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Law, Probability and Risk Advance Access originally published online on January 16, 2007
Law, Probability and Risk 2006 5(2):87-117; doi:10.1093/lpr/mgl014
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© The Author [2007]. Published by Oxford University Press. All rights reserved.

A scenario-driven decision support system for serious crime investigation

Qiang Shen{dagger}

Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK

Jeroen Keppens{ddagger}

Department of Computer Science, King's College London, London WC2R 2LS, UK

Colin Aitken§

School of Mathematics, University of Edinburgh, Edinburgh EH9 3JZ, UK

Burkhard Schafer

School of Law, University of Edinburgh, Edinburgh EH8 9YL, UK

Mark Lee||

Department of Computer Science, University of Wales, Aberystwyth SY23 3DB, UK

{dagger} Corresponding author. Email: qqs{at}aber.ac.uk

{ddagger} Email: jeroen.keppens{at}kcl.ac.uk

§ Email: c.g.g.aitken{at}ed.ac.uk

Email: bschafer{at}staffmail.ed.ac.uk

|| Email: mhl{at}aber.ac.uk

Received on 12 July 2006. Revised on 30 October 2006. Accepted on 8 November 2006.


  Abstract

Consideration of a wide range of plausible crime scenarios during any crime investigation is important to seek convincing evidence and hence to minimize the likelihood of miscarriages of justice. It is equally important for crime investigators to be able to employ effective and efficient evidence-collection strategies that are likely to produce the most conclusive information under limited available resources. An intelligent decision support system that can assist human investigators by automatically constructing plausible scenarios, and reasoning with the likely best investigating actions will clearly be very helpful in addressing these challenging problems. This paper presents a system for creating scenario spaces from given evidence, based on an integrated application of techniques for compositional modelling and Bayesian network-based evidence evaluation. Methods of analysis are also provided by the use of entropy to exploit the synthesized scenario spaces in order to prioritize investigating actions and hypotheses. These theoretical developments are illustrated by realistic examples of serious crime investigation.

Keywords: crime investigation; decision support; scenario generation; scenario fragments; Bayesian networks; evidence evaluation; conditional independence; system architecture; entropy


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