Blogging the Commons, Pt. 1

From Note 1 of Chapter 3 of Governing the Commons:

A substantial debate has been engendered among institutional economists and economic historians over the issue of whether or not long-enduring institutions are optimally efficient. The way the question is addressed in many instances leads to an automatic yes or no answer, depending on what variables are considered as constraints on the problem. If information and transaction costs are not considered, no real-world institution can ever be optimally efficient. If all information and transaction costs are included as fixed constraints, all long-enduring institutions are automatically optimally efficient. Neither position is very useful in evaluating institutions. I prefer to argue that optimality is not well defined in a changing environment, including the capacity to change the institutional rules themselves. One must use criteria other than optimal efficiency to evaluate long-enduring institutions.

(Emphasis added.)

This is probably more of a comment that is an important cautionary for myself than of practical use, but it reminds me of what I see as some of the limits of evidence-based approaches and certain kinds of scientific debates. That is, the question of whether or not certain situations or allocations are efficient is certainly a preoccupation in some circles, and a search for optimization/optimum solutions, while seemingly a logical place for science to contribute to problem-solving, may be leading us astray if it distracts from understanding how and why real institutions/situations work as well as they do. This latter may be different from (and possibly more practically important than)  than understanding how the theoretical perfect or ideal might work, or what the first principles of the situation might be in the abstract.

This is not quite what Ostrom is actually arguing, but it struck me in relationship to ideas about how science contributes to humankind’s improvement. It undoubtedly does, but sometimes I wonder if we get stuck in research around optimization rather than what might be rather different, but important research on how things actually play out (and how we should even go about doing our research–the process, not just the purpose!)

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