The risk of detection

The clearance rate is often used as an indicator of the risk of detection, in spite of the fact that these are different matters. This article suggests a method to make estimates of the risk of detection based on information from the Swedish crime statistics. The risk of detection is expressed as a function of the dark figure. Empirical estimates of the risk are given for drunken driving, residential burglary, and assault between strangers. These estimates are followed up with some calculations concerning the impact of crime activity level on the risk of getting caught. One major conclusion of the findings is that the risk of detection varies very moderately with the dark figure. This means that if there is some knowledge of the dark figure, it is often possible to make fairly good estimates of the risk of detection.     

A computational electoral competition model with social clustering and endogenous interest groups as information brokers

We extend the basic model of spatial competition in two directions. First, political parties and voters do not have complete information but behave adaptively. Political parties use polls to search for policy platforms that maximize the probability of winning an election and the voting decision of voters is influenced by social interaction. Second, we allow for the emergence of interest groups. These interest groups transmit information about voter preferences to the political parties, and they coordinate voting behavior. We use simulation methods to investigate the convergence properties of this model. We find that the introduction of social dynamics and interest groups increases the separation between parties platforms, prohibits convergence to the center of the distribution of voter preferences, and increases the size of the winning set.