Simulating Heterogeneous Farmer Behaviors
given by x then for a given amount of time we decay the belief reinforcement according to
The Cognitive Plane
A critical piece of the above formulation is the determination of each agent ’ s current cognitive state . The agent ’ s cognitive state is assumed to be a function of the agent ’ s need for cognitive elaboration , the agent ’ s certainty in its beliefs , and the public or private nature of the behavior in question . We assume that this comes from its most salient evaluation of an outcome associated with the behavior . As such , we query the AB ( t ) component that has the highest absolute value for . If this evaluation is close to zero , we assume the agent has a low need for cognitive elaboration ; i . e ., the agent is close to indifference regarding the outcome . We then check the strength of the belief to determine if the agent is in the repetitive state ( ) or the imitative state (< 0.5 ). If , on the other hand , the highest absolute evaluation is greater than , then we assume this agent has a high need for cognitive elaboration and thus the angle needs to be calculated as shown earlier in Figure 4 .
Agent Algorithm
The final algorithm that ties all the parts of the State system together can be summarized as a set of processes and decisions that are evaluated at each time step the agent is activated . Specifically , at each time step , an agent is activated the agent performs the following actions :
1 . Decay belief reinforcement according to Equation ( 10 ).
2 . Process new messages to update AB and PBC according to Equations ( 6 ) and ( 9 ).
3 . Process referrals from social network to update SN according to Equation ( 7 ). 4 . Update BI ( t ) . 5 . Check if action has been previously taken .
6 . If no action has been taken , check if BI ( t ) exceeds the threshold for action . If so , attempt action .
7 . Otherwise , check if action previously taken was successful . If so , update action status .
8 . If previous action was not successful , update PBC and decide whether to try again or not
9 . Schedule next time step to repeat these steps and stop .
The algorithm is shown graphically in Figure 5 .
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