Expertise-based ranking of experts: An assessment level approach

The quality of a formal decision is influenced by the level of expertise of the decision makers (DMs). The composition of a team of DMs can change when new members join or old members leave, based on their ranking. In order to improve the quality of decisions, this ranking should be based on their demonstrated expertise. This paper proposes using the experts’ expertise levels, in terms of ‘the ability to differentiate consistently’, to determine their ranking, according to the level at which they assess alternatives. The expertise level is expressed using the CWS-Index (Cochran-Weiss-Shanteau), a ratio between Discrimination and Inconsistency. The experts give their evaluations using pairwise comparisons of Fuzzy Preference Relations with an Additive Consistency property. This property can be used to generate estimators, and replaces the repetition needed to obtain the CWS-Index. Finally, a numerical example is discussed to illustrate the model for producing expertise-based ranking of experts

Triple-acyclicity in majorities based on difference in support

In this paper we study to what extent majorities based on difference in support leads to triple-acyclic collective decisions. These majorities, which take into account voters' intensities of preference between pairs of alternatives through reciprocal preference relations, require to the winner alternative to exceed the support for the other alternative in a difference fixed before the election. Depending on that difference, i.e., on the threshold of support, and on some requirements on the individual rationality of the voters, we provide necessary and sufficient conditions for avoiding cycles of three alternatives on the collective decision

Statistical properties of Lorenz like flows, recent developments and perspectives

We comment on mathematical results about the statistical behavior of Lorenz equations an its attractor, and more generally to the class of singular hyperbolic systems. The mathematical theory of such kind of systems turned out to be surprisingly difficult. It is remarkable that a rigorous proof of the existence of the Lorenz attractor was presented only around the year 2000 with a computer assisted proof together with an extension of the hyperbolic theory developed to encompass attractors robustly containing equilibria. We present some of the main results on the statisitcal behavior of such systems. We show that for attractors of three-dimensional flows, robust chaotic behavior is equivalent to the existence of certain hyperbolic structures, known as singular-hyperbolicity. These structures, in turn, are associated to the existence of physical measures: \emph{in low dimensions, robust chaotic behavior for flows ensures the existence of a physical measure}. We then give more details on recent results on the dynamics of singular-hyperbolic (Lorenz-like) attractors.Comment: 40 pages; 10 figures; Keywords: sensitive dependence on initial conditions, physical measure, singular-hyperbolicity, expansiveness, robust attractor, robust chaotic flow, positive Lyapunov exponent, large deviations, hitting and recurrence times. Minor typos corrected and precise acknowledgments of financial support added. To appear in Int J of Bif and Chaos in App Sciences and Engineerin

Learning implicit recommenders from massive unobserved feedback

In this thesis we investigate implicit feedback techniques for real-world recommender systems. However, learning a recommender system from implicit feedback is very challenging, primarily due to the lack of negative feedback. While a common strategy is to treat the unobserved feedback (i.e., missing data) as a source of negative signal, the technical difficulties cannot be overlooked: (1) the ratio of positive to negative feedback in practice is highly imbalanced, and (2) learning through all unobserved feedback (which easily scales to billion level or higher) is computationally expensive. To effectively and efficiently learn recommender models from implicit feedback, two types of methods are presented, that is, negative sampling based stochastic gradient descent (NS-SGD) and whole sample based batch gradient descent (WS-BGD). Regarding the NS-SGD method, how to effectively sample informative negative examples to improve recommendation algorithms is investigated. More specifically, three learning models called Lambda Factorization Machines (lambdaFM), Boosting Factorization Machines (BoostFM) and Geographical Bayesian Personalized Ranking (GeoBPR) are described. While regarding the WS-BGD method, how to efficiently use all unobserved implicit feedback data rather than resorting to negative sampling is studied. A fast BGD learning algorithm is proposed, which can be applied to both basic collaborative filtering and content/context-aware recommendation settings. The last research work is on the session-based item recommendation, which is also an implicit feedback scenario. However, different from above four works based on shallow embedding models, we apply deep learning based sequence-to-sequence model to directly generate the probability distribution of next item. The proposed generative model can be applied to various sequential recommendation scenarios. To support the main arguments, extensive experiments are carried out based on real-world recommendation datasets. The proposed recommendation algorithms have achieved significant improvements in contrast with strong benchmark models. Moreover, these models can also serve as generic solutions and solid baselines for future implicit recommendation problems

Implicit indefinite objects at the syntax-semantics-pragmatics interface: a probabilistic model of acceptability judgments

Optionally transitive verbs, whose Patient participant is semantically obligatory but syntactically optional (e.g., to eat, to drink, to write), deviate from the transitive prototype defined by Hopper and Thompson (1980). Following Fillmore (1986), unexpressed objects may be either indefinite (referring to prototypical Patients of a verb, whose actual entity is unknown or irrelevant) or definite (with a referent available in the immediate intra- or extra-linguistic context). This thesis centered on indefinite null objects, which the literature argues to be a gradient, non-categorical phenomenon possible with virtually any transitive verb (in different degrees depending on the verb semantics), favored or hindered by several semantic, aspectual, pragmatic, and discourse factors. In particular, the probabilistic model of the grammaticality of indefinite null objects hereby discussed takes into account a continuous factor (semantic selectivity, as a proxy to object recoverability) and four binary factors (telicity, perfectivity, iterativity, and manner specification). This work was inspired by Medina (2007), who modeled the effect of three predictors (semantic selectivity, telicity, and perfectivity) on the grammaticality of indefinite null objects (as gauged via Likert-scale acceptability judgments elicited from native speakers of English) within the framework of Stochastic Optimality Theory. In her variant of the framework, the constraints get floating rankings based on the input verb’s semantic selectivity, which she modeled via the Selectional Preference Strength measure by Resnik (1993, 1996). I expanded Medina’s model by modeling implicit indefinite objects in two languages (English and Italian), by using three different measures of semantic selectivity (Resnik’s SPS; Behavioral PISA, inspired by Medina’s Object Similarity measure; and Computational PISA, a novel similarity-based measure by Cappelli and Lenci (2020) based on distributional semantics), and by adding iterativity and manner specification as new predictors in the model. Both the English and the Italian five-predictor models based on Behavioral PISA explain almost half of the variance in the data, improving on the Medina-like three-predictor models based on Resnik’s SPS. Moreover, they have a comparable range of predicted object-dropping probabilities (30-100% in English, 30-90% in Italian), and the predictors perform consistently with theoretical literature on object drop. Indeed, in both models, atelic imperfective iterative manner-specified inputs are the most likely to drop their object (between 80% and 90%), while telic perfective non-iterative manner-unspecified inputs are the least likely (between 30% and 40%). The constraint re-ranking probabilities are always directly proportional to semantic selectivity, with the exception of Telic End in Italian. Both models show a main effect of telicity, but the second most relevant factor in the model is perfectivity in English and manner specification in Italian

ISIPTA'07: Proceedings of the Fifth International Symposium on Imprecise Probability: Theories and Applications

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