SoNARe
Modelling social and economic influences on the decision making of farmers
in the Odra case study region
Center for Environmental Systems Research,Kassel
2CAVES Meeting, 28 March 2007
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
3CAVES Meeting, 28 March 2007
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
4CAVES Meeting, 28 March 2007
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
6CAVES Meeting, 28 March 2007
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
7CAVES Meeting, 28 March 2007
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
8CAVES Meeting, 28 March 2007
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
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Agent-based model setup – Network types
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Agent Decision Making
• Farmers– IF
social support + economic success sufficiently lowTHEN switch LRS maintenance strategy (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 Initiator (WPI)– IF
number of farmers with big losses >= 3THENexert social influence pro LRSELSEdo not exert social influence
• Water Partnership (WP)– IF
number of farmers maintaining LRS >= 3THEN activate WPELSEdeactivate WP
11CAVES Meeting, 28 March 2007
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 influence3. perceive social influence and economic success4. decide (decisions are buffered => synchronised)5. commit to decisions
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Abstract Land Parcel Map
Flow direction of channel
Agents maintaining LRS
Agents neglecting LRS
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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
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Scenario A
12 24 36 48 60 72 84 96 108
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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
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Scenario B
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Scenario B
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Scenario B
84 96 120 132 144 180 192 360... ......
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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
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Scenario C
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Scenario C
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Scenario C
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Scenario C
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Scenario C
36 48
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Scenario C
168 180
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Scenario C
288 300
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Scenario C
336 480
28CAVES Meeting, 28 March 2007
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