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The problem of dependency.

It is a wide-spread assumption that multiple pieces of evidence, whether they involve measurements or testimony, provide stronger evidential support when they are independent than when they are not. The standard view is that non-independence creates redundancy and leads to over-confidence (see e.g.,...

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Published in:Synthese Vol. 205; no. 4; pp. 1 - 21
Main Authors: Pilditch, Toby D., Hahn, Ulrike, Lagnado, David
Format: Article
Published: Springer Nature Apr2025
Online Access:View this record in EBSCOhost
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        atl: The problem of dependency.
      aug:
        au:
          Pilditch, Toby D.
          Hahn, Ulrike
          Lagnado, David
        affil:
          https://ror.org/02jx3x895 Department of Experimental Psychology, University College London, 26 Bedford Way, WC1H 0AP, London, UK
          https://ror.org/052gg0110 School of Geography and the Environment, University of Oxford, South Parks Road, OX1 3QY, Oxford, UK
          https://ror.org/04cw6st05 Department of Psychological Sciences, Birkbeck, University of London, Malet Street, WC1E 7HX, London, UK
      sug:
      keyword:
        Dependencies
        Epistemics
        Reasoning under uncertainty
        Testimony
      ab: It is a wide-spread assumption that multiple pieces of evidence, whether they involve measurements or testimony, provide stronger evidential support when they are independent than when they are not. The standard view is that non-independence creates redundancy and leads to over-confidence (see e.g., Soll in Cogn Psychol 38(2):317–346, 1999; Nisbett and Ross in Human inference: strategies and shortcomings of social judgment. Prentice Hall, New Jersey, 1980). In keeping with this, fields as diverse as law, statistics, and philosophy have sought to understand the implications of non-independence and set out rules for dealing with it. In this paper, we set out how the challenges posed by non-independence are both far more wide-spread and more challenging than typically assumed. Specifically, we review how Bayesian probabilistic analysis reveals “simple” inference cases that conflict with wide-spread assumptions about the value of independence. These suggest that mere intuition is not a reliable guide in this arena. However, we then show that the same framework cannot (in its current form) be scaled to common real-world situations even in principle. We conclude with a discussion of the more modest, partial, solutions that are currently available and outline possible future directions.
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    language: English
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