One of my long-time gripes about the climate-economic models that outfits like the IPCC produce is that they ignore politics. More broadly, they ignore social change and the way it can both drive and be driven by technology and climate impacts.

This isn’t difficult to explain — unlike technology costs, biophysical feedbacks, and other easily quantifiable variables, the dynamics of social change seem fuzzy and qualitative, too soft and poorly understood to include in a quantitative model. Consequently, those dynamics have been treated as “exogenous” to models. Modelers simply determine those values, feed in a set level of policy change, and the models react. Parameters internal to the model can not affect policy and be affected by it in turn; models do not capture socio-physical and socio-economic feedback loops.

But we know those feedback loops exist. We know that falling costs of technology can shift public sentiment which can lead to policy which can further reduce the costs of technology. All kinds of loops like that exist, among and between climate, technology, and human social variables. Leaving them out entirely can produce misleading results.

At long last, a new research paper has tackled this problem head-on. Fran Moore, an assistant professor at UC Davis working at the intersection of climate science and economics, took a stab at it in a recent Nature paper, “Determinants of emissions pathways in the coupled climate–social system.” Moore, along with several co-authors, attempted to construct a climate model that includes social feedback loops, to help determine what kinds of social conditions produce policy change and how policy change helps change social conditions.

I am fascinated by this effort and by the larger questions of how to integrate social-science dynamics into climate analysis, so I was eager to talk to Moore about how she constructed her model, what kinds of data she drew on, and how she views the dangers and opportunities of quantifying social variables.