Overcoming the Knowledge Problem in Behavioral Law and Economics: Uncertainty, Decision Theory, and Autonomy

Science evolves in the long run. Law rules in the present. This potential temporal disconnect leads to a Hayekian “knowledge problem”, a challenge increasingly raised against behavioral law and economics: Empirical findings are deemed too uncertain to provide a solid basis for legal reasoning. This paper claims that in such cases, rules for decision making under risk and uncertainty have to enter the game. They can serve as analytic tools and generate positive solutions to the knowledge problem, thereby ensuring a rational treatment of the question of bounded rationality. The paper thus essentially applies a meta-decision theory to the question of what kind of decision theory (rational choice or behavioral decision theory) should govern legal reasoning. Generally, two routes may be pursued: First, in some situations it is possible to reduce uncertainty with the help of advanced empirical techniques. This prepares the ground for decision making under risk in an expected utility analysis. Second, in the majority of cases, reliable empirical data are indeed so far unavailable, necessitating decision making under uncertainty. In a decision theoretic maximin analysis, this paper demonstrates that in these cases of empirically unresolved uncertainty legal rules should presumptively assume the significant presence of bounded rationality among the relevant actors. The result can be successfully defended against the common objection that behaviorally informed interventions, such as debiasing or nudging, constitute an infringement of autonomy. Finally, implications of this methodologically novel justification of behaviorally informed legislation and adjudication for specific legal concepts in consumer law and product liability are discussed.