Friction and Decision Rules in Portfolio Decision Analysis

Abstract

In portfolio decision analysis, features comprise the objectives, alternatives, physics, and information that define a decision context. By modeling features, decision analysts forecast the expected utilities of the alternatives. A model is complete if it contains all the features. A model is well-calibrated if it correctly predicts the probability distributions of each alternative’s utility, whereas ill-calibrated models, like those that suffer the optimizer’s curse, do not. Friction identifies qualities of a situation that prevent decision analysts from creating complete, well-calibrated models. When friction is significant, can maximizing expected utility be a suboptimal decision rule? Is satisfying decision theory’s axioms a necessary or sufficient condition for good decision making? Can rules that violate the axioms outperform rules that satisfy them? A simulation study of how unbiased, imprecise forecasts of payoffs affect project selection finds that, for the example tested, the answers are yes, no, and yes, which suggests that further studies of friction may be worthwhile. Discussions of friction bookend the study, starting the paper by defining friction and concluding by presenting three frameworks, each one from a different field of study, that provide mathematical tools for studying friction.