A group decision-making methodology with incomplete individual beliefs applied to e-Democracy

We consider the situation where there are several alternatives for investing a quantity of money to achieve a set of objectives. The choice of which alternative to apply depends on how citizens and political representatives perceive that such objectives should be achieved. All citizens with the right to vote can express their preferences in the decision-making process. These preferences may be incomplete. Political representatives represent the citizens who have not taken part in the decision-making process. The weight corresponding to political representatives depends on the number of citizens that have intervened in the decision-making process. The methodology we propose needs the participants to specify for each alternative how they rate the different attributes and the relative importance of attributes. On the basis of this information an expected utility interval is output for each alternative. To do this, an evidential reasoning approach is applied. This approach improves the insightfulness and rationality of the decision-making process using a belief decision matrix for problem modeling and the Dempster?Shafer theory of evidence for attribute aggregation. Finally, we propose using the distances of each expected utility interval from the maximum and the minimum utilities to rank the alternative set. The basic idea is that an alternative is ranked first if its distance to the maximum utility is the smallest, and its distance to the minimum utility is the greatest. If only one of these conditions is satisfied, a distance ratio is then used

Hierarchical Concurrent Engineering in a Multiagent Framework

Our experience indicates coordination in concurrent engineering (CE) requires support for two types of relationships among decision makers supervisor/subordinate and peer-to-peer Supervisor/subordinate relationships are created by the standard hierarchical decomposition process that is required to solve any large design problem Peer-to-peer relationships arise when teams of decision makers must interact, without direct guidance, to achieve individual and common goals In this paper, we describe a general decision-making methodology, which we call hierarchical CE The emphasis of hierarchical CE is to provide support for both supervisor/subordinate and peer-to-peer relationships In addition to the concept of hierarchical CE, we present a supporting agent-based framework in which the preferences and constraints of a design supervi sor are distributed to design subordinates, who are expected to exploit their local expertise within the context provided by this global information A distinct separation between feasibility and value facilitates optimal decision-making by design agents, since the bounds on feasibility do not include arbitrary statements about value This distinction may prove useful for other problem domains as wellPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/68258/2/10.1177_1063293X9600400105.pd

Applying new uncertainty related theories and multicriteria decision analysis methods to snow avalanche risk management

International audienceMaking the best decision in the event of a snow avalanche encounters problems in the assessment and management process because of the lack of information and knowledge on natural phenomena and the heterogeneity and reliability of the information sources available (historical data, field measurements, and expert assessments). One major goal today is therefore to aid decision making by improving the quality, quantity, and reliability of the available information. This article presents a new method called evidential reasoning and multicriteria decision analysis (ER-MCDA) to help decision making by considering information imperfections arising from several more or less reliable and possibly conflicting sources of information. First, the principles of the existing methods are reviewed. Classical methods of multicriteria decision making and existing theories attempting to represent and propagate information imperfections are described. In a second point, we describe the principle of the ER-MCDA method combining multicriteria decision analysis (MCDA) to model the decision-making process and fuzzy sets theory, possibility theory, and evidence theory to represent, fuse and propagate information imperfections. Experts, considered more or less reliable, provide imprecise and uncertain evaluations of quantitative and qualitative criteria that are combined through information fusion. The method is applied to a simplified version of an existing system aiming to evaluate the sensitivity of avalanche sites. This new method makes it possible to consider both the importance of the information available and reliability in the decision process. It also contributes to improving traceability. Other developments designed to handle other assessment problems such as avalanche triggering conditions or data quality are in progress

A MAUT approach for reusing ontologies

Knowledge resource reuse has become a popular approach within the ontology engineering field, mainly because it can speed up the ontology development process, saving time and money and promoting the application of good practices. The NeOn Methodology provides guidelines for reuse. These guidelines include the selection of the most appropriate knowledge resources for reuse in ontology development. This is a complex decision-making problem where different conflicting objectives, like the reuse cost, understandability, integration workload and reliability, have to be taken into account simultaneously. GMAA is a PC-based decision support system based on an additive multi-attribute utility model that is intended to allay the operational difficulties involved in the Decision Analysis methodology. The paper illustrates how it can be applied to select multimedia ontologies for reuse to develop a new ontology in the multimedia domain. It also demonstrates that the sensitivity analyses provided by GMAA are useful tools for making a final recommendation

Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment

Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteria decision analysis (MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This review paper presents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if … then …” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches

Online Performance Tracking

This paper describes the conceptual framework, development process, and theoretical structure for an online performance tracking system. The principle factors influencing online performance tracking are described using the weighted sum model as computational method on measures of performance. Input data for the computational model were obtained directly from a real-time system in an actual organization that directly measured staff performance. In this multicriteria decision-making approach, the criteria weights are computed using the entropy information method and ranking of 15 alternatives (employees) is computed using the weighted sum model. Computational results obtained using the online performance appraisal system are evaluated and discussed relative to the weighted sum model

Multi-Attribute Decision Tree Evaluation in Imprecise and Uncertain Domains

Abstract We present a decision tree evaluation method integrated with a common framework for analyzing multi-attribute decisions under risk, where information is numerically imprecise. The approach extends the use of additive and multiplicative utility functions for supporting evaluation of imprecise statements, relaxing requirements for precise estimates of decision parameters. Information is modeled in convex sets of utility and probability measures restricted by closed intervals. Evaluation is done relative to a set of rules, generalizing the concept of admissibility, computationally handled through optimization of aggregated utility functions. Pros and cons of two approaches, and tradeoffs in selecting a utility function, are discussed