In “Complex Systems View of Educational Policy Research,” I thought the discussion of locale and policy implementation was interesting. The example of California mandating state-wide class size reductions based “partly on the basis of results of a randomized fi eld experiment in Tennessee,” encompassed micro- and macro-effects on a few levels. First, the Tennessee experiment was randomized, so some of the variability that occurs among student bodies was eliminated, something that was probably not true of the schools in California. Second, that small sample was taken and the state tried to replicate it with a population that was probably different in that it was not in Tennessee and also that it was not random. One better intermediate step between the experiment and the California regulation would have been to try to at least replicate the experiment multiple times in different samples throughout California.
In “Meltdown modeling,” I enjoyed the discussion of the adoption of ABM within the finance industry and the industry preference for econometrics. The discussion of familiarity was interesting, and seemed to mimic technology adoption curves. Industries tend to be slow to adopt innovations because of the higher costs associated with the risk of deviating from industry norms. But when I put myself in the position of decision-makers, my question was, “Why wouldn’t I at least try to run ABM alongside with my standard mathematical models?” Running to types of models would allow industry leaders to evaluate the effectiveness of both types of models side by side in the same situation. Instead, many seem incredibly ambivalent and inert. I think that’s to the disadvantage of those companies, but should ABM really take off, they’ll just be caught flat footed on the wrong side of the adoption curve.
