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Psychology and the Mined: A Case Study in Psychological Barriers to the Use of Statistical Analysis

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[abstract based on the first two paragraphs]

Despite the potential value of statistical analyses in making military decisions, there are formidable psychological barriers to their use. These psychological impediments have been demonstrated both in the context of historical examples and in the literature of experimental psychology. The purpose of this paper is to raise awareness of some of these impediments, and how they affect decision making, while also providing insights into how to overcome them.

As a case study, we focus on the use of statistics in naval mine warfare. There are a couple of reasons for focusing on this particular warfare area to demonstrate psychological biases which inhibit the effective use of statistics. First, it is a field which is often relegated to career specialists, but in which non-specialists are called upon to make key operational decisions (notably, whether or how to transit mined waters, as well as what risks and delays are acceptable). Second, the crux of the warfare area centers on uncertainty, and the use of statistics to overcome this uncertainty is critical. Mine countermeasures (MCM) forces have as their primary challenge the localization of static, hidden objects; assessing the number and locations of these objects, using statistical insights, is central to their efforts. Statistical analysis, together with mathematical or stochastic modeling, can provide a probabilistic distribution of risk which is invaluable to forces contending with a prospective minefield. When integrated with other sources of data (such as intelligence and insights gleaned from experience), such analysis can help to inform decision making and render it more effective.

Keywords: Application Areas: Analysis of Alternatives; Application Areas: Measures of Effectiveness; Application Areas: Modeling and Simulation; Application Areas: Operations Research and Intelligence; OR Methodologies: Decision Analysis; OR Methodologies: Pattern Recognition; OR Methodologies: Simulation; OR Methodologies: Stochastic Processes

Document Type: Research Article


Publication date: December 1, 2008

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