Rationally Biased Learning

TL;DR

This paper demonstrates that certain human decision biases, such as focusing on negative outcomes and status quo preference, can be explained as rational responses within an optimal decision-making framework under imperfect information.

Contribution

It provides a formal analysis showing that human-like biases can emerge from rational solutions to infinite horizon optimization problems with imperfect state observation.

Findings

Biases can be rational responses to imperfect information.

Conditions for the occurrence of biases are derived.

Biases are shown to be robust under various conditions.

Abstract

Humans display a tendency to pay more attention to bad outcomes, often in a disproportionate way relative to their statistical occurrence. They also display euphorism, as well as a preference for the current state of affairs (status quo bias). Based on the analysis of optimal solutions of infinite horizon stationary optimization problems under imperfect state observation, we show that such human perception and decision biases can be grounded in a form of rationality. We also provide conditions (boundaries) for their possible occurence and an analysis of their robustness.Thus, biases can be the product of rational behavior.