Uploaded August 2026 | Updated September 2026, 2 weeks ago

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Learning and the Price of Anarchy in Games
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We investigate repeated strategic interactions where participants use learning algorithms to guide their decisions. As machine learning increasingly powers online systems—from traffic and packet routing to ad auctions—it becomes essential to understand how strategic behavior affects performance, and how to design systems that ensure robust outcomes.Over the past two decades, researchers have developed powerful tools to quantify the inefficiency caused by selfish behavior, known as the Price of Anarchy. Foundational results show that when participants use learning algorithms satisfying the no-regret condition, the resulting inefficiency remains bounded—even in repeated games. However, these analyses typically assume that each round is independent, with no carryover effects from previous outcomes.In reality, many systems exhibit an evolving dynamic state. We explore such dynamic games, where outcomes in one round directly influence future interactions. We will highlight ongoing research studying this phenomenon in the context of a game modeling queuing system: routers compete for servers, and packets that fail to get served must be resent. This creates a feedback loop where the number of packets in each round depends on prior success, resulting in a highly dependent random process. We study how much excess server capacity is needed to guarantee system stability, even when participants behave selfishly and myopically.