1,720,965 research outputs found
Using Social Choice Function Vs. Social Welfare Function To Aggregate Individual Preferences In Group Decision Support Systems
Get PDFIn multi-criteria decision making, any Group Decision Support System (GDSS) requires a “social judgment model” for calculation of weights on decision alternatives, and tabulation of individual votes toward a consensus. One could assess a Social Welfare Function - such as Keeney’s - to aggregate individual cardinal preferences or utilities into a group preference. Alternatively, one could use Social Choice Functions - such as Condorcet, Borda, Copeland, and Eigenvector - to aggregate individual ordinal preferences or rankings into a group ranking. This study empirically investigates the consensus between individual preferences and the group preference derived from various aggregation methods
Learning And Predicting Individual Preferences In Multicriteria Decision Making With Neural Networks Vs. Utility Functions
Get PDFThis paper reports an empirical investigation into the performance of neural network technique vs. traditional utility theory-based method in capturing and predicting individual preference in multi-criteria decision making. As a universal function approximator, a neural network can assess individual utility function without imposing strong assumptions on functional form and behavior of the underlying data. Results of this study show that in all cases, the predictive ability of neural network technique was comparable to the multi-attribute utility theory-based models.
Forecasting macroeconomic models with artificial neural networks : an empirical investigation into the foundation for an intelligent forecasting system
Get PDFThis study investigates the foundation of an intelligent system using Artificial Intelligent (AI) technologies to assist decision makers in a specific business problem, namely business forecasting. In time series and macroeconomic modelling, there are many assumptions being imposed on the behavior and functional relationship of the underlying variables. In addition, one may face the complexity in the estimation of these models. This study uses Artificial Neural Network (ANN) and other AI technologies in an effective forecasting system in order to overcome the restrictions of traditional modelling and estimation methods. An ANN has been shown to be a universal function approximator (Cybenko, 1989; Hornik et al., 1989). It requires no prior assumptions on the behavior and functional form of the related variables but it is still able to capture the underlying dynamic and nonlinear relationships among variables in the problem space, ie. a macroeconomic model in this context. This study integrates the powerful ability of an ANN into an efficient framework incorporating Recurrent Algorithms (Jordan, 1986), Genetic Algorithms (Holland, 1975) in a Mixture-of-experts Architecture (Jacobs et al., 1991) to obtain accurate estimation and forecasts. As such, this study addresses the ability of a versatile intelligent technology to solve a general economic forecasting problem involving temporal and non-temporal variables. Using the contexts provided in the Klein Model I of the US interwar economy in 1921-1941 and the Klein-Goldberger Model of the US economy in 1929-1952, this study investigates the relative performance of the proposed system and traditional methods in modelling and forecasting a mix of economic variables. It extends these frameworks into the future to forecast with more recent data. The study specifies the conditions that will make the implementation of ANN more successful in estimation and forecasting. This study provides evidence on the effectiveness and efficiency of the proposed system. It asserts empirically the ability of the integrated ANN and GA in estimation and forecasting. The findings should contribute positively to the development of theory, methodology, and practice of using AI tools, particularly ANN and GA, to build intelligent forecasting systems
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Trust and Quality Assurance in Business-to-Consumer Electronic Commerce: Enhancing Consumer Acceptance and Participation
Get PDFBusiness Strategies in Credit Rating and the Control of Misclassification Costs in Neural Network Predictions
Get PDFLearning and Predicting Group Preference: Modeling Group Utility Function with Artificial Neural Networks
Get PDFThis paper reports on an empirical investigation into the ability of Artificial Neural Networks (ANN) in approximating group preference. Given the existence of a group utility function in a multi-criteria decision context, an ANN, as a universal function approximator, would be able to recognize group decision patterns, approximate the underlying functional relationship, and generalize over new cases. The merit of ANN over the traditional utility theory approach is in its ability to learn group preference without imposing strong assumptions on the functional form and behavior of decision patterns
On Learning and Predicting Preference with Artificial Neural Networks: Some Preliminary Results
Get PDFThis paper reports on an empirical investigation into the ability of Artificial Neural Network (ANN) technique in learning and predicting preference. ANNs were used to learn preference patterns of holistic judgments on a sample of multi-criteria decision alternatives defined by the orthogonal design. Then a comparative study was conducted with utility theory-based models and ANNs to predict decision makers’ choice. In all cases, the predictive ability of ANN was found to be as good as or better than those of utility theory-based models
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
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