Abstract: Complex systems include networks of decision-makers, processes, technologies, incentives, rules, and feedback loops that collectively shape individual and societal outcomes. My research aims to better understand human behavior by examining how people navigate and adapt within these complex systems, drawing on insights from basic decision research. For example, foundational studies highlight a significant “description-experience gap”: when confronted with abstract descriptions, people appear to overemphasize rare events, yet when relying on personal experience, they often dismiss low-probability risks (“it won’t happen to me”). I will discuss work that clarifies this gap and consider its implications for policy and intervention designs. Specifically, work that extends such insights to predict human behavior in optional stopping tasks and the unintended potential consequences of overtaxing reckless behaviors.

Bio: I am a decision scientist and Assistant Professor in the Department of Engineering and Public Policy at Carnegie Mellon University. I received my Ph.D. in behavioral sciences at the Technion–Israel Institute of Technology. My work combines experimental and computational methods to understand how behavior and learning are shaped by environmental features such as information, feedback, task design, and incentive systems. By integrating computational cognitive modeling with structural analyses of environments, I aim to predict the long-term impacts of policies, interventions, technologies, and structural changes. These insights are used to inform more effective policy evaluation and design.