2. Reversing the unsustainability spiral Guido van Hofwegen, Gertjan Becx, Joep van den Broek and Niek Koning A modeling study of the co-evolution of population, techniques, natural resources and institutions of an agrarian subsistence economy

3. Outline 1.Recapitulation of poverty spiral 2.Agent Based Modeling. 3.Overview SUBSA model 4.Issues related to (agent based) modelling 5.Conclusions

4. Africa struggles with economic- demographic-environmental vicious circles in its rural areas

5. We hypothesize that the dynamics in Africa are comparable to those in pre- industrial Europe:

6. population growth prices moderately high

7. population growth prices moderately high investment in sustainable intensification

8. population growth prices moderately high investment in sustainable intensification

9. population growth prices skyrocketing investment in sustainable intensification impoverishment

10. population growth prices skyrocketing investment in sustainable intensification soil degradation impoverishment

11. population growth prices skyrocketing investment in sustainable intensification soil degradation impoverishment

12. population growth prices skyrocketing investment in sustainable intensification soil degradation impoverishment distrust, conflict, paranoia

13. population growth prices skyrocketing investment in sustainable intensification soil degradation impoverishment distrust, conflict, paranoia

14. population collapse prices skyrocketing investment in sustainable intensification soil degradation impoverishment distrust, conflict, paranoia

15. population collapse prices collapse soil degradation impoverishment distrust, conflict, paranoia

16. population collapse prices collapse soil degradation impoverishment distrust, conflict, paranoia fall-back on extensive techniques

17. population recovers low prices extensive techniques

18. population growth prices recover extensive techniques

19. population growth prices moderately high extensive techniques investment in sustainable intensification

20. A vital element in these dynamics was an endogenous relation between population and prices. A continuous availability of cheap food the last decades locked Africa in a unsustainability trap.

21. population recovers low prices extensive techniques Cheap food imports

22. population boom prices remain low extensive techniques Cheap food imports

23. Getting out may not be as simple as rising price levels as the situation is embedded in Africa's institutions at all levels.

24. To elaborate and test the previously explained vision for the African context and explore its ramifications. Objective

25. To reach the objective we need a model that can handle the co- evolution of population, natural resources, technology and social strategizing at local regional and global scales.

26. Agent Based Model (ABM) “In agent based models agents are described as unique individual and autonomous entities which have a goal and base their behavior on adaptive decisions” (Grimm 2003)

27. ABM continued Agents described as unique and autonomous individuals Agents: –Are all different –Have a life history –Interact with other individuals –Make decisions which are adaptive and depend on the individuals and environments state –society emerges from their behavior

28. A simple example

29. Why Agent based modeling in this context? Allows for interdisciplinary (combining actor approaches with system approaches) Allows for local decisions Bottom up dynamics Inclusion of multiple levels

30. SUBSA model Highly abstract Decision making agents are farmer groups. Long time period (hundreds of years) IT IS NOT: A spatially explicit simulation of reality

31. Model levels Resource base Decision making level Macro institutional /environmental level Soil and crop Farmer groups In this case: world market price setting mechanisms.

32. agents Farmer groups Goal seeking Which are Represent two year utility maximizing Free time income Yield of own plot Result from trade Result from robberies Utility year 1 Utility year 2 Discount rate / Time preference Driving force behind agent behaviour

33. Agricultural Production

34. Agricultural production inputs capital labour land quality population free time Room for consumption and investment Weighting of current consumption against future harvests Technology consumption Agro ecological production landscape Time preference / discount rate

35. Resource Dynamics

36. Resource dynamics Land quality Short term inputs Is a broad concept Long term inputs jeopardize enhance Mix of inputs depends on time horizon Strong direct yield effect Yield effect indirect.

37. Technological Change

38. Technological change Memory of production technologies Most suitable technology for production Depending on: Labour availability Capital availability Land quality All related to time preference! Each group owns Technologies are forgotten When not used longer than a certain period Technologies can be imitated from neighbours new technologies Experimenting if expected future profit is high enough so depends on time preference again! Removes “ladder” from poverty trap

39. Trade & conflict

40. Each group Can Live in autarky trade World market Other groups Differences in labour and food availability Food for labour World market price Depending on Cooperation (trade) & conflict Can result in robbery Implemented as: No pay, or take double the salary. Increases future transaction costs Transactio n costs Conflict history distanceinfrastructure

41. Validation How to improve the validity of an abstract agent based model that runs at long time scales?

42. Earth testimonies

43. Earth testimonies II

44. Desk / Field studies Choice making behavior Interaction between trust agricultural development and migration.

45. Conclusion © Wageningen UR Models are always wrong Multi agents models can help in acquiring insights on multiple scale multiple driver phenomena These kinds of models contribute to integrated studies with a long term view Extra attention has to be paid to creative methods of validation