Presentation on theme: "Adam Arsenault Department of Agricultural Economics University of Saskatchewan UNIVERSITY OF SASKATCHEWAN Saskatoon, Saskatchewan, Canada. www.usask.ca."— Presentation transcript:
Adam Arsenault Department of Agricultural Economics University of Saskatchewan UNIVERSITY OF SASKATCHEWAN Saskatoon, Saskatchewan, Canada. www.usask.ca A Multi-Agent Systems Approach to Farmland Auction Markets
Department of Agricultural Economics The Issue Multiple farmland auction markets and interactions are poorly understood Current methodologies cannot account for: Interaction between farmers (agents) and their environment Heterogeneity of land and farmer characteristics Spatial and temporal aspects of land purchases and sales Little is known about the impacts of bidder learning on farmland prices
Department of Agricultural Economics Project Objectives Develop modern auction theory applicable to Canadian prairies farmland market Specifically, this entails : Incorporating heterogeneity into farmer characteristics and geographical landscape Incorporating learning mechanisms and feedback into bidding strategies for agricultural land Better understanding the effects of farmer interactions and adaptive learning in a repeated game of bidding in auction markets
Department of Agricultural Economics Difficulties with the “Classical Model” - Agents Classical models assume rational expectations and optimization in bidding strategies Optimization strategies tell us nothing about the negotiation and bidding process (Zeng et al 1998) Results in only strategic moves: No interaction or learning (Selten 2001, Selten and Neugebauer 2006) Classical models cannot find solutions to “analytically complex” problems (Tesfatsion 2002) Simple models of interactions and feedback (learning) become complex very quickly Nonlinearity
Department of Agricultural Economics Difficulties with the “Classical Model” - Land Space and the heterogeneity of land Law of one price may not always apply to land Land Price = F ( Location, Quality, Time, β ) Time is crucial in land acquisitions Land is a lumpy investment Desired land not always available Outcome of auction (win/lose) is critical in success of individual farms Bidding strategies and learning are paramount
Department of Agricultural Economics The Model Multi-Agent Systems (MAS)/ Agent-Based Computational Economics (ACE) “The computational study of economies modeled as evolving systems of autonomous interacting agents.” (Tesfatsion 2002) Agents have Goals, Actions, and Domain Knowledge (Stone and Veloso 2000) Dynamic model of heterogeneous agent interactions Learning in repeated games of bidding using behavioral adaptations Dynamic model of farmland markets with spatial and temporal factors Land is lumpy, heterogeneous, and non-transportable
Department of Agricultural Economics Why Agents Matter Heterogeneity, interaction, and feedback (Stone and Veloso 2000) Agents interact with one-another and surrounding environment – not simply market clearing prices Goals Actions Domain Knowledge Goals Actions Domain Knowledge Goals Actions Domain Knowledge Environment Feedback and Interaction
Department of Agricultural Economics System Flow Adaptiv e Learnin g Exogenous Rain Yield = F(Rain,Soil) Count Years Profit/Loss Continue Farming? Exit Sell Land: Skip To Sell Farm Based on Expectations Decide How Much to Seed Private Sale With Prob. T Private Sale Land? SellBuy Neither Seed Harvest Update Expectation About Land Reservation Price Bid Win/Lose Update Probabilities Feedback Learning START
Department of Agricultural Economics How Efficient Are Current Farmland Markets? How well does game theoretic predicted price map actual price? Is farmland available to those who need it when they need it? Do better informed farmers actually do better? Better bidders What salient features of farmer behavior/learning are driving trends in farmland prices?
Department of Agricultural Economics QUESTIONS? Thank You!