It is difficult or even impossible to construct models covering all aspects of (complex) problem domains of interest. A model is therefore most often an approximation of a problem domain that is designed to be applied according to the assumptions as determined by the background condition or context of the model. If a model is used under circumstances not consistent with the background condition, the results will in general be unreliable. The evidence need not be inconsistent with the model in order for the results to be unreliable. It may be that evidence is simply in conflict with the model. This implies that the model in relation to the evidence may be weak and therefore the results may be unreliable.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2008). Conflict Analysis. In: Bayesian Networks and Influence Diagrams. Information Science and Statistics. Springer, New York, NY. https://doi.org/10.1007/978-0-387-74101-7_9
Download citation
DOI: https://doi.org/10.1007/978-0-387-74101-7_9
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-74100-0
Online ISBN: 978-0-387-74101-7
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)