Multicriteria rankings of open-end investment funds and their stability

For research purposes, three multicriteria outranking methods (PROMETHEE, WSA and TOPSIS) were used to construct rankings of investment funds to assess their performance in the time period from January to July 2008. Nine indicators related to the distributions of return rates, purchase and management costs and to customers’ convenience were included in the set of criteria. The weight of each criterion was calculated on the basis of the relative volatility rate of the given criterion. In order to assess the stability of the rankings, the weight of a single criterion was changed (using each criterion in turn) and new rankings were constructed using the modified weights. The similarity of rankings built before and after these changes was assessed on the basis of the maximum difference between ranks and the Spearman correlation coefficient. The results obtained enable assessment not only of the stability of each outranking method, but the similarity of results obtained by different methods as well. All calculations were done using the SANNA software.investment funds, outranking methods, PROMETHEE method, WSA method, TOPSIS method, stability of rankings

A Multiple Stakeholder Multicriteria Decision Analysis in Diabetic Macular Edema Management: The MULTIDEX‑EMD Study

Background The clinical and economic management of retinal diseases has become more complex following the introduction of new intravitreal treatments. Multicriteria decision analysis (MCDA) offers the potential to overcome the challenges associated with traditional decision-making tools. Objectives A MCDA to determine the most relevant criteria to decision-making in the management of diabetic macular edema (DME) based on the perspectives of multiple stakeholders in Spain was developed. This MCDA was termed the MULTIDEX-EMD study. Methods Nineteen stakeholders (7 physicians, 4 pharmacists, 5 health authorities and health management experts, 1 psychologist, and 2 patient representatives) participated in this three-phase project. In phase A, an advisory board defined all of the criteria that could influence DME treatment decision-making. These criteria were then screened using a discrete choice experiment (DCE) (phase B). Next, a multinomial logit model was fitted by applying the backward elimination algorithm (relevant criteria: p value = 15 letters (p value < 0.001), effect duration per administration (p value = 0.008), retinal detachment (p value < 0.001), endophthalmitis (p value = 0.012), myocardial infarction (p value < 0.001), intravitreal hemorrhage (p value = 0.021), annual treatment cost per patient (p value = 0.001), health-related quality of life (HRQoL) (p value = 0.004), and disability level (p value = 0.021). Conclusions From a multi-stakeholder perspective, the selection of an appropriate treatment for DME patients should guarantee patient safety and maximize the visual acuity improvement and treatment effect duration. It should also contribute to system sustainability by being affordable, it should have a positive impact on HRQoL, and it should prevent disability

Multicriteria decision making for enhanced perception-based multimedia communication

This paper proposes an approach that integrates technical concerns with user perceptual considerations for intelligent decision making in the construction of tailor-made multimedia communication protocols. Thus, the proposed approach, based on multicriteria decision making (MDM), incorporates not only classical networking considerations, but, indeed, user preferences as well. Furthermore, in keeping with the task-dependent nature consistently identified in multimedia scenarios, the suggested communication protocols also take into account the type of multimedia application that they are transporting. Lastly, this approach also opens the possibility for such protocols to dynamically adapt based on a changing operating environment and user's preferences

Multicriteria analysis under uncertainty with IANUS - method and empirical results

IANUS is a method for aiding public decision-making that supports efforts towards sustainable development and has a wide range of application. IANUS stands for Integrated Assessment of Decisions uNder Uncertainty for Sustainable Development. This paper introduces the main features of IANUS and illustrates the method using the results of a case study in the Torgau region (eastern Germany). IANUS structures the decision process into four steps: scenario derivation, criteria selection, modeling, evaluation. Its overall aim is to extract the information needed for a sound, responsible decision in a clear, transparent manner. The method is designed for use in conflict situations where environmental and socioeconomic effects need to be considered and so an interdisciplinary approach is required. Special emphasis is placed on a broad perception and consideration of uncertainty. Three types of uncertainty are explicitly taken into account by IANUS: development uncertainty (uncertainty about the social, economic and other developments that affect the consequences of decision), model uncertainty (uncertainty associated with the prediction of the effects of decisions), and weight uncertainty (uncertainty about the appropriate weighting of the criteria). The backbone of IANUS is a multicriteria method with the ability to process uncertain information. In the case study the multicriteria method PROMETHEE is used. Since PROMETHEE in its basic versions is not able to process uncertain information an extension of this method is developed here and described in detail. --

Multicriteria Analysis of Neural Network Forecasting Models: An Application to German Regional Labour Markets

This paper develops a flexible multi-dimensional assessment method for the comparison of different statistical-econometric techniques based on learning mechanisms with a view to analysing and forecasting regional labour markets. The aim of this paper is twofold. A first major objective is to explore the use of a standard choice tool, namely Multicriteria Analysis (MCA), in order to cope with the intrinsic methodological uncertainty on the choice of a suitable statistical-econometric learning technique for regional labour market analysis. MCA is applied here to support choices on the performance of various models -based on classes of Neural Network (NN) techniques-that serve to generate employment forecasts in West Germany at a regional/district level. A second objective of the paper is to analyse the methodological potential of a blend of approaches (NN-MCA) in order to extend the analysis framework to other economic research domains, where formal models are not available, but where a variety of statistical data is present. The paper offers a basis for a more balanced judgement of the performance of rival statistical tests