How Smart Should Farms Be Modeled? Behavioral Foundation of Bidding Strategies in Agent-Based Land Market Models
AbstractLand markets play a crucial role in agricultural structural change. Because the dynamics of structural change and land markets, respectively, mainly depend on the interactions between individual farms, agent-based modeling (ABM) has been established as a tool for understanding and explaining structural change and land market dynamics. This is particularly so because of ABM's ability to capture heterogeneity, non-convexity and dynamics. Unfortunately, the behavioral foundation of economic actors in ABM, i.e., of the farms, is often specified as ad hoc or simply based on "expert knowledge". In this contribution, the highly-detailed ABM AgriPoliS - which uses a myopic normative behavioral foundation - is coupled with a genetic algorithm (GA) to detect market equilibria on a land market. This is done in the dynamic context of the model and its heterogeneous, non-convex production functions. This approach enables the creation of a benchmark with a sound economic foundation for evaluating alternative behavioral patterns. As results illustrate, forming a rational strategy requires bidders that are able to anticipate size effects and their growth potential in a competitive situation. Moreover, the contribution shows that reaching the market equilibrium would imply "aggressive" bidding strategies.
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Bibliographic InfoPaper provided by International Association of Agricultural Economists in its series 2006 Annual Meeting, August 12-18, 2006, Queensland, Australia with number 25446.
Date of creation: 2006
Date of revision:
agent-based modeling; genetic algorithms; land markets; behavioral economics; Land Economics/Use; Q12; C6;
Find related papers by JEL classification:
- Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
- C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
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