Of Mollusks, Matrices, Modeling

In the Science Museum of Minnesota this past weekend, I found myself pouring rather closely over the mollusk exhibit, if only because I could not fathom—pardon the pun—why there was such a grand display of them. I didn’t have to look far: Mollusks are often the leading indicators that an environment is in danger. The museum is home to a larger collection of mollusk shells, many of which reveal where mollusks once thrived but are now scarce or non-existent thanks to changes in the landscape brought about by agricultural or industrial contamination.

Leading indicators is, of course, quite popular right now, because a lot of people are interested in being able to predict the future based on the kinds of early successes we have had with machine learning whereby algorithms trained on a reasonably large dataset can discover the same patterns in new data. I am reminded of work reported by Peter Brooking and Singer on XXX as well as the “calculus of culture” being developed by Jianbao Gao.

What you would need of course is a reasonable definition of the parameters of your “socio-cultural matrix.” The social dimensions would be all those non-text data points that might be of interest and associated with humans either individually or collectively. The cultural dimensions would be texts and other discernible behaviors, again either described individually or collectively. We know this is possible because, to some degree, Cambridge Analytica has already done it, and we can be sure that other organizations are doing the same and just not talking about it. (In a moment when all this data is available, either by cash, hack, or crook, you would be a fool not to collect it all and compile it as many ways as you can possible imagine, and then some.)

Breaking off some piece of this larger matrix, or set of matrices, is something we all need to be better about doing: modeling complex environments is something that needs to get taught and practiced more widely—all the while reminding people never to mistake the map for the territory. To some degree the social sciences do some of this at the computational end, but the kind of statistical, and sometimes speculative, modeling suggested here is not as pervasive in public discourse as it should be.

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