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 computational software agents that simulate human attitudes and behavior. I discuss how we used this approach, which we call generative agents, to model a representative sample of 1,000 Americans and replicate their attitudes and behavior 85% as well as they replicate themselves two weeks later. Extending my line of argument, I explore 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.