Landscape Ecology

, Volume 22, Issue 10, pp 1447–1459 | Cite as

Agent-based land-use models: a review of applications

  • Robin B. Matthews
  • Nigel G. Gilbert
  • Alan Roach
  • J. Gary Polhill
  • Nick M. Gotts
Review

Abstract

Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear—it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools.

Keywords

Agent-based modelling Land-use Complexity Policy analysis Interactions Decision-making 

References

  1. An L, Linderman M, Qi J, Shortridge A, Liu J (2005) Exploring complexity in a human-environment system: an agent-based spatial model for multidisciplinary and multiscale integration. Ann Assoc Am Geogr 95(1):54–79CrossRefGoogle Scholar
  2. Axelrod R (1997) The complexity of cooperation: agent-based models of competition and collaboration. Princeton studies in complexity. Princeton University Press, Princeton, NJ, 232 ppGoogle Scholar
  3. Balmann A (1997) Farm-based modelling of a regional structural change: a cellular automata approach. Eur Rev Agric Econ 24:85–108Google Scholar
  4. Balmann A, Happe K, Kellermann K, Kleingarn A (2002) Adjustment costs of agri-environment policy switchings: an agent-based analysis of the German region Hohenlohe. In: Janssen M (ed) Complexity and ecosystem management: the theory and practice of multi-agent systems. Edward Elgar, Cheltenham, UK, pp 127–157Google Scholar
  5. Barreteau O, Bousquet F (2000) SHADOC: a multi-agent model to tackle viability of irrigated systems. Ann Oper Res 94:139–162CrossRefGoogle Scholar
  6. Barreteau O, Bousquet F, Attonaty J-M (2001) Role-playing games for opening the black box of multi-agent systems: method and lessons of its application to Senegal River Valley irrigated systems. J Artif Soc Soc Simul 4(2):http://www.soc.surrey.ac.uk/JASSS/4/2/5.html
  7. Becu N, Perez P, Walker A, Barreteau O, Page CL (2003) Agent based simulation of a small catchment water management in northern Thailand—description of the CATCHSCAPE model. Ecol Model 170(2–3):319–331CrossRefGoogle Scholar
  8. Berger T (2001) Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. Agric Econ 25:245–260CrossRefGoogle Scholar
  9. Berger T, Ringler C (2002) Trade-offs, efficiency gains, and technical change: modelling water management and land use within a multiple-agent framework. Q J Int Agric 41(1/2):119–144Google Scholar
  10. Boero R, Squazzoni F (2005) Does empirical embeddedness matter? Methodological issues on agent-based models for analytical social science. J Artif Soc Soc Simul 8(4):6 [online at: http://www.jasss.soc.surrey.ac.uk/8/4/6.html]Google Scholar
  11. Boissau S, Anh HL, Castella JC (2004) The SAMBA role play game in northern Vietnam: an innovative approach to participatory natural resource management. Mt Res Dev 24(2):101–105CrossRefGoogle Scholar
  12. Bousquet F, Barreteau O, d’Aquino P, Etienne M, Boissau S, Aubert S, Le Page C, Babin D, Castella J-C (2002) Multi-agent systems and role games: collective learning processes for ecosystem management. In: Janssen M (ed) Complexity and ecosystem management: the theory and practice of multi-agent systems. Edward Elgar, Cheltenham, UK, pp 248–285Google Scholar
  13. Bousquet F, Le Page C (2004) Multi-agent simulations and ecosystem management: a review. Ecol Model 176:313–332CrossRefGoogle Scholar
  14. Brown DG, Page SE, Riolo R, Rand W (2004) Agent-based and analytical modelling to evaluate the effectiveness of greenbelts. Environ Model Softw 19:1097–1109CrossRefGoogle Scholar
  15. Carpenter SR, Cottingham KL (2002) Resilience and the restoration of lakes. In: Gunderson LH, Pritchard L Jr (eds) Resilience and the behavior of large scale ecosystems. Island Press, Washington, DC, pp 51–70Google Scholar
  16. Castella JC, Trung TN, Boissau S (2005) Participatory simulation of land-use changes in the northern mountains of Vietnam: the combined use of an agent-based model, a role-playing game, and a geographic information system. Ecol Soc 10(1):27 [online at http://www.ecologyandsociety.org/vol10/iss1/art27/]Google Scholar
  17. d’Aquino P, Le Page C, Bousquet F, Bah A (2003) Using self-designed role-playing games and a multi-agent system to empower a local decision-making process for land use management: the SelfCormas experiment in Senegal. J Artif Soc Soc Simul 6(3):5 [online at http://www.jasss.soc.surrey.ac.uk/6/3/5.html]Google Scholar
  18. Deadman P, Robinson D, Moran E, Brondizio E (2004) Colonist household decisionmaking and land-use change in the Amazon Rainforest: an agent-based simulation. Environ Plan B: Plan Des 31(5):693–709CrossRefGoogle Scholar
  19. Dean JS, Gumerman GJ, Epstein JM, Axtell RL, Swedlund AC, Parket MT, McCarroll S (2000) Understanding Anasazi cultural change through agent-based modelling. In: Kohler TA, Gumerman GJ (eds) Dynamics in human and primate studies: agent-based modeling of social and spatial processes. Oxford University Press, New York, Oxford, pp 179–206Google Scholar
  20. Deffuant G, Huet S, Bousset JP, Henriot J, Amon G, Weisbuch G (2002) Agent-based simulation of organic farming conversion in Allier département. In: Janssen M (ed) Complexity and ecosystem management: the theory and practice of multi-agent systems. Edward Elgar, Cheltenham, UK, pp 158–187Google Scholar
  21. Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. Brookings Institute, Washington, DCGoogle Scholar
  22. Etienne M, Le Page C, Cohen M (2003) A step-by-step approach to building land management scenarios based on multiple viewpoints on multi-agent system simulations. J Artif Soc Soc Simul 6(2):2Google Scholar
  23. Evans TP, Kelley H (2004) Multi-scale analysis of a household level agent-based model of landcover change. J Environ Manage 72:57–72PubMedCrossRefGoogle Scholar
  24. Ferber J (1999) Multi-agent systems: an introduction to distributed artificial intelligence. Addison-Wesley Longman, Harlow, UK, 509 ppGoogle Scholar
  25. Gotts NM, Polhill JG, Law ANR (2003a) Agent-based simulation in the study of social dilemmas. Artif Intell Rev 19:3–92CrossRefGoogle Scholar
  26. Gotts NM, Polhill JG, Law ANR (2003b) Aspiration levels in a land use simulation. Cybern Syst 34(8):663–683CrossRefGoogle Scholar
  27. Grimm V, Wyszomirski T, Aikman D, Uchmanski J (1999) Individual-based modelling and ecological theory: synthesis of a workshop. Ecol Model 115:275–282CrossRefGoogle Scholar
  28. Happe K (2004) Agricultural policies and farm structures: agent-based modelling and application to EU policy reform (Studies on the Agricultural and Food Sector in Central and Eastern Europe, vol 30). Institut für Agrarentwicklung in Mittel- und Osteuropa (IAMO), Halle, Germany, 291 ppGoogle Scholar
  29. Hare M, Deadman P (2004) Further towards a taxonomy of agent-based simulation models in environmental management. Math Comput Simul 64:25–40CrossRefGoogle Scholar
  30. Hoffmann M, Kelley H, Evans T (2002) Simulating land-cover change in South-Central Indiana: an agent-based model of deforestation and afforestation. In: Janssen M (ed) Complexity and ecosystem management: the theory and practice of multi-agent systems. Edward Elgar, Cheltenham, UK, pp 218–247Google Scholar
  31. Huigen MGA (2004) First principles of the MameLuke multi-actor modelling framework for land use change, illustrated with a Philippine case study. J Environ Manage 72:5–21PubMedCrossRefGoogle Scholar
  32. Huston M, DeAngelis D, Post W (1988) New computer models unify ecological theory. Bioscience 38:682–691CrossRefGoogle Scholar
  33. Izquierdo L, Gotts NM, Polhill JG (2004) Case based reasoning, social dilemmas, and a new equilibrium concept. J Artif Soc Soc Simul 7(3):1 [online at: http://www.jasss.soc.surrey.ac.uk/7/3/1.html]Google Scholar
  34. Izquierdo LR, Gotts NM, Polhill JG (2003) FEARLUS-W: an agent-based model of river basin land use and water management. In: Dijst M, Schot P, de Jong K (eds) Framing land use dynamics: integrating knowledge on spatial dynamics in socio-economic and environmental systems for spatial planning in western urbanized countries. Faculty of Geographical Sciences, Utrecht University, Utrecht, The Netherlands, pp 163–165Google Scholar
  35. Janssen MA (2001) An exploratory integrated model to assess management of lake eutrophication. Ecol Model 140:111–124CrossRefGoogle Scholar
  36. Janssen MA, Walker BH, Langridge J, Abel N (2000) An adaptive agent model for analysing co-evolution of management and policies in a complex rangeland system. Ecol Model 131:249–268CrossRefGoogle Scholar
  37. Kohler TA, Gumerman GJ (2000) Dynamics in human and primate societies: agent-based modelling of social and spatial processes. Sante Fe Institute Studies in the Sciences of Complexity. Oxford University Press, Oxford, 398 ppGoogle Scholar
  38. Kohler TA, Kresl J, West CV, Carr E, Wilshusen RH (2000) Be there then: a modelling approach to settlement determinants and spatial efficiency among late ancestral populations of the Mesa Verde region, US Southwest. In: Kohler TA, Gumerman GJ (eds) Dynamics in human and primate studies: agent-based modeling of social and spatial processes. Oxford University Press, New York, Oxford, pp 145–178Google Scholar
  39. Lansing JS, Kremer JN (1993) Emergent properties of Balinese water temple networks: coadaptation on a rugged fitness landscape. Am Anthropol 95:97–114CrossRefGoogle Scholar
  40. Lempert R (2002) Agent-based modeling as organizational and public policy simulators. Proc Natl Acad Sci USA 99:7195–7196PubMedCrossRefGoogle Scholar
  41. Ligtenberg A, Wachowicz M, Bregt AK, Beulens A, Kettenis DL (2004) A design and application of a multi-agent system for simulation of multi-actor spatial planning. J Environ Manage 72:43–55PubMedCrossRefGoogle Scholar
  42. Loibl W, Toetzer T (2003) Modeling growth and densification processes in suburban regions—simulation of landscape transition with spatial agents. Environ Model Softw 18(6):553–563CrossRefGoogle Scholar
  43. Lynam T (2002) Scientific measurements and villagers’ knowledge: an integrative multi-agent model from the semi-arid areas of Zimbabwe. In: Janssen M (ed) Complexity and ecosystem management: the theory and practice of multi-agent systems. Edward Elgar, Cheltenham, UK, pp 188–217Google Scholar
  44. Manson SM (2004) The SYPR integrative assessment model: complexity in development. In: Turner BL, Geoghegan J, Foster DR (eds) Integrated land-change science and tropical deforestation in the Southern Yucatán: final frontiers. Oxford University Press, OxfordGoogle Scholar
  45. Manson SM (2005a) Agent-based modeling and genetic programming for modeling land change in the Southern Yucatán Peninsular Region of Mexico. Agric Ecosyst Environ 111(1–4):47–62CrossRefGoogle Scholar
  46. Manson SM (2005b) Land use in the southern Yucatan peninsular region of Mexico: scenarios of population and institutional change. Comput Environ Urban Syst 30(3):230–253CrossRefGoogle Scholar
  47. Matthews R, Selman P (2006) Landscape as a focus for integrating human and environmental processes. J Agric Econ 57(2):199–212CrossRefGoogle Scholar
  48. Matthews RB (2006) The People and Landscape Model (PALM): towards full integration of human decision-making and biophysical simulation models. Ecol Model 194(4):329–343CrossRefGoogle Scholar
  49. Matthews RB, Pilbeam CJ (2005) Modelling the long-term sustainability of maize/millet cropping systems in the mid-hills of Nepal. Agric Ecosyst Environ 111(1–4):119–139CrossRefGoogle Scholar
  50. Matthews RB, Stephens W (eds) (2002) Crop-soil simulation models: applications in developing countries. CAB International, Wallingford, UK, 277 ppGoogle Scholar
  51. McCown RL (2002a) Changing systems for supporting farmers’ decisions: problems, paradigms, and prospects. Agric Syst 74:179–220CrossRefGoogle Scholar
  52. McCown RL (2002b) Locating agricultural decision support systems in the troubled past and socio-technical complexity of ‘models for management’. Agric Syst 74:11–25CrossRefGoogle Scholar
  53. Milner-Gulland EJ, Kerven C, Behnke R, Wright IA, Smailov A (2006) A multi-agent system model of pastoralist behaviour in Kazakhstan. Ecol Complexity 3:23–36CrossRefGoogle Scholar
  54. Möhring M, Troitzsch KG (2001) Lake Anderson revisited by agents. J Artif Soc Soc Simul 4(3):1 [online at: http://www.jasss.soc.surrey.ac.uk/4/3/1.html]Google Scholar
  55. Moss S, Pahl-Wostl C, Downing T (2001) Agent-based integrated assessment modelling: the example of climate change. Integr Assess 2:17–30CrossRefGoogle Scholar
  56. Otter HS, van der Veen A, de Vriend HJ (2001) ABLOoM: location behaviour, spatial patterns, and agent-based modelling. J Artif Soc Soc Simul 4(4):2 (Online at: http://www.jasss.soc.surrey.ac.uk/4/4/2.html)Google Scholar
  57. Parker DC, Manson SM, Janssen MA, Hoffmann MJ, Deadman P (2002) Multi-agent systems for the simulation of land-use and land-cover change: a review. Ann Assoc Am Geogr 93(2):316–340Google Scholar
  58. Parker DC, Meretsky V (2004) Measuring pattern outcomes in an agent-based model of edge-effect externalities using spatial metrics. Agric Ecosyst Environ 101:233–250CrossRefGoogle Scholar
  59. Polhill JG, Gotts NM, Law ANR (2001) Imitative versus non-imitative strategies in a land use simulation. Cybern Syst 32(1–2):285–307CrossRefGoogle Scholar
  60. Prabhu R, Haggith M, Mudavanhu H, Muetzelfeldt R, Standa-Gunda W (2003) ZimFlores: a model to advise co-management of the Mafungautsi Forest in Zimbabwe. Small-scale For Econ Manage Policy 2(2):185–210Google Scholar
  61. Rajan KS, Shibasaki R (2001) A GIS based integrated land use/cover change model to study agricultural and urban land use changes. In: Proceedings of the 22nd Asian conference on remote sensing. Centre for Remote Imaging, Sensing, and Processing, National University of Singapore, Singapore, 5–9 November 2001Google Scholar
  62. Ramanath AM, Gilbert N (2004) The design of participatory agent-based social simulations. J Artif Soc Soc Simul 7(4):1 [online] URL: http://www.jasss.soc.surrey.ac.uk/7/4/1.html Google Scholar
  63. Sanders L, Pumain D, Mathian H, Guérin-Pace F, Bura S (1997) SIMPOP—a multi-agent system for the study of urbanism. Environ Plan B: Plan Des 24:287–305CrossRefGoogle Scholar
  64. Scheffer M, Carpenter SR (2003) Catastrophic regime shifts in ecosystems: linking theory to observation. Trends Ecol Evol 18(12):648–656CrossRefGoogle Scholar
  65. Sengupta R, Lant C, Kraft S, Beaulieu J, Peterson W, Loftus T (2005) Modeling enrollment in the conservation reserve program by using agents within spatial decision support systems: an example from southern Illinois. Environ Plan B Plan Des 32(6):821–834CrossRefGoogle Scholar
  66. Sinclair TR, Seligman NaG (1996) Crop modelling: from infancy to maturity. Agron J 88(5):698–703CrossRefGoogle Scholar
  67. Stephens W, Middleton T (2002) Why has the uptake of decision-support systems been so poor? In: Matthews RB, Stephens W (eds) Crop-soil simulation models: applications in developing countries. CAB International, Wallingford, UK, pp 129–147Google Scholar
  68. Torrens P (2002) SprawlSim: modelling sprawling urban growth using automata-based models. In: Parker DC, Berger T, Manson SM (eds) Agent-based models of land-use and land-cover change: report and review of an international workshop, LUCC Focus 1 Office, Anthropological Center for Training and Research on Global Environmental Change, Indiana University, Indiana, pp 72–78, 4–7 October 2001Google Scholar
  69. Van den Belt M (2004) Mediated modelling: a systems dynamic approach to environmental consensus building. Island Press, Washington, DC, 339 ppGoogle Scholar
  70. Vanclay JK, Haggith M, Colfer C (2003) Participation and model building: lessons learned from the Bukittinggi workshop. Small-scale For Econ Manage Policy 2(2):135–154Google Scholar
  71. Verburg PH (2006) Simulating feedbacks in land use and land cover change models. Landsc Ecol 21:1171–1183CrossRefGoogle Scholar
  72. Weisbuch G (2000) Environment and institutions: a complex dynamical systems approach. Ecol Econ 34:381–391CrossRefGoogle Scholar
  73. Weisbuch G, Boudjema G (1999) Dynamical aspects in the adoption of agri-environmental measures. Adv Complex Syst 2:11–36CrossRefGoogle Scholar
  74. Wu J, Hobbs R (2002) Key issues and research priorities in landscape ecology: an idiosyncratic synthesis. Landsc Ecol 17(4):355–365CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • Robin B. Matthews
    • 1
  • Nigel G. Gilbert
    • 2
  • Alan Roach
    • 2
  • J. Gary Polhill
    • 1
  • Nick M. Gotts
    • 1
  1. 1.Integrated Land Use Systems GroupMacaulay InstituteCraigiebuckler, AberdeenUK
  2. 2.Department of SociologyUniversity of SurreyGuildford, SurreyUK

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