Dynamic consistency and decision making under vacuous belief

TL;DR

This paper explores how artificial agents can make consistent decisions under ignorance by combining economic and computer science theories, formalizing sequential consistency with plausibility measures.

Contribution

It introduces a formal framework for dynamic consistency in decision making under ignorance, adapting the law of iterated expectation for plausibility measures.

Findings

Characterizes when certainty equivalence is sequentially consistent

Provides necessary and sufficient conditions for sequential consistency

Enhances understanding of decision models under uncertainty

Abstract

The ideas about decision making under ignorance in economics are combined with the ideas about uncertainty representation in computer science. The combination sheds new light on the question of how artificial agents can act in a dynamically consistent manner. The notion of sequential consistency is formalized by adapting the law of iterated expectation for plausibility measures. The necessary and sufficient condition for a certainty equivalence operator for Nehring-Puppe's preference to be sequentially consistent is given. This result sheds light on the models of decision making under uncertainty.