An extended minimax disparity to determine the OWA operator weights

This paper contributes to extend the minimax disparity to determine the ordered weighted averaging (OWA) model based on linear programming. It introduces the minimax disparity approach between any distinct pairs of the weights and uses the duality of linear programming to prove the feasibility of the extended OWA operator weights model. The paper finishes with an open problem. © 2006 Elsevier Ltd. All rights reserved.

Zero weights and non-zero slacks:different solution to the same problem

This paper re-assesses three independently developed approaches that are aimed at solving the problem of zero-weights or non-zero slacks in Data Envelopment Analysis (DEA). The methods are weights restricted, non-radial and extended facet DEA models. Weights restricted DEA models are dual to envelopment DEA models with restrictions on the dual variables (DEA weights) aimed at avoiding zero values for those weights; non-radial DEA models are envelopment models which avoid non-zero slacks in the input-output constraints. Finally, extended facet DEA models recognize that only projections on facets of full dimension correspond to well defined rates of substitution/transformation between all inputs/outputs which in turn correspond to non-zero weights in the multiplier version of the DEA model. We demonstrate how these methods are equivalent, not only in their aim but also in the solutions they yield. In addition, we show that the aforementioned methods modify the production frontier by extending existing facets or creating unobserved facets. Further we propose a new approach that uses weight restrictions to extend existing facets. This approach has some advantages in computational terms, because extended facet models normally make use of mixed integer programming models, which are computationally demanding.

Determining more realistic OWA weights

The concept of ordered weighted averaging (OWA) operator weights arises in uncertain decision making problems, however some weights may have a specific relationship with other. This information about the weights can be obtained from decision makers (DMs). This paper intends to introduce a theory of weight restrictions into the existing OWA operator weight models. Based on the DMs' value judgment the obtained OWA operator weights could be more realistic.

A method for automatically eliciting node weights in a hierarchical knowledge-based structure for reasoning with uncertainty

Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decisionmaking processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalised to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

A new fuzzy additive model for determining the common set of weights in Data Envelopment Analysis

The main advantage of Data Envelopment Analysis (DEA) is that it does not require any priori weights for inputs and outputs and allows individual DMUs to evaluate their efficiencies with the input and output weights that are only most favorable weights for calculating their efficiency. It can be argued that if DMUs are experiencing similar circumstances, then the pricing of inputs and outputs should apply uniformly across all DMUs. That is using of different weights for DMUs makes their efficiencies unable to be compared and not possible to rank them on the same basis. This is a significant drawback of DEA; however literature observed many solutions including the use of common set of weights (CSW). Besides, the conventional DEA methods require accurate measurement of both the inputs and outputs; however, crisp input and output data may not relevant be available in real world applications. This paper develops a new model for the calculation of CSW in fuzzy environments using fuzzy DEA. Further, a numerical example is used to show the validity and efficacy of the proposed model and to compare the results with previous models available in the literature.

Evolving weights for a new United Kingdom Divisia

Divisia money is a monetary aggregate that gives each component asset an assigned weight. We use an evolutionary neural network to calculate new Divisia weights for each component utilising the Bank of England monetary data for the U.K. We propose a new monetary aggregate using our newly derived weights to carry out quantitative inflation prediction. The results show that this new monetary aggregate has better inflation forecasting performance than the traditionally constructed Bank of England Divisa money. This result is important for monetary policymakers, as improved construction of monetary aggregates will yield tighter relationships between key macroeconomic variables and ultimately, greater macroeconomic control. Research is ongoing to establish the extent of the increased information content and parameter stability of this new monetary aggregate.

A scoring system for early prognostic assessment after neonatal seizures

OBJECTIVE: The aim of this study was to devise a scoring system that could aid in predicting neurologic outcome at the onset of neonatal seizures. METHODS: A total of 106 newborns who had neonatal seizures and were consecutively admitted to the NICU of the University of Parma from January 1999 through December 2004 were prospectively followed-up, and neurologic outcome was assessed at 24 months’ postconceptional age. We conducted a retrospective analysis on this cohort to identify variables that were significantly related to adverse outcome and to develop a scoring system that could provide early prognostic indications. RESULTS: A total of 70 (66%) of 106 infants had an adverse neurologic outcome. Six variables were identified as the most important independent risk factors for adverse outcome and were used to construct a scoring system: birth weight, Apgar score at 1 minute, neurologic examination at seizure onset, cerebral ultrasound, efficacy of anticonvulsant therapy, and presence of neonatal status epilepticus. Each variable was scored from 0 to 3 to represent the range from “normal” to “severely abnormal.” A total composite score was computed by addition of the raw scores of the 6 variables. This score ranged from 0 to 12. A cutoff score of =4 provided the greatest sensitivity and specificity. CONCLUSIONS: This scoring system may offer an easy, rapid, and reliable prognostic indicator of neurologic outcome after the onset of neonatal seizures. A final assessment of the validity of this score in routine clinical practice will require independent validation in other centers.

Improving minimax disparity model to determine the OWA operator weights

Determining the Ordered Weighted Averaging (OWA) operator weights is important in decision making applications. Several approaches have been proposed in the literature to obtain the associated weights. This paper provides an alternative disparity model to identify the OWA operator weights. The proposed mathematical model extends the existing disparity approaches by minimizing the sum of the deviation between two distinct OWA weights. The proposed disparity model can be used for a preference ranking aggregation. A numerical example in preference ranking and an application in search engines prove the usefulness of the generated OWA weights.

A comparison of antibiotic prescribing indicators and medicines management scoring in secondary care

Poster: - Robust prescribing indicators analogous to those used in primary care are not available currently in NHS hospital trusts - The Department of Health has recently implemented a scheme for self-assessment scoring medicines management processes (maximum 23) in NHS hospitals - There is no clear relationship between average values for two antibiotic prescribing indicators obtained in ten NHS hospital trusts in the West Midlands - There is no clear relationship between either indicator value and the corresponding self-assessment medicines management score - This study highlights the difficulties involved in assessing the medicines management processes in NHS hospitals; better medicines management evaluation systems are needed

The value of indirect ties in citation networks:SNA analysis with OWA operator weights

This paper seeks to advance the theory and practice of the dynamics of complex networks in relation to direct and indirect citations. It applies social network analysis (SNA) and the ordered weighted averaging operator (OWA) to study a patent citations network. So far the SNA studies investigating long chains of patents citations have rarely been undertaken and the importance of a node in a network has been associated mostly with its number of direct ties. In this research OWA is used to analyse complex networks, assess the role of indirect ties, and provide guidance to reduce complexity for decision makers and analysts. An empirical example of a set of European patents published in 2000 in the renewable energy industry is provided to show the usefulness of the proposed approach for the preference ranking of patent citations.