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change turns out to (so to speak) blow over, fossil fuels will not last forever. However, engineering viable replacements to fossil fuel energy is an expensive, long-term investment. While important, we should not allow ourselves to focus on it single-mindedly—just as important are more short-term interventions which, though possibly less dramatic, have the potential to contribute to an effective multi-level response to a possible threat. For instance, directing resources toward increases in efficiency of current energy expenditure might be more effective (at least in the short run) at making an impact. Innovations here can, like EMICs, take the form of highly specialized changes: the current work on piezoelectric pedestrian walkways (which harvest some of the kinetic energy of human foot impacting sidewalk or hallway and store it as electrical energy) is an excellent example[1]. Unfortunately, research programs like this are relatively confined to the sidelines of research, with the vast majority of public attention (and funding) going to things like alternative energy and the possibilities of carbon taxes. A more appropriate response requires us to first accept the permanent pluralism of climate science models, and to then search for a similarly pluralistic set of policy interventions.


There’s one last point I’d like to make connecting complexity modeling and public policy. In a way, it is the simplest point of the whole dissertation, and it has been lurking in the background of all of the preceding 200-some-odd pages. Indeed, it was perhaps best phrased way back in the first chapter: the world is messy, and science is hard. We’ve examined a number of senses in which that sentence is true, but there’s one sense in particular that’s been illuminated in the course of our discussion here. I want to close with a brief discussion of that sense.

  1. See, for example, Yi et. al. (2012)