CAUSAL INFERENCE IN LEGAL DECISION MAKING: EXPLANATORY COHERENCE VS. BAYESIAN NETWORKS
نویسندگان
چکیده
منابع مشابه
Causal Inference In Legal Decision Making: Explanatory Coherence Vs. Bayesian Networks
Reasoning by jurors concerning whether an accused person should be convicted of committing a crime is a kind of casual inference. Jurors need to decide whether the evidence in the case was caused by the accused’s criminal action or by some other cause. This paper compares two computational models of casual inference: explanatory coherence and Bayesian networks. Both models can be applied to leg...
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Decisions often rely on judgments about the probabilities of various explanations. Recent research has uncovered a host of biases that afflict explanatory inference: Would these biases also translate into decision-making? We find that although people show biased inferences when making explanatory judgments in decision-relevant contexts (Exp. 1A), these biases are attenuated or eliminated when t...
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ژورنال
عنوان ژورنال: Applied Artificial Intelligence
سال: 2004
ISSN: 0883-9514,1087-6545
DOI: 10.1080/08839510490279861