Trade -offs, efficiency gains and technical change - Modeling water management and land use within a multiple-agent framework

Summary Decisions on natural resource use are usually taken by individual resource users such as farm-households and landowners within an existing legal and policy framework; their consequences on the natural resource base, however, can sometimes be felt at much larger scales due to the complex interdependencies among these resources. Linking biophysical and socioeconomic processes, identifying their consequences at different scales or levels of analysis, and formulating suitable policy options based on these analyses remains a difficult research task. This article suggests to combine aggregate and disaggregate mathematical programming models that relate land and water resources within a multiple scale - multiple agent framework. Such an approach promises to generate valuable information for policy development as it captures more fully temporal and spatial scales of human-nature interactions; provides a way to address interrelated water and land use issues; and allows the inclusion of policy responses from farmers’ and other resource users’ points of view.

[1]  Arthur C. Graesser,et al.  Is it an Agent, or Just a Program?: A Taxonomy for Autonomous Agents , 1996, ATAL.

[2]  E. Rogers,et al.  Diffusion of Innovations , 1964 .

[3]  A. Balmann Farm-Based Modelling of Regional Structural Change: A Cellular Automata Approach , 1997 .

[4]  M. Janssen,et al.  Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review , 2003 .

[5]  John H. Holland,et al.  Emergence. , 1997, Philosophica.

[6]  M. Rosegrant,et al.  Modeling water resources management at the basin level: review and future directions , 2018 .

[7]  Wilhelm Brandes,et al.  On the limitations of armchair economics: Some views of an armchair agricultural economist , 1989 .

[8]  Ximing Cai,et al.  Integrated economichydrologic water modeling at the basin scale: the Maipo river basin , 2000 .

[9]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[10]  Tony Curzon Price,et al.  Emergence: From Chaos to Order by John H. Holland , 1998, J. Artif. Soc. Soc. Simul..

[11]  Thomas Berger,et al.  Agent-based spatial models applied to agriculture: A simulation tool , 2001 .

[12]  Jan L. Harrington C++ and the Object-Oriented Paradigm: An IS Perspective , 1995 .

[13]  Robert Kok,et al.  Incorporating Complexity in Ecosystem Modelling , 2000 .

[14]  C. Ringler OPTIMAL WATER ALLOCATION IN THE MEKONG RIVER BASIN , 2001 .

[15]  Ruerd Ruben,et al.  Technical coefficients for bio-economic farm household models: a meta-modelling approach with applications for Southern Mali , 2001 .

[16]  Joshua M. Epstein,et al.  Growing Artificial Societies: Social Science from the Bottom Up , 1996 .

[17]  Joshua M. Epstein,et al.  Growing artificial societies , 1996 .

[18]  François Bousquet,et al.  Agent-Based modelling, game theory and natural resource management issues , 2001 .

[19]  Peter B. R. Hazell,et al.  Mathematical Programming for Economic Analysis in Agriculture. , 1987 .

[20]  Elena G. Irwin,et al.  Economics and the Land Use-Environment Link , 1999 .

[21]  Richard H. Day,et al.  A Dynamic Model of Regional Agricultural Development , 1975 .