so cial n etworks of a gents’ re clamation of land center for environmental systems research...

Social Networks of Agents’ Reclamation of Land

Center for Environmental Systems ResearchKassel

CAVES Project Meeting, 25 September 2007

2CAVES Project Meeting, 25 September 2007

Outline

• Abstraction of case study evidence• The SoNARe agent-based model• Initial simulations• Two lines of study• Simulation results• Outlook


Abstraction of case study evidence The Simple Hydro-Agricultural Model


Abstraction of case study evidence – cont’d

• Social network integration– actors are embedded in social networks– propagate their opinion concerning LRS maintenance– perceive that of others

• Consider three actor types – Small Farmers and Commercial Farmers

• keep a balanced attention to– economic success indicated by their attained crop yields– social endorsement resulting from their opinion regarding LRS

maintenance• there are approximately twice as many small farmers as commercial farmers• commercial farmers own approximately ten times as much land as small

farmers– Water Partnership initiator (WPI)

• leader personalities with high reputation and a high degree of social network integration

• trigger social activation towards LRS maintenance• Actor decision rules

– three sets of decision rules– for WPI actors the rules reflect their reasoning about when to trigger collective

action– for farmer actors the rules reflect their decision making about partaking in LRS

maintenance under social and economic considerations.


The SoNARe agent-based model

• Agent types– Farmer (small, commercial)– WPI, not necessarily a farmer itself

• Environment– each farmer “manages” exactly one land parcel -> binary

decision about LRS maintenance– perceives feedback about the attained crop yield

• Networks– farmers are embedded in an acquaintance network– WPI is acquainted with, i.e. linked to, all farmers in a star-

like fashion– social links can span a number of hydrologically

independent channels


Economic aspects

• Farmer agents’ perception of economic success– appraise current yield as either “good” or ”bad” with

respect to a fixed yield perception threshold (symmetrical: “good year” and “bad year” cancel each other out exactly)

– store either a positive value (“good”) or a negative value (“bad”) in yield memory

– memory capacity is fixed for one agent but it may vary across individual agents

– sum of all the stored values in yield memory constitutes the agent’s current perception of economic success

– economic success is normalised to [0, 1]

• WPI uses social network to observe farmers’ economic success


Social aspects

• Farmers exert social influence– use outgoing network edges– positive influence

• 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)– social success is normalised to [0, 1]

• WPI may exert additional social influence pro LRS– higher intensity of exerted social influence


Farmer agent decision making

• Economically driven: “Win-Stay, Lose-Change”– IF notMaintaining AND sufficientEconomicSuccess THEN doNotMaintain– IF maintaining AND sufficientEconomicSuccess THEN doMaintain– IF notMaintaining AND veryLowEconomicSuccess THEN doMaintain– IF maintaining AND veryLowEconomicSuccess THEN doNotMaintain

• Socially driven: General opinion dynamics, social network– IF askedByInitiatorToJoinWP THEN doMaintain– IF notMaintaining AND manyOtherFarmersMaintainLRS THEN doMaintain– IF maintaining AND manyOtherFarmersNotMaintainLRS THEN

doNotMaintain

• Balancing between economic and social considerations– introduce parameter decisionBias from [0, 1]– calculate combined decision criterion

currentEconomicSuccess * decisionBias + currentSocialSupport*(1 – decisionBias)

– evidence indicates a decision bias of about 0.6


WPI agent decision making

• IF significantLossesOfAtLeastThreeFarmers THEN initiateWP

• IF WPExists THEN askFarmersToJoinWP

• IF noSignificantLossesOfFarmers THEN stopAskingFarmersToJoinWP


Initial simulations

• Passive scenario (baseline)– decision bias of farmer agents set to 0– social influence level of the WPI agent is set to 0– no opinion dynamics

• Selfish scenario– decision bias parameter is set to 1– farmer agents base their decisions only on their perceived

economic success. Thus, the WPI does not have any influence on the decision making.

– due to the hydrological dependencies farmers’ decisions may well affect other farmers’ economic success

• Socially active scenario– decision bias is set to 0.5 (neutral)– farmer agents use both their past economic success and the social

influence of other agents as a basis for their decision making.– WPI agent has a social influence level of 3 (i.e. it is three times as

influential as a farmer agent).


The selfish scenario


The socially active scenario


Two lines of study

• Motivations– under which circumstances do transitions from one scenario to

another occur? (Parameter changes along “storylines”)– which factors trigger such transitions? – what meta-phenomena can be observed? Volatility,

Resilience?– where do we start out from? What is the evidence?

• Two parallel but interrelated levels of study1. examine the present model

– in terms of meta-phenomena– performing sensitivity analyses

2. enhance the model to further approximate the case study situation– introducing commercial farmers– using available economic data – etc.


Channel Views


Simulation Results

No commercial farmers 50 commercial farmers


Simulation Results



Outlook

• Approximate the situation in the case study region in more detail

• Include case study data about economic aspects of the LRS– maintenance costs / allowances for LRS maintenance– compensation payments in case of yield loss

• Add new farmer types– commercial farmers / fish pond owners (effects of sluice-

gate operation)– characterised by land ownership, economic orientation,

and possibly a higher social influence

• Allow farmers to buy / sell / rent land to others– extend the decision-making process to an expansion

decision– study the implications, if social ties change


Outlook – cont‘d

• Further examine the model• Focus on the identification of meta-phenomena like

volatility, phase transitions, and possibly resilience– Start out from the three extreme scenarios and deal with

transitions between the scenarios– Formulate scenario shifts as parameter changes along

different “storylines”– Under which (social, economic, environmental)

circumstances does the passive scenario shift to the socially active scenario?

– Which circumstances inhibit or break-up a positive social lock-in?

– Finally: Does the model qualitatively reproduce historically encountered developments in the case study region?

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