Convergence of Learning Dynamics in Information Retrieval Games

  • Omer Ben-Porat Technion – Israel Institute of Technology
  • Itay Rosenberg Technion – Israel Institute of Technology
  • Moshe Tennenholtz Technion – Israel Institute of Technology

Abstract

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 betterresponse 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.

Published
2019-07-17
Section
AAAI Technical Track: Game Theory and Economic Paradigms