Baseball and Environmental Regulation

"Formal quantitative assessments of uncertainty can mark a truly significant step forward in enhancing regulatory analysis under Presidential Executive Orders."  This stuffy-sounding statement appeared today in a otherwise trenchant Huffington Post column by Harvard environmental economist Robert Stavins.  Dr. Stavin’s target in today’s piece is the so-called RIA–the Regulatory Impact Analysis required by Presidential Order for any proposed new piece of federal regulation.
Stavins’s concern is that current methods of evaluating proposed environmental regulations do not attempt to account in a meaningful way for uncertainty, especially uncertainty over time. His solution to this inadequacy is Monte Carlo simulation (which has over the past year been lambasted for its use in the financial sector for its own inadequacies–but never mind, anyone reading this is likely to understand that a risk analysis model is only as good as its inputs).

To make his point about environmental risk analysis, Stavins turns to an analogy in baseball: due to randomness over time, the "best" teams win over the full length of a season, while "hot" teams win during the much briefer period of the post-season.  Randomness is what links baseball outcomes to regulation outcomes, and according to Stavins, Monte Carlo software is in much greater use in baseball than in environmental policy making.  In fact, baseball has its own group dedicated to statistical analysis, SABR (Society for American Baseball Research) whose quantitative work is know as "sabermetrics."  You might think sabermetrics would be a discipline unto itself, but Slavins doesn’t.  He thinks federal regulators have a lot to learn from SABR.  

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