Our research approach involves laboratory experiments and cognitive computational models. We study human decision making processes by observing and collecting human choices in dynamic tasks; we also develop cognitive-computational models that reproduce such human decision making process. Models are used to explain and predict new unobserved human behavior.
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 and dynamic effects, overall averages of optimal behavior, overall risky behavior, variance in behavior, etc.). From this comparison of human and model choices, we derive conclusions 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. We have created DMGames in many diverse contexts, some of these are available 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 developing IBL models in Python. These are available for download and for use in research. Instance-Based Learning Models are developed within our theoretical approach: Instance-Based Learning Theory (IBLT): a general process by which individuals recognize new choice opportunities, evaluate alternatives, make choices and learn from the feedback provided in the environment.
The Poster below shows a summary of some of the research at the DDMLab.