Learning to Strategically Acquire Resources in Competition

2026-06-04

AI Summary

This study addresses strategic competition among multiple agents for divisible, scarce resources—such as financial assets or computational capacity—under conditions of asymmetric information or the absence of a common prior. By developing a game-theoretic framework that integrates market price dynamics, the work extends the existence, uniqueness, and computationally efficient characterization of Bayesian Nash equilibria to partial-information settings with a common prior, and further establishes convergence guarantees for simultaneous learning dynamics when no common prior is assumed. Theoretical contributions include a rigorous characterization of equilibrium properties and an upper bound on the Price of Anarchy. Empirically, simulations based on real-world financial data validate both the convergence of the proposed algorithms and the efficacy of the resulting strategic behaviors.

Abstract

We consider multiple agents competing to acquire some costly divisible resource (e.g. shares of a financial asset, compute resources, etc.) over time. Leveraging a standard model for price dynamics, we propose a novel game-theoretic model for this problem, generalizing settings studied in diverse literatures. Our analysis considers different assumptions on the information available to agents. Under partial-information with a common prior (which subsumes complete information as a special case), we establish the existence, uniqueness, and efficient computability of the Bayesian Nash equilibrium (BNE), and bound the price of anarchy. Next and more generally, we consider agents with no common prior learning to act optimally given realistic market feedback from repeated interactions. We provide sufficient conditions on agents doing simultaneous learning dynamics for last-iterate convergence to the BNE. For all settings, we provide simulations based on real financial data to illustrate our theoretical results and offer new insights on strategic behavior in the context of trading and resource acquisition.

Problem

• resource acquisition
• strategic competition
• Bayesian Nash equilibrium
• multi-agent learning
• price dynamics

Innovation

• Bayesian Nash equilibrium
• resource competition
• learning dynamics
• price of anarchy
• game-theoretic modeling