I am preparing an article, which presents, in a more elegant and disciplined form, that evolutionary model of technological change. I am going once again through all the observation, guessing and econometric testing. My current purpose is to find simple, intelligible premises that all my thinking started from. ‘Simple and intelligible’ means sort of hard, irrefutable facts, or, foggy, unresolved questions in the available literature. This is the point, in scientific research, when I am coining up statements like: ‘I took on that issue in my research, because facts A,B, C suggest something interesting, and the available literature remains silent or undecided about it’. So now, I am trying to reconstruct my own thinking and explain, to whomever would read my article, why the hell did I adopt that evolutionary perspective. This is the point when doing science as pure research is being transformed into scientific writing and communication.
Thus, facts should come first. The Schumpeterian process of technological progress can be decomposed into three parts: the exogenous scientific input of invention, the resulting replacement of established technologies, and the ultimate growth in productivity. Empirical data provides a puzzling image of those three sub-processes in the modern economy. Data published by the World Bank regarding science, research and development allow noticing, for example, a consistently growing number of patent applications per one million people in the global economy (see http://data.worldbank.org/indicator/IP.PAT.RESD ). On the other hand, Penn Tables 9.0 (Feenstra et al. 2015) make it possible to compute a steadily growing amount of aggregate amortization per capita, just as a growing share of aggregate amortization in the global GDP (see Table 1 in the Appendix). Still, the same Penn Tables 9.0, indicate unequivocally that the mean value of Total Factor Productivity across the global economy has been consistently decreasing since 1979 until 2014.
Of course, there are alternative views of measuring efficiency in economic activity. It is possible, for example, to consider energy efficiency as informative about technological progress, and the World Bank publishes the relevant statistics, such as energy use per capita, in kilograms of oil equivalent (see http://data.worldbank.org/indicator/EG.USE.PCAP.KG.OE ). Here too, the last decades do not seem to have brought any significant slowdown in the growth of energy consumption. The overall energy-efficiency of the global economy, measured with this metric, is decreasing, and there is no technological progress to observe at this level. A still different approach is possible, namely that of measuring technological progress at the very basic level of economic activity, in farming and food supply. The statistics reported by the World Bank as, respectively, the cereal yield per hectare ( see http://data.worldbank.org/indicator/AG.YLD.CREL.KG ), and the depth of food deficit per capita (see http://data.worldbank.org/indicator/SN.ITK.DFCT ), allow noticing a progressive improvement, at the scale of global economy, in those most fundamental metrics of technological performance.
Thus, the very clearly growing effort in research and development, paired with a seemingly accelerating pace of moral ageing in established technologies, occurs together with a decreasing Total Factor Productivity, decreasing energy efficiency, and just very slowly increasing efficiency in farming and food supply chains. Now, in science, there are basically three ways of apprehending facts: the why, the what, and the how. Yes, I know, there is a fourth way, the ‘nonsense!’ one, currently in fashion as ‘this is fake news! we ignore it’. Still, this fourth way is not really science. This is idiocy dressed fancily for an electoral meeting. So, we have three: the why, the what, and the how.
The why, or ‘Why are things happening the way they are?’, is probably the oldest way of starting science. ‘Why?’ is the intuitive way we have of apprehending things we don’t quite understand, like ‘Why is this piece of iron bending after I have left it close to a furnace?’. Probably, that intuitive tendency to ask for reasons reflects the way our brain works. Something happens, and some neurons fire in response. Now, they have to get social and to inform other neurons about that something having happened. Only in the world of neurons, i.e. in our nervous system, the category ‘other neurons to inform’ is quite broad. There are millions of them, in there. Besides, they need synapses to communicate, and synapses are an investment. Sort of a fixed asset. So, neurons have to invest in creating synapses, and they have a wide choice as for where exactly they should branch. As a result, neurons like fixed patterns of communication. Once they make a synaptic connection, they just use it. The ‘why?’ reflects this predilection, as in response we expect ‘Because things happen this way’, i.e. in response to this stimulus we fire that synaptic network, period.
The problem with the ‘why?’ is that it is essentially deterministic. We ask ‘why?’ and we expect ‘Because…’ in return. The ‘Because…’ is supposed to be reassuringly repetitive. Still, it usually is not. We build a ‘Because…’ in response to a ‘why?’, and suddenly something new pops up. Something, which makes the established ‘Because…’ look a little out of place. Something that requires a new ‘Because…’ in response to essentially the same ‘why?’. We end up with many becauses being attached to one why. Picking up the right because for the situation at hand becomes a real issue. Which because is the right because can be logically derived from observation, or illogically derived from our emotional stress due to cognitive dissonance. Did you know that the experience of cognitive dissonance can trigger, in a human being, stronger a stress reaction than the actual danger of death? This is probably why we do science. Anyway, choosing the right because on the illogical grounds of personal emotions leads to metaphysics, whilst an attempt to pick up the right because for the occasion by logical inference from observation leads to the next question: the ‘what?’. What exactly is happening? If we have many becauses to choose between, choosing the right one means adapting our reaction to what is actually taking place.
The ‘what?’ is slightly more modern than the ‘why?’. Probably, mathematics were historically the first attempt to harness the subtleties of the ‘what?’, so we are talking about settled populations, with a division of labour allowing some people to think about things kind of professionally. Anyway, the ‘what?’ amounts to describing reality so as the causal sequence of ‘because…’ is being decomposed as a sequence. Instead of saying ‘C happens because of B, and B happens because of A’, we state a sequence: A comes first, then comes B, and finally comes C. If we really mean business, we observe probabilities of occurrence and we can make those sequences more complex and more flexible. A happens with a probability of 20%, and then B can happen with a probability of 30%, or B’ can happen at 50% odds, and finally we have 20% of chances that B’’ happens instead. If it is B’’ than happens, it can branch into C, C’ or C’’ with the respective probabilities of X, Y, Z etc.
Statistics are basically a baby of the ‘what?’. As the ‘why?’ is stressful and embarrassingly deterministic, we dodge and duck and dive into the reassuringly cool waters of the ‘what?’. Still, I am not the only one to have a curious ape inside of me. Everyone has, and the curiosity of the curious ape is neurologically wired around the ‘why?’ pattern. So, just to make the ape calm and logical, whilst satisfying its ‘why’-based curiosity, we use the ‘how?’ question. Instead of asking ‘why are things happening the way they are?’, so instead of looking for fixed patterns, we ask ‘how are things happening?’. We are still on the hunt for links between phenomena, but instead of trying to shoot the solid, heavy becauses, we satisfy our ambition with the faster and more flexible hows. The how is the way things happen in a given context. We have all the liberty to compare the hows from different contexts and to look for their mutual similarities and differences. With enough empirical material we can even make a set of connected hows into a family, under a common ‘why?’. Still, even with such generalisations, the how is always different an answer from ‘because…’. The how is always context-specific and always allows other hows to take place in different contexts. The ‘because…’ is much more prone to elbow its way to the front of the crowd and to push the others out of the way.
Returning to my observations about technological change, I can choose, now, between the ‘why?’, the ‘what?’, and the “how?’. I can ask ‘Why is this apparent contradiction taking place between the way technological change takes place, and its outcomes in terms of productivity?’. Answering this question directly with a ‘Because…’ means building a full-fledged theory. I do not feel ready for that, yet. All these ideas in my head need more ripening, I can feel it. I have to settle for a ‘what?’, hopefully combined into context-specific hows. Hows run fast, and they change their shape, according to the situation. If you are not quick enough to run after a how, you have to satisfy yourself with the slow, respectable because. Being quick, in science, means having access to empirical data and be able to test quickly your hypotheses. I mean, you can be quick without access to empirical data, but then you just run very quickly after your own shadow. Interesting, but moderately productive.
So I am running after my hows. I have that empirical landscape, where a continuously intensifying experimentation with new technologies leads, apparently, to decreasing a productivity. There is a how, camouflaging itself in that landscape. This how assumes that we, as a civilisation, randomly experiment with new technologies, kind of which idea comes first, and then we watch the outcomes in terms of productivity. The outcomes are not really good – Total Factor Productivity keeps falling in the global economy – and we still keep experimenting at an accelerating pace. Are we stupid? That would be a tempting because, only I can invert my how. We are experimenting with new technologies at an increasing pace as we face disappointing outcomes in terms of productivity. If technology A brings, on the long run, decreasing productivity, we quickly experiment with A’, A’’, A’’’ etc. Something that we do brings unsatisfactory results. We have two options then. Firstly, we can stop doing what we do, or, in other words, in the presence of decreasing productivity we could stop experimenting with new technologies. Secondly, we can intensify experimentation in order to find efficient ways to do what we do. Facing trouble, we can be passive or try to be clever. Which option is cleverer, at the end of the day? I cast my personal vote for trying to be clever.
Thus, it would turn out that the global innovative effort is an intelligent, collective response to the unsatisfactory outcomes of previous innovative effort. Someone could say that this is irrational to go deeper and deeper into something that does not bring results. That is a rightful objection. I can formulate two answers. First of all, any results come with a delay. If something is not bringing results we want, we can assume it is not bringing them yet. Science, which allows invention, is in itself quite a recent invention. The scientific paradigm we know today has taken definitive shape in the 19th century. Earlier, we basically have been using philosophy in order to invent science. It makes some 150 years that we can use real science to invent new technologies. Maybe it has not been enough to learn how to use science properly. Secondly, there is still the question of what we want. The Schumpeterian paradigm assumes we want increased productivity but do we really? I can assume, very biologically, what I already signalled in my previous posts: any living species tends to maximize its hold on the environment by absorbing as much energy as possible. Maybe we are not that far from amoeba, after all, and, as a species, we collectively tend towards maximizing our absorption of energy from our environment. From this point of view, technological change that leads to increasing our energy use per capita and to engaging an ever growing amount of capital and labour into the process could be a perfectly rational behaviour.
All that requires assuming collective intelligence in the mankind. Proving the existence of intelligence is both hard and easy. On the one hand, culture is proof of intelligence: this is one of the foundational principles in anthropology. From that point of view, we can perfectly assume that the whole human species has collective intelligence. Still, an economist has a problem with this view. In economics, we assume individual choice. Can individual choice be congruent with collective intelligence, i.e. can individual, conscious behaviour change in step with collective decisions? Well, we did Renaissance, didn’t we? We did electricity, we did vaccines, we did religions, didn’t we? I use the expression ‘we did’ and not ‘we made’, because it wasn’t that one day in the 15th century we collectively decided that from now on, we sculpt people with no clothes on and we build cathedrals on pseudo-ancient columns. Many people made individual choices, and those individual choices turned out to be mutually congruent, and produced a coherent social change, and so we have Tesla and Barbie dolls today.
Now, this is the easy part. The difficult one consists in passing from those general intuitions, which, in the scientific world, are hypotheses, to empirical verification. Honestly, I cannot directly, empirically prove we are collectively intelligent. I reviewed thoroughly the empirical data I have access to and I found nothing that could serve as direct proof of collective intelligence in the mankind. Maybe this is because I don’t know how exactly could I formulate the null hypothesis, here. Would it be that we are collectively dumb? Milton Friedman would say that in such a case, I have to options: forget it or do just as if. In other words, I can drop entirely the hypothesis of collective intelligence, with all its ramifications, or construe a model implying its veracity, so treating this hypothesis as an actual assumption, and see how this model fits, in confrontation with facts. In economics, we have that assumption of efficient markets. Right, I agree, they are not necessarily perfectly efficient, those markets, but they arrange prices and quantities in a predictable way. We have the assumption of rational institutions. In general, we assume that a collection of individual acts can produce coherent social action. Thus, we always somehow imply the existence of collective intelligence in our doings. Dropping entirely this hypothesis would be excessive. So I stay with doing just as if.
 Feenstra, Robert C., Robert Inklaar and Marcel P. Timmer (2015), “The Next Generation of the Penn World Table” American Economic Review, 105(10), 3150-3182, available for download at www.ggdc.net/pwt