2012 NIPS NeurIPS 2012

Rational inference of relative preferences

Abstract

Statistical decision theory axiomatically assumes that the relative desirability of different options that humans perceive is well described by assigning them option-specific scalar utility functions. However, this assumption is refuted by observed human behavior, including studies wherein preferences have been shown to change systematically simply through variation in the set of choice options presented. In this paper, we show that interpreting desirability as a relative comparison between available options at any particular decision instance results in a rational theory of value-inference that explains heretofore intractable violations of rational choice behavior in human subjects. Complementarily, we also characterize the conditions under which a rational agent selecting optimal options indicated by dynamic value inference in our framework will behave identically to one whose preferences are encoded using a static ordinal utility function.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Interdisciplinary
🧭 Keyword Pioneer — relative desirability
🐝 Cross-Pollinator — Artificial Intelligence, Data Science & Analytics, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Reinforcement Learning
📈 Trend Setter — Game Theory
🐣 Hot Topic Early Bird — preference learning