Research: Three Fundamental Breakthroughs
Building this digital New York required solving three fundamental challenges:
1. The Scale vs. Detail Dilemma: We need to simulate millions of New Yorkers as distinct individuals with their unique circumstances and interactions. 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: We can now simulate all 8.4 million New Yorkers with their individual behaviors on a single GPU—600× 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 few thousands—recreating a digital New York for $500. We can now see how Maya's decision will ripple out across thousands of other businesses to shape city-wide health and economic outcomes—connections that were impossible to discover before.
2.The Puzzle Piece Challenge: Officials need to understand how Maya and millions like her would respond to new policies like stimulus checks or lockdowns. They have data fragments—restaurant bookings, mobility patterns, infection rates, compliance behaviors—but traditionally couldn't connect this real-world information with simulations without building simplified approximations (surrogates) that sacrifice critical understanding.
Our breakthrough: We've eliminated 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. 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.
Our breakthrough: We've transformed personal devices—like real Maya's phone—from passive data collectors into active simulation agent. This enables decentralized simulations that run across networks of real-world devices. Instead of bringing sensitive data to central systems, we bring computation directly to where information naturally exists, using secure multi-party computation to preserve privacy while estimating simulation outputs and gradients. 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. This establishes a practical collaboration between real and synthetic New Yorkers, where each improves the other. The result transforms simulations from isolated analysis tools into living systems embedded within real communities, providing timely insights that evolve with the changing world.
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).