Abstract
Decisions concerning proof of facts in criminal law must be rational because of what is at stake, but the decision-making process must also be cognitively feasible because of cognitive limitations, and it must obey the relevant legal-procedural constraints. In this topic three approaches to rational reasoning about evidence in criminal law are compared in light of these demands: arguments, probabilities, and scenarios. This is done in six case studies in which different authors analyze a manslaughter case from different theoretical perspectives, plus four commentaries on these case studies. The aim of this topic is to obtain more insight into how the different approaches can be applied in a legal context. This will advance the discussion on rational reasoning about evidence in law and will contribute more widely to cognitive science on a number of topics, including the value of probabilistic accounts of cognition and the problem of dealing with cognitive biases in reasoning under uncertainty in practical contexts.
Highlights
In the context of criminal law, decisions concerning legal proof of facts must be rational because of what is at stake, but the decision-making process must be cognitively feasible because of the decision-makers’ cognitive limitations, and it must obey the relevant legal–procedural constraints
One link with normative theories of evidential reasoning is through the requirement to test which scenario best explains the evidence
Work on using so-called idioms (Fenton et al, 2013) or templates for crime scenarios (Vlek et al, 2014) aims to support the construction of Bayesian networks, while Timmer, Meyer, Prakken, Renooij, and Verheij (2017) study the extraction of arguments from Bayesian networks in order to explain these networks to judges or prosecutors
Summary
In the context of criminal law, decisions concerning legal proof of facts must be rational because of what is at stake, but the decision-making process must be cognitively feasible because of the decision-makers’ cognitive limitations, and it must obey the relevant legal–procedural constraints The aim of this topic is to compare three approaches focusing, respectively, on arguments, probabilities, and scenarios, in light of these demands. The topic aims to more widely contribute to cognitive science on a number of topics It relates to the recent interest in probabilistic models as alternatives to deductive models of cognition (Chater, Tenenbaum, & Yuille, 2006) and to attempts to develop a Bayesian theory of argumentation (Hahn & Hornikx, 2016; Hahn & Oaksford, 2007; Harris, Hahn, Madsen, & Hsu, 2016). The question arises whether argumentation or scenario approaches are cognitively feasible alternatives to strict Bayesian thinking
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