Playing games against nature: optimal policies for renewable resource allocation

In this paper we introduce a class of Markov decision processes that arise as a natural model for many renewable resource allocation problems. Upon extending results from the inventory control literature, we prove that they admit a closed form solution and we show how to exploit this structure to speed up its computation. We consider the application of the proposed framework to several problems arising in very different domains, and as part of the ongoing effort in the emerging field of Computational Sustainability we discuss in detail its application to the Northern Pacific Halibut marine fishery. Our approach is applied to a model based on real world data, obtaining a policy with a guaranteed lower bound on the utility function that is structurally very different from the one currently employed.

在本文中，我们引入了一类马尔可夫决策过程，它作为许多可再生资源分配问题的自然模型而出现。通过扩展库存控制文献中的结果，我们证明了它们具有闭式解，并展示了如何利用这种结构来加快其计算速度。我们考虑将所提出的框架应用于在非常不同的领域中出现的几个问题，并且作为新兴的计算可持续性领域正在进行的工作的一部分，我们详细讨论了它在北太平洋大比目鱼海洋渔业中的应用。我们的方法被应用于一个基于现实世界数据的模型，得到了一个在效用函数上具有保证下限的策略，该策略在结构上与当前使用的策略有很大不同。