Playing games against nature: optimal policies for renewable resource allocation
It addresses resource management for sustainability, but is incremental as it extends existing inventory control results.
The paper tackles the problem of renewable resource allocation by introducing a class of Markov decision processes with a closed-form solution, applied to real-world data like the Northern Pacific Halibut fishery to obtain a policy with a guaranteed lower bound on utility.
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.