Convergence of Learning Dynamics in Information Retrieval Games
This addresses the stability of learning dynamics in information retrieval for strategic authors, but it is incremental as it builds on existing game-theoretic models and ranking principles.
The paper tackles the problem of strategic author behavior in information retrieval games, proving that under the probability ranking principle, better-response learning dynamics converge to a pure Nash equilibrium, while other ranking methods may not guarantee such convergence.
We consider a game-theoretic model of information retrieval with strategic authors. We examine two different utility schemes: authors who aim at maximizing exposure and authors who want to maximize active selection of their content (i.e. the number of clicks). We introduce the study of author learning dynamics in such contexts. We prove that under the probability ranking principle (PRP), which forms the basis of the current state of the art ranking methods, any better-response learning dynamics converges to a pure Nash equilibrium. We also show that other ranking methods induce a strategic environment under which such a convergence may not occur.