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.