1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. 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

9. 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?

10. Department of Agricultural Economics QUESTIONS? Thank You!