We use laboratory experiments and cognitive computational models. We study human behavior by observing and collecting human decisions in a dynamic task and develop cognitive models that reproduce that behavior and predict new unobserved behavior within the same task.
The figure below represents our technical approach. Data are collected from two sources: a human interacting with a task, and a computational model interacting with the same task. These are compared at many different levels (e.g., over time learning curves, overall averages of maximization behavior, overall risk behavior, variance in behavior, etc.), from which we derive conclusions and understanding regarding the human decision making process and the accuracy of our computational representations.
We have developed interactive computer simulations that may represent realistic but also abstract decision making situations. In the past, we have created DMGames in many diverse contexts. We offer a variety of DMGames for download and for use in research.
We have developed computational representations of human behavior according to Instance-Based Learning Theory (IBLT). These are generic representations of human choice which may be used to model behavior in multiple tasks. We offer implementations of IBL models and tools for download and for use in research.