Should the Model for Risk‐Informed Regulation be Game Theory Rather than Decision Theory?

Abstract

Risk analysts frequently view the regulation of risks as being largely a matter of decision theory. According to this view, risk analysis methods provide information on the likelihood and severity of various possible outcomes; this information should then be assessed using a decision-theoretic approach (such as cost/benefit analysis) to determine whether the risks are acceptable, and whether additional regulation is warranted. However, this view ignores the fact that in many industries (particularly industries that are technologically sophisticated and employ specialized risk and safety experts), risk analyses may be done by regulated firms, not by the regulator. Moreover, those firms may have more knowledge about the levels of safety at their own facilities than the regulator does. This creates a situation in which the regulated firm has both the opportunity-and often also the motive-to provide inaccurate (in particular, favorably biased) risk information to the regulator, and hence the regulator has reason to doubt the accuracy of the risk information provided by regulated parties. Researchers have argued that decision theory is capable of dealing with many such strategic interactions as well as game theory can. This is especially true in two-player, two-stage games in which the follower has a unique best strategy in response to the leader's strategy, as appears to be the case in the situation analyzed in this article. However, even in such cases, we agree with Cox that game-theoretic methods and concepts can still be useful. In particular, the tools of mechanism design, and especially the revelation principle, can simplify the analysis of such games because the revelation principle provides rigorous assurance that it is sufficient to analyze only games in which licensees truthfully report their risk levels, making the problem more manageable. Without that, it would generally be necessary to consider much more complicated forms of strategic behavior (including deception), to identify optimal regulatory strategies. Therefore, we believe that the types of regulatory interactions analyzed in this article are better modeled using game theory rather than decision theory. In particular, the goals of this article are to review the relevant literature in game theory and regulatory economics (to stimulate interest in this area among risk analysts), and to present illustrative results showing how the application of game theory can provide useful insights into the theory and practice of risk-informed regulation.

Optimal Food Safety Sampling Under a Budget Constraint

“…Although the curative effects of a lot acceptance sampling program may be weak, any analysis of its food safety impact that ignores the effect of economic pressures exerted by sampling is incomplete. Food safety inspection problems seem to call for a game‐theoretic treatment . However, the economic incentives can vary among firms, geographically, and over time and may be subject to legal constraints that vary by jurisdiction and to uncertain liability exposures.…”

China's Railway Transportation Safety Regulation System Based on Evolutionary Game Theory and System Dynamics

“…Researchers frequently view regulation as being largely a matter of decision theory (Bier & Lin, 2013). According to this view, risk analysis methods using decision theory provide information on the likelihood and severity of various possible outcomes; this information should then be assessed using a decision‐theoretic approach to determine whether the risks are acceptable and whether additional regulation is warranted.…”

“…However, this approach has been slow to be adopted in practice, in large part because of the uncertainties and subjectivity involved in risk analysis (Applegate, 1991) and the inability of regulators to directly measure the variable of interest (Chinander, Kleindorfer, & Kunreuther, 1998). In addition, in many industries, risk analyses may be performed by regulated firms, not by the regulator (Bier & Lin, 2013). This situation is also observed in the railway transportation industry, which creates a situation where the CR has both the opportunity and the motivation to provide inaccurate information to the SRA; thus, the SRA has reason to doubt the accuracy of the information provided by CR.…”

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