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market of electricity’, in order to resubmit it to the journal Applied Energy , by somehow fusing it with two other, unpublished pieces of my writing, namely: ‘Behavioural absorption of Black Swans: simulation with an artificial neural network’, and ‘The labour-oriented, collective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’.

>> Pizzeria >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Pizzeria-Business-plan.docx

>> Pancake Café >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Pancake-Cafe-Business-Plan.pptx

>> Never Alone >> https://discoversocialsciences.com/wp-content/uploads/2021/03/Never-Alone-business-plan.pdf

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

>> ToyBox >> https://discoversocialsciences.com/wp-content/uploads/2021/03/ToyBox-Business-Plan.pdf

>> 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:

>> Netflix >> https://ir.netflix.net/ir-overview/profile/default.aspx

>> 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

>> Pfizer >> https://investors.pfizer.com/investors-overview/default.aspx

>> 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:

>> https://discoversocialsciences.com/wp-content/uploads/2021/03/Switch-Park-Business-Plan.docx

>> https://discoversocialsciences.com/wp-content/uploads/2021/03/Peerket-Business-Plan.docx

>> https://discoversocialsciences.com/wp-content/uploads/2021/03/Foodies-Business-Plan.docx

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:

Subject DescriptorUnitsScale
Gross domestic product, constant pricesNational currencyBillions
Gross domestic product, constant pricesPercent change
Gross domestic product, current pricesNational currencyBillions
Gross domestic product, current pricesU.S. dollarsBillions
Gross domestic product, current pricesPurchasing power parity; international dollarsBillions
Gross domestic product, deflatorIndex
Gross domestic product per capita, constant pricesNational currencyUnits
Gross domestic product per capita, constant pricesPurchasing power parity; 2017 international dollarUnits
Gross domestic product per capita, current pricesNational currencyUnits
Gross domestic product per capita, current pricesU.S. dollarsUnits
Gross domestic product per capita, current pricesPurchasing power parity; international dollarsUnits
Gross domestic product based on purchasing-power-parity (PPP) share of world totalPercent
Implied PPP conversion rateNational currency per current international dollar
Total investmentPercent of GDP
Gross national savingsPercent of GDP
Inflation, average consumer pricesIndex
Inflation, average consumer pricesPercent change
Inflation, end of period consumer pricesIndex
Inflation, end of period consumer pricesPercent change
Volume of imports of goods and servicesPercent change
Volume of Imports of goodsPercent change
Volume of exports of goods and servicesPercent change
Volume of exports of goodsPercent change
Unemployment ratePercent of total labor force
PopulationPersonsMillions
Current account balanceU.S. dollarsBillions
Current account balancePercent of GDP

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.

One step out of the cavern

I have made one step further in my learning of programming. I finally have learn’t at least one method of standardising numerical values in a dataset. In a moment, I will show what exact method did I nail down. First, I want to share a thought of more general nature. I learn programming in order to enrich my research on the application of artificial intelligence for simulating collective intelligence in human societies. I have already discovered the importance of libraries, i.e. ready-made pieces of code, possible to call with a simple command, and short-cutting across many verses of code which I would have to write laboriously. I mean libraries such as NumPy, Pandas, Math etc. It is very similar to human consciousness. Using pre-constructed cognitive structures, i.e. using language and culture is a turbo boost for whatever we do of things that humans are supposed to do when being a civilisation.  

Anyway, I kept working with the dataset which I had already mentioned in my earlier updates, namely a version of Penn Tables 9.1., cleaned of all the rows with empty cells [see: Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, www.ggdc.net/pwt ]. Thus I started by creating an online notebook at JupyterLab (https://jupyter.org/try), with Python 3 as its kernel. Then I imported what I needed from Python in terms of ready-cooked culture, i.e. I went:

>> import numpy as np

>> import pandas as pd

>> import os

I uploaded the ‘PWT 9_1 no empty cells.csv’ file from my computer, and, just in case, I checked its presence in the working directory, with >> os.listdir(). I read the contents of the file into a Pandas Data Frame, which spells: PWT = pd.DataFrame(pd.read_csv(‘PWT 9_1 no empty cells.csv’)). Worked.  

In my next step, as I planned to mess up a bit with the columns of that dataset, I typed: PWT.columns. The thing nicely gave me back a list of columns, i.e. literally a list of labels in quotation marks [‘’]. I used that list to create a dictionary of columns with numerical values, and therefore the most interesting to me. I went:

>> Variables=[‘rgdpe’, ‘rgdpo’, ‘pop’, ’emp’, ’emp / pop’, ‘avh’,

       ‘hc’, ‘ccon’, ‘cda’, ‘cgdpe’, ‘cgdpo’, ‘cn’, ‘ck’, ‘ctfp’, ‘cwtfp’,

       ‘rgdpna’, ‘rconna’, ‘rdana’, ‘rnna’, ‘rkna’, ‘rtfpna’, ‘rwtfpna’,

       ‘labsh’, ‘irr’, ‘delta’, ‘xr’, ‘pl_con’, ‘pl_da’, ‘pl_gdpo’, ‘csh_c’,

       ‘csh_i’, ‘csh_g’, ‘csh_x’, ‘csh_m’, ‘csh_r’, ‘pl_c’, ‘pl_i’, ‘pl_g’,

       ‘pl_x’, ‘pl_m’, ‘pl_n’, ‘pl_k’]

The ‘Variables’ dictionary served me to make a purely numerical mutation of my dataset, namely: PWTVar=pd.DataFrame(PWT[Variables]).  

I generated the fixed components of standardisation in my data, i.e. maximums, means, and standard deviations across columns in PWTVar. It looked like this: 

>> Maximums=PWTVar.max(axis=0)

>> Means=PWTVar.mean(axis=0)

>> Deviations=PWTVar.std(axis=0)

The ‘axis=0’ part means that I want to generate those values across columns, not rows. Once that done, I made my two standardisations of data from PWTVar, namely: a) standardisation over maximums, like s(x) = x/max(x) and b) standardisation by mean-reversion, where s(x) = [x – avg(x)]/std(x)]. I did it as:

>> Standardized=pd.DataFrame(PWTVar/Maximums)

>> MR=pd.DataFrame((PWTVar-Means)/Deviations)

I used here the in-built function of Python Pandas, i.e. the fact that they automatically operate data frames as matrices. When, for example, I subtract ‘Means’ from ‘PWTVar’, the one-row matrix of ‘Means’ gets subtracted from each among the 3005 rows of ‘PWTVar’ etc. I checked those two data frames with commands such as ‘df.describe()’, ’df.shape’, and df.info(), just to make sure they are what I think they are. They are, indeed. 

Standardisation allowed me to step out of my cavern, in terms of programming artificial neural networks. The next step I took was to split my numerical dataset PWTVar into one output variable, on the one hand, and all the other variables grouped as input. As output, I took a variable which, as I have already found out in my research, is extremely important in social change seen through the lens of Penn Tables 9.1. This is ‘avh’ AKA the average number of hours worked per person per year. I did:  

>> Output_AVH=pd.DataFrame(PWTVar[‘avh’])

>> Input_dict=[‘rgdpe’, ‘rgdpo’, ‘pop’, ’emp’, ’emp / pop’, ‘hc’, ‘ccon’, ‘cda’,

        ‘cgdpe’, ‘cgdpo’, ‘cn’, ‘ck’, ‘ctfp’, ‘cwtfp’, ‘rgdpna’, ‘rconna’,

        ‘rdana’, ‘rnna’, ‘rkna’, ‘rtfpna’, ‘rwtfpna’, ‘labsh’, ‘irr’, ‘delta’,

        ‘xr’, ‘pl_con’, ‘pl_da’, ‘pl_gdpo’, ‘csh_c’, ‘csh_i’, ‘csh_g’, ‘csh_x’,

        ‘csh_m’, ‘csh_r’, ‘pl_c’, ‘pl_i’, ‘pl_g’, ‘pl_x’, ‘pl_m’, ‘pl_n’,

        ‘pl_k’] 

#As you can see, ‘avh’ is absent from the ‘Input-dict’ dictionary 

>> Input = pd.DataFrame(PWT[Input_dict])

The last thing that worked, in this episode of my learning, was to multiply the ‘Input’ dataset by a matrix of random float values generated with NumPy:

>> Randomized_input=pd.DataFrame(Input*np.random.rand(3006,41)) 

## Gives an entire Data Frame of randomized values

Waiting for interesting offers

In this update, I am attempting to introduce a general concept of social sciences, namely that of pooled risks, and, associated with them, pooled resources, and combine it with my research regarding collective intelligence. I open up by connecting to my last update, namely that titled ‘My NORMAL and my WEIRD’. Things happen all the time. Some of that happening we deem as normal, and anything outside normal is weird. We, humans, we have built a whole civilization around saving stuff for later. We started by saving bodily energy for later, and thus we invented shelter as a complex method of saving and investment. Today, we build shelter, and thus we expend more energy than strictly needed for survival, and yet the whole business pays off, because shelter allows us to save even more energy on the long run (lower heat loss when sleeping and resting, more stable conditions for keeping a fire up etc.). As we started building shelters for ourselves, we figured out that food could be conserved for later, too. Once again, spend a little bit more energy today, bro, to conserve that beautiful sabre-tooth tiger, as well as that root, which your distant descendants will call turnip, and tomorrow you will have food which you do not have to run after nor away from.

Over thousands of years, we, humans, have developed that pattern of countering adverse events by saving resources for later and investing them in something that pays off. At some point in time, we noticed, withing the realm of all the nasty s**t that happens, different grades of normality and weirdness. There is daily adversity, such as cold in winter, or the basic need for food (all year long), which needs to be addressed recurrently and sort of locally. Everyone needs local protection from cold, right? If, in a city, there is one shelter, it can work just for a tiny handful of homeless people, not for all the citizens. The latter need their individual places. Still, there is a different type of adversity, such as a flood, a hurricane, or a wedding, which all happen incidentally and are randomly local: they hit a specific subset of population, in a specific place. Although these events are abnormal, they happen abnormally with a pattern. The Black Swan theory, by Nassim Taleb (Taleb 2007[1]; Taleb & Blyth 2011[2]; see Black Swans happen all the time), argues that we, humans, have developed an otherwise amazing skill of incorporating into our collective culture the memories of unusual, catastrophic events so as to be prepared for the next time the s**t hits the fan. A lot of our structures and institutions are made for countering and compensating the sudden, and yet somehow predictable advent of those Black Swans. One of the simplest examples are buildings. The toughest architectural structures have historically developed, a bit surprisingly, in places with an abundant history of warfare and sieging, and not, as someone could expect, in places haunted by hurricanes and earthquakes. I could see that sort of in my front yard, when, in the early 2000s, I met Croatians who migrated abroad. Their default expectation for a building was reinforced concrete, brick was barely acceptable, and the American wooden structure was completely out of the question. It wasn’t until 2004, when I went to Croatia for the first time, and I saw the still-standing corpses of buildings damaged by bombs, that I understood where that preference for reinforced concrete was coming from.

What is even more puzzling is that abundant history of warfare and sieging has developed in places, where heavy construction materials, such as stone, lime, clay for making bricks, and wood for fuelling ovens to bake bricks, were just as abundant. In other words, we historically developed big, solid buildings in places where we used to destroy those buildings a lot through our own military effort, and, apparently, we used to destroy them a lot because we had a lot of raw materials at hand for rebuilding them. That’s what I call incorporating Black Swans in a culture.

Anyway, we have that thing about purposefully investing our resources in structures and institutions supposed to shield us against big, catastrophic events. There is another thing about those Black Swans: they happen in sort of a floating manner. If you know your climate, and that climate is prone to floods, you know there will be floods, you can estimate the magnitude of damage they are likely to inflict, you just don’t know where and when exactly it is going to flood. What you need is a s**t-shielding structure akin to an immune system: a lot of resources, hanging around, sort of held at the ready, able to channel themselves into the specific place where damage occurs.

Now, I jump. Like really. Intellectually, I mean. You probably know that governments of many countries are borrowing lots of money, this year, to counter the adverse impact of the COVID-19 pandemic. You can read more about it in the Fiscal Monitor published by the International Monetary Fund in September 2020, under the title ‘Fiscal Monitor: Policies for the Recovery

September 2020’ (https://www.imf.org/en/Publications/FM/Issues/2020/09/30/october-2020-fiscal-monitor ). I think this is the right move to make, right now, and right now, you, my readers, are, as we say in Poland, in your holy bandit’s right to ask ‘WTF?’. What does public borrowing have to do with floating adversities? Hold on, dear readers. One step at a time. We have those governments borrowing money, right? The first interesting thing is the way they borrow. When I borrow money from a bank, I do it against a written promise of paying back, possibly backed with a conditional claim I grant to the bank on some assets of mine. When governments borrow, they prevalently do it by issuing tradable financial securities called sovereign bonds AKA treasury bonds, which incorporate the government’s obligation to pay back the capital borrowed in more than 12 months. A minor part of public borrowing takes place through the issuance of sovereign notes (treasury notes), which differ from bonds just by their time of maturity, always shorter than 12 months. Hardly any public borrowing takes place in the form of classical loans, the kind which me or any other, non-political mortal could contract. Why is it so?

The first basic difference between a classical bank loan and a loan extended in exchange of tradable securities is that governments can do the latter, cities can do it, too, as well as large corporations. On the other hand, if I go to a bank and I propose to borrow money from them in exchange of bonds which I will issue, they will tell me: ‘Mr Wasniewski, with all the due respect, you have no business issuing any bonds, for one, and even if you had, no one would be really interested in buying your bonds from us, and so there is no point for us to accept bonds from you. If you really like making securities, you can sign a bill of exchange, if it is all the same for you, yet it will be just an additional collateral in the framework of a normal lending contract’.

I could go bitching and ranting about those horrible bankers. Yes, I could. Yet, I prefer understanding. Why can governments borrow in exchange of bonds, and I cannot? ‘Cause their bonds are wanted, whilst mine are not. This is called demand, and when demand pertains to a financial instrument, we call it liquidity. Financial institutions willingly buy sovereign bonds, thus give them liquidity, because these bonds have very low credit risk attached to it. Most governments are good payers, especially when they can schedule those payments way in advance.

Thus, we have a new concept in this update: credit risk. If you are a bank, and you extend 1000 loans of $5000 each, to 1000 different clients, some of them will simply not pay, or, as bankers say, will default on their loans. Realistically, you can expect between 4% and 7% of them to default. Let’s make it average between 4% and 7%, thus 5,5%, which makes 5,5% * 1000 borrowers = 55 loans in default, or 55 * $5000 = $275 000. When someone doesn’t pay, someone else has to pay for them, and the basic financial strategy is to spread those $275 000 over the remaining 94,5% of customers, dutiful and solvable. In other words, those remaining 945 customers will have to pay, in the interest the bank charges them, those $275 000 in default. That makes $291 per customer, over the top of the $5000 principal capital borrowed, or 5,82% of that capital. In even more other words, in this specific population of customers, there is a floating credit risk amounting to 5,82% of capital engaged in lending, or $275 000 for each $5000 000 of credit extended. The interest charged on each individual loan comprises, among other things, those 5,82% of spread credit risk.

The strategy of spreading credit risk is common sense, yet a bit unfair and easily tiltable out of its base. When credit risk in a population of clients rises sharply, e.g. when there is a wave of insolvencies and bankruptcies among small businesses, that spread credit risk starts spiralling up in an uncontrolled manner, and soon enters into a dysfunctional loop. Each additional 1% of customers defaulting on their loans creates the need to make credit even more expensive for all the others, which makes those others more likely to default etc. Someone could say that it is all because of bad banking. Just don’t lend to customers who default, that’s all. Yes, cool, it is an excellent advice and yet it is functional just in a certain number of cases. The historically accumulated wisdom of finance is that credit risk is essentially exogenous, i.e. it is a characteristic of the entire market rather than bad credit decisions in individual cases.

Banks lend to the non-financials the money they have borrowed from other banks. This is something to remember: when a bank extends you credit, they are trading you that money, not producing it. Banks don’t lend out of their equity; they lend out of their liabilities vis a vis other banks. That’s why there is a big market of interbank credit. If you are a bank and another bank asks you for a loan, you will easily do it. Instead of dealing with 1000 small loans of $5000 each, you just deal with one big loan of $5000 000, extended to another bank, thus to an organization which, you, a bunch of bankers, can much better predict the financial stance of than it is the case with the non-financial businesses. You just need to see that this other bank has financial reserves for credit risk, i.e. they do not rely exclusively on the market in managing its own credit risk. You’ve got 5,82% of credit risk, bro? Cool, happens all the time. Still, show me, in your official accounts, that you have secured a financial reserve for that, and the next thing you know, I lend you, no problem.

Thus, when you are exposed to credit risk, and you inevitably are, as a bank, you should keep financial reserves for that risk. Risk is a quantity, see? Now, you can be smart and do something else. You can invest part of your total capital in financial assets which are technically lending, and yet are way away, in terms of credit risk, from your average loan. Most governments are good payers, especially when they can schedule those payments way in advance. Lending to them, especially in exchange of tradable sovereign bonds, is low risk, usually estimated way below 1%. Let’s suppose that you, a bank, can buy sovereign bonds with a credit risk at 0,5%. You lend to all the others with a risk coefficient of 5,82%. You spread your total lending so as to have 30% extended to governments, at 0,5% risk, 30% to other banks, let’s say at 1% risk, and the remaining 40% lent to all them other folks, at a credit risk of 5,82%. Your compound weighted average credit risk amounts to 0,3*0,05% + 0,3*1% + 0,4*5,82% = 2,78%. See, with that portfolio of financial assets, you need to make financial reserves for just 2,78% of your total lending, instead of 5,82%. Moreover, in the distinguished company of government bonds, the baseline credit risk to be spread over loans granted to ordinary clients gets driven down from the initial 5,82% to just 0,4*5,82% = 2,33%. By acquiring and holding solid government bonds, you can stop and revert the upwards spiral of credit risk in the times of economic trouble among your clients. Government bonds suck in and hold part of the baseline credit risk induced by the market.

Governments know all that, and we all know there is a price to pay for anything valuable. Thus, when a government approaches you with an initial, usually unofficial proposal of credit against sovereign bonds, you are likely to hear something in the lines of: ‘Look, man, we offer those bonds with an interest rate of 2% a year, and those bonds have a credit risk of 0,5%. We know your baseline credit risk is 5,82%. Thus, by lending us money in exchange of our bonds, you gain 2% in the nominal interest we offer, and 5,82% – 0,5% = 5,32% of reduction in your nominal credit risk. That makes you a total gain of 2% + 5,32% = 7,32%. C’mon, man, let’s share 50/50. Out of that total gain of 7.32%, you take 3,66% and we take 3,66%. How do we take it? Simple. Nominally, we hand you bonds for $10 000 000, but you actually advance to us, in cash, $10 000 000 * (1 + 3,66%) = $10 366 000. Yes, that 3,66% is called discount and you give us that discount because we give you lower credit risk’.

You can take that proposal such as it is or you can bargain. If you take it, you will get 2% in nominal interest on those bonds and you will give away 3,66% in discount. At the end of the day, you get those sovereign bonds at a negative yield of 2% – 3,66% = – 1,66%. Ridiculous? Not at all. You can go through Fiscal Monitors, published by the International Monetary Fund (https://www.imf.org/en/Publications/FM ), and you will see by yourself: the total value of sovereign bonds endowed with a negative yield, after all is said and done as regards discount for low credit risk, has been growing rapidly over the last decade. You can also consult http://www.worldgovernmentbonds.com/ and see the actual numbers. The French government currently borrows at -0,339% on 10 years, and at -0,715% on 3 years. Germany is the big boss of that lot: they borrow at – 0,619% over 10 years.

You can bargain, too. Instead of accepting that initial proposal of 3,66% discount, you say: ‘Look, government. We like each other, and we acknowledge your bonds give us lower risk. Still, please remember that your bonds have that low risk attached to them just as long as we, all the banks, give you a high credit rating. When we decide to downgrade your rating, your credit risk will go from 0,5% to 1,5%, and that will be bad for everyone. Let’s stay reasonable and share 70% for us and 30% for you, instead of 50/50. You get a discount of 7,32% * 0,3 = 2,2%, which, with the nominal interest of 2,5% you offer, gives us a real positive yield of 2,5% – 2,2% = 0,3%, and then we don’t look as total losers’.

You bargain or you don’t, thus. As a banker, you can bargain with a government when you are strong and they are weak. In the world, there are two completely different markets of public debt. The debt of strong governments, which have a lot of political and economic leverage on banks, is something very distinct from the debt of weak, economically wobbly governments, who are clients to banks rather than equivalent players. Against that background, there is that claim: ‘Our children will have to pay the debts we are contracting now’. Let’s discuss it.

You are a bank. You have lent money to a big solid government, who sort of raped you financially, into negative yield on their bonds, and yet you stay on the top of it with the low credit risk they give you, their bonds. That government comes to you, one day, and they say: ‘Time’s up. The maturity of our bonds was 5 years, which have just elapsed, and thus we are buying our bonds back. Here is the cash. Bye, bye. Have fun’. For you, as a banker, it is a disaster. For years, you have been constructing that financial portfolio where sovereign bonds were compensating the credit risk attached to risky business loans, and all that thing sort of kept itself together. Now, you stay with a pile of cash, which you need to invest into something, only anything you will invest it in will have a higher credit risk, and thus you will have to charge a higher interest, and thus you will be less attractive as lender, and you will be more and more doomed to work with people really desperate for cash, and that will drive your overall credit risk even higher, and here the loop spirals into hell.

What you can do is to agree, with the government, for a deal called ‘roll-over of debt’. When sovereign bonds come to their maturity, the government can offer you to swap them against a next generation of bonds, just to keep your balance sheet stable risk-wise, and to keep their cash-flow stable. In that roll-over swap, the same game of nominal interest and discount recurs. When you, as a bank, deal with a strong government with a solid economic base, they are very likely to swap like $100 of face value in bonds against $98 of face value in the next generation of bonds, or 2,5% of nominal interest a year against 1,9% a year etc. At the end of the day, those strong governments, as long as they stay within the limits of reasonable, can keep borrowing without burdening any future generation with the necessity of paying back the debt, because no one really wants to see it paid back. On the other hand, weak governments, ruling over wobbly economies, are in the opposite position. They have to roll their debt over at systematically worse financial conditions than the previous generation of bonds was based on. Those ones, yes, they fall into a true debt trap.

The key, thus, is to have a strong economy. When business runs well, everything else runs well, too. A strong economy, today, means an innovative one, with a lot of real technological change going on. By ‘real’ I mean technological change which actually develops your national technological base and doesn’t just outsource to China. Technological change involves a lot of uncertainty, and therefore a lot of business risk to face, which, at the level of banks, translates into a lot of credit risk. The latter needs to be compensated by low-risk sovereign bonds, which bear the lowest risk when they come from the government sitting on an economic base endowed with quick technological change. The loop closes. Strong economies generate a lot of credit risk, and their governments can alleviate that risk by borrowing from their national banks. As long as the money borrowed by governments supports technological change, directly or indirectly, and as long as neither party in that game goes feral, the whole thing works. This is an old intuition, which was already phrased out by Adam Smith, in his ‘Inquiry Into The Nature and Causes of The Wealth of Nations’ (Book V, Chapter III): substantial public borrowing appears when there is a substantial amount of private capital accumulated and waiting for interesting offers.

I made a video lecture, mostly addressed to my students of Economic Policy, as those in the course of Managerial Economics, with active reading of selected passages in the Fiscal Monitor September 2020 (https://youtu.be/ODY6zl1Z1r4 ).


[1] Taleb, N. N. (2007). The black swan: The impact of the highly improbable (Vol. 2). Random house.

[2] Taleb, N. N., & Blyth, M. (2011). The black swan of Cairo: How suppressing volatility makes the world less predictable and more dangerous. Foreign Affairs, 33-39.

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.

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