8,508 research outputs found
Quasi Conjunction, Quasi Disjunction, T-norms and T-conorms: Probabilistic Aspects
We make a probabilistic analysis related to some inference rules which play
an important role in nonmonotonic reasoning. In a coherence-based setting, we
study the extensions of a probability assessment defined on conditional
events to their quasi conjunction, and by exploiting duality, to their quasi
disjunction. The lower and upper bounds coincide with some well known t-norms
and t-conorms: minimum, product, Lukasiewicz, and Hamacher t-norms and their
dual t-conorms. On this basis we obtain Quasi And and Quasi Or rules. These are
rules for which any finite family of conditional events p-entails the
associated quasi conjunction and quasi disjunction. We examine some cases of
logical dependencies, and we study the relations among coherence, inclusion for
conditional events, and p-entailment. We also consider the Or rule, where quasi
conjunction and quasi disjunction of premises coincide with the conclusion. We
analyze further aspects of quasi conjunction and quasi disjunction, by
computing probabilistic bounds on premises from bounds on conclusions. Finally,
we consider biconditional events, and we introduce the notion of an
-conditional event. Then we give a probabilistic interpretation for a
generalized Loop rule. In an appendix we provide explicit expressions for the
Hamacher t-norm and t-conorm in the unitary hypercube
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A quantum theoretical explanation for probability judgment errors
A quantum probability model is introduced and used to explain human probability judgment errors including the conjunction, disjunction, inverse, and conditional fallacies, as well as unpacking effects and partitioning effects. Quantum probability theory is a general and coherent theory based on a set of (von Neumann) axioms which relax some of the constraints underlying classic (Kolmogorov) probability theory. The quantum model is compared and contrasted with other competing explanations for these judgment errors including the representativeness heuristic, the averaging model, and a memory retrieval model for probability judgments. The quantum model also provides ways to extend Bayesian, fuzzy set, and fuzzy trace theories. We conclude that quantum information processing principles provide a viable and promising new way to understand human judgment and reasoning
Probabilistic entailment in the setting of coherence: The role of quasi conjunction and inclusion relation
In this paper, by adopting a coherence-based probabilistic approach to
default reasoning, we focus the study on the logical operation of quasi
conjunction and the Goodman-Nguyen inclusion relation for conditional events.
We recall that quasi conjunction is a basic notion for defining consistency of
conditional knowledge bases. By deepening some results given in a previous
paper we show that, given any finite family of conditional events F and any
nonempty subset S of F, the family F p-entails the quasi conjunction C(S);
then, given any conditional event E|H, we analyze the equivalence between
p-entailment of E|H from F and p-entailment of E|H from C(S), where S is some
nonempty subset of F. We also illustrate some alternative theorems related with
p-consistency and p-entailment. Finally, we deepen the study of the connections
between the notions of p-entailment and inclusion relation by introducing for a
pair (F,E|H) the (possibly empty) class K of the subsets S of F such that C(S)
implies E|H. We show that the class K satisfies many properties; in particular
K is additive and has a greatest element which can be determined by applying a
suitable algorithm
Surprisingly Rational: Probability theory plus noise explains biases in judgment
The systematic biases seen in people's probability judgments are typically
taken as evidence that people do not reason about probability using the rules
of probability theory, but instead use heuristics which sometimes yield
reasonable judgments and sometimes systematic biases. This view has had a major
impact in economics, law, medicine, and other fields; indeed, the idea that
people cannot reason with probabilities has become a widespread truism. We
present a simple alternative to this view, where people reason about
probability according to probability theory but are subject to random variation
or noise in the reasoning process. In this account the effect of noise is
cancelled for some probabilistic expressions: analysing data from two
experiments we find that, for these expressions, people's probability judgments
are strikingly close to those required by probability theory. For other
expressions this account produces systematic deviations in probability
estimates. These deviations explain four reliable biases in human probabilistic
reasoning (conservatism, subadditivity, conjunction and disjunction fallacies).
These results suggest that people's probability judgments embody the rules of
probability theory, and that biases in those judgments are due to the effects
of random noise.Comment: 64 pages. Final preprint version. In press, Psychological Revie
Interference Effects in Quantum Belief Networks
Probabilistic graphical models such as Bayesian Networks are one of the most
powerful structures known by the Computer Science community for deriving
probabilistic inferences. However, modern cognitive psychology has revealed
that human decisions could not follow the rules of classical probability
theory, because humans cannot process large amounts of data in order to make
judgements. Consequently, the inferences performed are based on limited data
coupled with several heuristics, leading to violations of the law of total
probability. This means that probabilistic graphical models based on classical
probability theory are too limited to fully simulate and explain various
aspects of human decision making.
Quantum probability theory was developed in order to accommodate the
paradoxical findings that the classical theory could not explain. Recent
findings in cognitive psychology revealed that quantum probability can fully
describe human decisions in an elegant framework. Their findings suggest that,
before taking a decision, human thoughts are seen as superposed waves that can
interfere with each other, influencing the final decision.
In this work, we propose a new Bayesian Network based on the psychological
findings of cognitive scientists. We made experiments with two very well known
Bayesian Networks from the literature. The results obtained revealed that the
quantum like Bayesian Network can affect drastically the probabilistic
inferences, specially when the levels of uncertainty of the network are very
high (no pieces of evidence observed). When the levels of uncertainty are very
low, then the proposed quantum like network collapses to its classical
counterpart
The Temporal Logic of the Tower Chief System
The purpose is to describe the logic used in the reasoning scheme employed in the Tower Chief system, a runway configuration management system. First, a review of classical logic is given. Defensible logics, truth maintenance, default logic, temporally dependent propositions, and resource allocation and planning are discussed
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