Uncertainty and the de Finetti tables

By Jean Baratgin, David E. Over, Guy Politzer. 2013

73 citations recorded by the source corpus.

The new paradigm in the psychology of reasoning adopts a Bayesian, or probabilistic, model for studying human reasoning. Contrary to the traditional binary approach based on truth functional logic, with its binary values of truth and falsity, a third value that represents uncertainty can be introduced in the new paradigm. A variety of three-valued truth table systems are available in the formal literature, including one proposed by de Finetti. We examine the descriptive adequacy of these systems for natural language indicative conditionals and bets on conditionals. Within our framework the so-called “defective” truth table, in which participants choose a third value when the antecedent of the indicative conditional is false, becomes a coherent response. We show that only de Finetti's system has a good descriptive fit when uncertainty is the third value.

Subfields: Artificial Intelligence, Computational Theory and Mathematics, General Decision Sciences

View Paper: https://www.semanticscholar.org/paper/96c931598d0639553e36a0e253409a9c8e440ad8

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In Artificial Intelligence

In General Decision Sciences

Citations in the corpus

Bayesian reasoning with ifs and ands and ors (2015, background): “…between categorical belief in the truth, or falsity, of propositions to the full range of degrees of belief, or subjective probabilities (Evans and Over, 2004, 2013; Oaksford and Chater, 2007, 2012, 2013; Pfeifer and Kleiter, 2010, 2011; Baratgin et al., 2013; Pfeifer, 2013; Over, in press).”