Modeling human cognition is challenging because there are infinitely many mechanisms
that can generate any given observation. Some researchers address this by constraining the …

All biological and artificial agents must act given limits on their ability to acquire and process
information. As such, a general theory of adaptive behavior should be able to account for the …

Resource rationality holds great promise as a unifying principle across theories in
neuroscience, cognitive science, and economics. The target article clearly lays out this …

Throughout the cognitive-science literature, there is widespread agreement that decision-
making agents operating in the real world do so under limited information-processing …

A default assumption in the design of reinforcement-learning algorithms is that a decision-
making agent always explores to learn optimal behavior. In sufficiently complex …

The project of justifying all the limits and failings of human cognition as inevitable
consequences of strategies that are actually" optimal" relative to the limits on computational …

The commentaries raised questions about normativity, human rationality, cognitive
architectures, cognitive constraints, and the scope or resource rational analysis (RRA). We …

Resource-rational approaches offer much promise for understanding human cognition,
especially if they can reach beyond the confines of individual minds. Language allows …

Resource rationality may explain suboptimal patterns of reasoning; but what of “anti-
Bayesian” effects where the mind updates in a direction opposite the one it should? We …

We propose an alternative and unifying framework for decision-making that, by using
quantum mechanics, provides more generalised cognitive and decision models with the …