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An Agent-Based Approach to Providing Tourism Planning Support

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  • Peter A Johnson
  • Renee E Sieber

Abstract

Agent-based modeling (ABM) is a computer simulation approach that can be used to represent real-world systems and create planning scenarios to examine possible future outcomes of present-day decisions. This approach can be applied in tourism planning, where destinations are exposed to a variety of externalities, and must develop strategies to adapt to changing operational conditions. We describe the development of TourSim, an ABM of tourism dynamics set in the Canadian province of Nova Scotia. We present an overview of the data sources and techniques used to inform agent behavior and the destination landscape, as well as consider aspects of system representation and validation and how these may affect the use of TourSim. TourSim is used to generate three scenarios of tourism dynamics; a base-case scenario, one that simulates the effect of a decrease in visitation from American markets as a result of economic crisis, and the use of advertising as a response to this lower level of visitation. These scenarios are used to evaluate ABM in comparison with other computer-based methods of modeling tourism, namely geographic information systems and system dynamics models.

Suggested Citation

  • Peter A Johnson & Renee E Sieber, 2011. "An Agent-Based Approach to Providing Tourism Planning Support," Environment and Planning B, , vol. 38(3), pages 486-504, June.
    • Handle: RePEc:sae:envirb:v:38:y:2011:i:3:p:486-504
    DOI: 10.1068/b35148
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    1. Nuno David & Jaime Simão Sichman & Helder Coelho, 2005. "The Logic of the Method of Agent-Based Simulation in the Social Sciences: Empirical and Intentional Adequacy of Computer Programs," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 8(4), pages 1-2.
    2. Alison Heppenstall & Andrew Evans & Mark Birkin, 2006. "Using Hybrid Agent-Based Systems to Model Spatially-Influenced Retail Markets," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 9(3), pages 1-2.
    3. Joshua M. Epstein & Robert L. Axtell, 1996. "Growing Artificial Societies: Social Science from the Bottom Up," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262550253, December.
    4. Peterson, Garry, 2000. "Political ecology and ecological resilience:: An integration of human and ecological dynamics," Ecological Economics, Elsevier, vol. 35(3), pages 323-336, December.
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    Cited by:

    1. Zhai, Xueting & Zhong, Dixi & Luo, Qiuju, 2019. "Turn it around in crisis communication: An ABM approach," Annals of Tourism Research, Elsevier, vol. 79(C).

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