1. Agent-based Modelling in Agricultural Economics Perspectives and Challenges Alfons Balmann JRSS 2012, Toulouse, December 14, 2012 View Video: http://ut-capitole.ubicast.tv/videos/agent_based-modelling/http://ut-capitole.ubicast.tv/videos/agent_based-modelling/

2. 2 Structure Motivation: Challenges of understanding structural change topical conceptual Agent-based modeling Idea Case study I: AgriPoliS Case study II: SpAbCoM Conclusions

3. 3 Challenges of Structural Change General trends New markets Food, fuel, fibres … Rapid growth of emerging countries Globalization Verticalization of supply chains New technologies: GMO, … Increasing knowledge intensity of agricultural production Policy changes Can structural change keep pace? How do society and policy respond?

4. 4 Number of farms with livestock production in Germany 2000=100% Source: Statistisches Bundesamt, own calculations Challenges of Structural Change - 41 % - 58 %

5. 5 Challenges of Structural Change Size distribution of hog farms (Germany 2010) Source: Statistisches Bundesamt, own calculations

6. 6 Herausforderungen des Agrarstrukturwandels Verteilung Größenklassen Schweine haltender Betriebe (2010) Quelle: Statistisches Bundesamt, eigene Berechnungen

7. 7 Structural change in poultry production (North-west Germany) Source:Quelle: www.noz.de

8. 8 Challenges of Structural Change

9. 9 Stakeholder analysis dairy production Source: Ostermeyer 2011 Farmers Experts Public stakeholders Altmark (Saxony Anhalt) Abhängigkeit der Milchproduktion im Ostallgäu von dauerhafter staatlicher Unterstützung gering sehr hoch Bedeutung der Milchproduktion im Ostallgäu sehr hoch gering Ostallgäu (Bavaria) dependence of dairy farms on permanent subsidies regional importance of dairy production low high regional importance of dairy production low high

10. 10 Agricultural structures as complex adaptive systems manifold dimensions manifold levels: individuals, enterprises, institutions, sectors, regions,… subjective perceptions, bounded rationality dynamics, non-linearities, discontinouities Evolutionary process with limited foresights! Analysis requires specific (also heterodox) approaches! Agent-Based Modelling (ABM) interaction time space Challenges of Structural Change

11. 11 (computer-)models, consisting of artificial entities (agents), which communicate and interact in an environment (Ferber, 1999) interactions Agent-based Models are …

12. 12 Agents are … sub-systems which perceive parts of their environment and respond autonomously ??? (Ferber, 1999)

13. 13 Agent-based Modelling Bottom-up approach allows flexible assumptions on individual level (e.g. heterogenous agents, bounded rationality) allows for flexible frameworks (e.g. non-convex functions, imperfect markets) Self-organization spontanous order endogenous change Particular perspectives for the analysis of emergence and change of structures organization and coordination problems Discovery of "islands in the chaos"

14. 14 Agent-based Modelling Several predecessors in economics and social sciences Recursive Programming and Production Response (Day 1963) Micromotives and Macrobehavior (Schelling 1978) Nonlinear dynamics, chaos, erratic behavior (Benhabib & Day 1981) The Evolution of Cooperation (Axelrod 1984) Positive Feedbacks in the Economy (Arthur 1990) Computational Economics driven by curiosity on complex social and economic processes driven by increasing power and availability of computers

15. 15 Agent-based Modelling Clock rate 3,8 GHz 60 MHz 0,7 MHz x 1000 000 x 5000

16. 16 Agent-based Modelling ABM examples from social sciences and economics Santa Fe Artificial Stock Markets (Palmer et al. 1993) Genetic Algorithm and the Cobweb Model (Arifovic 1994) Growing Artificial Societies (Sugarscape) (Epstein and Axtell 1996) The Complexity of Cooperation (Axelrod 1997) … Understanding complexity of markets and society Study complex games and economics Relaxing micro-economic assumptions (convexity, rationality)

17. 17 Agent-based Modelling In agricultural economics used since some 20 years CORMAS rather a modeling platform with focus on common pool resource management developed at CIRAD by Francios Bousquet et al. since early 1990s participatory approach (compagnion modelling) linking ABM with role-playing games broad international community of users AgriPoliS developed in Göttingen, Berlin and at IAMO since 1991 somehow in the tradition of recursive programming models (Day 1963) focus on analysis of structural change several clones and extensions exist, e.g. MAS-MP (Berger 2001)

18. 18 AgriPoliS Policy Environment Markets (labor, capital, land, quotas) Actions Maximization of profit/ household income Perceptions Resources Interactions Leave? Invest? Grow?

19. 19 Agricultural Policy Simulator agent-based (bottom-up) spatial (land market) dynamic (farm growth, exits, investments) Farm level adjustments are considered! Endogenous structural change! AgriPoliS

20. 20 Effects of capping direct payments on the Altmark region ~ 270.000 ha UAA ~ 980 farms > 10 ha Ø farm size ~ 280 ha > 45 % of land in farms > 1000 ha 1,4 WU / 100 ha AgriPoliS Case study: EU CAP after 2013

21. 21 Scenario REF CAPPING Description No modulation after 2013 Devision of direct payments (352 /ha) in base payment (70%) and greening component (30%) Like REF, but: Capping base payment after deduction of wage costs (20,000 /WU): 150.000-200.000 : 20% 200.000-250.000 : 40% 250.000-300.000 : 70% >300.000 : 100% AgriPoliS Case study: EU CAP after 2013

22. 22 Development of direct payments, profits and land rents AgriPoliS Case study: EU CAP after 2013

23. 23 AgriPoliS Case study: EU CAP after 2013 Distribution of land in 2025 according to farm size classes

24. 24 AgriPoliS Case study: EU CAP after 2013 Effects of capping on the Altmark region Only a few large farms affected Adjustments allow to avoid capping Adjustments cause negative long-term effects on efficiency and profitability Some large farms even benefit Hardly any benefits for small and medium-sized farms (10-200ha) Regional effects Just marginal losses of direct payments! Losses in efficiency and profitability higher!

25. 25 AgriPoliS Conclusions Contribution to understanding of structural change and policies Powerful opportunities and broad scope for scenario analyses on structural change on distributional issues (sizes, incomes, rents) on productivity and efficiency Opportunity to use it also for participatory stakeholder interaction! Use is very demanding! programming (AgriPoliS 3.0) adaptation and calibration (regions, scenarios, maintainance, updates) validation analysis of results (not just pushing a button – apply theory and statistics!) communication of assumptions and results

26. 26 SpAbCom agricultural products (raw milk) are spatially distributed transport is costly many producers face few but also spatially distributed processors Location of milk processors in Germany

27. 27 SpAbCom Location of milk processors in Germany milk market: uniform delivered pricing (udp) (Alvarez et al. 2000) farmers receive the same price irrespective of location to dairy price discrimination What determines different spatial price strategies in agricultural markets?

28. 28 SpAbCom Spatial price theory (monopsony) local price p(r) : mill price m less a portion of the transport costs tr = (m, ) is the spatial pricing strategy of a firm distance r price p(r) odp fob udp R fob,udp R odp zpl… zero profit line r... distance to processors location fob... free-on-board pricing udp... uniform delivered pricing odp... optimal dicriminatory pricing R... market radius of the processor t... transport rate 1 ( =1) ( =0) ( =1/2) zpl = local prices differ by transport costs = farmers receive same price irrespective of distance = local prices differ by less than transport costs

29. 29 SpAbCom Spatial competition (duopsony) price p(r) odp fob udp 1 zpl distance r AB zpl A zpl B odp fob udp price p(r) standard assumptions ( Espinosa 1992, Zhang/Sexton 2001 ): distance AB=1 linear supply at each location q(r)=p(r) price of the finished good is 1 linear transport rate t What are the optimal strategies in terms of m and under spatial competition? 1

30. 30 SpAbCom Spatial competition normalized transport costs (t) 00.51.01.52.0 Perfect competition distance r A B t=0

31. 31 Local Monopsony t>2 SpAbCom Spatial competition normalized transport costs (t) 00.51.01.52.0 Perfect competition distance r A B 2.0 Local Monopsony

32. 32 0<t1 SpAbCom Spatial competition normalized transport costs (t) 00.51.01.52.0 Spatial competitionLocal Monopson Perfect competition distance r A B 2.0 Local Monopsony Spatial Competition

33. 33 1<t2 SpAbCom Spatial competition normalized transport costs (t) 00.51.01.52.0 Spatial competitionLocal Monopson Perfect competition distance r A B 2.0 Local Monopsony Spatial Competition

34. 34 SpAbCom Prior studies Perfect com- petition Spatial competition Local Monopson t 00.40.61.14/35/32 ZSfob/fob udp/fob fob/udp udp/udp udp or fob any combination of fob and udp ZS = Zhang and Sexton (2001) J IND ECON Spatial competition Perfect competition Local Monopsony

35. 35 ZS = Zhang and Sexton (2001) J IND ECON SpAbCom Prior studies Perfect com- petition Spatial competition Local Monopson t 00.40.61.14/35/32 ZSfob/fob udp/fob fob/udp udp/udp udp or fob any combination of fob and udp Prior studies only consider fob ( α=1) and udp ( α =0) as pricing options but nothing in-between!!! What comes out for 0 < α<1?

36. 36 SpAbCom Methodology Agent-based modeling farmers processors Genetic algorithm (GA) one GA per agent selection of most profitable strategies 1 zpl B zpl A distance r AB F0F0 Agents of type farmer: max p(r) F1F1 F3F3 F9F9 F 10 F6F6 F5F5 F7F7 F4F4 F2F2 m m Generation 1 best in population Agents of type processor: max PROFIT(Γ A, Γ B ) 1 p(r)

37. 37 Generation 2 SpAbCom Methodology Agent-based modeling farmers processors Genetic algorithm (GA) one GA per agent selection of most profitable strategies creation of new strategies (recombination, mutation) 1 zpl B zpl A distance r AB m m new strategy 1 p(r)

38. 38 Generation n SpAbCom Methodology Agent-based modeling farmers processors Genetic algorithm (GA) one GA per agent selection of most profitable strategies creation of new strategies (recombination, mutation) 1 zpl B zpl A distance r AB m m optimum 1 p(r)

39. 39 fierce competition (low transport costs): high price discrimination less competition (high transport costs): price (discrimination) increases (diminishes) SpAbCom Results normalized transport costs (t) m 00.51.01.52.0 0.2 0.4 0.6 0.8 1.0 Spatial competitionLocal Monopson Perfect competition 2.0 Local Monopsony udp partial freight absorption (FA) m,

40. 40 SpAbCom Compared to prior studies Perfect com- petition Spatial competition Local Monopson t 00.40.61.14/35/32 ZSfob/fob udp/fob fob/udp udp/udp udp or fob any combination (of fob and udp) GBS udp/udppartial FA*odp/odp ZS = Zhang and Sexton (2001) J IND ECON GBS = Graubner, Balmann, and Sexton (2010) * FA = freight absorption (0< α <1/2)

41. 41 Perfect com- petition Spatial competition Local Monopson t 00.40.61.14/35/32 SpAbCom Real world observations Perfect com- petition Spatial competition Local Monopson t 00.40.61.14/35/32 ZSfob/fob udp/fob fob/udp udp/udp udp or fob any combination (of fob and udp) GBS udp/udppartial FA*odp/odp GBS = Graubner, Balmann, and Sexton (2010) * FA = freight absorption (0< α <1/2) e.g., on markets of: raw milk, almonds, canning peaches and pears, rice, sugar beets (in Germany A and B Quota), processing tomatoes, wine grapes, corn for ethanol e.g., on markets of sugar beets (in Germany C-Quota), milk market? And some markets were commonly fob-pricing is assumed?

42. 42 SpAbCom Summary of findings Spatial pricing in agricultural markets pricing depends on the competitiveness of the market (distance, measured by normalized transport costs) prevalence of spatial price discrimination if production and processing is spatially distributed ud pricing under fierce competition partial freight absorption if competition is less intense results are consistent with observations on many agricultural markets

43. 43 Agent-based Modelling SpAbCom Contribution to agricultural economics Micro-economic approach for problems without "closed-form solutions" analyses on abstract level spatial market power, spatial allocation,…

44. 44 Agent-based Modelling SpAbCom Contribution to agricultural economics Micro-economic approach for problems without "closed-form solutions" analyses on abstract level spatial market power, location strategies,… further applications and extensions land markets (large farms, land funds) repeated games for analysis of collusion real options, auctions Challenges very high computational needs, particularly if many dimensions considered (space, time, interactions) design of experiments demanding: parametrization validation and communication of model and results

45. 45 Summary Agent-based models provide broad scope for interesting analyses Micro-economic approaches for problems without "closed-form solutions" behavioral foundation of agents based on computational intelligence (e.g. GA) interesting perspectives for combination with human experiments to study games Scenario analyses for understanding systems and for decision support policies, prices, technologies, institutions, … behavioral foundation of agents usually based on optimization rules calibration, scenario definition and validation can be linked to participatory analyses e.g. Compagnion Modelling linking ABM and role-playing games (Bousquet et al. 1999) Use is demanding! Convincing addressees of results is demanding!

46. 46 Market outcome SpAbCom Methodology L L L L L L L L A B Candidate solutionsGA operators Selection Mutation Cross-over Generation t+1 Selection Mutation Cross-over Generation t+1 L L Equilibrium after many generations

47. 47 Oligopsony

48. 48 SpAbCom Location and pricing equilibria Competition in unbounded space with price elastic supply

49. 49 SpAbCom Location and pricing equilibria Competition in unbounded space with price perfectly inelastic supply