Complexity Aversion

TL;DR

This paper introduces a decision-making model where agents consider cognitive complexity costs, which can be updated with new information, affecting their act rankings under uncertainty.

Contribution

It develops an axiomatic updating rule for complexity costs, called the Minimal Complexity Aversion representation, integrating cognitive constraints into decision models.

Findings

Characterizes an axiom-based updating rule for complexity costs

Shows agents update complexity costs based on the lowest-cost act with the same partition

Provides a formal framework for complexity-aware decision-making under uncertainty

Abstract

This paper proposes a model of decision-making under uncertainty in which an agent is constrained in her cognitive ability to consider complex acts. We identify the complexity of an act according to the corresponding partition of state space. The agent ranks acts according to the expected utility net of complexity cost. A key feature of this model is that the agent is able to update her complexity cost function after the arrival of new information. The main result characterizes axiomatically an updating rule for complexity cost function, the Minimal Complexity Aversion representation. According to this rule, the agent measures the complexity cost of an act conditional on the new information by using the cost of another act that gives exactly the same partition of the event but with the lowest ex-ante cost.