Bounded Optimality and Rational Metareasoning in Human Cognition

Human decision-making is often described as irrational, being the result of applying error-prone heuristics. I will argue that this is partly a consequence of the use of an unrealistic standard of rationality, and that the notion of bounded optimality from the artificial intelligence literature provides a better framework for understanding human behaviour. Within this framework a rational agent seeks to execute the best algorithm for solving a problem, taking into account available computational resources and the cost of time. We find that several classic heuristics from the decision-making literature are bounded optimal, assuming people have access to particular computational resources. This establishes a new problem: how do people find such good heuristics? I will discuss how this problem can be addressed via rational metareasoning, which examines how rational agents should decide what algorithm to use in solving a problem. The result is a view of human decision-making in which people are intelligently and flexibly making the most of their limited computational resources.