Agent-Based Simulation of Stakeholder Behaviour through Evolutionary Game Theory

  • Yngve Svalestuen
  • Pinar Öztürk
  • Axel Tidemann
  • Rachel Tiller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8955)

Abstract

Aquaculture organizations establish facilities at the coast in Frøya, Norway. The facilities block the surrounding area from fishing and cause environmental damage to close natural resources. Fishers who depend on those natural resources get the opportunity to influence the aquaculture expansion through complaints about the municipality’s coastal plan. Statistics show that fishers don’t complain as much as expected. This work aims to investigate why. An agent-based simulation is developed in order to model the fishers as intelligent agents with complex interaction. Fishermen’s decision making is simulated through an artificial neural network which adapts its behavior (i.e. weights) by “learning-by-imitation”, a method in evolutionary game theory, from other stakeholders’ behavior in the environment. The promising results show that with further development the simulation system may be part of a decision support system that promotes policies that are fair for the stakeholders.

Keywords

computational intelligence agent-based model simulation learning by imitation evolutionary game theory artificial neural network strategical decision making 

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References

  1. 1.
    Davidsson, P.: Agent based social simulation: A computer science view. Journal of Artificial Societies and Social Simulation 5(1) (January 2002)Google Scholar
  2. 2.
    Smith, M.: Evolution and the Theory of Games. Cambridge University Press (1982)Google Scholar
  3. 3.
    Axelrod, R., Hamilton, W.D.: The evolution of cooperation. Science 211, 1390–1396 (1981)CrossRefMATHMathSciNetGoogle Scholar
  4. 4.
    Tiller, R., Richards, R., Salgado, H., Strand, H., Moe, E., Ellis, J.: Assessing stakeholder adaptive capacity to salmon aquaculture in Norway. Consilience: The Journal of Sustainable Development 11(1), 62–96 (2014)Google Scholar
  5. 5.
    Cleland, D., Dray, A., Perez, P., Cruz-Trinidad, A., Geronimo, R.: Simulating the dynamics of subsistence fishing communities: REEFGAME as a learning and data-gathering computer-assisted role-play game. Simulation and Gaming 43(1), 102–117 (2012)CrossRefGoogle Scholar
  6. 6.
    Groner, M.L., Cox, R., Gettinby, G., Revie, C.W.: Use of agent-based modelling to predict benefits of cleaner fish in controlling sea lice, lepeophtheirus salmonis, infestations on farmed Atlantic salmon, Salmo salar L. Journal of Fish Diseases 36(3), 195–208 (2013)CrossRefGoogle Scholar
  7. 7.
    Rebaudo, F., Crespo-Perez, V., Silvain, J.-F., Dangles, O.: Agent-based modeling of human-induced spread of invasive species in agricultural landscapes: Insights from the potato moth in Ecuador. Journal of Artificial Societies and Social Simulation 14(3), 7 (2011)Google Scholar
  8. 8.
    Nannen, V., van den Bergh, J.C.J.M.: Policy instruments for evolution of bounded rationality: Application to climate-energy problems. Technological Forecasting and Social Change 77(1), 76–93 (2010)CrossRefGoogle Scholar
  9. 9.
    Touza, J., Drechsler, M., Smart, J.C.R., Termansen, M.: Emergence of cooperative behaviours in the management of mobile ecological resources. Environmental Modelling and Software 45, 52–63 (2013)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yngve Svalestuen
    • 1
  • Pinar Öztürk
    • 1
  • Axel Tidemann
    • 1
  • Rachel Tiller
    • 1
  1. 1.Norwegian University of Science and TechnologyTrondheimNorway

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