Cognitive bias in intelligence analysis : testing the analysis of competing hypotheses method (Book, 2020) [WorldCat.org]
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Cognitive bias in intelligence analysis : testing the analysis of competing hypotheses method

Author: Martha Whitesmith
Publisher: Edinburgh : Edinburgh University Press, 2020.
Series: Intelligence, surveillance and secret warfare.
Edition/Format:   Print book : EnglishView all editions and formats
Summary:
Tests whether the analysis of competing hypotheses reduces cognitive bias, and proposes a more effective approach. Reveals that a key element of current training provided to the UK and US intelligence communities (and likely all 5-EYES and several European agencies) does not have a proven ability to mitigate cognitive biases. Demonstrates that judging the credibility of information from human sources means that  Read more...
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Document Type: Book
All Authors / Contributors: Martha Whitesmith
ISBN: 9781474466349 1474466346 1474466354 9781474466356
OCLC Number: 1173787433
Description: viii, 294 pages ; 24 cm.
Contents: Part 1. Intelligence, bias and belief acquisition : a theoretical perspective. 1. Intelligence and belief acquisition : a taxonomy of intelligence ; intelligence as an epistemological act ; how can knowledge be acquired? ; epistemological implications for intelligence --
2. The efficacy of ACH in establishing epistemic justification and mitigating cognitive bias : the ACH method ; the ability of ACH to reduce epistemic complexity ; intelligence, bias and belief acquisition : a theoretical perspective --
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Part 2. Intelligence, bias and belief acquisition : an empirical perspective. 3. The efficacy of ACH in mitigating serial position effects and confirmation bias in an intelligence analysis scenario ; procedure ; results ; discussion and conclusions --
4. Predictiing serial position effects : experimental conditions ; results and discussion --
an examination of previous serial position effects prediction models ; the impact of analytical conditions on serial position effects --
5. Predicting confirmation bias : additional experimental conditions ; results and discussion ; an examination of previous confirmation bias prediction models ; the impact of analytical conditions on confirmation bias ; intelligence, bias and belief acquisition : an empirical perspective --
6. Reducing the risk of cognitive bias in intelligence analysis : recommendations for future research and best practice for intelligence analysis.
Series Title: Intelligence, surveillance and secret warfare.
Responsibility: Martha Whitesmith.

Abstract:

Tests whether the analysis of competing hypotheses reduces cognitive bias, and proposes a more effective approach. Reveals that a key element of current training provided to the UK and US intelligence communities (and likely all 5-EYES and several European agencies) does not have a proven ability to mitigate cognitive biases. Demonstrates that judging the credibility of information from human sources means that intelligence analysis faces greater complexity and cognitive strain than non-intelligence analysis. Explains the underlying causes cognitive biases, based on meta-analyses of existing research. Shows that identifying the ideal conditions for intelligence analysis is a more effective way of reducing the risk of cognitive bias than the use of ACH. Recent high-profile intelligence failures - from 9/11 to the 2003 Iraq war - prove that cognitive bias in intelligence analysis can have catastrophic consequences. This book critiques the reliance of Western intelligence agencies on the use of a method for intelligence analysis developed by the CIA in the 1990s, the Analysis of Competing Hypotheses (ACH). The author puts ACH to the test in an experimental setting against two key cognitive biases with unique empirical research facilitated by UK's Professional Heads of Intelligence Analysis unit at the Cabinet Office, and finds that the theoretical basis of the ACH method is significantly flawed. Combining the insight of a practitioner with over 11 years of experience in intelligence with both philosophical theory and experimental research, the author proposes an alternative approach to mitigating cognitive bias that focuses on creating the optimum environment for analysis, challenging current leading theories.

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