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Can Agent-Based Modelling Really be Useful?

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Cooperative Agents

Part of the book series: Theory and Decision Library ((TDLA,volume 32))

Abstract

Agent-based modelling on a computer appears to have a special role to play in the development of social science and the formulation of social policy. It offers a means of discovering general and applicable social theory, and grounding it in precise assumptions and derivations, whilst addressing those elements of individual cognition that are central to human society. However, there are important questions to be asked and difficulties to be overcome in achieving this potential. What differentiates agent-based modelling from traditional computer modelling? What different types of agent-based models are there, and what are the structural relationships between them (if any)? Which model types should be used in which circumstances? If it is appropriate to use a complex model, for example one incorporating “deliberative” agents, how can it be validated? If it can only be validated in general terms, does this mean that we are forced into a “theory building” mode in which the focus of the investigation lies in the model’s properties? If so, what types of parameter space may a complex model have? How best can very large parameter spaces be explored? Some of these questions are here addressed and are illustrated by reference to recent agent-based models for the environment. A particular application is then considered in some detail: agent-based modelling of intervention strategies for integrated ecosystem management, especially management of the Fraser River watershed in British Columbia.

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References

  • Axtell, R. (2001). Effects of Interaction Topology and Activation Regime in Several Multi-Agent Systems. In: 33–48.

    Google Scholar 

  • Bousquet, F., Cambier, C., Mullon, C., Morand, P., and Quensiere, J. (1994). Simulating Fishermen’s Society. In: Gilbert, N. and Doran, J.E. (eds.) Simulating Societies. UCL Press, London. pp. 143–164.

    Google Scholar 

  • Carpenter, S., Brock, W. and Hanson, P. (1999). Ecological and Social Dynamics in Simple Models of Ecosystem management. Conservation Ecology 3(2): 4. http://www.consecol.org/vol3/iss2/art4

  • Christie, P. and White, A.T. (1997). Trends in Development of Coastal Area Management in Tropical Countries: From Central to Community Orientation. Coastal Management, 25, 155–181.

    Article  Google Scholar 

  • Doran, J.E. (1997). From Computer Simulation to Artificial Societies. Transactions of the Society for Computer Simulation International, 14, 69–78.

    Google Scholar 

  • Doran, J.E. (1998). Simulating Collective Misbelief. Journal of Artificial Societies and Social Simulation, Vol 1(1) http://www.soc.surrey.ac.uk/JASSS/1/1/3.html

    Google Scholar 

  • Doran, J.E. (2000). Trajectories to Complexity in Artificial Societies: Rationality, Belief and Emotions. In: T. A. Kohler and G. J. Gummerman (eds.). Dynamics in Human and Primate Societies. Oxford University Press, Oxford. pp. 89–105.

    Google Scholar 

  • Doran, J.E. and Palmer, M. (1995). The EOS Project: Integrating Two Models of Palaeolithic Social Change. In: Gilbert, N. and Conte, R. (eds.). Artificial Societies. UCL Press, London. pp. 103–125.

    Google Scholar 

  • Dorcey, A.H.J. (1997). Collaborating Towards Sustainability Together: The Fraser Basin Management Board and Program. In: Shrubsole, D. and Mitchell, B. (eds.). Practising Sustainable Water Management: Canadian and International Experiences. Canadian Water Resources Association. Available at http://www.interchg.ubc.ca/dorcey/chcwra/fccwra.html

    Google Scholar 

  • Downing, T.E., Moss, S., and Pahl-Wostl, C. (2001). Understanding Climate Policy Using Participatory Agent-Based Social Simulation. In: Moss, S. and Davidsson, P. (eds.). Multi-Agent Based Simulation. Springer, Berlin. LNAI 1979. pp. 198–213.

    Google Scholar 

  • Epstein, J.M. and Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom Up. Washington, D.C. The Brookings Institution Press and MIT Press, Cambridge, MA.

    Google Scholar 

  • FIRMA (2000). See web page at http://www.cpm.mmu.ac.uk/firma/index.html

    Google Scholar 

  • Fishwick, P.A., Sanderson, J.G. and Wolff, W.F. (1998). A Multimodeling Basis for AcrossTrophic-Level Ecosystem Modeling: The Florida Everglades Example. SCS Transactions on Simulation, 15 (2), 76–98.

    Google Scholar 

  • Gilbert, G.N. (2000). Modelling Sociality: The View from Europe. In: T. A. Kohler and G. J. Gummerman (eds.). Dynamics in Human and Primate Societies. Oxford University Press, Oxford. pp. 355–372.

    Google Scholar 

  • Gilbert, G.N. and Troitzsch K.G., (1999). Simulation for the Social Scientist. Open University Press.

    Google Scholar 

  • Hales, D. (2001). Memetic Evolution and Sub-Optimisation. PhD Thesis, Department of Computer Science, University of Essex, Colchester, UK

    Google Scholar 

  • Hall, K. et al (2000) Brunette Basin Watershed Plan. Policy and Planning Department, Greater Vancouver Regional District.

    Google Scholar 

  • Healey, M. (1998). Paradigms, Policies, and Prognostications about the Management of Watershed Ecosystems In: Naiman, R.J. and Bilby, R.E. (eds.) River Ecology and Management. Springer. Pp. 662–682.

    Google Scholar 

  • Healey, M. (1998a). Barriers and Bridges to Sustainability in the Fraser Basin. Invited address to the State of the Basin conference, November 20 and 21`x, 1998. Vancouver.

    Google Scholar 

  • Healey, M. (ed.) (1999). Seeking Sustainability in the Lower Fraser Basin: Issues and Choices. Institute for Resources and the Environment, Westwater Research Centre, University of British Columbia, Vancouver.

    Google Scholar 

  • Jennings, N.R., and Wooldridge, M.J. (eds.) (1998). Agent Technology. Springer, Berlin.

    MATH  Google Scholar 

  • Kluver, J. and Schmidt, J. (1999). Topology, Metric and Dynamics of Social Systems. Journal of Artificial Societies and Social Simulation, vol. 2, no. 3, http://www.soc.surrey.ac.uk/JASSS/2/3/7.html

  • Kohler, T.A., Kresl, J., Van West, C., Carr E., and Wilshusen, R.H. (2000). Be There Then: a Modelling Approach to Settlement Determinants and Spatial Efficiency Among Late Ancestral Pueblo Populations of the Mesa Verde Region, U.S. In: T. A. Kohler and G. J. Gummerman (eds.). Dynamics in Human and Primate Societies. Oxford University Press. pp. 145–178.

    Google Scholar 

  • Lansing, J.S. (2000). Anti-Chaos, Common Property, and the Emergence of Cooperation. In: T. A. Kohler and G. J. Gummerman (eds.). Dynamics in Human and Primate Societies. Oxford University Press. pp. 207–223.

    Google Scholar 

  • Lansing, J.S, Kremer, J.N, and Smuts, B.B. (1998). System-Dependent Selection, Ecological Feedback and the Emergence of Functional Structure in Ecosystems. J Theor. Biol. 192, 377–391.

    Article  Google Scholar 

  • Lawson, B.G. and Park, S. (2000). Asynchronous Time Evolution in an Artificial Society Model. [ONLINE] JASSS, Vol. 3, No. 1.

    Google Scholar 

  • Lee, K. and Fishwick, P.A. (1997). A Methodology for Dynamic Model Abstraction Transactions of the Society for Computer Simulation International, 13 (4), 217–229.

    Article  Google Scholar 

  • Marshall, D. (1998) Watershed management in British Columbia: The Fraser Basin Experience. Environments, Vol. 25, No. 2 /3, 64–79.

    Google Scholar 

  • Moss, S. (1998). Critical Incident Management: An Empirically Derived Computational Model. [ONLINE] JASSS, Vol. 1, No. 4.

    Google Scholar 

  • Moss S. (2001). Messy Systems: The Target for Multi Agent Based Simulation. In: Moss, S. and Davidsson, P. (eds.). Multi-Agent Based Simulation. Springer, Berlin. LNAI 1979. pp. 1–14.

    Google Scholar 

  • Nielsen, J.R. and Vedsmand, T. (1999). User Participation and Institutional Change in Fisheries Management: a Viable Alternative to the Failures of ‘Top-Down’ Driven Control? Ocean Coastal Management, 42, 19–37.

    Article  Google Scholar 

  • Nilsson, N.J. (1971). Problem-Solving Methods in Artificial Intelligence. McGraw-Hill.

    Google Scholar 

  • Norling, E., Sonenberg, L. and Ronnquist, R. (2001). Enhancing Multi-Agent Based Simulation with Human-Like Decision Making Strategies. In: Moss, S. and Davidsson, P. (eds.). MultiAgent Based Simulation. Springer, Berlin. LNAI 1979. pp. 214–228

    Google Scholar 

  • Ostrom, E. (1990). Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press, Cambridge.

    Book  Google Scholar 

  • Ostrom, E. (1995). Constituting Social Capital and Collective Action. In: R. O. Kehane and E. Ostrom (eds.). Local Commons and Global Interdependence: Heterogeneity and Cooperation in Two Domains. Sage Publications, London. pp. 125–160.

    Google Scholar 

  • Prietula, M.J., Carley, K.M., and Gasser, L. (1998). Simulating Organisations. AAAI and MIT Press.

    Google Scholar 

  • Rouchier, J., Bousquet, F., Le Page, C., and Bonnefoy, J.-L. (2001). Multi-Agent Modelling and Renewable Resource Issues: the Relevance of Shared Representations for Interacting Agents. In: Moss, S. and Davidsson, P. (eds.). Multi-Agent Based Simulation. Springer, Berlin. LNAI 1979. pp. 181–197.

    Google Scholar 

  • Russell, S. and Norvig, P. (1995). Artificial Intelligence: a Modern Approach. Prentice-Hall.

    Google Scholar 

  • Teran, O., Edmunds, B., and Wallis, S. (2001). Mapping the Envelope of Social Simulation Trajectories. In: Moss, S. and Davidsson, P. (eds.). Multi-Agent Based Simulation. Springer, Berlin. LNAI 1979. pp. 229–243.

    Google Scholar 

  • Walters, C.J. (1986). Adaptive Management of Renewable Resources. Macmillan, New York. WCED (1987). Our Common Future. Oxford University Press, Oxford.

    Google Scholar 

  • Weiss, G. (ed.) (1999). Multiagent Systems. The MIT Press, Cambridge, MA and London.

    Google Scholar 

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Doran, J.E. (2001). Can Agent-Based Modelling Really be Useful?. In: Saam, N.J., Schmidt, B. (eds) Cooperative Agents. Theory and Decision Library, vol 32. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1177-7_5

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  • DOI: https://doi.org/10.1007/978-94-017-1177-7_5

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-5902-4

  • Online ISBN: 978-94-017-1177-7

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