Thresholds of Behavioral Flexibility in Turbulent Environments for Individual and Group Success
and success , over each type . We will see far more of the successful type of agent than the less successful , which usually just makes the successful types even more successful . This is particularly startling if the system began slightly biased in favor of the type that ends up losing . The lack of diversity appears here , too , for in these cases we see an average of 91 % of agents taking on the dominant type , leaving only 9 % stuck in the minority with no way out given the constraints of the model .
The key to all of these results is the threshold ( given we know p , q , r , and v ). A higher threshold means you keep switching until you hit a high enough score . The higher θ is , the more the agent is an optimizer . The lower θ , the more the agent is a satisficer .
Result 2 : Exogenous and Endogenous Environmental Turbulence
The second major result is that the environmental affects outcomes in two
ways : physical and social . The first is with respect to the physical landscape , or topology , itself . As mentioned in the model description , when v = 2 there is little hope for any spreading agent to receive high K S regardless of where on the lattice it moves . Even if we allow these unfortunate spreading agents to change types through increasing r or increasing θ , it turns out that unless θ = 0 and r = 1 , there will always be a not insignificant period during which agents who started out as spreading agents earn low K S that they never regain compared to their peers .
The additional way that environment affects outcomes is social : unsurprisingly , how well one type performs in the model depends on what the other type is doing . This effect exhibits sensitivity to initial conditions . Often if clustering and spreading agents are earning similar utility scores for the first few runs , all it takes is a slight lead for one type before the system suddenly turns to favor that type .
Agent-based modeling offers the advantage of being able to observe processes unfold in real time , which can help enlighten the causal principles behind the outcomes we observe . In this case , it turns out that if spreading agents end up going to cells on some turns that earn them enough points to pass a threshold ( and this result is robust to the range of θ levels ), there will be more spreading types in the next turn , as some clustering agents will switch to spreading . This , in turn , means that on the next time step , spreading agents will do even better , as there will be more of them spreading out rather than clustering . Moreover , for the same reason , clustering agents will do even worse , as there will now be even fewer agents with which to cluster .
Note that half the time the reverse holds : if clustering agents get even a slight advantage on one turn — for whatever reason — the population quickly veers toward favoring clusterers , regardless of the topological environment .
In this way , we have created a kind of endogenous turbulence .
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