I just came in from standing on the deck, under clear skies, a partial moon, and the most amazing windstorm. The moon made visible the big waves crashing on the rocks below me, and the whitecaps out in the channel. It’s been blowing hard all day, without cease, and I’m happy to be inside with a wood stove and food on the stove. A brief respite at home before another stretch at the office. I haven’t quite figured out the optimal amount of time to spend down in Seattle, but I’m pretty sure it’s shorter than I’ve been spending as things heat up at work. Seeing friends and doing things in Seattle is great, but I miss the island. The slow process of meeting people and “becoming a local” has all but stopped as I commute back and forth.
I haven’t written much here since late December, but only because life has reached a fever pitch again, and the brief times I have free away from a full schedule need to be devoted to research and my dissertation, not idle contemplation for my website. But we’re in the thick swamp of an election season, unseasonably early of course, and I haven’t written anything about the candidates, the primaries, the debates, as I did for much of 2004. I can’t promise to get back to regular posting before Super Tuesday, but I hope to soon thereafter. Or as soon as I can get my two projects more firmly underway (one paper, one poster) for the SAA (Society for American Archaeology) meetings in late March in Vancouver. Both are co-authored with Alex Bentley and Carl Lipo, and we’re working on the statistical consequences of expressing formal models of cultural transmission within realistic social networks.
For those unfamiliar with cultural transmission, this is the observation that humans are not born with a hard-coded set of cultural behaviors (in the sense of genetically transmitted) but learn, over the course of child development and throughout life, ways of behaving and believing and thinking through interaction with others in our social groups. In a formal sense, cultural transmission is modeled mathematically through analogues of haploid population genetics models (Wright-Fisher and Moran processes), replicator dynamics and allied models from evolutionary game theory, and the contact and voter models in the study of “interacting particle systems” or spatial stochastic processes by probability theorists and statistical physicists. An open question, whose likely answer is “yes,” is that these methods of modeling cultural learning and transmission are formally equivalent, given appropriate variations of population structure and the focus on deterministic versus stochastic models. But more of that in future posts, hopefully.
Basically, I’m working with some collaborators studying models of social learning and communication, for predictive ensemble or spatial statistical “signatures” in cultural data which are mapped spatially and dated temporally. A “signature” would be a unique pattern of statistical properties which tells us how a given population was structured (in terms of social networks) given the results of how cultural information flowed within the population, and came to be reflected in material objects or artifacts. An example would be a model in which we learn about, and adopt, preferences for songs and music from our social network of friends, but in an unbiased fashion — we occasionally adopt the preferences of a colleague or associate. What statistical properties does this local process of imitation have, when projected into a “global” perspective — statistical patterns within a population, spatial patterns in kinds of data we can map and chart?
Of course, we all know that the model I just described is pretty simplistic. Nobody “just copies” their friends, let alone doing so without any filters, biases, and on a strict “coin flip” or probabilistic basis. But it turns out it sure can look that way when you aggregate the results of many people imitating, choosing, learning, and adopting ideas. So this kind of model is a good “null hypothesis” for a simplistic kind of cultural communication — anything more realistic will have to depart from this simple random model in striking, hopefully unique ways.
Being able to find unique, predictive patterns from more complex models of cultural learning and communication is possible, but not guaranteed — it is easily possible (maybe even likely) that several different kinds of social situations could lead to the same overall patterns at a local, regional, or even global level. We call this problem “equifinality” — the data we have are insufficient to distinguish between several possible processes, so given our models and data, each process is “equally likely” to have caused the observed pattern.
This type of research is what I’ve been engaged in for a long time — at least since 1995, with conference papers, publications, and Carl Lipo’s dissertation research covering some of the results. Now I’m extending our previous work and learning a lot of math, probability, and population genetics in the process. It’s fascinating stuff, but in addition to the job at GridNetworks the work keeps me pretty busy.
This is all by way of explanation for my longish absences from writing something here. I hope to remedy that, as I said, but there’s some serious work between now and then.