Coherence and probability in legal evidence

Dahlman, Christian; Mackor, Anne‐Ruth

doi:10.1093/lpr/mgz016

Cited by 5 publications

(5 citation statements)

References 22 publications

(27 reference statements)

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“…Second, our results identify two additional contributing factors, namely the base rate-dependent neglect of false alarm rates, and the binary decision-dependent overweighting of the false-alarm rate. Previous studies have reported the neglect of false alarm rates (which the reports referred to as ‘miss rate neglect’) in the context of legal judgements ( Dahlman et al, 2016 ; Dahlman and Mackor, 2019 ). But to our knowledge, our study is the first to report the contribution of the above two factors and to report such conditional underweighting/overweighting.…”

Section: Discussionmentioning

confidence: 99%

Toward a more nuanced understanding of probability estimation biases

Branch

Hegdé

2023

Front. Psychol.

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In real life, we often have to make judgements under uncertainty. One such judgement task is estimating the probability of a given event based on uncertain evidence for the event, such as estimating the chances of actual fire when the fire alarm goes off. On the one hand, previous studies have shown that human subjects often significantly misestimate the probability in such cases. On the other hand, these studies have offered divergent explanations as to the exact causes of these judgment errors (or, synonymously, biases). For instance, different studies have attributed the errors to the neglect (or underweighting) of the prevalence (or base rate) of the given event, or the overweighting of the evidence for the individual event (‘individuating information’), etc. However, whether or to what extent any such explanation can fully account for the observed errors remains unclear. To help fill this gap, we studied the probability estimation performance of non-professional subjects under four different real-world problem scenarios: (i) Estimating the probability of cancer in a mammogram given the relevant evidence from a computer-aided cancer detection system, (ii) estimating the probability of drunkenness based on breathalyzer evidence, and (iii & iv) estimating the probability of an enemy sniper based on two different sets of evidence from a drone reconnaissance system. In each case, we quantitatively characterized the contributions of the various potential explanatory variables to the subjects’ probability judgements. We found that while the various explanatory variables together accounted for about 30 to 45% of the overall variance of the subjects’ responses depending on the problem scenario, no single factor was sufficient to account for more than 53% of the explainable variance (or about 16 to 24% of the overall variance), let alone all of it. Further analyses of the explained variance revealed the surprising fact that no single factor accounted for significantly more than its ‘fair share’ of the variance. Taken together, our results demonstrate quantitatively that it is statistically untenable to attribute the errors of probabilistic judgement to any single cause, including base rate neglect. A more nuanced and unifying explanation would be that the actual biases reflect a weighted combination of multiple contributing factors, the exact mix of which depends on the particular problem scenario.

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Section: Discussionmentioning

confidence: 99%

Toward a more nuanced understanding of probability estimation biases

Branch

Hegdé

2023

Front. Psychol.

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“…This absence of an inhibitory link requirement allows researchers to evaluate individual theories. In addition, some have advocated to use IMEC analogous to models of probabilistic reasoning such as Bayesian networks (Dahlman & Mackor, 2019). In this case the principle of competition can also be useful to model "explaining 10 To acknowledge Gilbert Harman, who conceptualized inference to the best explanation, Thagard uses the word Harmany rather than the more common word harmony.…”

Section: Revising Tecmentioning

confidence: 99%

Comparing theories with the Ising model of explanatory coherence.

Maier¹,

Dongen²,

Borsboom³

2024

Psychological Methods

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Theories are among the most important tools of science. Lewin (1943) already noted “There is nothing as practical as a good theory.” Although psychologists discussed problems of theory in their discipline for a long time, weak theories are still widespread in most subfields. One possible reason for this is that psychologists lack the tools to systematically assess the quality of their theories. Thagard (1989) developed a computational model for formal theory evaluation based on the concept of explanatory coherence. However, there are possible improvements to Thagard’s (1989) model and it is not available in software that psychologists typically use. Therefore, we developed a new implementation of explanatory coherence based on the Ising model. We demonstrate the capabilities of this new Ising model of Explanatory Coherence (IMEC) on several examples from psychology and other sciences. In addition, we implemented it in the R-package to assist scientists in evaluating the quality of their theories in practice.

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“…24 In response to that critique, defenders of explanation-based approaches have referred to epistemologists and philosophers of science who have done work on epistemic virtues such as simplicity, robustness and coherence, showing how these virtues can be truth-conducive (Thagard, 1978;McMullin, 1996;Douglas, 2009;Cabrera, 2017). These insights can also be applied in the context of criminal law (Mackor, 2017, Dahlman & Mackor, 2019. We will briefly return to this topic in Section 21.5.…”

Section: E Good Versus Probable Explanationsmentioning

confidence: 99%

“…We discuss their critique of probabilistic interpretation of the proof standard in Section 21.4. 9This section is an adaptation ofDahlman and Mackor (2019), section 2.2.…”

mentioning

confidence: 99%