Creating simplified models of a system to better understand its workings has long been an effective method of solving complex problems (Bradley and Schaefer 1998; Gilbert and Troitzsch 2005). Models help to formalize theories and can be used to test hypotheses before committing to implementation (Holling 1978). Agent-based Modelling (ABM) is one branch of computerized simulation modelling that shows particular promise as a tool for planning support. Recent work has explored the application of ABM to study volatile gasoline market dynamics (Heppenstall et al. 2006), urban sprawl (Benenson and Torrens 2004), the conversion of Amazonian forest into farmland (Deadman et al. 2004) and economic development in response to climate change in the remote Canadian north (Berman et al. 2004). These research examples push the use of ABM from simplified theoretical models towards more detailed representations that incorporate real-world data (Alessa et al. 2006). These next generation examples of ABM have the potential to fill a role as a planning support system (PSS) within variety of planning and policy development areas.
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References
Alessa, L.N., Laituri, M. and Barton, M. (2006) An “all hands” call to the social science community: establishing a community framework for complexity modeling using agent based models and cyberinfrastructure, Journal of Artificial Societies and Social Simulation, 9(4): 6. www.jasss.soc.surrey.ac.uk/9/4/6.html.
An, L., Linderman, M., Qi, J., Shortridge, A. and Liu, J. (2005) Exploring complexity in a humanenvironment system: an agent-based spatial model for multidisciplinary and multiscale integration, Annals of the Association of American Geographers, 95(1): 54–79.
Bankes, S., Lempert, R. and Popper, S. (2002) Making computational social science effective: epistemology, methodology and technology, Social Science Computer Review, 20(4): 377–388.
Batty, M. (2004) Dissecting the streams of planning history: technology versus policy through models, Environment and Planning B, 31: 326–330.
Benenson, I. and Torrens, P.M. (2004) Geosimulation: object-based modeling of urban phenomena. Computers, Environment and Urban Systems, 28(1): 1–8.
Berger, T. and Schreinemachers, P. (2006) Creating agents and landscapes for multiagent systems from random samples. Ecology and Society, 11(2): 19. www.ecologyandsociety.org/vol11/iss2/art19/.
Berman, M., Nicholson, C., Kofinas, G., Tetlichi, J. and Martin, S. (2004) Adaptation and sustainability in a small Arctic community: results of an agent-based simulation model, Arctic, 57(4): 401–414.
Bolte, J.P., Hulse, D.W., Gregory, S.V. and Smith, C. (2006) Modeling biocomplexity – actors, landscapes and alternative futures, Environmental Modelling and Software, 22: 570–579.
Bonabeau, E. (2002) Agent-based modeling: methods and techniques for simulating human systems, Proceedings of the National Academy of Sciences USA, 99(3): 7280–7287.
Bradley, W.J. and Schaefer, K.C. (1998) The Uses and Misuses of Data and Models: The Mathematizations of the Human Sciences, SAGE Publications, Thousand Oaks, California.
Brown, D.G. (2001) Project SLUCE, www.cscs.umich.edu/research/projects/sluce/.
Brown, D.G., Riolo, R., Robinson, D.T., North, M. and Rand, W. (2005) Spatial process and data models: toward integration of agent-based models and GIS, Journal of Geographical Systems, 7: 25–45.
Budic, Z.D. (1994) Effectiveness of geographic information systems in local planning, Journal of the American Planning Association, 60(2): 244–263.
Deadman, P. and Gimblett, H.R. (1994) The role of goal-oriented autonomous agents in modeling people-environment interactions in forest recreation, Mathematical and Computer Modelling, 20(8): 121–133.
Deadman, P., Robinson, D., Moran, E. and Brondizio, E. (2004) Colonist household decisionmaking and land-use change in the Amazon rainforest: an agent-based simulation, Environment and Planning B, 31: 693–709.
Gilbert, N. and Troitzsch, K.G. (2005) Simulation for the Social Scientist (Second edition), Open University Press, Maidenhead.
Grimm, V. and Railsback, S.F. (2005) Individual-based Modeling and Ecology, Princeton University Press, Princeton, New Jersey.
Haklay, M. and Tobon, C. (2003) Usability evaluation and PPGIS: towards a user-centered design approach, International Journal of Geographical Information Science, 17(6): 577–592.
Hall, C.M. (2000) Tourism Planning: Policies, Processes and Relationships, Prentice Hall, Harlow.
Heppenstall, A., Evans, A. and Birkin, M. (2006) Using hybrid agent-based systems to model spatially-influenced retail markets, Journal of Artificial Societies and Social Simulation, 9(3): 2. www.jasss.soc.surrey.ac.uk/9/3/2.html.
Holling, C.S. (1978) Adaptive Environmental Assessment and Management, John Wiley and Sons, New York.
Jamal, T., Borges, M. and Figueiredo, R. (2004) Systems-based modeling for participatory tourism planning and destination management, Tourism Analysis, 9(1–2): 77–89.
Lempert, R. (2002) Agent-based modeling as organizational and public policy simulators, Proceedings of the National Academy of Sciences USA, 99(3): 7195–7196.
Ligmann-Zielinska, A. and Jankowski, P. (2007) Agent-based models as laboratories for spatially explicit planning policies, Environment and Planning B, 34: 316–335.
Manson, S.M. (2006) Bounded rationality in agent-based models: experiments with evolutionary programs, International Journal of Geographical Information Science, 20(9): 991–1012.
McIntosh, B., Seaton, R.A F. and Jeffrey, P. (2007) Tools to think with? Towards understanding the use of computer-based support tools in policy relevant research, Environmental Modelling and Software, 22: 640–648.
Nedovic-Budic, Z. (1998) The impact of GIS technology, Environment and Planning B, 25(5): 681–692.
Parker, D.C., Manson, S.M., Janssen, M.A., Hoffmann, M.J. and Deadman, P. (2003) Multi-agent systems for the simulation of land-use and land-cover change: a review, Annals of the Association of American Geographers, 93(2): 314–337.
Robinson, D.T., Brown, D.G., Parker, D.C., Schreinemachers, P., Janssen, M.A., Huigen, M. et al. (2007) Comparison of empirical methods for building agent-based models in land use science, Journal of Land Use Science, 2(1): 31–55.
Talen, E. (2000) Bottom-up GIS: a new tool for individual and group expression in participatory planning, Journal of the American Planning Association, 66(3): 279–285.
Vonk, G., Geertman, S. and Schot, P. (2005) Bottlenecks blocking widespread usage of planning support systems, Environment and Planning A, 37: 909–924.
Walker, P.A., Greiner, R., McDonald, D. and Lyne, V. (1999) The tourism futures simulator: a systems thinking approach, Environmental Modelling and Software, 14: 59–67.
Wooldridge, M. and Jennings, N.R. (1995) Intelligent agents: theory and practice, The Knowledge Engineering Review, 10(2): 115–152.
Yeoman, I., Galt, M. and McMahon-Beattie, U. (2005) A case study of how VisitScotland prepared for war, Journal of Travel Research, 44(1): 6–20.
Zellner, M.L. (2007) Generating policies for sustainable water use in complex scenarios: an integrated land-use and water-use model of Monroe County, Michigan, Environment and Planning B, 34: 664–686.
Additional Reading
Axelrod, R. and Tesfatsion, L. (2006) On-line guide for newcomers to agent-based modeling in the social science. www.econ.iastate.edu/tesfatsi/abmread.htm.
Bankes, S. (2002). Agent-based modeling: a revolution? Proceedings of the National Academy of Sciences USA, 99(3): 7199–7200.
Epstein, J.M. and Axtell, R. (1996) Growing Artificial Societies: Social Science from the Bottom Up, The Brookings Institution, Washington, D.C.
Gimblett, H.R. (2002) Integrating Geographic Information Systems and Agent-Based Modeling Techniques for Simulating Social and Ecological Processes, Oxford University Press, New York.
Milne, S. and Ateljevic, I. (2001) Tourism, economic development and the global-local nexus: theory embracing complexity, Tourism Geographies, 3(4): 369–393.
Reed, M.G. (1999). Collaborative tourism planning as adaptive experiments in emergent tourism settings, Journal of Sustainable Tourism, 7(3–4): 331–356.
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Johnson, P.A., Sieber, R. (2009). Agent-Based Modelling: A Dynamic Scenario Planning Approach to Tourism PSS. In: Geertman, S., Stillwell, J. (eds) Planning Support Systems Best Practice and New Methods. The GeoJournal Library, vol 95. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8952-7_11
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