This course provides an overview of behavioural and computational approaches to value-based decision-making. It examines how decision variables (such as expected utility and its components, including value, probability, risk, and delay) are combined to guide choices.
Particular emphasis is placed on the use of computational models to formalise hypotheses about decision-making and to connect observed behaviour with underlying cognitive processes. The course introduces key modelling frameworks used to describe how individuals evaluate options, learn from experience, and make decisions under risk and uncertainty. It also considers how behavioural observations can reveal systematic departures from normative predictions and how these deviations can be incorporated into descriptive and mechanistic models of choice.
The final part of the course extends these approaches to contemporary questions concerning human–machine interaction, including how people evaluate, use, and respond to recommendations or decisions generated by computational systems.