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Structured Deliberation for Dynamic Uncertain Inference

  • Paul Snow
  • Marianne Belis
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 94)

Abstract

Dynamic uncertain inference is the formation of opinions based upon evidence or argument whose availability is neither disclosed to the analyst in advance nor disclosed all at once. Normative accounts of belief change, which work well when the analyst has prior notice of well-designed experiments and their possible outcomes, may not be applicable to less tidy occasions of inference. In addition, there is the clerical challenge of keeping track of what has been observed, what relates to what, and how. This Article begins with a discussion of subjective valuation in general. An approach to deliberation, similar to what is practiced in the multiattribute utility modeling community, is then suggested for dynamic credibility assessment. Features of the proposed technique are explained through their application to a celebrated French murder investigation. The method presented here may be reconciled with Bayesian belief models by noting that the latter lack a consensus view of how stable beliefs form in the first place. Thus, the ideas discussed here may be taken as an account of original belief formation, and so complementary rather than antagonistic to subjective probability methods.

Keywords

Supra Note Directed Acyclic Graph Belief Change Multiattribute Utility Simple Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Physica-Verlag Heidelberg 2002

Authors and Affiliations

  • Paul Snow
  • Marianne Belis

There are no affiliations available

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