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Distributing scarce resources among agents in a way that maximizes the social welfare of the group is a computationally hard problem when the value of a resource bundle is not linearly decomposable. Furthermore, the problem of determining the value of a resource bundle can be a significant computational challenge in itself, such as for an agent operating in a stochastic environment, where the value of a resource bundle is the expected payoff of the optimal policy realizable given these resources. Recent work has shown that the structure in agents' preferences induced by stochastic policy-optimization problems (modeled as MDPs) can be exploited to solve the resource-allocation and the policy-optimization problems simultaneously, leading to drastic (often exponential) improvements in computational efficiency. However, previous work used a flat MDP model that scales very poorly. In this work, we present and empirically evaluate a resource-allocation mechanism that achieves much better scaling by using factored MDP models, thus exploiting both the structure in agents' MDP-induced preferences, as well as the structure within agents' MDPs.

Resource allocation among agents with preferences induced by factored MDPs