In changing, dynamic environments, people must often make multiple, interdependent decisions in real time, while tracking external changes and the results of their own past decisions. At the DDMLab, we use cognitive computational models and laboratory experiments to help explain and predict how people make such decisions. We also develop recommendations for how people can make such decisions better.
We study how humans make choices, learn and use their experiences to make decisions in dynamic environments.
We also study humans making decisions in a wide range of decision contexts that we bring to the laboratory in the form of dynamic simulations (MicroWorlds or DMGames)
Our driving theory is the Instance-Based Learning Theory (IBLT), which in essence proposes that people make choices by retrieving the best outcomes from their past experience. The process involves: