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Improving Intelligence Analysis With Decision Science.

Mandeep K Dhami1, David R Mandel2, Barbara A Mellers3

  • 1Middlesex University, London, UK m.dhami@mdx.ac.uk.

Perspectives on Psychological Science : a Journal of the Association for Psychological Science
|November 20, 2015
PubMed
Summary
This summary is machine-generated.

Intelligence analysis improves policy decisions through better forecasting and uncertainty communication. Decision science can enhance accuracy and stakeholder understanding, recommending quantitative monitoring and use of numerical probabilities.

Keywords:
decision scienceforecastingintelligence analysisuncertainty

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Area of Science:

  • Decision Science
  • Intelligence Studies
  • Cognitive Psychology

Background:

  • Intelligence analysis is crucial for policy decision-making.
  • Key functions include forecasting, uncertainty characterization, and communication.
  • Current methods may be suboptimal.

Purpose of the Study:

  • Review decision research on probabilistic forecasting and uncertainty communication.
  • Identify potential reforms for intelligence processes and oversight.
  • Provide actionable recommendations for the intelligence community.

Main Methods:

  • Literature review of decision science research.
  • Analysis of findings related to forecasting accuracy and uncertainty communication.
  • Synthesis of recommendations for intelligence community practices.

Main Results:

  • Decision research offers strategies to improve forecasting accuracy via data and behavioral interventions.
  • Current reliance on verbal probabilities for uncertainty communication is ineffective.
  • Quantitative monitoring of forecasting accuracy is recommended.

Conclusions:

  • The intelligence community (IC) should adopt decision science principles.
  • Implement regular, quantitative monitoring of forecasting performance.
  • Transition towards more effective uncertainty communication, including numerical probabilities.