Multi-Agent Simulations to Explore Rules for Rural Credit Management in a Highland Farming Community of Northern Thailand
Thanks to recent advances in the field of distributed artificial intelligence, agentbased models (ABM) can now be used to run simulations of social phenomena based on their computerized representations, and to apply experimental methods in social sciences (Axelrod 1997, Gilbert and Troitzsch 1999, Jager 2000). In the field of renewable resource management and environmental sciences, several ABM simulation platforms offer the possibility to explore interactions between social and ecological dynamics (Costanza and Ruth 1998, Bousquet et al. 1998, Lansing 2002). In these complex systems, the social and economic dynamics can be viewed as a set of interactions among heterogeneous agents, generating aggregate phenomena that are different from the behaviour of groups of average individuals considered in classical economic thinking (Rouchier and Bousquet 1998). Such a view was adopted in the research presented here.
KeywordsUnify Modelling Language Local Stakeholder Perennial Crop Grace Period Social Simulation
Unable to display preview. Download preview PDF.
- Axelrod R (1997) Advancing the Art of Simulation in the Social Sciences. Lecture notes in Economics and Mathematical systems 456: 21–40.Google Scholar
- Barnaud C, Promburom P, Trébuil G, Bousquet F (in press). An evolving simulation and gaming process to facilitate adaptive watershed management in mountain northern Thailand. Simulation & gamingGoogle Scholar
- Barreteau O, Antona M, d’Aquino P, Aubert S, Boissau S, Bousquet F, Dare W, Etienne M, Le Page C, Mathevet R, Trébuil G, Weber J (2003) Our companion modelling approach. Journal of Artificial Societies and Social Simulation. http://jasss.soc.surrey.ac.uk/6/2/l.html URLGoogle Scholar
- Barreteau O, Bousquet F, Attonaty JM (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. Journal of Artificial Societies and Social Simulation. http://www.soc.surrey.ac.Uk/JASSS/4/2/5.html URLGoogle Scholar
- Bousquet F, Bakam I, Proton H, Le Page C (1998) CORMAS: Common-pool Resources and Multi-Agent Systems. Lecture Notes in Artificial Intelligence, Springer. 1416: 826–838.Google Scholar
- Bousquet F, Trébuil G, Hardy B (2005) Companion Modeling and Multi-Agent Systems for Integrated Natural Resource Management in Asia. IRRI, Los BafiosGoogle Scholar
- Gilbert N, Troitzsch K (1999) Simulation for the social scientist. Buckingham: Open University Press.Google Scholar
- Jager W (2000) Modelling consumer behaviour. Universal Press, The Netherlands.Google Scholar
- Lansing JS (2002) Artificial societies and social simulations. Santa Fe Institute for Complex Studies, Santa Fe.Google Scholar
- Moss S, Downing T, Rouchier J (2000) Demonstrating the Role of Stakeholder Participation: An Agent Based Social Simulation Model of Water Demand Policy and Response. Report 00–76. Centre for Policy Modelling, Manchester Metropolitan University.Google Scholar
- Rouchier J, Bousquet F (1998) Non merchant economy and multi-agent systems: an analysis of structuring exchanges. Lecture Notes in Articicial Intelligence 1534: 111–124.Google Scholar
- Trébuil G, Ekasingh B, Bousquet F, Thong-Ngam C. (2002) Multi-Agent Systems Companion Modeling for Integrated Watershed Management: A Northern Thailand Experience. In: Jianchu X, Mikesell S (eds) Landscapes of diversity. Yunnan Science and Technology Press, China.pp 349–358.Google Scholar