Journal on Policy and Complex Systems
cases to explore different strategies with a relatively small investment in data . It allows us to ask “ what if ” questions and explore consequences for paths and outcomes of various anticipated changes in context and in circumstances that drive individual and group attitudes . The results are relatively easy to communicate to stakeholders and interveners , which is not always the case with more intricate models .
The same simple quality is also a limitation : this model is not of the explanatory kind , but is rather anticipatory . Our two-group model produced scenarios that were realized in the Brexit and U . S . elections cases , but did not explain what social and political factors led to them . Similarly , our exploration of the BiH conflict yielded scenarios consistent with reality , without offering causal linkages among the many historical , political , and economic factors that likely contributed to the 2018 election outcomes . Those searching for the causes of various intractable conflicts will not find them here . Instead , this is an “ as if ” model that produces results by different means than direct links between causes and their effects , which are difficult , if not impossible , to discover in complex systems .
An added limitation of this model is the quick rise in the computational difficulty as we add groups . To get around this challenge , we explored specific value regions rather than plumbing the entire parameter space . In other cases with multiple groups , we may be able to examine paths and outcomes if some groups are sufficiently aligned that we can consider collapsing them into one group . That can be done in the US-M-CA dispute , where , for purposes of exploring migration scenarios , we could consider the three Central American countries to comprise one group , even if in reality they differ along many dimensions .
We plan to refine and continue to develop the multiple group model . We are considering the introduction of principal-agent effects , as we add a layer of negotiators to the disputing group layers . We will explore leadership effects and situations where an entire group takes the position of its leader ( as in dictatorships ), as opposed to groups who may diverge from their leaders ’ positions ( as in democracies ), resulting in strong or impaired cohesion levels between group members and their respective leaders . We also plan to examine sparse networks , where not every individual interacts with every other in the group . We will also work to unpack “ temperature ” by identifying specific components that can be affected by disputants or interveners .
References
Barnes T . J ., & Wilson M . W . ( 2014 ). Big data , social physics , and spatial analysis : The early years . Big Data & Society , 1 ( 1 ). doi : 10.1177 / 2053951714535365
Bernstein S ., Lebow , R . N ., Stein J . G ., & Weber S . ( 2000 ). God gave physics the easy problems : Adapting social science to an unpredictable world . European
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