Yesterday, in my update in French, I started discussing some literature, which I came by recently, devoted to the issue of quantitative research in long-term social changes (see “La guerre, l’espace, et l’évolution des sociétés” ). As we are talking long-term, this stream of research comes mostly from history. I am currently reviewing one of those papers, entitled ‘War, space, and the evolution of Old World complex societies’ (Turchin et al. 2013). To me, science is, at the end of the day, a method of discovering things. When I see a piece of research done by other scientists, I most of all look for methods. In this precise case, the method is quite illuminating for my own purposes in research. At the baseline of their methodology, Turchin et al. divide big populations in big territories into basic, local cells, equivalent to local communities, and assess three essential probabilities, namely that of coordination occurring between two or more cells, as opposed to the probability of disintegration in such coordinated structures, as well as the probability of at least one cell being destroyed by others. Two other probabilities come as instrumental in calculating the fundamental three: the probabilities of social mutation. Turchin et al. construe the concept of social mutation around that of valuation. At any given moment, there is a set of traits, in a society, which make this society optimally competitive, accounting for requirements stemming from the environment. Any given society develops its own traits through valuating them in its own culture, or, conversely, disintegrates some traits by culturally denying their value. As I understand this methodology by Turchin et al., the concept of valuing some societal traits or disvaluing them is a compound, covering both the strictly spoken ethical valuation, and the actions informative about it (investment, creation or disintegration of specific social structures etc.).
In short, social mutation is supposed to be something akin genetic mutation. There is a set of traits, in a society, and each of those traits can be switched on, or switched off. This is the social code. I am trying to represent it below, in a semi-graphical example, where ‘1’ stands for the given trait being switched on, and ‘0’ to its deactivation.
Trait A >> 1
Trait B >> 0
Trait C >> 0
Trait D >> 1 etc.
Each society has such a social code, and, in the background, there is some kind of implicitly optimal code, the one that makes the top dog in the pack of societies. The local, social code, observable in any given society displays some Euclidean distance from this optimal code. Putting it simply, in this world, when you are a society, you have all the interest in having the right traits switched on at ‘1’, with the not-quite-favourable ones switched off, i.e. at ‘0’. What Turchin et al. assess, for any given local society studied empirically, is the probability of favourable traits passing from 0 to 1 (µ01: functional mutation), or, conversely, being deactivated from 1 to 0 (µ10: dysfunctional mutation). This specific methodology allows setting baseline probabilities as well. If the general assumption is that societies have a tendency to f**k things up, rather than figuring them out correctly (this is, by the way, what Turchin et al. assume), then µ10 > µ10. If, on the other hand, we have some optimism as for collective intelligence, we can settle for µ10 < µ10. Of course, µ10 = µ10 is a compromise at the weakest possible level of assuming anything. Anyway, the proportions between those probabilities, namely µ01 and µ10, make the overall likelihood for the emergence of large political structures, the ‘ultrasocial’ ones, as Turchin et al. call them (you know: army, taxes, government etc.) in a given set of local communities. Those chances are calculated as: u = ((µ01/(µ01 + µ10)). The ‘u’ symbol comes from that ‘ultrasocial’ adjective. The baseline probabilities in the model, as they come from empirical tests, are: µ01 = 0,0001 and µ10 = 0,002. That makes the likelihood u = 0,05. In other words, in a given set of local communities, a priori not connected by ultrasocial institutions, which, in turn, could stimulate the emergence of political systems, the likelihood that such institutions are triggered on is like 5%.
On the grounds of these findings by Turchin et al., I start my own reasoning. Just hold on to something, ‘cause my reasoning, it can really get some swing, on the account of me having that curious ape inside of me. Anyway, I am translating that tiny u = 0,05 likelihood into the possible behaviour of large human populations living in a territory. Some 5% of those humans, whoever they are, is likely to develop social traits, which can turn them into ultrasocial political systems. This, in turn, means that in every large collection of local communities a relatively small, ultrasocial core is likely to emerge, and this core is going to agglutinate around itself consecutive local communities, to make something really political. Still, the actual empirical results obtained by Turchin et al. are way above those baseline probabilities. The likelihood of turning on the right ultrasocial genes in a given society turns out to be like 0,47 =< µ01 =< 0,51, and the probability µ10 of switching them off ranges from 0,49 to 0,52. That makes the likelihood u = ((µ01/(µ01 + µ10)) floating consistently close to 50%. In other words, if you take 1 million primitive, proto-political people (voters), the baseline likelihood of some among them turning into serious political players, i.e. of turning on the right ultrasocial traits is like µ01 = 0,0001, whilst the probability of them consistently not giving a s***t about going political is µ10 = 0,002, which, in turn, makes that likelihood u = 0,05 of anything seriously political going on in those 1 million people. Now, my internal curious ape spots a detail in the article: those baseline probabilities correspond to something that Turchin et al. call ‘equilibrium’. As an economist, I have a very ground-to-ground approach to equilibriums: it would be nice if they existed in reality, but most of the times they don’t, and we have just a neighbourhood of equilibrium, and still, it is if we are lucky.
I put, now, those two sets of numbers back to back, i.e. the parameters of equilibrium against those empirically inferable from actual historical data. One conclusion jumps to the eye: in real life, we, humans, tend to be some 10 times more prone to do politics in large structures, than we are technically expected to be in the state of equilibrium (whatever is being balanced in that equilibrium). By the way, and to be quite honest in relation to that article by Turchin et al., agglutination around the political core is not the only option actually available. Ethnocide is another one, and, sadly enough, quite recurrent in the historical perspective. Recurrence means, in the results obtained by Turchin et al., a likelihood of ethnocide varying between emax = 0,41 and emax = 0,56 in communities, which had not triggered on their ultrasocial traits at the right moment. This is sad, but seems to be rock solid in that empirical research.
 Turchin P., Currie, T.E., Turner, E. A. L., Gavrilets, S., 2013, War, space, and the evolution of Old World complex societies, Proceedings of The National Academy of Science, vol. 110, no. 41, pp. 16384 – 16389