Welcome to the Dynamic Decision Making Laboratory!
Dynamic Decision Making (DDM) refers to how people assess and choose among alternatives in environments that evolve over time. In these settings, decisions unfold sequentially, under uncertainty, and with feedback that shapes future choices.
Our research shows that people rely heavily on experience when navigating dynamic tasks. Rather than computing optimal solutions from scratch, they draw on memories of past situations, retrieve actions that worked before, and adapt those strategies based on outcomes. This process is formalized in Instance-Based Learning (IBL) theory, a cognitive framework that enables us to build computational models of human decision-making.
Using IBL, we model and predict behavior across a wide range of domains, including cybersecurity, dynamic resource allocation, control systems, search and rescue, and exploration–exploitation problems. These models provide a unified account of how people learn and act in complex, changing environments.
Our work is organized around three core questions:
- How do humans make decisions, learn, and adapt in dynamic environments?
- How can these cognitive processes be represented and tested computationally?
- How can we design systems that support and improve human decision-making?
Explore how we address these questions through our research program, publications, cognitive modeling, decision making games, and our .
