Handling Preference Information for Multiple-Criteria Decision-Making: Theory and Applications in Management, Economics and Finance
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Multidisciplinary Applications".
Deadline for manuscript submissions: 30 October 2024 | Viewed by 83
Special Issue Editors
Interests: negotiation; negotiation support; multi-criteria decision-making; fuzzy multi-criteria decision-making
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Making complex decisions based on multiple criteria affects all social and economic life disciplines. Hence, decision theory has developed numerous alternative methods for multiple-criteria decision support. These methods are based on preference information provided by the decision-maker, which can take various forms. This information may vary in terms of data representation: crisp, fuzzy, grey, taking into account uncertainty or imprecision, thereby necessitating the use of different algorithms for its processing, including those based on information theory and entropy. Preference information may be objective, based on actual data acquired by the decision-maker, or subjective, stemming from their expert assessment of the problem. It may also be provided by multiple decision-makers, requiring the need for consensus building, seeking common ground, or solving through game theoretical models. This diversity of preference information poses a challenge for modifying existing and developing new, often hybrid, multi-criteria decision-making (MCDM) methods.
Given the above, this Special Issue aims to present works that focus on various techniques of preference information analysis employed in individual and group multiple-criteria decision-making processes. Papers are welcome to be theoretical, addressing the development of methodologies, as well as applied, demonstrating the utilization of current and new MCDM techniques in solving real-life decision-making problems. We are particularly interested in applications in the sectors of management, economics, and finance. Topics include, but are not limited to, the following:
- Methods of preference information analysis and processing;
- Entropy and preference information;
- Artificial intelligence and machine learning in preference analysis;
- Analyzing and aggregating group preferences;
- Game theory models with complex preference information;
- New trends in MCDM methods for handling various preference information;
- Objective preference information in MCDM;
- Subjective preference information in MCDM;
- Entropy-based MCDM;
- Processing individual preferences in novel MCDM algorithms;
- Methods for determining weights in MCDM problems;
- Uncertainty and fuzziness in MCDM;
- Preference information and decision support systems (DSS), negotiation support systems (NSS), and group decision support systems (GDSS).
Prof. Dr. Ewa Roszkowska
Prof. Dr. Tomasz Wachowicz
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- preference information
- preference analysis
- weight determination
- entropy
- uncertainty
- fuzziness
- MCDM
- fuzzy MCDM
- group decision-making
- machine learning
- artificial intelligence
- game theory
- decision support systems
