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Applied Sciences
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Decision-Making Methods: Applications and Perspectives
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Decision-Making Methods: Applications and Perspectives

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 2220

Special Issue Editor

Dr. Konrad Kulakowski

E-Mail Website
Guest Editor
Department Of Applied Computer Science, AGH University of Krakow, 30-059 Kraków, Poland
Interests: decision-making methods; pairwise comparisons; decision inconsistency; algorithms; parallel programming; computational complexity; intelligent control systems and their applications in robotics; collective intelligence; multi-agent architectures
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Despite intensive scientific development in many areas, such as cognitive science, artificial intelligence, and machine learning, computer systems do not (and probably never will) make crucial decisions independently. Their actions result in inconclusive recommendations, often one of many. Therefore, there remains a lot of room for decision-making methods that allow for combining and processing expert knowledge from different sources. 

In this Special Issue, we invite authors to submit papers reflecting on the future of decision-making methods from the perspective of advances in various computational techniques. In particular, our interest concerns (but is not limited to) the following: 

  • Hybrid decision-making methods combining different techniques;
  • Methods for calculating rankings based on different types of decision data;
  • Novel applications of existing decision-making methods;
  • Frameworks and software for decision making; 
  • Decision-making methods using machine learning and artificial intelligence;
  • Security of the decision-making process, and resistance of methods to manipulation and fraud;
  • Methods for reaching and developing consensus;
  • Methods using pairwise comparisons of alternatives.

Dr. Konrad Kulakowski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • decision-making methods
  • machine learning
  • artificial intelligence

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Published Papers (3 papers)

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Research

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14 pages, 337 KiB  
Article
Beyond the Arbitrariness of Drug-Likeness Rules: Rough Set Theory and Decision Rules in the Service of Drug Design
by Grzegorz Miebs, Adam Mielniczuk, Miłosz Kadziński and Rafał A. Bachorz
Appl. Sci. 2024, 14(21), 9966; https://doi.org/10.3390/app14219966 - 31 Oct 2024
Viewed by 692
Abstract
Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these [...] Read more.
Lipinski’s Rule of Five and Ghose filter are empirical guidelines for evaluating the drug-likeness of a compound, suggesting that orally active drugs typically fall within specific ranges for molecular descriptors such as hydrogen bond donors and acceptors, weight, and lipophilicity. We revisit these practices and offer a more analytical perspective using the Dominance-based Rough Set Approach (DRSA). By analyzing representative samples of drug and non-drug molecules and focusing on the same molecular descriptors, we derived decision rules capable of distinguishing between these two classes systematically and reproducibly. This way, we reduced human bias and enabled efficient knowledge extraction from available data. The performance of the DRSA model was rigorously validated against traditional rules and available machine learning (ML) approaches, showing a significant improvement over empirical rules while achieving comparable predictive accuracy to more complex ML methods. Our rules remain simple and interpretable while being characterized by high sensitivity and specificity. Full article
(This article belongs to the Special Issue Decision-Making Methods: Applications and Perspectives)
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20 pages, 2300 KiB  
Article
Detection of Decision-Making Manipulation in the Pairwise Comparison Method
by Michał Strada, Sebastian Ernst, Jacek Szybowski and Konrad Kułakowski
Appl. Sci. 2024, 14(19), 8946; https://doi.org/10.3390/app14198946 - 4 Oct 2024
Viewed by 677
Abstract
Most decision-making models, including the pairwise comparison method, assume the honesty of the decision-maker. However, it is easy to imagine a situation where the decision-maker tries to manipulate the ranking results. This problem applies to many decision-making methods, including the pairwise comparison method. [...] Read more.
Most decision-making models, including the pairwise comparison method, assume the honesty of the decision-maker. However, it is easy to imagine a situation where the decision-maker tries to manipulate the ranking results. This problem applies to many decision-making methods, including the pairwise comparison method. This article proposes three simple algorithmic methods for manipulating data using the pairwise comparison method. The proposed solutions try to mimic the behavior of a dishonest decision-maker who, acting under time pressure, chooses a simple strategy that leads to pushing through a given alternative. We also test the susceptibility to detection of the proposed manipulation strategies. To this end, we propose a convolutional neural network architecture, which we train based on generated data consisting of the original random pairwise comparison matrices and their manipulated counterparts. Our approach treats the pairwise comparison matrices as two- or three-dimensional images specific to the decision situation. In the latter case, the matrices are initially transformed into a three-dimensional map of local inconsistencies, and only data processed in this way are subjected to analysis using neural networks. The experiments indicate a significant level of detection of the proposed manipulations. In numerical tests, the effectiveness of the presented solution ranges from 88% to 100% effectiveness, depending on the tested algorithm and test parameters. The measured average computation time for the single case analyzed oscillated below one millisecond, which is a more than satisfactory result of the performance of the built implementation. We can successfully use the neural networks trained on synthetic data to detect manipulation attempts carried out by real experts. Preliminary tests with respondents also indicated high effectiveness in detecting manipulation. At the same time, they signaled the difficulty of distinguishing actual manipulation from a situation in which an expert strongly prefers one or more selected alternatives. Full article
(This article belongs to the Special Issue Decision-Making Methods: Applications and Perspectives)
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Review

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33 pages, 748 KiB  
Review
A Comprehensive Exploration of Hellwig’s Taxonomic Measure of Development and Its Modifications—A Systematic Review of Algorithms and Applications
by Ewa Roszkowska
Appl. Sci. 2024, 14(21), 10029; https://doi.org/10.3390/app142110029 - 3 Nov 2024
Viewed by 554
Abstract
This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and [...] Read more.
This paper presents an original and comprehensive investigation into the Taxonomic Measure of Development (TMD), introduced by Hellwig in 1968, enriching both its theoretical foundations and practical applications. It provides an overview of various variants of the Hellwig method, including their extensions and applications, while also exploring recent trends across multiple research domains. Primarily developed as a method for multidimensional analysis, TMD has evolved into a pivotal tool in multi-criteria decision-making. It is widely used for evaluating and ranking alternatives, particularly in the analysis of complex socio-economic phenomena and decision-making scenarios involving multiple criteria. This study systematically reviews the original algorithm and its subsequent extensions and modifications, including adaptations for fuzzy sets, intuitionistic fuzzy sets, and interval-valued fuzzy sets. Furthermore, it explores an integrated multi-criteria approach based on Hellwig’s method and its practical applications across various domains. This paper introduces an original approach by conducting a detailed, step-by-step analysis of the TMD framework. This process-oriented analysis is a novel contribution that sets this study apart from typical reviews based on statistical or bibliometric data. By examining key steps in the TMD framework—such as data collection, criterion weighting, data normalization, ideal value determination, distance calculation, and normalization factor—this paper highlights the method’s versatility in addressing complex, real-world decision-making problems. Although similar to the widely used Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method in its reliance on distance to evaluate alternatives, Hellwig’s approach is unique in focusing exclusively on proximity to an ideal solution, without considering distance from a negative ideal. This distinctive emphasis has led to numerous adaptations and extensions that address specific issues such as criterion dependencies, uncertainty, and rank reversal. The findings underscore the continued relevance of the Hellwig method, its recent extensions, and its growing international recognition. Full article
(This article belongs to the Special Issue Decision-Making Methods: Applications and Perspectives)
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