Policy and Complex Systems
Figure 6 . Percent of human trafficking victims successfully detected . The plot shows the percent of the migrant population that were willing to cooperate ( and the quota was not yet full ) when the state agent interacted with them .
The outputs suggest however that the economic immigration policy yields more cooperation among trafficking victims and a slightly improved rate of detecting those victims . A satisfactory explanation of this outcome is still being investigated . Previously cited studies on international migration offer some theoretical basis for validation . For example , Kugler , Boussalis , and Coan ( 2012 ) suggest that migrants prioritize the opportunity for economic success over access to public goods .
This may also suggest a tradeoff between more permissive immigration policies ( represented in this model by the humanitarian policy ) and a victim-centered approach to human trafficking . In other words , policies that admit migrants based on broad or commonly occurring attributes ( such as humanitarian suffering ) might lead states to more quickly fill their immigration quotas and deplete resources , while more selective immigration policies might afford
state authorities more time and resources to successfully employ a victim-centered approach .
Conclusions
Our research aimed to provide insight
into some of the tradeoffs governments must balance in the context of large migration flows . We developed and presented a proof-of-concept model to better understand of how a state ’ s immigration policy might affect the willingness of human trafficking victims to self-identify ( i . e ., cooperate ) and , in turn , the state ’ s ability to identify victims among the flow of migrants . Ours is a parsimonious model that is focused on representing a population environment ( i . e ., migrants ) where agents are sensitive to spatial and network-based communication , adaptation , and learning .
Our experiments identified a tipping point of initial cooperation levels at which
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