We describe the use of synthetic information for doing prescriptive and predictive analytics. We discuss in detail how synthetic information is created by combining data from multiple sources, and then describe its role in an an ongoing disaster resilience study where we simulate the aftermath of a hypothetical nuclear detonation in Washington DC.

A distinction is made between two forms of morality on the basis of approach-avoidance differences in self-regulation. Prescriptive morality is sensitive to positive outcomes, activation-based, and focused on what we should do. Proscriptive morality is sensitive to negative outcomes, inhibition-based, and focused on what we should not do. Seven studies profile these two faces of morality, suppo...

Concrete (for example, a customer database was seen as concrete and organisational information as abstract). Prescriptive-Flexible (for example, legal and regulatory information was seen as prescriptive whereas organisational information was seen as flexible).

So far, ontologies in the Semantic Web and models in model-driven engineering have been developed mainly in isolation. It seems that due to a lack of communication between communities, modelling concepts have been designed similarly in both paradigms without ensuring their orthogonality. On the long run, this will replicate efforts and cannot be afforded by either community. Hence, this chapter...

This checklist is for the review of evidence syntheses for treatment efficacy used in decision making based on either efficacy or cost-effectiveness. It is intended to be used for pairwise meta-analysis, indirect comparisons, and network meta-analysis, without distinction. It does not generate a quality rating and is not prescriptive. Instead, it focuses on a series of questions aimed at reveal...

Business research practice is witnessing a surge in the integration of predictive modeling and prescriptive analysis. We describe framework JANOS that seamlessly integrates two streams analytics, allowing researchers practitioners to embed machine learning models an end-to-end optimization framework. allows for specifying model using standard elements such as constraints variables. The key nove...