Synthese 189 (S1):17-28 (2012)

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Abstract
Many of our decisions pertain to causal systems. Nevertheless, only recently has it been claimed that people use causal models when making judgments, decisions and predictions, and that causal Bayes nets allow us to formally describe these inferences. Experimental research has been limited to simple, artificial problems, which are unrepresentative of the complex dynamic systems we successfully deal with in everyday life. For instance, in social interactions, we can explain the actions of other's on the fly and we can generalize from limited observations to predict future actions and their consequences. Our main argument is that none of these inferences (i.e., induction, generalization, explanation, and prediction) can be achieved without causal reasoning. As a case in point we use the popular television series desperate housewives and show how causal Bayes nets are able to explain the inferences made in social contexts. Crucially, causal Bayes nets also allow us to understand why we can infer so much from so little when making sense of a protagonist's behavior.
Keywords Causal reasoning  Bayes nets  Rational models  Everyday decision making
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DOI 10.1007/s11229-012-0162-3
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References found in this work BETA

Causality: Models, Reasoning and Inference.Judea Pearl - 2000 - Cambridge University Press.
Thagard’s Coherentism. [REVIEW]Majid Amini - 2000 - Philosophical Books 43 (2):136-140.

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Citations of this work BETA

The Oxford Handbook of Causal Reasoning.Michael R. Waldmann (ed.) - 2017 - Oxford University Press.

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