Strategic Multi-Agent Dynamics in Competitive IR

In the Web ecosystem, document authors often strategically modify content to maximize visibility in search rankings. These ranking-incentivized modifications pose challenges to retrieval effectiveness, content diversity, and long-term stability. At the same time, the rise of large language models (LLMs) is transforming IR: queries and documents that were once assumed to be generated exclusively by humans can now also be created by automated agents [5]. These agents can formulate queries, generate documents, and perform ranking. This introduces a variety of new challenges and opportunities in competitive search environments, where interactions between strategic agents can substantially influence outcomes.