‘Belief-Consistent Information Processing’ vs. ‘Coherence-Based Reasoning’: Pragmatic Frameworks for Exposing Common Cognitive Biases in Intelligence Analysis

Barrows, Matthew and Ellison, George orcid iconORCID: 0000-0001-8914-6812 (2024) ‘Belief-Consistent Information Processing’ vs. ‘Coherence-Based Reasoning’: Pragmatic Frameworks for Exposing Common Cognitive Biases in Intelligence Analysis. UK Journal of Intelligence Analysis . (Submitted)

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Official URL: https://doi.org/10.20944/preprints202401.1338.v1

Abstract

Aileen Oeberst and Roland Imhoff’s remarkable paper (Perspectives on Psychological Science 2023; 18: 1464-87). should be compulsory reading for all intelligence analysts. It offers a tantalisingly parsimonious explanation comprising the combination of strongly held prior 'beliefs' coupled with subsequent 'belief-consistent information processing' (akin to confirmation bias) - an explanation that simplifies understanding of a vast array of cognitive biases, and thereby suggests that all of these might be attenuated through the application of accessible analytical and assessment practices. What this means for intelligence analysis will be determined by the explicit and implicit 'beliefs' (or, more generally, ‘value[s]’) attributed to any evidence available, and any insight generated, to reduce decision-makers’ uncertainty and unsubstantiated certainty, and thereby offer them future advantage. Analysts are well aware that different sources of evidence (whether empirical, theoretical or entirely speculative) can assign different types and amounts of ‘prior value’ to the information available for intelligence analysis and assessment. Acknowledging such value as a potential driver of subsequent confirmation bias should help analysts guard against any inherent tendency to preference evidence solely on the basis that this was initially considered most ‘valuable’. Instead they should subject all evidence and all insight – regardless of perceived ‘value’ – to a consistent battery of systematic, rigorous and robust evaluation.


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