Decision Support Systems of DIDAS Family

Lewandowski, Andrzej; Kreglewski, Tomasz; Rogowski, T.; Wierzbicki, Andrzej P.

doi:10.1007/978-3-662-21637-8_2

Cited by 14 publications

(9 citation statements)

References 6 publications

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“…Such approaches were further generalized by the methodology of reference point or aspiration-led optimization (e.g. Wierzbicki, 1980Wierzbicki, , 1986Wierzbicki, , 1992bLewandowski and Wierzbicki, 1989). This methodology uses both optimization and adaptive formation of aspirations, but rather as instruments of interaction between the decision maker and the computer than prescriptive assumptions concerning human behaviour.…”

Section: The Concept Of Rationality In Decision Theory and Philosophymentioning

confidence: 99%

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On the Role of Intuition in Decision Making and Some Ways of Multicriteria Aid of Intuition

Wierzbicki

1997

J. Multi-Crit. Decis. Anal.

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Practical experiences in decision-making Ð including the experience in the reform of the system of funding of scientific research in Poland Ð lead to the conclusion that it is important to understand better the role of intuition in decision-making processes. For this purpose a more specific definition of intuition is proposed. One of the conclusions from this definition is that multicriteria aid for decisions should concentrate more on supporting earlier stages of decision processes that precede choice. The role of and possible tools for multicriteria aid for model simulation and analysis are presented.

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Section: The Concept Of Rationality In Decision Theory and Philosophymentioning

confidence: 99%

“…Lewandowski and Wierzbicki, 1989), another essential phase of implementation was added. In order to better understand creative or strategic, intuitive decision processes, we should propose a different distinction of their phases.…”

Section: Phases Of Creative or Strategic Intuitive Decision Processesmentioning

confidence: 99%

On the Role of Intuition in Decision Making and Some Ways of Multicriteria Aid of Intuition

Wierzbicki

1997

J. Multi-Crit. Decis. Anal.

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“…There is a great variety of DSS's classifications: (Sawaragi, 1986;Naisbitt, 1982;Van Hee, 1986;Lewandowski et al, 1986). We shall follow the most practical (Sawaragi, 1986) classification, according to which DSS could be divided into two main groups: mathematical modelling and multiple criteria decision making systems.…”

Section: Introductionmentioning

confidence: 99%

Methodology and Software for Interactive Decision Support

Lewandowski¹,

Stanchev²

1989

Lecture Notes in Economics and Mathematical Systems

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The workshop was held in Albena, on the Black Sea coast. More than 80 scientists from 15 countries attended the workshop; 50 lectures were presented and 17 computer demonstration sessions took place. This Workshop is one of a series of meetings organized by nASA with the collaboration of scientific institutions from the National Member Organization countries. The previous meetings took place in Austria (1983), Hungary (1984) and the German Democratic Republic (1985). All proceedings of these meetings have been published by Springer Verlag in the series Lecture Notes in Economics and Mathematical Systems.The research on decision support systems has long tradition at nASA. The Institute, being the forum for common research of scientists from East and West, with different cultural backgrounds and different experiences with real life applications of their results, operates the international network of scientific institutions involved in research related to the methodology of decision analysis and decision support systems. The approach to this research assumes high level of synergy between three main components: methodological and theoretical backgrounds, computer implementation of decision support systems and real life applications. This synergy is reflected in the subject of papers presented during the workshop as well as in the structure of the Proceedings which is divided in three main sections.In the first section, Theory and Algorithms for Multiple Criteria Optimization, new theoretical developments in multiple criteria optimization are presented. Tanino presents new approach for application of multiple objective optimization in hierarchical systems, Wierzbicki discusses the relation between simulation and gaming for conflict resolution as well as proposes the interactive framework for supporting this kind of decision problems. The other group of papers addresses purely theoretical questionsNakayama presents recent developments in sensitivity and trade-offs analysis in multiobjective programming, Ramesh and others discuss the degeneracy problem. Finally, several papers are more algorithmic oriented -like paper by Katoh with presentation of new algorithms for bicriteria combinatorial optimization or the paper presenting the reachable set approach by £otov. Finally, Steuer presents the current state-of-the art in the interactive multiple criteria optimization.In the second section, Theory, Methodology and Software for Decision Support Systems, the principles of building decision support systems are presented as well as software IV tools constituting the building components of such systems. Moreover, several papers are devoted to the general methodology of building such systems or present experimental design of systems supporting certain class of decision problems. For example, paper by Makowski presents the HYBRID system for solving dynamic linear multiple criteria problems using the aspiration level paradigm. This system has been used as component of several practical implementations of DSS. Similar nature has ...

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“…It provides an opportunity to define expectations in fuzzy terms like: "minimize the probability of shortfall to medium profit." Moreover, due to aggregation on intervals (4), the following assertion is valid.…”

Section: Fuzzy Targetsmentioning

confidence: 85%

“…This can be achieved with the so-called quasisatisficing approach to multiple criteria optimization introduced as the reference point method [15] and later extended the aspiration/reservation based decision support (ARBDS) approach with many successful applications [4]. In the ARBDS interactive scheme the decision maker (DM) specifies requirements in terms of aspiration and reservation levels, i.e., by introducing acceptable and required values for several criteria.…”

Section: Reference Point Methods and Fuzzy Chancesmentioning

confidence: 99%

On Fuzzy Driven Support for SD-Efficient Portfolio Selection

Ogryczak

Romaszkiewicz

Adaptive and Natural Computing Algorithms

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Abstract. The stochastic dominance (SD) is based on an axiomatic model of risk-averse preferences and therefore, the SD-efficiency is an important property of selected portfolios. As defined with a continuum of criteria representing some measures of failure in achieving several targets, the SD does not provide us with a simple computational recipe. While limiting to a few selected target values one gets a typical multiple criteria optimization model approximating the corresponding SD approach. Although, it is rather difficult to justify a selection of a few target values, this difficulty can be overcome with the effective use of fuzzy target values. While focusing on the first degree SD and extending the target membership functions to some monotonic utility functions we get the multiple criteria model which preserves the consistency with both the first degree and the second degree SD. Further applying the reference point methodology to the multiple criteria model and taking advantages of fuzzy chance specifications we get the method that allows to model interactively the preferences by fuzzy specification of the desired distribution. The model itself guarantees that every generated solution is efficient according to the SD rules.

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Decision Support Systems of DIDAS Family

Cited by 14 publications

References 6 publications

On the Role of Intuition in Decision Making and Some Ways of Multicriteria Aid of Intuition

On the Role of Intuition in Decision Making and Some Ways of Multicriteria Aid of Intuition

Methodology and Software for Interactive Decision Support

On Fuzzy Driven Support for SD-Efficient Portfolio Selection

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