Credit risk assessment and meta-judgment

Decision-making problems in credit risk assessment and authorization are often too complex to be processed by conventional methods. They involve judgmental procedures which are by nature non-deterministic. Artificial intelligence methods can be used to approach the problem from a different angle. This paper shows the feasibility of a multi-expert approach driven by a meta-model in assessing business risk. More precisely it describes the meta-expert as part of a knowledge-based system being developed to assist bank loan officers in assessing the activities of firms applying for a loan. The use of a meta-model to represent the expert's strategic reasoning has the advantage of offering adaptable systems capable of building dynamically the resolution strategy most suited to the problem to be solved.     

Navigating Israeli citizenship: how do Arab-Palestinian teachers civicize their pupils?

This study examines citizenship education in Israel from the point of view of Arab teachers, as they rework and negotiate the content and boundaries of their Israeli citizenship. Specifically, the paper studies how teachers of citizenship education in Arab high schools in Israel perceive their sociopolitical reality, how they respond to it in their classrooms, and how they conceptualize Israeli citizenship for their pupils. In doing so, the paper ponders the pedagogical strategies and emphases of these teachers, as they mediate the citizenship education curriculum, with its heavy emphasis on the ethno-national character of Israel, to their Arab pupils.     

Local linear regression with adaptive orthogonal fitting for the wind power application

Short-term forecasting of wind generation requires a model of the function for the conversion of meteorological variables (mainly wind speed) to power production. Such a power curve is nonlinear and bounded, in addition to being nonstationary. Local linear regression is an appealing nonparametric approach for power curve estimation, for which the model coefficients can be tracked with recursive Least Squares (LS) methods. This may lead to an inaccurate estimate of the true power curve, owing to the assumption that a noise component is present on the response variable axis only. Therefore, this assumption is relaxed here, by describing a local linear regression with orthogonal fit. Local linear coefficients are defined as those which minimize a weighted Total Least Squares (TLS) criterion. An adaptive estimation method is introduced in order to accommodate nonstationarity. This has the additional benefit of lowering the computational costs of updating local coefficients every time new observations become available. The estimation method is based on tracking the left-most eigenvector of the augmented covariance matrix. A robustification of the estimation method is also proposed. Simulations on semi-artificial datasets (for which the true power curve is available) underline the properties of the proposed regression and related estimation methods. An important result is the significantly higher ability of local polynomial regression with orthogonal fit to accurately approximate the target regression, even though it may hardly be visible when calculating error criteria against corrupted data.     

Cognitive differentiation in consumer product judgments

The investigations reported in this article examined some of the individual and situational determinants of the level of cognitive product differentiation in consumer products judgements, Subjects consisted of four large samples of male and female consumers in two large metropolitan areas in the USA. These four independent studies found empirical support for the following hypothesis: consumers tend to have more differentiated judgments of negatively valenced products than positively valenced products. These findings were discussed in the context of the “vigilance”. “justification” and “Pollyanna” hypotheses. Finally, as a promising direction for future research. the need for an interactionist approach to cognitive functioning was emphasized.