Research: Three Fundamental Breakthroughs
LPMs address the core challenges of modeling complex systems with millions of interacting agents. To show how, consider Maya facing H5N1 bird flu—a disease spreading from poultry to humans, threatening both food security and public health. This disease emerges from a series of interconnected events. Wild birds migrate and carry the virus to farms. Farmers must then decide whether to cull their flocks to limit the spread or risk further losses. For Maya, Egg shortages raise her costs, and visits to the farmers’ market expose her to health risks. Here’s how LPMs help.
1. Modeling Millions of Individuals: We need to understand millions of people—farmers, supply chain workers, consumers—and their interactions across multiple networks. Traditional simulations forced an impossible tradeoff: either model realistic behaviors for a few hundred individuals OR track simplified movements for millions—but never both at once. It's like trying to simultaneously film an entire stadium while capturing each person's facial expressions.
Our breakthrough: LPMs can simulate 8.4 million individuals, each with their own behaviors, on a single GPU - 600 times faster than previously possible - without sacrificing the rich detail of each person's unique situation. First, we efficiently process billions of interactions simultaneously across customer, supply chain and community networks - in minutes instead of hours. Second, we learn behavioral patterns across individuals, allowing us to accurately capture unique decisions for millions of people while separately modeling only a few thousands - recreating a digital New York for $500. This helps see how Maya's decisions ripple out across the city - shaping consumer behavior and public health.
2.Learning from Multi-scale Data: Managing H5N1 requires combining varied data sources: migration patterns of wild birds, production losses on farms, logistics records, and consumer purchasing trends. Traditionally, these sources remained fragmented. Researchers built surrogate models—simplified approximations—to align simulations with real-world data, but these approximations sacrificed the detailed mechanics of how disease moves from birds to farms, or how farm losses disrupt supply chains. This led to imprecise predictions and incomplete solutions.
Our breakthrough: LPMs eliminate the need for simplified approximations by making our simulations differentiable—transforming months of computation into minutes. This allows simulations to learn directly from diverse real-world data sources—hospital records, mobility patterns, economic indicators—providing 2-20x better precision and 3000x faster calibration over traditional surrogate models. When Maya's restaurant sees fewer customers, our model rapidly determines whether this resulted from rising infections, new restrictions, or consumer confidence changes—and projects how specific interventions might help her business while improving public health.
3. Closing the Simulation vs. Reality Gap: Traditional simulations treat agents like Maya purely as synthetic entities that mimic real people. This creates a fundamental disconnect—the digital Maya can never truly reflect how the real Maya adapts to changing conditions, and insights from the simulation can't easily reach the real Maya when she needs them. By the time data is collected, cleaned, and fed into models, the real world has already moved on. During COVID-19, contact tracing apps collected data on exposures and sent it to central servers for processing, only notifying users days later. These apps could only provide post-hoc notifications ("You were exposed 3 days ago"), when the damage was already done. This created a critical gap between simulation insights and real-world action, severely limiting adoption and effectiveness. A similar centralized approach for H5N1 would repeat this flaw, hindering efforts to protect health and food systems.
Our breakthrough: LPMs can decentralize the simulation by transforming Maya’s personal device into active simulation nodes. Through advanced cryptographic techniques, we can use Maya’s real-time behavior and interactions to execute simulations locally without compromising her privacy. The simulations run on networks of phones, not central servers, reflecting people’s actions as they happen. This creates a powerful two-way connection: Maya's actual restaurant decisions help update our digital New York in real-time, while insights from millions of simulated scenarios provide her with personalized recommendations. With H5N1, this means a phone can warn, “Avoid the market tomorrow; risk is up,” using live data from farmers and shoppers. It’s instant, keeps data private, and helps prevent health and food problems before they grow, unlike COVID-19’s delays.
LPMs realize this vision by making fundamental advances in agent-based modeling, decentralized computation and machine learning. Our research has resulted in several publications at top-tier AI conferences and journals, and received multiple best-paper awards. Our work has received research awards from industry (e.g. JP Morgan, Adobe) and government (e.g. NSF).