Boundedly rational backward induction

Boundedly Rational Dynamic Programming

This paper proposes a way to model boundedly rational dynamic programming in a parsimonious and tractable way. The framework is quite general, and has at its core a behavioral version of the Bellman equation, in which the agent uses a simplified model of the world and the consequences of his actions. It is then applied to some of the canonical models in macroeconomics and finance. In the consum...

Toward Boundedly Rational Analysis

The Bayesian program in cognitive science has been subject to criticism, due in part to puzzles about the role of rationality and approximation. While somewhat sympathetic with these concerns, I propose that a thoroughgoing boundedly rational analysis strategy can answer to some of them. Through simulation results I illustrate the method by showing how one can retrodict recently reported result...

Boundedly versus Procedurally Rational Expectations

Economists who have relied on the rational expectations hypothesis are now seeking to demonstrate that rational expectiations equilibria can emerge in models with agents who are artificually intelligent. Agents’ intelligence is represented by genetic algorithms. However, these algorithms misrepresent current understanding of human cognition as well as well known and long standing evidence from ...

A Boundedly Rational Decision Algorithm

Bayesian Networks and Boundedly Rational Expectations∗

I present a framework for analyzing decision making under imperfect understanding of correlation structures and causal relations. A decision maker (DM) faces an objective long-run probability distribution p over several variables (including the action taken by previous DMs). He is characterized by a subjective causal model, represented by a directed acyclic graph over the set of variable labels...