DDMLab Research
Understanding and Enhancing Human Decision Making in a Dynamic World
In today’s world, we constantly face decisions in environments that are complex, fast-changing, and filled with uncertainty. Choices are rarely presented all at once—instead, they unfold over time and space, requiring us to explore, adapt, and act under pressure. Whether navigating overwhelming amounts of information, dealing with strict time constraints, or managing unpredictable changes, our ability to make sound decisions is continually put to the test.
At the Dynamic Decision Making Laboratory (DDMLab), our mission is to uncover how people make decisions in these dynamic settings. We develop robust theoretical models to explain the cognitive processes behind human choice and translate this knowledge into real-world applications. By bridging theory and practice, we aim to support and enhance decision-making across critical domains such as healthcare, emergency response, cybersecurity, and more.
Our Research Approach: Bridging Human Behavior and Cognitive Modeling
At the DDMLab, we combine laboratory experiments with cognitive computational models to understand and improve human decision making in dynamic environments.
In our experiments, individuals and teams interact with dynamic decision making tasks that unfold over time and space. These studies reveal behavioral patterns and adaptive strategies people use under uncertainty.
We complement this with cognitive models that simulate the decision-making process through algorithms. These models operate in the same tasks as humans, allowing us to compare their behavior across multiple dimensions—such as learning, risk-taking, and optimality.
By aligning human data with model predictions, we gain deep insights into cognitive mechanisms and use these findings to develop practical applications in areas like cybersecurity, climate change, phishing prevention, and human-machine interaction.
Instance-Based Learning: How Experience Shapes Our Decisions
At the DDMLab, we use a powerful cognitive theory called Instance-Based Learning Theory (IBLT) (Gonzalez, Lerch, & Lebiere, 2003) to understand how people make decisions based on past experiences. IBLT explains how we draw on memories of similar situations (i.e., instances) we've encountered before, to make decisions..
As we face new challenges, our brain searches through these stored instances to find ones that match the current situation. We then weigh the options based on past outcomes and select the one that seems most promising. After making a choice, we update our memory with the result, helping us improve future decisions.
This process is driven by a mathematical equation rooted in the ACT-R cognitive architecture, which captures how recent, frequent, and similar experiences influence what we remember—and how accurately we recall it.
To support researchers and developers, we’ve created PyIBL — a Python-based platform that brings IBLT to life. PyIBL makes it easy to build and test models that simulate human decision-making. To try it yourself visit our Cognitive Modeling page to access PyIBL, a detailed manual, and a library of example models.