I noticed it is one month that I did not post anything on my blog. Well, been doing things, you know. Been writing, and thinking by the same occasion. I am forming a BIG question in my mind, a question I want to answer: how are we going to respond to climate change? Among all the possible scenarios of such response, which are we the most likely to follow? When I have a look, every now and then, at Greta Thunberg’s astonishingly quick social ascent, I wonder why are we so divided about something apparently so simple? I am very clear: this is not a rhetorical question from my part. Maybe I should claim something like: ‘We just need to get all together, hold our hands and do X, Y, Z…’. Yes, in a perfect world we would do that. Still, in the world we actually live in, we don’t. Does it mean we are collectively stupid, like baseline, and just some enlightened individuals can sometimes see the truly rational path of moving ahead? Might be. Yet, another view is possible. We might be doing apparently dumb things locally, and those apparent local flops could sum up to something quite sensible at the aggregate scale.
There is some science behind that intuition, and some very provisional observations. I finally (and hopefully) nailed down the revision of the article on energy efficiency. I have already started developing on this one in my last update, entitled ‘Knowledge and Skills’, and now, it is done. I have just revised the article, quite deeply, and by the same occasion, I hatched a methodological paper, which I submitted to MethodsX. As I want to develop a broader discussion on these two papers, without repeating their contents, I invite my readers to get acquainted with their PDF, via the archives of my blog. Thus, by clicking the title Energy Efficiency as Manifestation of Collective Intelligence in Human Societies, you can access the subject matter paper on energy efficiency, and clicking on Neural Networks As Representation of Collective Intelligence will take you to the methodological article.
I think I know how to represent, plausibly, collective intelligence with artificial intelligence. I am showing the essential concept in the picture below. Thus, I start with a set of empirical data, describing a society. Well in the lines of what I have been writing, on this blog, since early spring this year, I assume that quantitative variables in my dataset, e.g. GDP per capita, schooling indicators, the probability for an average person to become a mad scientist etc. What is the meaning of those variables? Most of all, they exist and change together. Banal, but true. In other words, all that stuff represents the cumulative outcome of past, collective action and decision-making.
decided to use the intellectual momentum, and I used the same method with a
different dataset, and a different set of social phenomena. I took Penn Tables
9.1 (Feenstra et al. 2015), thus a well-known base
of macroeconomic data, and I followed the path sketched in the picture below.
Long story short, I have two big surprises. When I look upon energy efficiency and its determinants, turns out energy efficiency is not really the chief outcome pursued by the 59 societies studied: they care much more about the local, temporary proportions between capital immobilised in fixed assets, and the number of resident patent applications. More specifically, they seem to be principally optimizing the coefficient of fixed assets per 1 patent application. That is quite surprising. It sends me back to my peregrinations through the land of evolutionary theory (see for example: My most fundamental piece of theory).
When I take a look at the collective intelligence (possibly) embodied in Penn Tables 9.1, I can see this particular collective wit aiming at optimizing the share of labour in the proceeds from selling real output in the first place. Then, almost immediately after, comes the average number of hours worked per person per year. You can click on this link and read the full manuscript I have just submitted with the Quarterly Journal of Economics.
Wrapping it (provisionally) up, as I did some social science with the assumption of collective intelligence in human societies taken at the level of methodology, and I got truly surprising results. That thing about energy efficiency – i.e. the fact that when in presence of some capital in fixed assets, and some R&D embodied in patentable inventions, we seem caring about energy efficiency only secondarily – is really mind-blowing. I had already done some research on energy as factor of social change, and, whilst I have never been really optimistic about our collective capacity to save energy, I assumed that we orient ourselves, collectively, on some kind of energy balance. Apparently, we do only when we have nothing else to pay attention to. On the other hand, the collective focus on macroeconomic variables pertinent to labour, rather than prices and quantities, is just as gob-smacking. All economic education, when you start with Adam Smith and take it from there, assumes that economic equilibriums, i.e. those special states of society when we are sort of in balance among many forces at work, are built around prices and quantities. Still, in that research I have just completed, the only kind of price my neural network can build a plausibly acceptable learning around, is the average price level in international trade, i.e. in exports, and in imports. All the prices, which I have been taught, and which I taught are the cornerstones of economic equilibrium, like prices in consumption or prices in investment, when I peg them as output variables of my perceptron, the incriminated perceptron goes dumb like hell and yields negative economic aggregates. Yes, babe: when I make my neural network pay attention to price level in investment goods, it comes to the conclusion that the best idea is to have negative national income, and negative population.
Returning to the issue of climate change and our collective response to it, I am trying to connect my essential dots. I have just served some like well-cooked science, and not it is time to bite into some raw one. I am biting into facts which I cannot explain yet, like not at all. Did you know, for example, that there are more and more adult people dying in high-income countries, like per 1000, since 2014? You can consult the data available with World Bank, as regards the mortality of men and that in women. Infant mortality is generally falling, just as adult mortality in low, and middle-income countries. It is just about adult people in wealthy societies categorized as ‘high income’: there are more and more of them dying per 1000. Well, I should maybe say ‘more of us’, as I am 51, and relatively well-off, thank you. Anyway, all the way up through 2014, adult mortality in high-income countries had been consistently subsiding, reaching its minimum in 2014 at 57,5 per 1000 in women, and 103,8 in men. In 2016, it went up to 60,5 per 1000 in women, and 107,5 in men. It seems counter-intuitive. High-income countries are the place where adults are technically exposed to the least fatal hazards. We have virtually no wars around high income, we have food in abundance, we enjoy reasonably good healthcare systems, so WTF? As regards low-income countries, we could claim that adults who die are relatively the least fit for survival ones, but what do you want to be fit for in high-income places? Driving a Mercedes around? Why it started to revert since 2014?
Intriguingly, high income countries are also those, where the difference in adult mortality between men and women is the most pronounced, in men almost the double of what is observable in women. Once again, it is something counter-intuitive. In low-income countries, men are more exposed to death in battle, or to extreme conditions, like work in mines. Still, in high-income countries, such hazards are remote. Once again, WTF? Someone could say: it is about natural selection, about eliminating the weak genetics. Could be, and yet not quite. Elimination of weak genetics takes place mostly through infant mortality. Once we make it like through the first 5 years of our existence, the riskiest part is over. Adult mortality is mostly about recycling used organic material (i.e. our bodies). Are human societies in high-income countries increasing the pace of that recycling? Why since 2015? Is it more urgent to recycle used men than used women?
There is one thing about 2015, precisely connected to climate change. As I browsed some literature about droughts in Europe and their possible impact on agriculture (see for example All hope is not lost: the countryside is still exposed), it turned out that 2015 was precisely the year when we started to sort of officially admitting that we have a problem with agricultural droughts on our continent. Even more interestingly, 2014 and 2015 seem to have been the turning point when aggregate damages from floods, in Europe, started to curb down after something like two decades of progressive increase. We swapped one calamity for another one, and starting from then, we started to recycle used adults at more rapid a pace. Of course, most of Europe belongs to the category of high-income countries.
See? That’s what I call raw science about collective intelligence. Observation with a lot of questions and very remote idea as for the method of answering them. Something is apparently happening, maybe we are collectively intelligent in the process, and yet we don’t know how exactly (are we collectively intelligent). It is possible that we are not. Warmer climate is associated with greater prevalence of infectious diseases in adults (Amuakwa-Mensah et al. 2017), for example, and yet it does not explain why is greater adult mortality happening in high-income countries. Intuitively, infections attack where people are poorly shielded against them, thus in countries with frequent incidence of malnutrition and poor sanitation, thus in the low-income ones.
am consistently delivering good, almost new science to my readers, and love
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 Amuakwa-Mensah, F., Marbuah, G., & Mubanga, M. (2017). Climate variability and infectious diseases nexus: Evidence from Sweden. Infectious Disease Modelling, 2(2), 203-217.
 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