Round 1: FIGHT

I don’t know about you, but when I was in High School, I was treated to a close-up of more than a few fights (none including me, of course).  If you’d asked me, if those fights were totally random I probably would have said no: the two guys – and it was almost always guys – had something between them which festered for a while before they eventually went at it.

Just like humans, macaques live in extremely social environments.  Also just like people, macaques engage in fights which can be one-on-one or just be a straight-up gang war.  Since we can’t just sit the macaques down and ask them what it was that made them throw down, we can turn to statistics to figure out: what makes a monkey fight?

Jessica Flack studies the patterns and dynamics of social systems.  In a paper from her lab, Daniels et al. examined the statistics of monkey fights.  There are a few ways to analyze this, so the group examined three different possible strategies that the monkeys could be using.  First, they could just go at it willy-nilly; this was encoded in the form of a maximum entropy model – a model that basically assumes there are no correlations unless absolutely required.  This model assumed the only thing important to a fight was how often that particular individual fought.  On the other hand, a monkey could get in a fight because it hated the guts of some other guy, or because it had an ally it needed to defend; this was also examined with a maximum entropy model, albeit one that included the direct interactions between two individuals.  Finally, it’s possible that there are other more complex interactions – your buddy really wants you to go fight for that third guy, even though you don’t really know him.  This was tested with a ‘sparse coding’ model, the specifics of which aren’t actually important here.

What they find is that, just like people, it’s the direct connections that matter.  On virtually every metric, the model that includes the interactions between individuals is better than the one that just assumes random acts of violence.  But not only that, the direct interactions between individuals is mostly what’s important – when you include more than that, the only thing that you can do a better job of predicting is how many individuals there are in a fight in general, though not how big a fight is given a specific individual is in it.  In other words, you recruit your allies, they don’t do recruiting for you.

One of the advantages of using these models is that they can be used to estimate how complex the socialization is.  If one of these chimps wanted to remember the details of every fight with perfect fidelity, it would take 23,500 bits – roughly equivalent to a note written using only 3000 total letters (kind of; letters in words aren’t actually uncertain so it would probably take many more than this).  But if you only need to take into account these correlations, you can compress it to 1000 bits, or only 125 letters, and still do almost as well.  Which means that maybe social interactions aren’t as complicated as you might have thought – there is a lot of structure to them.

Of course, this raises the point that the ‘good’ predictions are only right 15% of the time.  Should we call that a good prediction?  For the complexity of what we’re trying to predict, maybe, but clearly it means that there is a lot more going on than the models let on.  Social interactions happen more than just because of general feelings between individuals; they are likely triggered by specific – or spontaneous – events.  But if a simple model can explain 15% of all of a social behavior in a large group of individuals?  And give an estimate of how complex those interactions actually are?  Well I’d say that’s pretty interesting.

References

Daniels BC, Krakauer DC, & Flack JC (2012). Sparse code of conflict in a primate society PNAS DOI: 10.1073/pnas.1203021109

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