Equilibrium

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Equilibrium

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Game Theory

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Imperfect-Information Games

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Nash Equilibrium

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Zero--Sum Differential Game

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254 papers

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Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation
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May 13, 2026Claire Chen, Yuheng Zhang, Xinyu Liu +3Nash Equilibrium Mirror Descent
The fitness landscape of social norms in social dilemmas
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A Theory of Multilevel Interactive Equilibrium in NeuroAI
May 11, 2026Zhe Sage Chen, Quanyan ZhuGame Theory Multi-Agent Systems

Jun 11, 2026 · cs.LGJ/K move · Enter open · S save

Once-for-All: Scalable Simultaneous Forecasting via Equilibrium State Estimation

Beinan Xu, Andy Song, Jiti Gao, Feng Liu

We introduce Equilibrium State Estimation (ESE), a novel paradigm for simultaneous prediction, where multiple interacting systems require separate yet coordinated forecasts. Such scenarios often arise in real-world settings such as economics and healthcare modeling. Unlike existing approaches that predict one system at a time, ESE forecasts all systems in a single pass. It first estimates the equilibrium state across systems, then generates holistic forecasts based on the difference between the current state and the estimated equilibrium. Extensive experiments on synthetic and real-world datasets, including currency exchange and COVID-19 spread modeling, demonstrate that ESE is at least as accurate as state-of-the-art (SOTA) methods while being significantly faster. In addition, ESE integrates seamlessly with conventional predictors, combining their accuracy with its exceptional efficiency and delivering a 10-70x speedup. With linear-time complexity, ESE scales far better than SOTA methods as the number of systems increases. Moreover, it remains accurate under diverse perturbations, establishing ESE as a fast, generalizable, robust, and scalable multi-prediction method.

Llm-Based Forecasting Methods Load Forecasting State Estimation Equilibrium

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