EURO 2024 Copenhagen
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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, 12:30-14:00
Room: 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

Status: accepted

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