Competitive Collaborative Learning

Baruch Awerbuch, Robert D. Kleinberg

Abstract: We develop algorithms for a community of users to make decisions about selecting products or resources, in a model characterized by two key features: We formulate such learning tasks as an algorithmic problem based on the multi-armed bandit problem, but with a set of users (as opposed to a single user), of whom a constant fraction are honest and are partitioned into coalitions such that the users in a coalition perceive the same expected quality if they sample the same resource at the same time. Our main result exhibits an algorithm for this problem which converges in polylogarithmic time to a state in which the average regret (per honest user) is an arbitrarily small constant.