Memento Ludi: Information Retrieval from a Game-Theoretic Perspective

TL;DR

This paper introduces a game-theoretic macro-model of information retrieval, representing users and systems as players, and proposes methods to analyze and influence user behavior through Nash equilibrium strategies.

Contribution

It develops a novel macro-model using Game Theory for IR, introduces the Alpha model, and proposes the shifting method to control user behavior.

Findings

Modeling IR as a game between users and systems.

Application of Nash equilibrium to IR data.

A new method to influence user behavior in IR environments.

Abstract

We develop a macro-model of information retrieval process using Game Theory as a mathematical theory of conflicts. We represent the participants of the Information Retrieval process as a game of two abstract players. The first player is the `intellectual crowd' of users of search engines, the second is a community of information retrieval systems. In order to apply Game Theory, we treat search log data as Nash equilibrium strategies and solve the inverse problem of finding appropriate payoff functions. For that, we suggest a particular model, which we call Alpha model. Within this model, we suggest a method, called shifting, which makes it possible to partially control the behavior of massive users. This Note is addressed to researchers in both game theory (providing a new class of real life problems) and information retrieval, for whom we present new techniques to control the IR environment.