Alexis Tsoukiàs

Multicriteria decision-aid

The Set of Actions. Preference Modeling. The Basic Concepts of Multicriteria Decision--Aid. Multiple Attribute Utility Theory. Outranking Methods. Interactive Methods. Miscellaneous Questions. Bibliography. Index to Main Subject Areas.

Incomplete Information Tables and Rough Classification

The rough set theory, based on the original definition of the indiscernibility relation, is not useful for analysing incomplete information tables where some values of attributes are unknown. In this paper we distinguish two different semantics for incomplete information: the “missing value” semantics and the “absent value” semantics. The already known approaches, e.g. based on the tolerance relations, deal with the missing value case. We introduce two generalisations of the rough sets theory to handle these situations. The first generalisation introduces the use of a non symmetric similarity relation in order to formalise the idea of absent value semantics. The second proposal is based on the use of valued tolerance relations. A logical analysis and the computational experiments show that for the valued tolerance approach it is possible to obtain more informative approximations and decision rules than using the approach based on the simple tolerance relation.

On the Extension of Rough Sets under Incomplete Information

The rough set theory, based on the conventional indiscernibility relation, is not useful for analysing incomplete information. We introduce two generalizations of this theory. The first proposal is based on non symmetric similarity relations, while the second one uses valued tolerance relation. Both approaches provide more informative results than the previously known approach employing simple tolerance relation.

From decision theory to decision aiding methodology

The paper presents the author’s partial and personal historical reconstruction of how decision theory is evolving to a decision aiding methodology. The presentation shows mainly how “alternative” approaches to classic decision theory evolved. In the paper it is claimed that all such decision “theories” share a common methodological feature, which is the use of formal and abstract languages as well as of a model of rationality. Different decision aiding approaches can thus be defined, depending on the origin of the model of rationality used in the decision aiding process. The concept of decision aiding process is then introduced and analysed. The paper’s ultimate claim is that all such decision aiding approaches can be seen as part of a decision aiding methodology.

Evaluation and Decision Models

1. Introduction. 2. Choosing on the basis of several opinions. 3. Building and aggregating evaluations. 4. Constructing measures. 5. Assessing competing projects. 6. Comparing on several attributes. 7. Deciding automatically. 8. Dealing with uncertainty. 9. Supporting decisions. Appendix A. Appendix B. 10. Conclusion. Bibliography. Index.

IusWare: a methodology for the evaluation and selection of software products

IusWare (IUStitia SoftWARis) is a methodology designed to evaluate software products in a formal and rigorous way. The methodology is based on the multicriteria decision aid approach and encompasses activities such as the comparison, assessment and selection of software artefacts. The methodology defines an evaluation process which consists of two main phases: designing an evaluation model and applying it. The design phase is made up of the following activities: (1) identifying the actors that are relevant to the evaluation, their roles, the purpose of the evaluation, the resources available and the object(s) of the evaluation; (2) identifying the type of evaluation required: either a formal description of products or the ranking of products from the most preferred to the least preferred, or a partitioning into two sets of the best and the remaining products; (3) defining a nonredundant hierarchy of evaluation attributes, often corresponding with the quality characteristics of quality models; (4) associating a measure, a criterion scale and a function to transform the measure scale into the criterion scale to each basic attribute; and (5) choosing an aggregation technique so as to aggregate values on criteria to form a recommendation for the selection. In the application phase, attributes of products are measured, the measures are transformed into values on criteria and aggregated to form a recommendation.

On the concept of decision aiding process: an operational perspective

Abstract The paper presents the concept of decision aiding process as an extension of the decision process. The aim of the paper is to analyse the type of activities occurring between a “client” and an “analyst” both engaged in a decision process. The decision aiding process is analysed both under a cognitive point of view and an operational point of view: i.e. considering the “products”, or cognitive artifacts the process will deliver at the end. Finally the decision aiding process is considered as a reasoning process for which the update and revision problems hold.

A new axiomatic foundation of partial comparability

The paper presents some results obtained in searching for a new axiomatic foundation for partial comparability (PC) in the frame of non-conventional preference modeling. The basic idea is to define an extended preference structure able to represent lack of information, uncertainty, ambiguity, multidimensional and conflicting preferences, using formal logic as the basic formalism.A four-valued paraconsistent logic is therefore described in the paper as a more suitable language for the purposes of the research. The concepts of partition, general binary relations properties, fundamental relational system of preferences (f.r.s.p.), maximal f.r.s.p. and well founded f.r.s.p. are then introduced and some theorems are demonstrated in order to provide the axiomatic foundation of PC. The main result obtained is a preference structure that is a maximal well founded f.r.s.p. This preference structure facilitates a more flexible, reliable and robust preference modeling. Moreover it can be viewed as a generalization of the conventional approach, so that all the results obtained until now can be used under it.Two examples are provided at the end of the paper in order to give an account of the operational potentialities of the new theory, mainly in the area of multicriteria decision aid and social choice theory. Further research directions conclude the paper.

ESSE: an expert system for software evaluation

Abstract Solving software evaluation problems is a particularly difficult software engineering process and many different—often contradictory—criteria must be considered in order to reach a decision. This paper presents ESSE, a prototype expert system for software evaluation that embodies various aspects of the Multiple-Criteria Decision Aid (MCDA) methodology. Its main features are the flexibility in problem modeling and the built-in knowledge about software problem solving and software attribute assessment. Evaluation problems are modeled around top-level software attributes, such as quality and cost. Expert assistants guide the evaluator in feeding values to the decision model. ESSE covers all important dimensions of software evaluation through the integration of different technologies.

Knowledge based evaluation of software systems: a case study☆

Solving software evaluation problems is a particularly difficult software engineering process and many contradictory criteria must be considered to reach a decision. Nowadays, the way that decision support techniques are applied suffers from a number of severe problems, such as naive interpretation of sophisticated methods and generation of counter-intuitive, and therefore most probably erroneous, results. In this paper we identify some common flaws in decision support for software evaluations. Subsequently, we discuss an integrated solution through which significant improvement may be achieved, based on the Multiple Criteria Decision Aid methodology and the exploitation of packaged software evaluation expertise in the form of an intelligent system. Both common mistakes and the way they are overcome are explained through a real world example.