Improved equilibria via public service advertising

Many natural games have both high and low cost Nash equilibria: their Price of Anarchy is high and yet their Price of Stability is low. In such cases, one could hope to move behavior from a high cost equilibrium to a low cost one by a “public service advertising campaign” encouraging players to follow the low-cost equilibrium, and if every player follows the advice then we are done. However, the assumption that everyone follows instructions is unrealistic. A more natural assumption is that some players will follow them, while other players will not. In this paper we consider the question of to what extent can such an advertising campaign cause behavior to switch from a bad equilibrium to a good one even if only a fraction of people actually follow the given advice, and do so only temporarily. Unlike the “value of altruism” model, we assume everyone will ultimately act in their own interest. We analyze this question for several important and widely studied classes of games including network design with fair cost sharing, scheduling with unrelated machines, and party affiliation games (which include consensus and cut games). We show that for some of these games (such as fair cost sharing), a random α fraction of the population following the given advice is sufficient to get a guarantee within an O(1/α) factor of the price of stability for any α > 0. For other games (such as party affiliation games), there is a strict threshold (in this case, α < 1/2 yields almost no benefit, yet α > 1/2 is enough to reach near-optimal behavior). Finally, for some games, such as scheduling, no value α < 1 is sufficient. We also consider a “viral marketing” model in which certain players are specifically targeted, and analyze the ability of such targeting to influence behavior using a much smaller number of targeted players. School of Computer Science, Carnegie Mellon University. Supported in part by NSF grant CCF-0514922, by an IBM Graduate Fellowship, and by a Google Research Grant. School of Computer Science, Carnegie Mellon University. Supported in part by NSF grant CCF-0514922 and by a Google Research Grant School of Computer Science, Tel Aviv University and Google Research. This work was supported in part by the IST Programme of the European Community, under the PASCAL Network of Excellence, IST-2002506778, by a grant no. 1079/04 from the Israel Science Foundation and by a grant from United States-Israel Binational Science Foundation (BSF). This publication reflects the authors’ views only.