Modeling Real Behavior in Two-Person Differential Games
In the behavioral sciences, games and game theory have long been the tools of choice for studying strategic behavior. However, the most commonly studied games involve only small numbers of discrete choices and well-defined rounds, while real-world strategic behaviors are continuous and extended in time. Differential game theory attempts to model these phenomena, but the theoretical and empirical properties of differential games are comparatively poorly understood. In this talk, I'll present recent work from my lab modeling the empirical behavior of real agents (humans and monkeys) in one such game. By combining approaches from control theory and physics with scalable Bayesian inference, we are able to fit generative models that not only produce realistic new examples of behavior but also decompose players' strategies into scientifically meaningful components.
Bio: John Pearson is Assistant Professor in the Department of Biostatistics and Bioinformatics at Duke, with appointments in Electrical and Computer Engineering, Neurobiology, and Psychology & Neuroscience. He did his PhD in theoretical physics, working on string theory and quantum gravity, before switching to neuroscience, where he taught monkeys to gamble, had patients play video games during brain surgery, and modeled jury decision-making. His lab works at the intersection of machine learning and neuroscience, from social decisions in humans to information processing in the retina.