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RESEARCH PRODUCT
Probability Propagation in Selected Aristotelian Syllogisms
Niki PfeiferGiuseppe Sanfilipposubject
Discrete mathematicsSettore MAT/06 - Probabilita' E Statistica Matematica05 social sciencesProbabilistic logicSyllogismConditional probability02 engineering and technologyCoherence (statistics)Settore MAT/01 - Logica MatematicaImprecise probabilityAristotelian syllogismFigure III050105 experimental psychologyConstraint (information theory)Premise0202 electrical engineering electronic engineering information engineeringImprecise probability020201 artificial intelligence & image processing0501 psychology and cognitive sciencesConditional eventDefault reasoningCoherenceSentenceMathematicsdescription
This paper continues our work on a coherence-based probability semantics for Aristotelian syllogisms (Gilio, Pfeifer, and Sanfilippo, 2016; Pfeifer and Sanfilippo, 2018) by studying Figure III under coherence. We interpret the syllogistic sentence types by suitable conditional probability assessments. Since the probabilistic inference of $P|S$ from the premise set ${P|M, S|M}$ is not informative, we add $p(M|(S ee M))>0$ as a probabilistic constraint (i.e., an ``existential import assumption'') to obtain probabilistic informativeness. We show how to propagate the assigned premise probabilities to the conclusion. Thereby, we give a probabilistic meaning to all syllogisms of Figure~III. We discuss applications like generalised quantifiers (like Most $S$ are $P$) and (negated) defaults
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2019-01-01 |