MESOB: Balancing Equilibria & Social Optimality
This work addresses bid recommendation in online ad auctions, offering a novel approach to model complex multi-agent interactions, though it is incremental in combining existing concepts like mean-field approximation and bi-objective optimization.
The paper tackles the problem of balancing competition and cooperation in multi-agent games, such as online ad auctions, by proposing the MESOB framework and MESOB-OMO method, which achieve approximately Pareto efficient solutions in simulated experiments, showing advantages over baselines that focus on only one aspect.
Motivated by bid recommendation in online ad auctions, this paper considers a general class of multi-level and multi-agent games, with two major characteristics: one is a large number of anonymous agents, and the other is the intricate interplay between competition and cooperation. To model such complex systems, we propose a novel and tractable bi-objective optimization formulation with mean-field approximation, called MESOB (Mean-field Equilibria & Social Optimality Balancing), as well as an associated occupation measure optimization (OMO) method called MESOB-OMO to solve it. MESOB-OMO enables obtaining approximately Pareto efficient solutions in terms of the dual objectives of competition and cooperation in MESOB, and in particular allows for Nash equilibrium selection and social equalization in an asymptotic manner. We apply MESOB-OMO to bid recommendation in a simulated pay-per-click ad auction. Experiments demonstrate its efficacy in balancing the interests of different parties and in handling the competitive nature of bidders, as well as its advantages over baselines that only consider either the competitive or the cooperative aspects.