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AbstractPolice corruption, especially in the form of bribery, is a severe social problem in many societies. However, neither the extent nor the factors contributing to police bribery are well understood because of data limitation issues. Understandably, it is incredibly challenging to observe and quantify such bribery, as it is usually considered illegal and/or unethical for police to accept and/or ask for bribes. Agent‐based modelling can solve such data limitation issues because it allows for the realistic modelling of hidden behaviours. This study uses an agent‐based modelling technique to investigate a threshold model of police corruption, more specifically, bribery. The authors assume that agents have a threshold regarding bribery, which may be conceptualised as either an honesty threshold or a risk threshold. The threshold value is a dynamic variable randomly assigned to each agent, and each interaction between citizens and officers possesses the potential to change the threshold of each agent.

AbstractObjectiveThis article examines the diffusion of paramilitary police units across the United States to consider the computational turn in social science.MethodsThe process of police diffusion is modeled by comparing traditional predictive models with agent‐based modeling (ABM) under constant and flexible binding conditions. The data are drawn from the Census of State and Local Law Enforcement Agencies 2000, 2004 and the United States Cities Database.ResultsABM was not superior to traditional predictive models when it came to predicting policy diffusion and that flexible binding provides superior predictive capabilities over constant binding.ConclusionsPublic policy scholars should exercise caution in adopting computational methods as they may not offer advantages over traditional predicative analysis. Policy diffusion of police militarization is not driven by geographical proximity but may reflect hierarchical influences in the form of federal policy or the influence of far‐flung organizations perceived to be similar. This may cause inappropriate policies and practices to be adopted.