Extracting and Leveraging Value from a Decision Interdependency Network (DIN) in a Policing/Law Enforcement Setting

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Proceedings - IEEE International Enterprise Distributed Object Computing Workshop, EDOCW


The choices we make constrain other choices. Certain combinations of decisions lead to better outcomes than other combinations of decisions. These observations hold in a large class of problem domains, but are especially compelling in policing and law enforcement. However, the problem of identifying the optimal choices has received relatively little attention in the literature. The nearest point of departure is the class of Distributed Constraint Optimisation Problems (DCOP), but these offer a limited vocabulary for describing the final outcome (we are only able to assess the value of a shared objective function when a complete assignment of values to all variables of interest has been arrived at). In many settings, the final state of affairs that is achieved after deploying a sequence of decisions is a critical determinant of the effectiveness of the choices made. To address these gaps in the literature, we introduce a novel notion of a Decision Interdependency Network (DIN). We show how these can be extracted from readily available artefacts such as business process models. We then illustrate how we might leverage value from a DIN using a substantive policing/law enforcement example.

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