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.
Meet Maya, a restaurant owner in Brooklyn. Every day during the pandemic, she faced difficult decisions: Should she raise prices as supply costs increased? Reduce staff hours? Pivot to takeout-only? Her decisions weren't made in isolation—they were influenced by her customers' willingness to dine indoors, staff availability, supply chain disruptions, and government policies.
Current AI research has made remarkable progress creating "digital humans" —machines that mimic Maya's reasoning and decision-making—but has largely overlooked the critical next step: understanding how millions of individuals like her 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, including thousands like Maya, 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, Maya 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).