Abstract

Effective models of human attitudes and behavior can empower applications ranging from immersive environments to social policy simulation. However, traditional simulations have struggled to capture the complexity and contingency of human behavior. I argue that modern artificial intelligence models allow us to re-examine this limitation. I make my case through generative agents: computational software agents that simulate believable human behavior. Generative agents enable us to populate an interactive sandbox environment inspired by The Sims, a small town of twenty five agents. Our generative agent architecture empowers agents to remember, reflect, and plan. Extending my line of argument, I explore how we might reason about the accuracy of these models, and how modeling human behavior and attitudes can help us design more effective online social spaces, understand the societal disagreement underlying modern AI models, and better embed societal values into our algorithms.

Biography

Michael Bernstein is an Associate Professor of Computer Science at Stanford University, where he is a Bass University Fellow and Interim Director of the Symbolic Systems program. His research focuses on designing social, societal, and interactive technologies. This research has been reported in venues such as The New York Times, Wired, Science, and Nature. Michael has been recognized with an Alfred P. Sloan Fellowship, the UIST Lasting Impact Award, and the Computer History Museum’s Patrick J. McGovern Tech for Humanity Prize. He holds a bachelor’s degree in Symbolic Systems from Stanford University, as well as a master’s degree and a Ph.D. in Computer Science from MIT.

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Michael Bernstein · Stanford HCI