Bargaining with limited computation: Deliberation equilibrium

摘要

We develop a normative theory of interaction—negotiation in particular—among self-interested computationally limited agents where computational actions are game theoretically treated as part of an agent's strategy. We focus on a 2-agent setting where each agent has an intractable individual problem, and there is a potential gain from pooling the problems, giving rise to an intractable joint problem. At any time, an agent can compute to improve its solution to its own problem, its opponent's problem, or the joint problem. At a deadline the agents then decide whether to implement the joint solution, and if so, how to divide its value (or cost). We present a fully normative model for controlling anytime algorithms where each agent has statistical performance profiles which are optimally conditioned on the problem instance as well as on the path of results of the algorithm run so far. Using this model, we introduce a solution concept, which we call deliberation equilibrium. It is the perfect Bayesian equilibrium of the game where deliberation actions are part of each agent's strategy. The equilibria differ based on whether the performance profiles are deterministic or stochastic, whether the deadline is known or not, and whether the proposer is known in advance or not. We present algorithms for finding the equilibria. Finally, we show that there exist instances of the deliberation–bargaining problem where no pure strategy equilibria exist and also instances where the unique equilibrium outcome is not Pareto efficient.