Computational Complexity and Human Decision-Making

The rationality principle postulates that decision-makers always choose the best action available to them. It underlies most modern theories of decision-making. The principle does not take into account the difficulty of finding the best option. Here, we propose that computational complexity theory (CCT) provides a framework for defining and quantifying the difficulty of decisions. We review evidence showing that human decision-making is affected by computational complexity. Building on this evidence, we argue that most models of decision-making, and metacognition, are intractable from a computational perspective. To be plausible, future theories of decision-making will need to take into account both the resources required for implementing the computations implied by the theory, and the resource constraints imposed on the decision-maker by biology. New research showing that the quality of human decision-making decreases with the computational complexity of decision problems challenges the core assumption of most models of decision-making: that decision-makers always optimise. CCT can help explain behavioural biases, such as choice overload and negative elasticity of labour supply. Integrating CCT with decision theory and neurobiology promises to lay the foundations of a more realistic theory of decision-making and metacognition.