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3929. Data-scarce identification of game dynamics via sum-of-squares optimization
Invited abstract in session WC-40: Experimental economics and game theory 2, stream Experimental economics and game theory.
Wednesday, 12:30-14:00Room: 96 (building: 306)
Authors (first author is the speaker)
1. | Antonios Varvitsiotis
|
Engineering Systems and Design, Singapore University of Technology and Design |
Abstract
Understanding how players adjust their strategies in games, based on past experience, is a crucial tool for policymakers. It enables them to forecast the system's eventual behavior, exert control over the system, and evaluate counterfactual scenarios. The task becomes increasingly difficult when only a limited number of observations are available or difficult to acquire. In this work, we introduce the Side-Information Assisted Regression (SIAR) framework, designed to identify game dynamics in multi-player normal-form games only using data from a short run of a single system trajectory. To enhance system recovery in the face of scarce data, we integrate side-information constraints into SIAR, which restrict the set of feasible solutions to those satisfying game-theoretic properties and common assumptions around strategic interactions. SIAR is solved using sum-of-squares optimization, resulting in a hierarchy of approximations that provably converge to the true dynamics of the system.
We showcase that the SIAR framework accurately predicts player behavior across a spectrum of normal-form games, widely-known families of game dynamics, and strong benchmarks, even if the unknown system is chaotic.
Keywords
- Game Theory
- Convex Optimization
- Control Theory
Status: accepted
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