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An Agent-based Model of the South African Offshore Hake Trawl Industry: Part I Model Description and Validation

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  • Cooper, Rachel
  • Jarre, Astrid

Abstract

The most valuable component in South Africa's fishing industry is its hake fishery, which targets two species, the shallow-water (Merluccius capensis) and deep-water (M. paradoxus) Cape hakes. Modelling provides a means to assist in understanding the dynamics of the economic system of this fishery and identify potential links to the ecological system in future, which can inform management. This study develops and describes a novel agent-based model of the South African offshore hake trawl industry, HakeSim, which captures drivers such as fuel price, catch per unit effort, export markets, exchange rate, industrial organization and uncertainty in catches as a proxy for environmental uncertainty. It allows identification of key drivers and their relative importance to the industry to be assessed. It has desirable and realistic sensitivities and it can successfully reproduce profitability scenarios for the industry under different fuel prices. Fuel prices above ZAR18.783 per litre, which could result from increased prices or reduced subsidies, are demonstrated to push the modelled fishing companies to making losses, which could potentially reduce employment. This model represents a strategic tool for management and significant advancements over existing bio-economic and agent-based models of fisheries.

Suggested Citation

  • Cooper, Rachel & Jarre, Astrid, 2017. "An Agent-based Model of the South African Offshore Hake Trawl Industry: Part I Model Description and Validation," Ecological Economics, Elsevier, vol. 142(C), pages 268-281.
  • Handle: RePEc:eee:ecolec:v:142:y:2017:i:c:p:268-281
    DOI: 10.1016/j.ecolecon.2017.06.026
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    1. McDonald, A.D. & Little, L.R. & Gray, R. & Fulton, E. & Sainsbury, K.J. & Lyne, V.D., 2008. "An agent-based modelling approach to evaluation of multiple-use management strategies for coastal marine ecosystems," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 401-411.
    2. Nigel Gilbert & Pietro Terna, 2000. "How to build and use agent-based models in social science," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 1(1), pages 57-72, March.
    3. Arnason, Ragnar, 2009. "Fisheries management and operations research," European Journal of Operational Research, Elsevier, vol. 193(3), pages 741-751, March.
    4. J. Doyne Farmer & Duncan Foley, 2009. "The economy needs agent-based modelling," Nature, Nature, vol. 460(7256), pages 685-686, August.
    5. Christophe Deissenberg & Sander van Der Hoog & Herbert Dawid, 2008. "EURACE: A Massively Parallel Agent-Based Model of the European Economy," Working Papers halshs-00339756, HAL.
    6. Crosoer, David & van Sittert, Lance & Ponte, Stefano, 2006. "The integration of South African fisheries into the global economy: Past, present and future," Marine Policy, Elsevier, vol. 30(1), pages 18-29, January.
    7. Beecham, J.A. & Engelhard, G.H., 2007. "Ideal free distribution or dynamic game? An agent-based simulation study of trawling strategies with varying information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 628-646.
    8. Hare, M & Deadman, P, 2004. "Further towards a taxonomy of agent-based simulation models in environmental management," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(1), pages 25-40.
    9. James Nolan & Dawn Parker & G. Cornelis Van Kooten & Thomas Berger, 2009. "An Overview of Computational Modeling in Agricultural and Resource Economics," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 57(4), pages 417-429, December.
    10. Cooper, Rachel & Jarre, Astrid, 2017. "An Agent-based Model of the South African Offshore Hake Trawl Industry: Part II Drivers and Trade-offs in Profit and Risk," Ecological Economics, Elsevier, vol. 142(C), pages 257-267.
    11. Cooper, Rachel & Leiman, Anthony & Jarre, Astrid, 2014. "An analysis of the structural changes in the offshore demersal hake (Merluccius capensis and M. paradoxus) trawl fishery in South Africa," Marine Policy, Elsevier, vol. 50(PA), pages 270-279.
    12. Little, L. Richard & Punt, André E. & Mapstone, Bruce D. & Begg, Gavin A. & Goldman, Barry & Williams, Ashley J., 2009. "An agent-based model for simulating trading of multi-species fisheries quota," Ecological Modelling, Elsevier, vol. 220(23), pages 3404-3412.
    13. Sophie Gourguet & Claire Macher & Luc Doyen & Olivier Thébaud & M. Bertignac & Olivier Guyader, 2013. "Managing mixed fisheries for bio-economic viability," Post-Print hal-00835634, HAL.
    14. Cabral, Reniel B. & Geronimo, Rollan C. & Lim, May T. & Aliño, Porfirio M., 2010. "Effect of variable fishing strategy on fisheries under changing effort and pressure: An agent-based model application," Ecological Modelling, Elsevier, vol. 221(2), pages 362-369.
    15. Cheilari, Anna & Guillen, Jordi & Damalas, Dimitrios & Barbas, Thomas, 2013. "Effects of the fuel price crisis on the energy efficiency and the economic performance of the European Union fishing fleets," Marine Policy, Elsevier, vol. 40(C), pages 18-24.
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    4. Cooper, Rachel & Jarre, Astrid, 2017. "An Agent-based Model of the South African Offshore Hake Trawl Industry: Part II Drivers and Trade-offs in Profit and Risk," Ecological Economics, Elsevier, vol. 142(C), pages 257-267.

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