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Truth, Probability, and Evidence in Judicial Reasoning: The Case of the Conjunction Fallacy

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Judicial Decision-Making

Part of the book series: Economic Analysis of Law in European Legal Scholarship ((EALELS,volume 14))

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Abstract

In recent decades, empirical investigation has increasingly illuminated how experts in the legal domain, including judges, evaluate evidence and hypotheses, reason and decide about them. Research has highlighted both the cognitive strategies employed in legal reasoning, and the cognitive pitfalls judges and other experts tend to fall prey to. In this paper, we focus on the “conjunction fallacy”, a widespread phenomenon showing that human reasoners systematically violate the rules of probability calculus. After presenting the fallacy as documented in judicial reasoning, we present two formal accounts of the phenomenon, respectively based on the notions of confirmation (evidential support) and truthlikeness (closeness to the truth) as studied in the philosophy of science. With reference to the “story-model” of legal decision-making, we clarify the role that “cognitive utilities” like truth, probability, and information play in legal reasoning, and how it can account for the documented fallacies. We conclude by suggesting some directions for further investigation.

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Notes

  1. 1.

    For a survey, see Rachlinski and Wistrich (2017).

  2. 2.

    Tversky and Kahneman (1983).

  3. 3.

    Guthrie et al. (2009); Wojciechowski and Pothos (2018).

  4. 4.

    Tversky and Kahneman (1982, 1983).

  5. 5.

    See Tversky and Kahneman (1983), p. 301, and Wedell and Moro (2008) for a survey.

  6. 6.

    See, e.g., Teichman and Zamir (2014); Rachlinski and Wistrich (2017).

  7. 7.

    Guthrie et al. (2009).

  8. 8.

    While already discussed by Tversky and Kahneman (1983), double conjunction fallacies have been rarely investigated in the literature, also because they are hardly reconciled with most suggested accounts of the (single) conjunction fallacy (Crupi et al. 2018a). The phenomenon is also reported in a recent study of legal decision-making by Wojciechowski and Pothos (2018), even if it is limited to a group of participants with no legal background.

  9. 9.

    In this connection, it is perhaps worth noting that the discussion in Guthrie et al. (2009) does not fully clarify whether the Dina experiment is construed as an instance of the “M-A paradigm” or of the “A-B paradigm”, in the terminology of Tversky and Kahneman (1983, pp. 304 ff.). Roughly, the difference is that, in the former experimental paradigm, a “model” (e.g., Linda story) is positively associated (in terms of representativeness, probability, etc.) to one of the conjuncts (“feminist”) and negatively associated to the other (“bank teller”); whereas in the A-B paradigm, one conjunct is positively associated to the other, even if the latter is not positively associated with the model. Since the two paradigms have different theoretical implications, this point would need further discussion in order to properly assess and interpret the experimental results.

  10. 10.

    See, e.g., Gigerenzer and The ABC group (1999); Kahneman and Frederick (2002); Samuels et al. (2002).

  11. 11.

    E.g., Levi (1985); Bovens and Hartmann (2003); Hintikka (2004); Crupi et al. (2008); Cevolani and Crupi (2015).

  12. 12.

    Cevolani and Crupi (2015).

  13. 13.

    Tversky and Kahneman (1983), p. 311.

  14. 14.

    Carnap’s (1962), pp. xv–xx.

  15. 15.

    Cf. Fitelson (2005).

  16. 16.

    See, e.g., Peijnenburg (2012) for a recent discussion.

  17. 17.

    Good (1968), p. 134.

  18. 18.

    Crupi et al. (2008). Further developments are in Tentori and Crupi (2012) and Tentori et al. (2013), who also refer to a number of earlier contributions that are more or less strictly related (see Lagnado and Shanks 2002; Levi 1985, 2004; Sides et al. 2002; Tenenbaum and Griffiths 2001).

  19. 19.

    Crupi (2020); Sprenger and Hartmann (2019).

  20. 20.

    Crupi et al. (2008), p. 188.

  21. 21.

    Popper (1963, ch. 10) proposed the notion of truthlikeness in order to defend the idea that, while likely false, scientific hypotheses and common beliefs can still be close the truth, thus making possible the progress of science and human knowledge in general as a gradual approximation to the truth. His ideas were further elaborated and refined by other scholars (Niiniluoto 1987, 1998; Oddie 2016). For recent discussion, see Cevolani (2017) and Cevolani and Festa (2020).

  22. 22.

    A point also hinted at by Tversky and Kahneman themselves: (1983), p. 312.

  23. 23.

    Cevolani et al. (2010, 2011).

  24. 24.

    Cevolani et al. (2010).

  25. 25.

    Tversky and Kahneman (1983), p. 312; also see Kahneman and Frederick (2002).

  26. 26.

    The long and spirited debate between two leading philosophers like Karl Popper and Rudolf Carnap (and their followers) is an example of such a discussion; since then, scholars working on so called cognitive decision theory have explored these issues in great detail, providing a solid formal background to the analysis of different cognitive utilities (Levi 1967; Niiniluoto 1987, ch. 12, 2011).

  27. 27.

    Popper (1934/1959, 1963).

  28. 28.

    Scholars in different fields have proposed different measures of the information content of a hypothesis h; for all of them, if h has greater content than g, then h is not more probable than g. One simple such measure, proposed by Popper (1934/1959) among others, amounts to defining informativeness as the plain improbability 1 – P(h), thus making obvious the above inverse relation between the two notions. For a survey of different formal accounts of information as applied to human cognition and information search, see Crupi et al. (2018b).

  29. 29.

    See, for instance, Huber (2008) and Kuipers (2012).

  30. 30.

    E.g., Pennington and Hastie (1986, 1993); Teichman and Zamir (2014); Vorms and Lagnado (2019).

  31. 31.

    It “has all its parts”, Pennington and Hastie (1993), pp. 198–199.

  32. 32.

    Vorms and Lagnado (2019), p. 108.

  33. 33.

    Of course, the model is not without its critics; for assessments of its adequacy, from the perspectives of different disciplines, see for instance Griffin (2013) and Vorms and Lagnado (2019).

  34. 34.

    Cf. Simon (1998).

  35. 35.

    Cf. Heller (2006).

  36. 36.

    Rachlinski and Wistrich (2017).

  37. 37.

    Cf. Guthrie et al. (2009); Wojciechowski and Pothos (2018).

  38. 38.

    Cf. Vorms and Lagnado (2019), p. 104.

  39. 39.

    Thagard (2000); Simon (1998).

  40. 40.

    Schum (2001).

  41. 41.

    Bovens and Hartmann (2003); Lagnado (2011); Taroni et al. (2014).

  42. 42.

    Cf. Vorms and Lagnado (2019), p. 118.

  43. 43.

    Cf. Cevolani and Crupi (2015).

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Authors and Affiliations

  1. IMT School for Advanced Studies Lucca, Lucca, Italy

    Gustavo Cevolani

  2. Center for Logic, Language, and Cognition, Turin, Italy

    Gustavo Cevolani & Vincenzo Crupi

  3. Department of Philosophy and Education, University of Turin, Torino, Italy

    Vincenzo Crupi

Authors
  1. Gustavo Cevolani
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  2. Vincenzo Crupi
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Correspondence to Gustavo Cevolani .

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Editors and Affiliations

  1. Jagiellonian University, Kraków, Poland

    Piotr Bystranowski

  2. University of Silesia in Katowice, Katowice, Poland

    Bartosz Janik

  3. Jagiellonian University, Kraków, Poland

    Maciej Próchnicki

Additional information

Gustavo Cevolani acknowledges financial support from the Italian Ministry of Education, Universities and Research (MIUR) through the grant n. 201743F9YE (PRIN 2017 project “From models to decisions”).

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Cevolani, G., Crupi, V. (2022). Truth, Probability, and Evidence in Judicial Reasoning: The Case of the Conjunction Fallacy. In: Bystranowski, P., Janik, B., Próchnicki, M. (eds) Judicial Decision-Making. Economic Analysis of Law in European Legal Scholarship, vol 14. Springer, Cham. https://doi.org/10.1007/978-3-031-11744-2_6

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