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market of electricity’, in order to resubmit it to the journal Applied Energy , by somehow fusing it with two other, unpublished pieces of my writing, namely: ‘Behavioural absorption of Black Swans: simulation with an artificial neural network’, and ‘The labour-oriented, collective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

I needed that

It’s been quite a few days without me writing and posting anything new on my blog. This is one of those strange moments, when many different strands of action emerge, none is truly preponderant over the others, and I feel like having to walk down many divergent paths all at once. As such an exercise can end up in serious injuries, the smart way to go is to make those divergent paths converge at some point.

As usually in such situations of slight chaos in my head, I use the method of questions to put some order in it. Let’s do it. What do I want? I want to develop my theoretical concept of collectively intelligent social structure into a workable, communicable, and reproducible methodology of research. I want to use that methodology as intellectual core for a big project of research and development. The development part would be some kind of digital tool which, using an otherwise very simple version of artificial neural network, can run the diagnosis of a society (e.g. a city), regarding: a) the collective outcomes pursued by the collective intelligence of that society b) the patterns of collective learning, and more specifically the phenomena which are likely to knock that society out of balance as opposed to those which make it stabilize.

As I am writing these words, I intuitively guess that my investment in the stock market, such as I consistently do it, is successfully based on the hypothesis of collective intelligence in the stock market, and in the industries which I invest in. As I consistently oscillate around 50% of annual return on the cash invested in the stock market, that hypothesis of collective intelligence seems to be workable. When I think about my recipe for success, it strangely resembles the findings of my scientific research. In a paper published with the journal ‘Energy’, titled ‘Energy efficiency as manifestation of collective intelligence in human societies’, I found out that the coefficient of fixed assets per one patentable invention is a key variable that societies optimize, and prioritize over energy efficiency. When I look at my investment portfolio, and what seems to work in it, it is precisely about some kind of balance between innovation and assets. When that sweet spot is there, the company’s stock brings me nice return.

I want to develop my concept of collectively intelligent social structure into a method of teaching social sciences, and to interweave that teaching into the canonical subjects I teach: microeconomics, macroeconomics, international trade etc. I wonder how I can use that concept e.g. in business planning or in the analysis of contracts and legal acts.

What am I afraid of? What can possibly go wrong with my plans? Good question. My fears are essentially those of publicly acknowledged failure on my part. I am shit scared of being labelled as a loser, but also of being seen as someone who fails to take any challenge at all. There is another deep fear in me, and this is a strange fear, as it is interwoven with hope: it is both the fear and the hope of deep change in my existence, like changing my professional occupation for a radically new one, or moving to live in another place, that kind of thing. It looks like I dread two types of suffering: that coming from socially recognized failure in building my position in social hierarchy, and that coming from existential change. Yet, my apprehension vis a vis those two types of suffering is different. Socially recognized failure is something I simply want to avoid. Existential change is that strange case of love and hate, a bit like my practice of the Wim Hof method. As I think of it, overcoming the fear of change can lead me to discovering new, wonderful things in my life, and this is what I want.

As I connect the dots I have just written down, turns out that what I really need to do is to utilise my research on collective intelligence as a platform for deep existential change. What specific kind of change would both scare me and thrill me in the best possible combination? What kinds of change can I take into account at all? Change of job inside the same occupation, i.e. inside the academia, for one. Further reaching a change of occupation, thus going outside academia, is the next level of professional change. The slightly fantasque move in that department would be to transform my investment in the stock market into a small investment fund for innovative projects, like a start-up fund. Moving to another place – a different city or a different country – is another option. Change of environment can be enormously stimulating, I know it by experience. Besides, my home country, Poland, is progressively turning into a mix of a catholic version of Iran, i.e. a religious state, with what I remember from the times of communism. A big part of the Polish population seems to be delighted with the process, and I am not delighted at all. I intuitively feel that compulsive thinking about how much ours is what we have means heading towards a disaster, and we just serve ourselves a lot of tranquilizing pills to kill the otherwise quite legitimate fear. It is all becoming both scary and suffocating, and I feel like getting out of the swamp before I sink too deep. Still, I know that geographical move has to be backed with realistic assumptions as for my social role: job, family etc. I am the kind of big, steady animal, like a moose, and it is both physical and existential. Jumping from one rooftop to another, parkour-style, is something I like watching but I completely suck at. I need a path and a structure to achieve change. 

I am exploring my deeply hidden drivers, and I am trying to be honest with myself and my readers. Which of those existential moves looks the most tempting to me? I think that a progressive transition, or, I should rather say: expansion, of out the academia is the most thrilling to me. I want it to be a progressive expansion, with a path of progress and learning. What do I need to learn in that process? In order to answer that question, I need to define my endgame, i.e. the target state I am working up to. In other words, how will I know I have what I want? I know I have a method when it has been intersubjectively validated, either by publication or by practical use in a collective research project.  How will other people know I have what I want? How will other people know I have a valid method? They need to buy into its logic, and acknowledge it as fit for publication or for application in a collective research project.

Here comes a fortunate coincidence, which has just knocked me out of philosophizing and closer to actual life. A scientific journal, Applied Energy, has just rejected positively my manuscript titled ‘Climbing the right hill – an evolutionary approach to the European market of electricity’, and I am sort of happy about it. Why being happy about rejection? Well, in the world of science, there are two types of rejection: the ‘f**k you, man!’ type, and the maybe-if-you-improve-and-develop type. With that specific manuscript, I have already knocked at the doors of many scientific journals, and each time I received the former type of rejection letter. This time, with Applied Energy, it is the latter type. The editorial letter I have just received states ‘While your submission is of interest to Applied Energy, your manuscript does not meet the following criteria, we are returning the manuscript to you before the review:

*Lack of scientific originality/novelty:

The novelty/originality shall be justified by highlighting that the manuscript contains sufficient contributions to the new body of knowledge. The knowledge gap needs to be clearly addressed in Introduction.

*Literature survey is not sufficient to present the most updated R&D status for further justification of the originality of the manuscript. You should carry out a thorough literature survey of papers published in a range of top energy journals in the last three/four years so as to fully appreciate the latest findings and key challenges relating to the topic addressed in your manuscript and to allow you to more clearly present your contributions to the pool of existing knowledge. In the case the subject is really novel and few or no specific references are found, the novelty of the subject, the methodology used and the similarity to other older or newer subjects should be explicitly addressed.

At this time, your submission will be rejected from Applied Energy but please feel free to re-submit to the journal once the aforementioned comments have been addressed’.

The journal Applied Energy is top of the food chain in as journals about energy economics come. Such a nice and polite rejection from them is an invitation to dialogue. At last! I really needed that.

As I am preparing teaching material for the next semester, and I am interweaving that stream of work with my research on collective intelligence in human societies. I drop by some published science, just to chat with Berghout, S., & Verbitskiy, E. (2021). On regularity of functions of Markov chains. Stochastic Processes and their Applications. https://doi.org/10.1016/j.spa.2020.12.006 . There is a state of reality Xn = {x1, x2, …, xn}, which we cannot observe directly; {Xn} slips easily of our observational capacity. Thus, instead of chasing ghosts, we nail down a set of observables {Yn} such that Yn = π (Xn), the π being a coding map of Xn so as we can observe through the lens of Yn.

These are the basic assumptions expressed in the paper by Berghout & Verbitskiy, and this is an important building bloc in my research and in my teaching. If I want to teach my hypothesis of collective intelligence to undergraduate students, I need to make it simple, and to show immediate benefits of using an analytical method based on it. I want to focus, for a moment, on the latter component, thus on practical applications. The hypothesis of collective intelligence implies that human societies are intelligent structures, and they learn new stuff by experimenting with many alternative versions of themselves. That capacity of learning by experimenting with ourselves, whilst staying structurally coherent, is precisely the gain out of being collectively intelligent. Here, I go a bit far with my next claim: I think we can enhance our capacity of collective learning if we accurately grasp and communicate the exact way we learn collectively, i.e. the exact way we experiment with many alternative versions of us doing things together. That hypothesis comes from my observation about myself, and about some other people I know: when I narrate to myself the way I learn something, my learning speeds up. What if we, humans being together, can speed up the process of our collective learning by narrating to ourselves the exact way we learn?

Here, I stress the ‘exact way’ part. We have culture, which recently turns into outrage culture, with a lot of moralizing and little action. Here, I allow myself to quote one of my students. The guy comes from Rwanda, Africa, and in the class of management, when we were discussing different business concepts my students come up with, he gave the example of an actual business model which apparently grows like hell in Rwanda and in Africa as a whole. You buy a small fleet of electric cars, like 5 – 10, you rent them, and you assure full technical support to your clients, and you build a charging station for those cars, powered by a solar farm just next door. Investment goes into five types of assets: land, solar farm with full equipment (big batteries for storage included), electric cars, and equipment for their maintenance. You sell rental hours, additional maintenance services, and energy from the charging station. Simple, clean, workable, just the way I like it.

When I heard that story from my student, I had one of those ‘F**k!’ realizations. In Europe, and I think in North America as well, when we want to do something for the planet and the climate, we start by bashing each other about how bad we are at it and how necessary it is to turn vegan, then we burn thousands of tons of fuel to gather in one place and do a big march for the planet, then we do a strike for climate, and finally we claim that the government should do something about the climate, and, by the way, it would be a good thing if Jeff Bezos gave away some of his wealth. In Rwanda, when those people realize they should take care of the climate and the planet, they develop businesses which do. I think their way is somehow more promising.

I come back to the exact way we learn collectively. There is the Greta-Thunberg-way of caring about the planet, and there is the Rwandan way. Both exist, both are different experimental versions of ourselves, and both get reinforced by communication. One march for the planet, properly covered by the media, incites further marches for the planet, and, in the same way, disseminating that business model – involving a small fleet of electric vehicles, charging stations and solar farms – is likely to speed up its development. Narrating to ourselves the ways we develop new technologies can speed up their development.

The exact way we learn collectively is made, in the first place, of the specific, alternative versions of the social structure. When I want to know the exact way we learn collectively, I need to look at the alternative versions (of our collective) which we are experimenting with, thus at the actual degrees of freedom we have in that experimentation. Those alternative versions are described in terms of observables that Yn = π (Xn), which, in turn, are our best epistemological take on the otherwise unobservable reality {Xn}, through the coding map π.

I can see something promising here, I mean in that notion of actual experimental versions of ourselves. My scientific discipline, i.e. social sciences with a strong edge of economics and management, is plagued by claims that things ‘should be done’ in a given way just because it worked locally. Recently, I witnessed a heated debate between some acquaintances of mine, on Facebook, as for which economic model is better: the American one or the Scandinavian one. You know, the thing about education, healthcare, economic equality and stuff. As I was observing the ball of thoughts being played between those people, I had the impression of seeing an argument without common field. One camp argued that because something works in Sweden or Finland, it should be applied everywhere, whilst their opponents claimed exactly the same about the American economic model. In the middle of that, I was watching the protagonists flexing their respective intellects, and I couldn’t help thinking about my own research on economic models. I found empirical evidence that economic systems, across the board, aim for optimizing the average number of hours worked per person per year, and the amount of education one needs to get into the job market. All the rest is apparently instrumental.

F**k! I got distracted once again. I am supposed to show practical applications of my hypothesis regarding collective intelligence. Here comes an idea for a research project, with some potential for acquiring a research grant, which is as practical an application as there can be, in science. In my update titled ‘Out-of-the-lab monsters’, I hypothesised that economic recovery after the COVID-19 pandemic will be somehow slower than we expect, and certainly very different in terms of business models and institutions. The pandemic has triggered accelerated change as regards the use of digital technologies, the prevalence of biotechnology as business, and as regards social roles that people can endorse. Therefore, it would be a good thing to know which specific direction that change is going to take.

My idea is to take a large sample of business entities listed in public stock markets, which disclose their activity via the mechanism of investor relations, and to study their publicly disclosed information in order to discover the exact way they take in their business models. I am formulating the following hypothesis: in the economic conditions peculiar to the COVID-19 pandemic, business entities build up their reserves of cash and cash-equivalent securities in order to reinforce their strategic flexibility as regards technological change

Out-of-the-lab monsters

That period, end of January, beginning of February, is usually a moment of reassessment for me. This might be associated with my job – I am a scientist and an academic teacher – and right now, it is the turn of semesters in my country, Poland. I need to have some plan of teaching for the next semester, and, with the pandemic still around, I need to record some new video material for the courses of the Summer semester: Macroeconomics, International Trade, and International Management.

That being said, I think that formulating my current research on collective intelligence in terms of teachable material could help me to phrase out those thoughts of mine coherently and intelligibly enough to advance with the writing of my book on the same topic. I feel like translating a few distinct pieces of scientific research into teaching. The theoretical science of Markov chains is the first one. The empirically observed rise of two technologically advanced industry, namely biotechnology and electric vehicles comes as the second big thing. Thirdly, and finally, I want to develop on the general empirical observation that money tends to flow towards those new technologies even if they struggle to wrap themselves into operationally profitable business models. Next comes a whole set of empirical observations which I made à propos of the role of cities in our civilization. Finally, the way we collectively behave amidst the pandemic is, of course, the most obvious piece of empirical science I need connecting to in my teaching. 

In discussing those pieces of science in a teachable form, I feel like using the method I have been progressively forming in my research over the last 2 years or so. I use simple artificial neural networks as simulators of collectively intelligent behaviour. I have singled out a few epistemological regularities I feel like using in my teaching. Large datasets of socio-economic variables seem to have privileged orientations: they sort of wrap themselves around some specific variables rather than others. When disturbed with a random exogenous factor, the same datasets display different ways of learning, depending, precisely, on the exact variable I make them wrap themselves around. One and the same dataset, annoyingly disturbed by the buzz of a random disturbance, displays consistent learning when oriented on some variables, and goes haywire when oriented on others.

On the top of all that, I want to use in my teaching the experience I have collected when investing in the stock market. This is mostly auto-narrative experience, about my own behaviour and my own reactions when sailing in my tiny boat across the big ocean, filled with sharks, of the stock market.

What exactly do I want to teach my students? I mean, I know the labels: Macroeconomics, International Trade, International Management. These are cool labels. Yet, what do I want to teach in terms of real skills and understanding? I think that my core message is that science is f**king amazing, and when we combine scientific thinking with good, old-fashioned perseverance and grit, great things emerge. My students are young people, and having been their age, back in the day, I know that entering adulthood and developing personal independence is a lot about pretending, and a lot about finding one’s place in a fluid, essentially chaotic reality. That place is called a social role. I think I can deliver valuable teaching as for how to use the basic tools of social sciences in order to make ourselves good, functional social roles.

Concurrently to that purpose, I have another one, about mathematics. I can see many of my students the same kind of almost visceral, and yet visibly acquired abhorrence of mathematics, which I used to have in my mind. I think this is one of the failures in our educational system: early at school, we start learning mathematics as multiplication tables, which quite thoroughly kills the understanding that mathematics are a language. It is a language which speaks about the structure of reality, just a bit less convivially than spoken languages do. That language proves being bloody useful when talking about tough and controversial, such as ways of starting a new business from scratch (hence engaging people’s equity into something fundamentally risky), ways of getting out of an economic crisis, or ways of solving a political conflict.     

I think I can teach my students to perceive their existence as if they were travelling engineers in the small patch social reality around them, particularly engineers of their own social role. Look around you, across the surrounding social landscape. Find your bearings and figure out your coordinates on those bearings. Formulate a strategy: set your goals, assess your risks, make the best-case scenario and the worst-case scenario. What is your action? What can you do every day in order to implement that strategy? Therefore, what repetitive patterns of behaviour should you develop and become skilful at, in order to perform your action with the best possible outcomes? Let’s be clear: it is not about being world champion in anything (although it wouldn’t hurt), it is about being constructively optimistic, with a toolbox close at hand.  

What do I really know about macroeconomics, international trade, and international management? This is a fundamental question. Most of what I know, I know from the observation of secondary sources. Periodical financial reports of the companies, coupled with their stock prices, and with general economic reports, such as the World Economic Outlook, published by the International Monetary Fund, are my basic sources of information about what’s up in business and economics. What I know in those fields is descriptive knowledge.    

Where do I start? We, humans, form collectively intelligent structures which learn by experimenting with many alternative versions of themselves. Those versions are built around a fundamental balance between two institutional orders: the institutions of agriculture, which serve as a factory of food, and the institutions of cities, whose function consists in creating and sustaining social roles, whilst speeding up technological change. We collectively experiment with ourselves by creating demographic anomalies: abnormally dense populations in cities, next door to abnormally dispersed populations in the countryside. I think this is the fundamental distinction between the populations of hunters-gatherers, and the populations of settlers. Hunters-gatherers live in just one social density, whilst settlers live in two of them: the high urban density coexisting with low rural density.

I can put it in a different way. We, humans, interact with the natural environment, and interact with each other.  When we interact with each other a lot, in highly dense networks of social relations, we reinforce each other’s learning, and start spinning the wheel of innovation and technological change. Abundant interaction with each other gives us new ideas for interacting with the natural environment.

Cities have peculiar properties. Firstly, by creating new social roles through intense social interaction, they create new products and services, and therefore new markets, connected in chains of value added. This is how the real output of goods and services in a society becomes a complex, multi-layered network of technologies, and this is how social structures become self-propelling businesses. The more complexity in social roles is created, the more products and services emerge, which brings the development in greater a number of markets. That, in turn, gives greater a real output, greater income per person, which incentivizes to create new social roles etc. This how social complexity creates the phenomenon called economic growth.

The phenomenon of economic growth, thus the quantitative growth in complex, networked technologies which emerge in relatively dense human settlements, has a few peculiar properties. You can’t see it, you can’t touch it, and yet you can immediately feel when its pace changes. Economic growth is among the most abstract concepts of social sciences, and yet living in a society with real economic growth at 5% per annum is like a different galaxy when compared to living in a place where real economic growth is actually a recession of -5%. The arithmetical difference is just 10 percentage points, around the top of something underlying which makes the base of 1. Still, lives in those two contexts are completely different. At +5% in real economic growth, starting a new business is generally a sensible idea, provided you have it nailed down with a business plan. At – 5% a year, i.e. in recession, the same business plan can be an elaborate way of committing economic and financial suicide. At +5%, political elections are usually won by people who just sell you the standard political bullshit, like ‘I will make your lives better’ claimed by a heavily indebted alcoholic with no real career of their own. At -5%, politics start being haunted by those sinister characters, who look and sound like evil spirits from our dreams and claim they ‘will restore order and social justice’.

The society which we consider today as normal is a society of positive real economic growth. All the institutions we are used to, such as healthcare systems, internal security, public administration, education – all that stuff works at least acceptably smoothly when complex, networked technologies of our society have demonstrable capacity to increase their real economic output. That ‘normal’ state of society is closely connected to the factories of social roles which we commonly call ‘cities’. Real economic growth happens when the amount of new social roles – fabricated through intense interactions between densely packed humans – is enough for the new humans coming around. Being professionally active means having a social role solid enough to participate in the redistribution of value added created in complex technological networks. It is both formal science and sort of accumulated wisdom in governance that we’d better have most of the adult, able bodied people in that state of professional activity. A small fringe of professionally inactive people is somehow healthy a margin of human energy free to be professionally activated, and when I say ‘small’, it is like no more than 5% of the adult population. Anything above becomes both a burden and a disruption to social cohesion. Too big a percentage of people with no clear, working social roles makes it increasingly difficult to make social interactions sufficiently abundant and complex to create enough new social roles for new people. This is why governments of this world attach keen importance to the accurate measurement of the phenomenon quantified as ‘unemployment’.  

Those complex networks of technologies in our societies, which have the capacity to create social roles and generate economic growth, work their work properly when we can transact about them, i.e. when we have working markets for the final economic goods produced with those technologies, and for intermediate economic goods produced for them. It is as if the whole thing worked when we can buy and sell things. I was born in 1968, in a communist country, namely Poland, and I can tell you that in the absence of markets the whole mechanism just jams, progressively to a halt. Yes, markets are messy and capricious, and transactional prices can easily get out of hand, creating inflation, and yet markets give those little local incentives needed to get the most of human social roles. In the communist Poland, I remember people doing really strange things, like hoarding massive inventories of refrigerators or women’s underwear, just to create some speculative spin in an ad hoc, semi-legal or completely illegal market. It looks as if people needed to market and transact for real, amidst the theoretically perfectly planned society.   

Anyway, economic growth is observable through big sets of transactions in product markets, and those transactions have two attributes: quantities and prices AKA Q an P. It is like Q*P = ∑qi*pi. When I have – well, when we have – that complex network of technologies functionally connected to a factory of social roles for new humans, that thing makes ∑qi*pi, thus a lot of local transactions with quantities qi, at prices pi. The economic growth I have been so vocal about in the last few paragraphs is the real growth, i.e. in quantity Q = ∑qi. On the long run, what I am interested in, and my government is interested in, is to reasonably max out on ∆ Q = ∆∑qi. Quantities change slowly and quite predictably, whilst prices tend to change quickly and, mostly on the short term, chaotically. Measuring accurately real economic growth involving kicking the ‘*pi’ component out of the equation and extracting just ∆ Q = ∆∑qi. Question: why bothering with the observation of Q*P = ∑qi*pi when the real thing we need is just ∆ Q = ∆∑qi? Answer: because there is no other way. Complex networks of technologies produce economic growth by creating increasing diversity in social roles in concurrence with increasing diversity in products and their respective markets. No genius has come up, so far, with a method to add up, directly, the volume of visits in hairdresser’s salons with the volume of electric vehicles made, and all that with the volume of energy consumed.

Cities trade. Initially, they trade with the surrounding farms, out in the countryside, but, with time, the zone of trade relations tends to extend, and, interestingly enough, its extent is roughly proportional to the relative weight of the given city’s real output in the overall economic activity of the whole region. It is as if cities were developing some sort of gravitational field around them. The bigger the city as compared to other cities in the vicinity, the greater share of overall trade it takes, both in terms of exports and imports. Countries with many big cities trade a lot with other countries.     

There is an interesting relationship between exports and imports. Do I, as a person, import anything? Sure, I import plenty of goods. This software I am writing in is an imported good, to start with. Bananas which I ate for breakfast are imported. I drive a Honda, another imported good. My washing machine is a Samsung, my dish washer is a Siemens, and my phone and computer both come from Apple. I am a walking micro-hub of imports. Do I export anything? Almost nothing. One could argue that I export intellectual content with my blog. Still, as I am not being paid (yet) for my blog, it is rather voluntary cultural communication than exports. Well, there is one thing that creates a flow of export and import in me: my investment in the stock market. The money which I invested in the stock market is mostly placed in US-based companies, a few German and Dutch, and just a tiny bit is invested in Poland. Why? Because there is nothing happening in the Polish stock market, really. Boring. Anyway, I sort of export capital.

Cities and countries import a whole diversified basket of goods, but they usually export just a few, which they are really good at making and marketing. There is something like structural asymmetry between exports and imports. As soon as economic sciences started to burgeon, even before they were called economics and had been designated as ‘political economy’, social thinkers were trying to explain that phenomenon. Probably the best known is the explanation by David Ricardo, namely the notion of comparative advantage AKA productive specialization. There are exceptions, called ‘super exporters’, e.g. China or South Korea. These are countries which successfully export virtually any manufactured good, mostly due to low labour costs. However we label that phenomenon, here it is: whilst the global map of imports look like a very tight web, the map of exports is more like a few huge fountains of goods, pouring their output across the world. Practically every known imported good has its specialized big exporters. Thus, if my students ask me what international trade is, I am more and more prone to answer that trade is a structural pattern of the human civilization, where some places on Earth become super-efficient at making and marketing specific goods, and, consequently, the whole planetary civilization is a like team of people, with clearly assigned roles.

What is international management in that context? What is the difference between international management and domestic management, actually? What I can see, for example in the companies whose stock I invest my savings in, there is a special phase in the development of a business. It is when you have developed a product or service which you start marketing successfully at the international scale, thus you are exporting it, and there comes a moment when branching abroad with your organisational structure looks like a good idea. Mind you, there are plenty of business which, whilst growing nicely and exporting a lot, remain firmly domestic. If I run a diamond mine in Botswana – to take one of the most incongruous examples that come to my mind – I mind those diamonds in order to export them. There is no point in mining diamonds in Botswana just to keep those diamonds in Botswana. Export is the name of the game, here. Still, do I need to branch out internationally? My diamonds go to Paris, but is it a sensible idea to open a branch office in Paris? Not necessarily, rents for office space are killers over there. Still, when I run a manufacturing business in Ukraine, and I make equipment for power grids, e.g. electric transformers, and I export that equipment across Europe and to US, it could be a good idea to branch out. More specifically, it becomes a good idea when the value of my sales to a given country makes it profitable to be closer to the end user. Closer means two things. I can clone my original manufacturing technology in the target market, thus instead of making those transformers in Ukraine and shipping them to Texas, I can make them in Texas. On the other hand, closer means more direct human interaction, like customer support. 

Good. I got carried away a bit. I need to return to the things I want to teach my students, i.e. to skills I want to develop in them when teaching those three courses: Macroeconomics, International Trade, and International Management. Here is my take on the thing. These three courses represent three levels of work with quantitative data. Doing Macroeconomics in real life means reading actively macroeconomic reports and data, for the purposes of private business or those of public policy. It means being able to interpret changes in real output, inflation, unemployment, as well as in financial markets.

Doing International Trade for real might go two different ways: either you work in international trade, i.e. you do the technicalities of export and import, on the one hand, or you work about and around international trade, namely you need to nail down some kind of business plan or policy strongly related to export and import. That latter aspect involves working with data much more than the former, which, in turn, is more about documents, procedures and negotiation. I am much more at home with data analysis, contracts, and business planning than with the very technicalities of international trade. My teaching of international trade will go in that direction.

As for International Management, my only real experience is that of advising, doing market research and business planning for people who are about to decide about branching out abroad with their business. This is the only real experience I can communicate to my students.

I want to combine that general drift of my teaching with more specific a take on the current social reality, i.e. that of pandemic, economic recession and plans for recovery, and technological change combined with a modification of established business models. That last phenomenon, namely new technologies coming to the game and forcing a change in business structures is the main kind of understanding I want to provide my students with, as regards current events. Digital technologies, biotechnologies, and complex power systems increasingly reliant on both renewable energies and batteries of all kinds, are the thread of change. On and around that thread, cash is being hoarded, in unusually big cash-oriented corporate balance sheets. Cash is king, and science is the queen, so to say, in those newly developing business models. That’s logical: deep and quick technological change creates substantial risks, and increased financial liquidity is a normal response thereto.

Whatever will be happening over the months and years to come, in terms of economic recovery after the epidemic recession, will be happening through and in businesses which hoard important amounts of cash, and constantly look for the most competitive digital technologies. When governments say ‘We want to support the bouncing back of our domestic businesses’, those governments have to keep in mind that before investing in new property, plant, equipment, and in new intangible intellectual property, those businesses will be bouncing back by accumulating cash. This time, economic recovery will be probably very much non-Keynesian. Instead of unfreezing cash balances and investing them in new productive assets, microeconomic recovery of local business structures will involve them juicing themselves with cash. I think this is to take or to leave, as the French say. Bitching and moaning about ‘those capitalists who just hoard money with no regard for jobs and social gain’ seems as pointless as an inflatable dartboard.

Those cash-rich balance sheets are going to translate into strategies oriented on flexibility and adaptability more than anything else. Business entities are naturally flexible, and they are because they have the capacity to build, purposefully, a zone of proximal development around their daily routines. It is a zone of manageable risks, made of projects which the given business entity can jump into on demand, almost instantaneously. I think that businesses across the globe will be developing such zones of proximal development around themselves: zones of readiness for action rather than action itself. There is another aspect to that. I intuitively feel that we are entering a period of increasingly quick technological change. If you just think about the transformation of manufacturing processes and supply chains in the pharmaceutical industry, so as to supply the entire global population with vaccines, you can understand the magnitude of change. Technologies need to break even just as business models do. In a business model, breaking even means learning how to finance the fixed costs with the gross margin created and captured when transacting with customers and suppliers. In a technology, breaking even means to drive the occurrence of flukes and mistakes, unavoidable in large-scale applications, down to an acceptable level. This, in turn, means that the aggregate costs of said flukes and mistakes, which enters into the fixed costs of the business structure, is low enough to be covered by the gross margin generated from the technological process itself.

That technological breaking even applies to the digital world just as it applies to industrial processes. If you use MS Teams, just as I and many other people do, you probably know that polity enquiry which Teams address you after each video call or meeting: ‘What was the call quality?’. This is because that quality is really poor, with everybody using online connections much more than before the pandemic (much worse than with Zoom, for example), and Microsoft is working on it, as far as I know. Working on something means putting additional effort and expense into that thing, thus temporarily pumping up the fixed costs.

Now, suppose that you are starting up with a new technology, and you brace for the period of breaking even with it. You will need to build up a cushion of cash to finance the costs of flukes and mistakes, as well as the cost of adapting and streamlining your technology as the scale of application grows (hopefully).

We live in a period when a lot of science breaks free out of experimental labs much earlier and faster than it was intended to. Vaccines against COVID-19 are the best example. You probably know those sci fi movies, where some kind of strange experimental creature, claimed to be a super-specimen of a new super-species, and yet strangely ill-adapted to function in the normal world, breaks out of a lab. It wreaks havoc, it causes people to panic, and it unavoidably attracts the attention of an evil businessperson who wants to turn it into a weapon or into a slave. This is, metaphorically, what is happening now and what will keep happening for quite a while. Of course, the Sars-Cov-2 virus could very well be such an out-of-the-lab monster, still I think about all the technologies we deploy in response, vaccines included. They are such out-of-the-lab monsters as well. We have, and we will keep having, a lot of out-of-the-lab monsters running around, which, in turn, requires a lot of evil businesspeople to step in and deploy they demoniac plots.

All that means that the years to come are likely to be bracing, adapting and transforming much more than riding a rising wave crest of economic growth. Recovery will be slower than the most optimistic scenarios imply. We need to adapt to a world of fence-sitting business strategies, with a lot of preparation and build-up in capacity, rather than direct economic bounce-back. When preparing a business plan, we need to prepare for investors asking questions like ‘How quickly and how specifically can you adapt if the competitor A implements the technology X faster than predicted? How much cash do we need to shield against that risk? How do we hedge? How do we insure?’, rather than questions of the type ‘How quickly will I have my money back?’. In such an environment, substantial operational surplus in business is a rarity. Profits are much more likely to be speculative, based on trading corporate stock and other financial instruments, maybe on trading surpluses of inventories.

Educational (very educational): embarrassing questions about monetary systems

My editorial

This particular update on my blog is both a piece of educational content, and a piece of general research methodology in social sciences. It regards monetary systems. In terms of education, it mostly addresses those 3rd year students, Undergraduate, whom I am currently lecturing about Economic Policy. Still, the graduate Master’s students in the curriculum of International Economic Transactions can have some benefits out of it. I start with an old and classical one: the quantitative monetary equilibrium, or, in fancy economic writing:

P*T = M*V

P – the index of prices

T – the volume of transactions in the economy

M – the supply of money (monetary mass) in the economy

V – the velocity of money

This equation is both a mindfuck and a useful tool to understand how money works at the macroeconomic level. As for being a mindfuck, it is simple: both sides of this equation are equal to Q, or nominal aggregate output, measured in current prices. So you essentially start with the not-too-risky assumption that Q = Q and then you unleash yourself on maths. The nominal output is equal to the real output (i.e. the physical volume of goods and services produced), and it gets nominal by being nominated in a currency, i.e. by being multiplied by the current prices of things inclusive in the real output. Thus, Q = P*T is pretty intuitive. Now, the right side of the equation is more based on the current empirical observation. If you care to have a look at a statistic published by the World Bank under the label of supply of broad money as % of the GDP (you know, you click on the underlined phrase), you will get the current proportion between nominal output and the monetary mass supplied to the economy. The first observation is that it is never equal. In other words, you have very little chances to hit Q = P*T = M, with velocity V =1. When the equation of quantitative monetary equilibrium was being formulated, back in the 1950ies and 1960ies, the supply of money to the economy was consistently lower than the nominal output of the economy, i.e. we recurrently had: Q = P*T > M. Intuitively, you could guess that money supplied to the economy serves more than one transaction a year, or, in other words, money was seen as something circulating pretty quickly across the economy. There were even people who claimed that it circulates at a constant velocity. The well-known master of economic methodology, Milton Friedman, used to be one of those people. Still, as time passes, things change. Since the 1950ies the proportion between nominal output and the supply of money (look up that statistic with the World Bank) has been consistently shrinking as for the global economy. The monetary mass supplied made 50,465% of the global GDP in 1960 (velocity V = 1/0,50465 = 1,981571386). In 1990, the proportion climbed to 88.01%, thus velocity fell to V = 1/0,8801 = 1,136234519. Around 2007 – 2008, the global economy passed the magical threshold of Q = M. Interestingly, the global financial crisis broke out just then. In 2016 the supply of money in the world made 116,411% of the global GDP, with the velocity sadly falling down to V = 1/1,16411 = 0,85902535. Thus, in real life, money works differently in the economy, depending on the period of time. Currently, it seems to slow down its circulation. If people have more goods than money, so if we have like M = 50%*Q, you accumulate real goods rather than money, and you make those coins spin quickly, just to have all your goods well financed. Yet, when you have more money than real goods (case of the present day), you accumulate money and you speed up the flow of goods, so as to have any grounds for having the money. Over the last 70 years, the global monetary system has shifted from one monetary paradigm to another one, and we still don’t understand completely what has actually happened.

Now, when you switch from differences in time to those across space, and you take a snapshot from 2016, you have, for example: Argentina M = 28,9% * Q, Australia M = 118,8% * Q, Hong Kong M = 363% * Q, China M = 208,3%*Q, United Kingdom M = 144%*Q, United States M = 90,6%*Q. You can see that money works very differently across space. Each country seems to be a highly idiosyncratic monetary system. Good, so we keep on asking embarrassing questions. In textbooks, and in my lectures in the first year, you could have learnt that the supply of money is practically equal to the supply of credit from the banking system. It is generally true to the extent, that when banks get profuse on lending money, you can immediately see prices rise in the economy, and one of the best ways to slow down inflation is to make credit more expensive in terms of interest rates. Still, let’s check. In 2016, the global supply of credit (you know, click), from banks to the real side of the global economy, made 177,421% of the global GDP. Simple arithmetic indicate that we had [177,421/116,411] = 1,52 times more credit than money supplied. Back in 1990, the credit supplied from all banks in the world made 126,138% of the global GDP. Once again, we check credit for its attendance to the money being supplied, and we get [Credit/Money] = [126,138/88,01] = 1,433. Interesting: there seems to be more and more credit who lost its way from banks to purses (happens usually on a late hour at night), and there seems to be more and more credit in that awkward situation. Let’s snapshot across space in 2016. Argentina, credit = 38,8%*Q, credit/M = 38,8/28,9 = 1,34; Australia, credit = 183,4%*Q, credit/M = 183,4/118,8 = 1,544; Hong Kong, credit = 212%*Q, credit/M = 212/363 = 0,584; China, credit = 215%*Q, credit/M = 215/208,3 = 1,032; United Kingdom, credit = 167,8%*Q, credit/M = 167,8/144 = 1,1653; United States, credit = 242,6%*Q, credit/M = 242,6/90,6 = 2,677. Each country has a different system of transmission from credit lent to money supplied.

Now, if you are a government, you want two things on the left, P*T side of monetary equilibrium. You want to see your real output, or the volume of transactions T, gallop joyfully forward, i.e. grow like hell, whilst controlling the level of prices P. In order to do that, you need to control, somehow, the way your national monetary system works. As you can see from the numbers presented above, this is not obvious at all. The basic leverages you have are (check them at Wikipedia or elsewhere): the supply of currency through the central bank, the interest rates on credit, the ratio of mandatory reserves (the % of deposits held from customers that commercial banks have to hold, in turn, at the central bank), open market operations by the central bank and sometimes by the national Treasury (Minister of Finance in continental Europe), and the so-called quantitative ease (this is when the government buys financial assets in the domestic market; it acts on financial markets like a toilet plunger, you know, that big rubber sucker that you use to make your plumbing cooperative again).