<p>Natural and political disasters, including earthquakes, hurricanes, and tsunamis, but also migration and refugees crisis, need quick and coordinated responses in order to support vulnerable populations. In such disasters, nongovernmental organizations compete with each other for financial donations, while people who need assistance suffer a lack of coordination, congestion in terms of logistics, and duplication of services. From a theoretical point of view, this problem can be formalized as a generalized Nash equilibrium (GNE) problem. This is a generalization of the Nash equilibrium problem, where the agents’ strategies are not fixed but depend on the other agents’ strategies. In this paper, we show that membrane computing can model humanitarian relief as a GNE problem. We propose a family of P systems that compute GNE in this context, and we illustrate their capabilities with Hurricane Katrina in 2005 as a case study.</p>

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An application of membrane computing to humanitarian relief via generalized Nash equilibrium

  • Alejandro Luque-Cerpa,
  • David Orellana-Martín,
  • Miguel Á. Gutiérrez-Naranjo

摘要

Natural and political disasters, including earthquakes, hurricanes, and tsunamis, but also migration and refugees crisis, need quick and coordinated responses in order to support vulnerable populations. In such disasters, nongovernmental organizations compete with each other for financial donations, while people who need assistance suffer a lack of coordination, congestion in terms of logistics, and duplication of services. From a theoretical point of view, this problem can be formalized as a generalized Nash equilibrium (GNE) problem. This is a generalization of the Nash equilibrium problem, where the agents’ strategies are not fixed but depend on the other agents’ strategies. In this paper, we show that membrane computing can model humanitarian relief as a GNE problem. We propose a family of P systems that compute GNE in this context, and we illustrate their capabilities with Hurricane Katrina in 2005 as a case study.