Many of society's most pressing challenges—from pandemic response to supply chain disruptions to climate adaptation—emerge from the collective behavior of millions of individuals making decisions over time. Understanding these complex systems requires seeing how individual choices combine to create outcomes that no one person intended.
Current AI research, driven by Large Language Models (LLMs), has made remarkable progress creating increasingly sophisticated "digital humans," but has largely overlooked the critical next step: understanding how these individuals combine to form "digital societies." This is where Large Population Models (LPMs) come in—a new computational approach that simulates entire populations with their complex interactions and emergent behaviors.
Imagine a digital microscope revealing an entire city—8.4 million synthetic New Yorkers living their daily lives in a computational world. In this virtual society, each person makes decisions based on their unique circumstances: a nurse weighs the risks of commuting on crowded subways, a restaurant owner adjusts prices as supply costs rise, families decide whether a $500 stimulus check means they can afford to stay home during a pandemic surge. As these millions of individual choices ripple through networks of interactions, patterns emerge that no single decision-maker could foresee. This living laboratory of human behavior is the vision behind Large Population Models (LPMs).