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Bart Selman - One of the best experts on this subject based on the ideXlab platform.

  • Risk-sensitive policies for sustainable renewable resource allocation
    IJCAI International Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    Markov Decision Processes arise as a natural model for many renewable resources allocation problems. In many such problems, high stakes decisions with potentially catastrophic outcomes (such as the collapse of an entire ecosystem) need to be taken by carefully balancing social, economic, and ecologic goals. We introduce a broad class of such MDP models with a risk averse attitude of the decision maker, in order to obtain policies that are more balanced with respect to the welfare of future generations. We prove that they admit a closed form solution that can be efficiently computed. We show an application of the proposed framework to the Pacific Halibut Marine Fishery, obtaining new and more cautious policies. Our results strengthen findings of related policies from the literature by providing new evidence that a policy based on periodic closures of the Fishery should be employed, in place of the one traditionally used that harvests a constant proportion of the stock every year.

  • Playing games against nature: optimal policies for renewable resource allocation
    UAI: Conference on Uncertainty in Artificial Intelligence, 2010
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    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.

Stefano Ermon - One of the best experts on this subject based on the ideXlab platform.

  • Risk-sensitive policies for sustainable renewable resource allocation
    IJCAI International Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    Markov Decision Processes arise as a natural model for many renewable resources allocation problems. In many such problems, high stakes decisions with potentially catastrophic outcomes (such as the collapse of an entire ecosystem) need to be taken by carefully balancing social, economic, and ecologic goals. We introduce a broad class of such MDP models with a risk averse attitude of the decision maker, in order to obtain policies that are more balanced with respect to the welfare of future generations. We prove that they admit a closed form solution that can be efficiently computed. We show an application of the proposed framework to the Pacific Halibut Marine Fishery, obtaining new and more cautious policies. Our results strengthen findings of related policies from the literature by providing new evidence that a policy based on periodic closures of the Fishery should be employed, in place of the one traditionally used that harvests a constant proportion of the stock every year.

  • Playing games against nature: optimal policies for renewable resource allocation
    UAI: Conference on Uncertainty in Artificial Intelligence, 2010
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    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.

Jon Conrad - One of the best experts on this subject based on the ideXlab platform.

  • Risk-sensitive policies for sustainable renewable resource allocation
    IJCAI International Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    Markov Decision Processes arise as a natural model for many renewable resources allocation problems. In many such problems, high stakes decisions with potentially catastrophic outcomes (such as the collapse of an entire ecosystem) need to be taken by carefully balancing social, economic, and ecologic goals. We introduce a broad class of such MDP models with a risk averse attitude of the decision maker, in order to obtain policies that are more balanced with respect to the welfare of future generations. We prove that they admit a closed form solution that can be efficiently computed. We show an application of the proposed framework to the Pacific Halibut Marine Fishery, obtaining new and more cautious policies. Our results strengthen findings of related policies from the literature by providing new evidence that a policy based on periodic closures of the Fishery should be employed, in place of the one traditionally used that harvests a constant proportion of the stock every year.

  • Playing games against nature: optimal policies for renewable resource allocation
    UAI: Conference on Uncertainty in Artificial Intelligence, 2010
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    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.

Carla Gomes - One of the best experts on this subject based on the ideXlab platform.

  • Risk-sensitive policies for sustainable renewable resource allocation
    IJCAI International Joint Conference on Artificial Intelligence, 2011
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    Markov Decision Processes arise as a natural model for many renewable resources allocation problems. In many such problems, high stakes decisions with potentially catastrophic outcomes (such as the collapse of an entire ecosystem) need to be taken by carefully balancing social, economic, and ecologic goals. We introduce a broad class of such MDP models with a risk averse attitude of the decision maker, in order to obtain policies that are more balanced with respect to the welfare of future generations. We prove that they admit a closed form solution that can be efficiently computed. We show an application of the proposed framework to the Pacific Halibut Marine Fishery, obtaining new and more cautious policies. Our results strengthen findings of related policies from the literature by providing new evidence that a policy based on periodic closures of the Fishery should be employed, in place of the one traditionally used that harvests a constant proportion of the stock every year.

  • Playing games against nature: optimal policies for renewable resource allocation
    UAI: Conference on Uncertainty in Artificial Intelligence, 2010
    Co-Authors: Stefano Ermon, Carla Gomes, Jon Conrad, Bart Selman
    Abstract:

    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.

Alexander Tewfik - One of the best experts on this subject based on the ideXlab platform.

  • Biological evaluation of Marine protected area: Evidence of crowding effect on a protected population of Queen Conch in the Caribbean
    Marine Ecology, 2003
    Co-Authors: Christophe Béné, Alexander Tewfik
    Abstract:

    This study provides a first evaluation of the biological impact of a Marine Fishery reserve on the stock of queen conch (Strombus gigas ) in the Turks and Caicos Islands. The density and the shell length of the population living in the reserve are compared with those of the individuals living in the surrounding fished areas. The results show that the adult density is six times higher in the reserve than in the fished areas. The shell length analysis shows that both adults and juveniles are significantly smaller in the reserve than in the fished area. This unexpected result suggests the existence of a crowding effect (i. e. a high density-induced reduction in growth rate) within the -reserve. It is hypothesised that this crowding effect is due to the superimposition of two factors leading to very high density values in the reserve: (a) the reduced fishing mortality following the creation of the reserve, (b) the existence of natural barriers that impede the emigration of adults outside the reserve. These results are then discussed in relation to current considerations on Marine fisheries reserves