Agent-based modeling in ecological economics

Annals of the New York Academy of Sciences

Volumn 1185, Issue , 2010, Pages 39-53

  Heckbert, Scott ^a   Baynes, Tim ^a   Reeson, Andrew ^a  

^a CSIRO   (Australia)

Author keywords

Decision making; Emergent properties; Multi agent systems; Validation

Indexed keywords

ADAPTATION; AGENT BASED MODELING; BEHAVIOR CHANGE; COMPUTER MODEL; CONSUMER ATTITUDE; DECISION MAKING; ECOLOGY; ECONOMICS; ENVIRONMENTAL MANAGEMENT; HUMAN; LAND USE; MODEL; RESOURCE MANAGEMENT; REVIEW; TECHNOLOGY; URBAN AREA; VALIDATION PROCESS;

EID: 76849104256     PISSN: 00778923     EISSN: 17496632     Source Type: Book Series    
DOI: 10.1111/j.1749-6632.2009.05286.x     Document Type: Review

References (110)

1. Grimm, V. 1999. Ten years of individual-based modelling in ecology: what have we learned and what could we learn in the future? Ecol. Mod. 115: 129-148.
2. DeAngelis, D.L. &W.M.Mooij. 2005. Individual-based modeling of ecological and evolutionary processes. Ann. Rev. Ecol, Evol. and Syst. 36: 147-168.
3. Tesfatsion,L.&K. Judd, Eds. 2006.Handbook ofComputational Economics, Vol.2: Agent-Based Computational Economics. North Holland/Elsevier. Amsterdam.
4. Spash, C. 2009. Social Ecological Economics. Socio-Economics and the Environment in Discussion. CSIRO Working Paper Series 2009-2103
5. Vicsek, T. 2002. The bigger picture: complexity. Nature 418: 131.
6. LeBaron, B. & L. Tesfatsion. 2008. Modeling macroeconomies as open-ended dynamic systems of interacting agents. Amer. Econ. Rev. 98: 246-250.
7. Janssen, M.A. & W. Jager. 2001. Fashions, habits and changing p: simulation of psychological factors affecting market dynamics. J. Econ. Psyc. 22: 745-772.
8. Jager, W., M.A. Janssen, H.J.M. De Vries, et al. 2000. Behaviour in commons dilemmas: Homo economicus and Homo psychologicus in an ecological-economic model. Ecol. Econ. 35: 357-379.
9. Costanza, R., B. Fisher, S. Ali, et al. 2007. Quality of life: an approach integrating opportunities, human needs, and subjective well-being. Ecol. Econ. 61: 267-276.
10. Parker, D.C., S.M. Manson, M.A. Janssen, et al. 2003. Multi-agent systems for the simulation of land-use and land-cover change: a review. Annals of the Association of American Geographers 93: 314-337.
11. Polhill, J.G., D.C. Parker & N. Gotts. 2008. Effects of land markets on competition between innovators and imitators in land use: results from FEARLUS-ELMM. In Social Simulation Technologies: Advances and New Discoveries, Information Science Reference. Bruce Edmonds, Cesareo Hernandez, Klaus G. Troitzch, IGI Global, USA. 81-97.
12. Schlüter, M. & C. Pahl-Wostl. 2007. Mechanisms of resilience in common-pool resource management systems: an agent-basedmodel of water use in a river basin. Ecol. Soc. 12: 4.
13. Leigh, Tesfatsion & Kenneth, L. Judd (Ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 30, pages 1465-1509 Elsevier.
14. Batty, M. 2005. Cities and Complexity: Understanding Cities with Cellular Automata, Agent based Models, and Fractals. MIT Press. Cambridge, USA.
15. Batty, M. 2008. The size, scale, and shape of cities. Science 319: 769-771.
16. Brown, D.G. & D.T. Robinson. 2006. Effects of heterogeneity in residential p on an agent-based model of urban sprawl. Ecol. & Soc. 11: 37.
17. Holland, J.H. 1992. Complex adaptive systems. Daedalus 121: 17-30.
18. Janssen, M.A. & E. Ostrom. 2006. Empirically based, agent-based models. Ecol. & Soc. 11: 37.
19. Matthews, R.B., N.G. Gilbert, A. Roach, et al. 2007. Agent-based land-usemodels: a review of applications. Land. Ecol. 22: 1447-1459.
20. Epstein, J.M. & R. Axtell. 1996. Growing Artificial Societies: Social Science from the Bottom Up. MIT Press. Cambridge, MA.
21. Axelrod, R. 1997. Advancing the art of simulation in the social sciences. In Simulating Social Phenomena. R. Conte, R. Hegselmann & P. Terna, Eds. Springer. Washington D.C. 208.
22. Grimm, V. & S.F. Railsback. 2005. Individual-Based Modeling and Ecology. Princeton University Press. Princeton, NJ. 480.
23. Duffy, J. & M. Unver. 2008. Internet auctions with artificial adaptive agents: a study on market design. J. Econ. Behav. Org. 67: 394-417.
24. Tesfatsion, L. 2007. Agents come to bits: towards a constructive comprehensive taxonomy of economic entities. J. Econ. Behav. & Org. 63: 333-346.
25. Simon, H. 1997. Models of Bounded Rationality. Vol 3: Empirically Grounded Economic Reason. MIT Press. Cambridge, MA. 479.
26. Kahneman, D. & A. Tversky. 1984. Choices, values, and frames. Amer. Psyc. 39: 341-350.
27. Gintis, H. 2000. Beyond Homo economicus: evidence from experimental economics. Ecol. Econ. 35: 311-322.
28. Robinson, D.T., D.G. Brown, D.C. Parker, et al. 2007. Comparison of empirical methods for building agentbased models in land use science. J. Land Use Sci. 21: 31-55.
29. Bousquet, F. & C. Le Page. 2004. Multi-agent simulations and ecosystem management: a review. Ecol. Mod. 176: 313-332.
30. Heckbert, S.,W.Adamowicz, P. Boxall&D.Hanneman. 2009. Cumulative effects and emergent properties of multiple-use natural resources. In Proceedings: MABS 2008, Budapest, Hungary, May 10-15.
31. Robinson, D.T. & D.G. Brown. in press. Evaluating the effects of land-use development policies on ex-urban forest cover: an integrated agent-based GIS approach. International Journal of Geographical Information Science, volume 23, issue 9 September 2009, pages 1211-1232.
32. Gotts, N., J.G. Polhill & A.N.R. Law. 2003. Aspiration levels in a land use simulation. Cyber. Syst. 34: 663-683.
33. Ebenhöh, E. & C. Pahl-Wostl. 2006. Agent-based modeling with boundedly rational agents. Handbook of Research on Nature Inspired Computing for Economy and Management. In J.P. Rennard, Ed. Idea Group. Philadelphia.
34. Brown, D.G., D.T. Robinson, L. An, et al. 2008. Exurbia from the bottom-up: confronting empirical challenges to characterizing a complex system. Geoforum 39: 805-818.
35. Deadman, P., D.T. Robinson, E.Moran & E. Brondizio. 2004. Colonist household decisionmaking and landuse change in the Amazon Rainforest: an agent-based simulation. Env. Plan. B 31: 693-709.
36. Berger, T. 2001. Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Ag. Econ. 25: 245-260.
37. Little, R. & D. McDonald. 2007. Simulations of agents in social networks harvesting a resource. Ecol.Mod. 20: 379-386.
38. Boschetti, F. & M. Brede. 2008. An information-based adaptive strategy for resource exploitation in competitive scenarios. Tech. Fore. Soc. Chan. 76: 525-532.
39. Brede, M., F. Boschetti&D.McDonald. 2008. Strategies for resource exploitation. Ecol. Compl. 5: 22-29.
40. Ledyard, J.O. 1995. Public goods: a survey of experimental research. In The Handbook of Experimental Economics. J.H. Kagel & A.E. Roth, Eds. Princeton University Press. New Jersey. 111-194.
41. Berg, J., J. Dickhaut & K. McCabe. 1995. Trust, reciprocity and social-history. Games Econ. Behav. 10: 122-142.
42. Fehr, E. & K.M. Schmidt. 1999. A theory of fairness, competition, and cooperation. Quart. J. Econ. 114: 817-868.
43. Fehr, E. & U. Fischbacher. 2002. Why social p matter-The impact of non-selfish motives on competition, cooperation and incentives. Econ. J. 112: C1-C33.
44. Gilbert, N. & K. Troitzsch. 2005. Simulation for the Social Scientist. Open University Press. Buckingham. 309.
45. Costanza, R., R. Leemans, R.M.J. Boumans, & E. Gaddis. 2006. Integrated global models. In Sustainability or Collapse? An Integrated History and Future of People on Earth. R. Costanza, L.J. Graumlich & W. Steffen, Eds. MIT Press. Cambridge, MA. 417-446.
46. Van den Belt, M. 2004. Mediated Modeling: A System Dynamics Approach to Environmental Consensus Building. Island Press.Washington.
47. Epstein, J. 2006. Generative Social Science. Princeton University Press. Princeton, NJ. 352.
48. Grimm, V., E. Revilla, U. Berger, et al. 2005. Patternoriented modeling of agent-based complex systems: lessons from ecology. Science 310: 987-991.
49. Levin, S. A. 1992. The problem of pattern and scale in ecology. Ecology 73: 1943-1967.
50. Arthur,W.B., S.N. Durlauf & D. Lane. 1997. The Economy as an Evolving Complex SystemII. Santa Fe Institute Studies in the Science of Complexity. Addison-Wesley. Reading, MA.
51. Tesfatsion, L. 2001. Introduction to the special issue on agent-based computational economics. J. Eco. Dyn. Con. 25: 281-293.
52. Berry, B.J.L., L.D. Kiel & E. Elliott. 2002. Adaptive agents, intelligence, and emergent human organization: capturing complexity through agent-based modeling. PNAS 99: 7187-7188.
53. Janssen, M.A., Ed. 2002. Complexity and Ecosystem Management: The Theory and Practice of Multi-Agent Systems. Edward Elgar Publishers. Cheltenham, UK.
54. Janssen, M.A. 2005. Agent-based modeling. In Modeling in Ecological Economics. Safonov, Ed. Edward Elgar Publishing. Cheltenham, UK. 155-172.
55. Grimm, V., U. Berger, F. Bastiansen, et al. 2006. A standard protocol for describing individual-based and agent-based models. Ecol. Modell. 198: 115-126.
56. Parker,D.C.,D.Brown, J.G. Polhill, et al. 2008. Illustrating a new 'conceptual design pattern' for agent-based models and land use viafive case studies: theMr.Potatohead framework. In Agent-basedModelleling in Natural Resource Management. C.H. Iglesias, Ed. Universidad de Valladolid. Valladolid, Spain. 29-62.
57. Rouchier, J., C. Cioffi-Revilla, J.G. Polhill & K. Takadama. 2008. Progress in model-to-model analysis. JASSS 11: 8.
58. Railsback, S.F., S.L. Lytinen&S.K. Jackson. 2006.Agentbased simulation platforms: review and development recommendations. Simulation 82: 609-623.
59. Crooks, A., C. Castle & M. Batty. 2008. Key challenges in agent-based modelling for geo-spatial simulation. Comp. Env. Urb. Syst. 32: 417-430.
60. Manson, S.M. & T. Evans. 2007. Agent-based modeling of deforestation in southern Yucatan, Mexico, and reforestation in the Midwest United States. PNAS 104: 20678-20683.
61. Heckbert, S. in review. Water quality and coastal land use change: Agent-based modelling of emissions trading for landscapes in transition.
62. Brown, D.G., S. Page, R. Riolo, et al. 2005. Path dependence and the validation of agent-based spatial models of land use. Int. J. Geo. Info. Sci. 19: 153-174.
63. Reynolds, C.W. 1987. Flocks, herds and schools: a distributed behavioural network. Comp.Graph. 21: 25-34.
64. Huston, M., D. Deangelis & W. Post. 1988. New computer models unify ecological theory. Bioscience 38: 682-691.
65. Polhill, J.G., D.C. Parker, D. Brown & V. Grimm. 2008. Using the ODD protocol for describing three agentbased social simulation models of land-use change. JASSS 11: 3.
66. Duffy, J. & M. Unver. 2006. Asset price bubbles and crashes with near-zero-intelligence traders. Econ. Theroy 27: 537-563.
67. Filatova, T., D.C. Parker & A. Van Der Veen. 2009. Agent-based urban land markets: agent's pricing behavior, land prices and urban land use change. JASSS 12: 3.
68. Berger, T., R. Birner, J. Diaz, N. McCarthy & H. Wittmer. 2007. Capturing the complexity of water uses and water users within amulti-agent framework. Water Resources Management 20:129-148.
69. Bithell, M.&J. Brasington. 2009. Coupling agent-based models of subsistence farming with individual-based forest models and dynamic models of water distribution. Env. Mod. Soft. 24: 173-190.
70. Evans, T.P.,W.J. Sun&H. Kelley. 2006. Spatially explicit experiments for the exploration of land-use decisionmaking dynamics. Int. J. Geog. Info. Sci. 20: 1013-1037.
71. Happe, K., K. Kellermann & A. Balmann. 2006. Agentbased analysis of agricultural policies: an illustration of the agricultural policy simulator AgriPoliS, its adaptation and behavior. Ecol & Soc. 11: 49.
72. Happe, K., A. Balmann, K. Kellermann & C. Sahrbacher. 2008. Does structure matter? The impact of switching the agricultural policy regime on farm structures. J. Econ. Behav. Org. 67: 431-444.
73. An, L., M. Linderman, J. Qi, et al. 2005. Exploring complexity in a human-environment system: an agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers 95: 54-79.
74. Benenson, I. & P.M. Torrens. 2004. Geosimulation: Automata-Based Modeling of Urban Phenomena. John Wiley & Sons. London.
75. Baynes, T. & S. Heckbert. 2009.Micro-scale simulation of the macro urban form: opportunities for exploring urban change and adaptation. In Proceedings: MABS 2008, Budapest, Hungary, May 10-15.
76. Irwin, E. & N. Bockstael. 2007. The evolution of urban sprawl: evidence of spatial heterogeneity and increasing land fragmentation. PNAS 104: 20672-20677.
77. Li, Y. & B. Muller. 2007. Residential location and the biophysical environment: exurban development agents in a heterogeneous landscape. Env. & Plan. B 34: 279-295.
78. Guzy, M.R., C.L. Smith, J.P. Bolte, et al. 2008. Policy research using agent-based modeling to assess future impacts of urban expansion into farmlands and forests. Ecol. Soc. 13: 37.
79. Zellner, M., S.E. Page, W. Rand, et al. 2009. The emergence of zoning policy games in exurban jurisdictions: informing collective action theory. Land Use Pol. 26: 356-367.
80. Daniell, K.A., A.B. Kingsborough, D.J. Malovka, et al. 2006. Sustainability assessment of housing developments: a new methodology. In Complex Science for a Complex World: Exploring Human Ecosystems with Agents. D.F. Batten & P. Perez, Eds. ANU Press. Canberra, Australia 113-146.
81. Baynes, T.M. 2009. Complexity in urban development and management. J. Ind. Ecol. 13: 214-227.
82. Marks, R. 2008.Validating simulation models: a general framework and four applied examples. Comp. Econ. 30: 265-290.
83. Windrum, P., G. Fagiolo & A. Moneta. 2007. Empirical Validation of Agent-BasedModels: Alternatives and Prospects. JASSS 10: 8.
84. Fagiolo, G., C. Birchenhall & P.Windrum. 2007. Introduction to the special issue on empirical validation in agent-based models. Comp. Econ. 30: 189-194.
85. Moss, S. 2008. Alternative approaches to the empirical validation of agent-based models. JASSS 11: 15.
86. Couclelis, H. 2001. Why I no longer work with agents: a challenge for ABMs of human-environment interaction. In Agent-Based Models of Land-Use and Land-Cover Change, LUCC Report Series no. 6: 3-5. D.C. Parker, T. Berger & S.M.Manson, Eds. Indiana University.
87. Dawid, H., & Fagiolo, G. 2008. Agent-based models for economic policy design: introduction to the special issue. J. Econ. Behav. Org. 67: 351-354.
88. Abel, N., H. Ross & P. Walker. 1998. Mental models in rangeland research, communication and management. Rang. J. 20: 77-91.
89. Wilensky, U. &W. Rand. 2007. Making models match: Replicating an agent-based model. JASSS 10: 2.
90. Polhill, J.G., L.R. Izquierdo & N.M. Gotts. 2005. The ghost in the model and other effects of floating point arithmetic. JASSS 8: 5.
91. Polhill, J.G., L.R. Izquierdo & N.M. Gotts. 2006. What every agent-basedmodeller should knowabout floating point arithmetic. Env. Mod. Soft. 21: 283-309.
92. Rouchier, J. 2003. Re-implementation of a multi-agent model aimed at sustaining experimental economic research: the case of simulations with emerging speculation. JASSS 6: 7.
93. Gintis, H. 2007. Review of: Handbook of Computational Economics, Volume II: Agent-Based Computational Economics. JASSS 10: 1.
94. Valbuena, D., P.H. Verburg & A.K. Bregt. 2008. A method to define a typology for agent-based analysis in regional land-use research. Ag. Ecosys. Env. 128: 27-36.
95. Huigen, M., K.P. Overmars & W.T. de Groot. 2006. Multiactor modeling of settling decisions and behavior in the San Mariano watershed, the Philippines: a first application with the MameLuke framework. Ecol Soc. 11: 33.
96. Berger, T. & P. Schreinemachers. 2006. Creating agents and landscapes for multiagent systems from random samples. Ecol & Soc. 11: 19.
97. Barreteau, O. 2003. Our companion modelling approach. JASSS 6: 1.
98. Castella, J.C., S.P. Kam, D.D. Quang, et al. 2007. Combining top-down and, bottom-up modelling approaches of land use/cover change to support public policies: application to sustainable management of natural resources in northern Vietnam. Land Use Pol. 24: 531-545.
99. Becu, N., A. Neef, P. Schreinemachers & C. Sangkapitux. 2008. Participatory computer simulation to support collective decision-making: potential and limits of stakeholder involvement. Land Use Pol. 25: 498-509.
100. Guyot, P. & S. Honiden. 2006. Agent-based participatory simulations: merging multi-agent systems and role-playing games. JASSS 9: 8.
101. Janssen, M., R. Goldstone, F. Menczer & E. Ostrom. 2008. Effect of rule choice in dynamic interactive spatial commons. Int. J. Comm. 2: 288-312.
102. Heckbert, S. 2009. Experimental economics and agentbasedmodels. InProceedings:MODSIM09, 18thWorld IMACS Congress. Cairns, Australia. 13-17 July 2009.
103. Gode, D.K. & S. Sunder. 1993. Allocative efficiency of markets with zero-intelligence traders-market as a partial substitute for individual rationality. J. Pol. Eco. 101: 119-137.
104. Miller,E.J., J.D.Hunt, J.E.Abraham&P.A. Salvini. 2004. Microsimulating urban systems. Comp. Env. Urb. Syst. 28: 9-44.
105. Geertman, S., J. Stillwell & H. Charles, Eds. 2009. Planning support systems best practice and new methods. GeoJournal Library 95: 490.
106. Noailly, J. 2008. Coevolution of economic and ecological systems. J. Evol. Econ. 18: 1-29.
107. Conover,D., 2007. Fisheries:Nets versus nature. Nature 450: 179-180.
108. Darimonta, C., S. Carlsonc, M. Kinnisond, et al. 2009. Human predators outpace other agents of trait change in the wild. PNAS 106: 952-954.
109. Wu, J. & E.G. Irwin. 2008. Optimal land development with endogenous environmental amenities.Amer. J. Ag. Econ. 90: 232-248.
110. Brown, D., R. Riolo, D.T. Robinson, et al. 2005. Spatial process and data models: toward integration of agentbased models and GIS. J. Geog. Syst. 7: 25-47.