M.Sc Student | Rosenberg Itay |
---|---|
Subject | Learning Dynamics in Information Retrieval Games |
Department | Department of Industrial Engineering and Management | Supervisor | Professor Moshe Tennenholtz |
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. In addition, we show a lower bound in the worst case for the time required until convergence is
guaranteed. We also show that other ranking methods
induce a strategic environment under which such a convergence may not
occur. Our study is also concerned with another aspect of the dynamics of the system, by considering situations where the number of
authors can increase. We prove that under some
natural assumptions, the PRP is the only ranking method that the social
welfare cannot decrease when the number of authors is increasing. Together, the results in this thesis expose two fundamental strong
properties of PRP, comparing to alternative
mediators. This is in contradiction to the previous fndings on weaknesses of the PRP.