Rui Da Silva Neves

Testing the descriptive validity of possibility theory in human judgments of uncertainty

Many works in the past showed that human judgments of uncertainty do not conform very well to probability theory. The present paper reports four experiments that were conducted in order to evaluate if human judgments of uncertainty conform better to possibility theory. At first, two experiments investigate the descriptive properties of some basic possibilistic measures. Then a new measurement apparatus is used, the Ψ-scale, to compare possibilistic vs. probabilistic disjunction and conjunction. Results strongly suggest that a human judgment is qualitative in essence, closer to a possibilistic than to a probabilistic approach of uncertainly. The paper also describes a qualitative heuristic, for conjunction, which was used by expert radiologists.

An Overview of Possibilistic Handling of Default Reasoning, with Experimental Studies

Abstract.This paper first provides a brief survey of a possibilistic handling of default rules. A set of default rules of the form, “generally, from α deduce β”, is viewed as the family of possibility distributions satisfying constraints expressing that the situation where α and β is true has a greater plausibility than the one where a and - β is true. When considering only the subset of linear possibility distributions, the well-known System P of postulates proposed by Kraus, Lehmann and Magidor, has been obtained. We also present two rational extensions: one based on the minimum specificity principle and the other is based on the lexicographic ordering. The second part of the paper presents an empirical study of three desirable properties for a consequence relation that capture default reasoning: Rationality, Property Inheritance and Ambiguity Preservation. An experiment is conducted to investigate 13 patterns of inference for the test of these properties. Our experimental apparatus confirms previous results on the relevance of System P, and enforces the psychological relevance of the studied properties.

Predicting causality ascriptions from background knowledge: model and experimental validation

A model is defined that predicts an agents ascriptions of causality (and related notions of facilitation and justification) between two events in a chain, based on background knowledge about the normal course of the world. Background knowledge is represented by non-monotonic consequence relations. This enables the model to handle situations of poor information, where background knowledge is not accurate enough to be represented in, e.g., structural equations. Tentative properties of causality ascriptions are discussed, and the conditions under which they hold are identified (preference for abnormal factors, transitivity, coherence with logical entailment, and stability with respect to disjunction and conjunction). Empirical data are reported to support the psychological plausibility of our basic definitions.

A Comparative Study of Six Formal Models of Causal Ascription

Ascribing causality amounts to determining what elements in a sequence of reported facts can be related in a causal way, on the basis of some knowledge about the course of the world. The paper offers a comparison of a large span of formal models (based on structural equations, non-monotonic consequence relations, trajectory preference relations, identification of violated norms, graphical representations, or connectionism), using a running example taken from a corpus of car accident reports. Interestingly enough, the compared approaches focus on different aspects of the problem by either identifying all the potential causes, or selecting a smaller subset by taking advantages of contextually abnormal facts, or by modeling interventions to get rid of simple correlations. The paper concludes by a general discussion based on a battery of criteria (several of them being proper to AI approaches to causality).