, ISSN 0890-8567, https://doi.org/10.1097/CHI.0b013e3181985068

[3] Lin, C. Y., Broström, A., Griffiths, M. D., & Pakpour, A. H. (2020). Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia. Internet interventions, 21, 100345, https://doi.org/10.1016/j.invent.2020.100345

# New, complete course of Business Planning

I have just finished putting together a complete course of Business Planning. You can find the link on the sidebar. In a series of video lectures combined with Power Point presentations, you will go through all the basic skills of business planning: pitching and modelling your business concept, market research and its translation into financials, assessment of the optimal capital base, and thorough reflection on the soft side of the business plan, i.e. your goals, your risks, your people etc.

Click, dive into, dig through and enjoy.

# Cautiously bon-vivant

I keep developing on a few topics in parallel, with a special focus on two of them. Lessons in economics and management which I can derive for my students, out of my personal experience as a small investor in the stock market, for one, and a broader, scientific work on the civilizational role of cities and our human collective intelligence, for two.

I like starting with the observation of real life, and I like ending with it as well. What I see around gives me the initial incentive to do research and makes the last pitch for testing my findings and intuitions. In my personal experience as investor, I have simply confirmed an initial intuition that giving a written, consistent and public account thereof helps me nailing down efficient strategies as an investor. As regards cities and collective intelligence, the first part of that topic comes from observing changes in urban life since COVID-19 broke out, and the second part is just a generalized, though mild an intellectual obsession, which I started developing once I observed the way artificial neural networks work.

In this update, I want to develop on two specific points, connected to those two paths of research and writing. As far as my investment is concerned, I am seriously entertaining the idea of broadening my investment portfolio in the sector of renewable energies, more specifically in the photovoltaic. I can notice a rush on the solar business in the U.S. I am thinking about investing in some of those shares. I already have, and have made a nice profit on the stock of First Solar (https://investor.firstsolar.com/home/default.aspx ) as well as on that of SMA Solar (https://www.sma.de/en/investor-relations/overview.html ). Currently, I am observing three other companies: Vivint Solar (https://investors.vivintsolar.com/company/investors/investors-overview/default.aspx ),  Canadian Solar (http://investors.canadiansolar.com/investor-relations ), and SolarEdge Technologies (https://investors.solaredge.com/investor-overview ). Below, I am placing the graphs of stock price over the last year, as regards those solar businesses. There is something like a common trend in those stock prices. March and April 2020 were a moment of brief jump upwards, which subsequently turned into a shy lie-down, and since the beginning of August 2020 another journey into the realm of investors’ keen interest seems to be on the way.

Before you have a look at the graphs, here is a summary table with selected financials, approached as relative gradients of change, or d(x).

There are two fundamental traits of business models which I am having a close look at. Firstly, it is the correlation between changes in market capitalization, and changes in assets. I am checking if the solar businesses I want to invest in have their capital base functionally connected to the financial market. Looks a bit wobbly, as for now. Secondly, I look at current operational efficiency, measured with operational cash flow. Here, I can see there is still a lot to do. Here is the link to You Tube video with all that topic developed: Business models in renewable energies #3 Solar business and investment opportunities [Renew BM 3 2020-09-06 09-20-30 ; https://youtu.be/wYkW5KHQlDg ].

Those business models seem to be in a phase of slow stabilization. The industry as a whole seems to be slowly figuring out the right way of running that PV show, however the truly efficient scheme is still to be nailed down. Investment in those companies is based on reasonable trust in the growth of their market, and in the positive impact of technological innovation. Question: is it a good move to invest now? Answer: it is risky, but acceptably rational; once those business models become really efficient, the industry will be in or close to the phase of maturity, which, in turn, does not really allow expecting abnormally high return on investment.

This is a very ‘financial’, hands-off approach to business models. In this case, business models of those photovoltaic businesses matter to me just to the extent of being fundamentally predictable. I don’t want to run a solar business, I just want to have elementary understanding of what’s going on, business-wise, to make my investment better grounded. Looking from inside a business, such an approach is informative about the way that a business model should ‘speak’ to investors.

At the end of the day, I think I am most likely to invest in SolarEdge. It seems to have all the LEGO blocks in place for a good opening. Good cash flow, although a bit sluggish when it comes to real investment.

As regards COVID-19 and cities, I am formulating the following hypothesis: COVID-19 has awakened some deeply rooted cultural patterns, which date back to the times of high epidemic risk, long before vaccines, sanitation and widespread basic healthcare. Those patterns involve less spatial mobility in the population, and social interactions within relatively steady social circles of knowingly healthy people. As a result, the overall frequency of social interactions in cities is likely to decrease, and, as a contingent result, the formation of new social roles is likely to slow down. Then, either digital technologies take over the function of direct social interactions and new social roles will be shaping themselves via your average smartphone, with all the apps it is blessed (haunted?) with, or the formation of new social roles will slow down in general. In that last case, we could have hard times with keeping up our pace of technological change. Here is the link to You Tube video which summarizes what is written below: Urban Economics and City Management #4 COVID and social mobility in cities [ Cities 4 2020-09-06 09-43-06 ; https://youtu.be/m3FZvsscw7A  ].

I want to gain some insight into the epidemiological angle of that claim, and I am passing in review some recent literature. I start with: Gatto, M., Bertuzzo, E., Mari, L., Miccoli, S., Carraro, L., Casagrandi, R., & Rinaldo, A. (2020). Spread and dynamics of the COVID-19 epidemic in Italy: Effects of emergency containment measures. Proceedings of the National Academy of Sciences, 117(19), 10484-10491 (https://www.pnas.org/content/pnas/117/19/10484.full.pdf ). As it is usually the case, my internal curious ape starts paying attention to details which could come as secondary for other people, and my internal happy bulldog follows along and bites deep into those details. The little detail in this specific paper is a parameter: the number of people quarantined as a percentage of those positively diagnosed with Sars-Cov-2. In the model developed by Gatto et al., that parameter is kept constant at 40%, which is, apparently, the average level empirically observed in Italy during the Spring 2020 outbreak. Quarantine is strict isolation between carriers and (supposedly) non-carriers of the virus. Quarantine can be placed on the same scale as basic social distancing. It is just stricter, and, in quantitative terms, it drives much lower the likelihood of infectious social interaction. Gatto el al. insist that testing effort and quarantining are essential components of collective defence against the epidemic. I generalize: testing and quarantine are patterns of collective behaviour. I check whether people around me are carriers or not, and then I split them into two categories: those whom I strongly suspect to host and transmit Sars-Cov-2, and all the rest. I define two patterns of social interaction with those two groups: very restrictive with the former, and cautiously bon vivant with the others (still, no hugging). As the technologies of testing will be inevitably diffusing across the social landscape, that structured pattern is likely to spread as well.

Now, I pay a short intellectual visit to Jiang, P., Fu, X., Van Fan, Y., Klemeš, J. J., Chen, P., Ma, S., & Zhang, W. (2020). Spatial-temporal potential exposure risk analytics and urban sustainability impacts related to COVID-19 mitigation: A perspective from car mobility behaviour. Journal of Cleaner Production, 123673 https://doi.org/10.1016/j.jclepro.2020.123673 . Their methodology is based on correlating spatial mobility of cars in residential areas of Singapore with the risk of infection with COVID-19. A 44,3% ÷ 55,4% decrease in the spatial mobility of cars is correlated with a 72% decrease in the risk of social transmission of the virus. I intuitively translate it into geometrical patterns. Lower mobility in cars means a shorter average radius of travel by the means of available urban transportation. In the presence of epidemic risk, people move across a smaller average territory.

In another paper (or rather in a commented dataset), namely in Pepe, E., Bajardi, P., Gauvin, L., Privitera, F., Lake, B., Cattuto, C., & Tizzoni, M. (2020). COVID-19 outbreak response, a dataset to assess mobility changes in Italy following national lockdown. Scientific data, 7(1), 1-7. https://www.nature.com/articles/s41597-020-00575-2.pdf?origin=ppub , I find an enlarged catalogue of metrics pertinent to spatial mobility. That paper, in turn, lead me to the functionality run by Google: https://www.google.com/covid19/mobility/ . I went through all of it a bit cursorily, and I noticed two things. First of all, countries are strongly idiosyncratic in their social response to the pandemic. Still, and second of all, there are common denominators across idiosyncrasies and the most visible one is cyclicality. Each society seems to have been experimenting with the spatial mobility they can afford and sustain in the presence of epidemic risk. There is a cycle experimentation, around 3 – 4 weeks. Experimentation means learning and learning usually leads to durable behavioural change. In other words, we (I mean, homo sapiens) are currently learning, with the pandemic, new ways of being together, and those ways are likely to incrust themselves into our social structures.

The article by Kraemer, M. U., Yang, C. H., Gutierrez, B., Wu, C. H., Klein, B., Pigott, D. M., … & Brownstein, J. S. (2020). The effect of human mobility and control measures on the COVID-19 epidemic in China. Science, 368(6490), 493-497 (https://science.sciencemag.org/content/368/6490/493 ) shows that without any restrictions in place, the spatial distribution of COVID-19 cases is strongly correlated with spatial mobility of people. With restrictions in place, that correlation can be curbed, however it is impossible to drive down to zero. In plain human, it means that even as stringent lockdowns as we could see in China cannot reduce spatial mobility to a level which would completely prevent the spread of the virus.

By the way, in Gao, S., Rao, J., Kang, Y., Liang, Y., & Kruse, J. (2020). Mapping county-level mobility pattern changes in the United States in response to COVID-19. SIGSPATIAL Special, 12(1), 16-26 (https://arxiv.org/pdf/2004.04544.pdf ), I read that the whole idea of tracking spatial mobility with people’s personal smartphones largely backfired because the GDS transponders, installed in the average phone, have around 20 metres of horizontal error, on average, and are easily blurred when people gather in one place. Still, whilst the idea went down the drain as regards individual tracking of mobility, smartphone data seems to provide reliable data for observing entire clusters of people, and the way those clusters flow across space. You can consult Jia, J. S., Lu, X., Yuan, Y., Xu, G., Jia, J., & Christakis, N. A. (2020). Population flow drives spatio-temporal distribution of COVID-19 in China. Nature, 1-5.  (https://www.nature.com/articles/s41586-020-2284-y?sf233344559=1) .

Bonaccorsi, G., Pierri, F., Cinelli, M., Flori, A., Galeazzi, A., Porcelli, F., … & Pammolli, F. (2020). Economic and social consequences of human mobility restrictions under COVID-19. Proceedings of the National Academy of Sciences, 117(27), 15530-15535 (https://www.pnas.org/content/pnas/117/27/15530.full.pdf ) show an interesting economic aspect of the pandemic. Restrictions in mobility give the strongest economic blow to the poorest people and to local communities marked by relatively the greatest economic inequalities. Restrictions imposed by governments are one thing, and self-imposed limitations in spatial mobility are another. If my intuition is correct, namely that we will be spontaneously modifying and generally limiting our social interactions, in order to protect ourselves from COVID-19, those changes are likely to be the fastest and the deepest in high-income, low-inequality communities. As income decreases and inequality rises, those adaptive behavioural modifications are likely to weaken.

As I am drawing a provisional bottom line under that handful of scientific papers, my initial hypothesis seems to hold. We do modify, as a species, our social patterns, towards more encapsulated social circles. There is a process of learning taking place, and there is no mistake about it. That process of learning involves a downwards recalibration in the average territory of activity, and smart selection of people whom we hang out with, based on what we know about the epidemic risk they convey. This is a process of learning by trial and error, and it is locally idiosyncratic. Idiosyncrasies seem to be somehow correlated with differences in wealth. Income and accumulated capital visibly give local communities an additional edge in the adaptive learning. On the long run, economic resilience seems to be a key factor in successful adaptation to epidemic risk.

Just to end up with, here you have an educational piece as regards Business models in the Media Industry #4 The gaming business[ Media BM 4 2020-09-02 10-42-44; https://youtu.be/KCzCicDE8pc]. I study the case of CD Projekt (https://www.cdprojekt.com/en/investors/ ), a Polish gaming company, known mostly for ‘The Witcher’ game and currently working on the next one, Cyberpunk, with Keanu Reeves giving his face to the hero. I discover a strange business model, which obviously has hard times to connect with the creative process at the operational level. As strange as it might seem, the main investment activity, for the moment, consists in terminating and initiating cash bank deposits (!), and one of the most important operational activities is to push further in time the moment of officially charging customers with some economically due receivables. On the top of all that, those revenues deferred into the future are officially written in the balance sheet as short-term liabilities, which CD Projekt owes to…whom exactly?