Center for Environmental Systems Research, Kassel

1. SoNAReModelling social and economic influences on the decision making of farmers in the Odra case study region Center for Environmental Systems Research,Kassel

2. Outline • Motivation • The CAVES Odra case study • Spatially explicit biophysical model (developed by WUT) • The „finer grained“ agent-based model • Explicit empirically supported farmer decision rules • Modelling economic and social aspects of decision making • First simulation run(s) • Outlook CAVES Meeting, 28 March 2007

3. Motivation • Asymmetric dependency relation requires collective action • Evidence for different farmer types and their respective sets of decision rules • Explicitly contrast social and economic influences on decision making • Make social pressure explicit in order to model the influence of water partnership initiators (WPIs) and model general opinion dynamics CAVES Meeting, 28 March 2007

4. Biophysical model setup • Land parcels • Located along a channel, uniform size • Upstream-downstream neighbouring relationship between owners • LRS condition, LU type, sluice gate • Channels • Uniform slope • No branching, no interconnections • Number of land parcels per channel • Same for all channels • Weather conditions • Normal, drought, flooding, set yearly • Different weather sequences CAVES Meeting, 28 March 2007

5. Agent-based model setup • Agents • Farmer • WPI, not necessarily a farmer itself • Networks • Dependency “network” reflects spatial neighbourhood relationship • Farmers are embedded in an acquaintance network • WPI is acquainted with, i.e. linked to, all farmers in a star-like fashion CAVES Meeting, 28 March 2007

6. Agent-based model setup – economic Aspects • Farmer agents recall their past economic success • Number of years memorised • Yield threshold • defines “good years” or “bad years” • Economic sensitivity • determines how much “good”/”bad” yields affect the perceived economic success • Economic success • good years memorised increase the perceived economic success, bad years decrease it • WPI uses social network to observe farmers’ economic success CAVES Meeting, 28 March 2007

7. Agent-based model setup – Social aspects • Farmers exert social influence • Use outgoing network edges • Positive influence, endorsement • acquaintances using the same LRS-strategy are supported • Negative influence • acquaintances using the opposite LRS-strategy are pressured into switching the strategy • Farmers perceive their present level of social support • Use incoming network edges • (sum of) social influences received from neighbours in the acquaintance network (including WPI) • WPI may exert additional social influence pro LRS CAVES Meeting, 28 March 2007

8. Agent-based model setup – Network types CAVES Meeting, 28 March 2007

9. Agent Decision Making • Water Partnership Initiator (WPI) • IFnumber of farmers with big losses>= 3THENexert social influence pro LRSELSEdo not exert social influence • Farmers • IF social support + economic success sufficiently lowTHEN switch LRS maintenancestrategy (maintain/¬maintain) • IFWP exists and maintain LRSTHENjoin / stay in WPELSEdo not join / leave WP • (always exert social influence in favour of own strategy; possibly higher influence when member of WP) • Water Partnership (WP) • IF number of farmers maintaining LRS >= 3THEN activate WPELSEdeactivate WP CAVES Meeting, 28 March 2007

10. Model Execution Cycle • May: plant crops • October: harvest crops • December: make decisions for the coming year, i.e. 1. perceive and memorise yield 2. exert social influence 3. perceive social influence and economic success 4. decide (decisions are buffered => synchronised) 5. commit to decisions CAVES Meeting, 28 March 2007

11. Abstract Land Parcel Map Agents maintaining LRS Flow direction of channel Agents neglecting LRS CAVES Meeting, 28 March 2007

12. Scenario A • Baseline scenario, 1 channel, 10 farmers • 2 normal years followed by 1 year of flooding • Farmers do not rate their economic success • No social influence • Thus: no opinion dynamics CAVES Meeting, 28 March 2007

13. Scenario A 12 24 36 48 60 72 84 96 108 CAVES Meeting, 28 March 2007

14. Scenario B • 1 channel, 10 farmers • 2 normal years followed by 1 year of flooding • Farmers rate their economic success • yieldThreshold = 9.0 • No social influence CAVES Meeting, 28 March 2007

15. Scenario B CAVES Meeting, 28 March 2007

16. Scenario B CAVES Meeting, 28 March 2007

17. Scenario B ... ... ... 84 192 96 120 132 144 180 360 CAVES Meeting, 28 March 2007

18. Scenario C • 10 channels, each 10 farmers • 2 normal years followed by 1 year of flooding • Farmers rate their economic success • yieldThreshold = 9.0 • Scale-Free topology for acquaintance network • WPI (linked to all farmers in a star-like fashion) • Farmers and WPI exert social influence CAVES Meeting, 28 March 2007

19. Scenario C CAVES Meeting, 28 March 2007

20. Scenario C CAVES Meeting, 28 March 2007

21. Scenario C CAVES Meeting, 28 March 2007

22. Scenario C CAVES Meeting, 28 March 2007

23. Scenario C 36 48 CAVES Meeting, 28 March 2007

24. Scenario C 168 180 CAVES Meeting, 28 March 2007

25. Scenario C 288 300 CAVES Meeting, 28 March 2007

26. Scenario C 336 480 CAVES Meeting, 28 March 2007

27. Outlook • calibrate the model • include allowances and compensation payments • include sluice gate operation / fish ponds • include additional land use types • distribute farmer types heterogenously • apply different network topologies • perform sensitivity analyses CAVES Meeting, 28 March 2007