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Abstrakty
The aim of the study is to investigate and evaluate user preferences regarding two visual representations of uncertainty estimates for decision-making purposes. The research is concerned with the perception of fuzzy numbers, which are depicted either as triangles or as specifically constructed vectors. The study involves a series of pairwise comparisons in which participants must determine which representation reflects the change in the most possible value in a more salient way. The results are then analyzed and formally verified statistically. The study shows that there are specific circumstances where vector representations are more desirable than their triangle-based counterparts. The findings also suggest that there may be some differences in assessing these representations depending on gender. This examination expands our understanding of how subjects perceive different graphical methods for presenting change in a selected parameter uncertainty feature. From a practical standpoint, the findings offer suggestions for designing graphical user interfaces that present fuzzy data to users.
Rocznik
Tom
Strony
269--274
Opis fizyczny
Bibliogr. 33 poz., wykr., tab.
Twórcy
autor
- Wroclaw University of Science and Technology, Faculty of Management 27 Wybrzeże Wyspiańskiego st., 50-370 Wrocław, Poland
autor
- Wroclaw University of Science and Technology, Faculty of Management 27 Wybrzeże Wyspiańskiego st., 50-370 Wrocław, Poland
autor
- Wroclaw University of Science and Technology, Faculty of Management 27 Wybrzeże Wyspiańskiego st., 50-370 Wrocław, Poland
autor
- Wroclaw University of Science and Technology, Faculty of Management 27 Wybrzeże Wyspiańskiego st., 50-370 Wrocław, Poland
Bibliografia
- 1. M. Masmoudi and A. Haït, “Project scheduling under uncertainty using fuzzy modelling and solving techniques,” Eng Appl Artif Intell, vol. 26, no. 1, pp. 135–149, 2013, http://dx.doi.org/10.1016/j.engappai.2012.07.012.
- 2. E. W. Larson and C. F. Gray, Project management: the managerial process, 8th ed. McGraw-Hill Education, 2021.
- 3. P. M. Rola and D. Kuchta, “Application of fuzzy sets to the expert estimation of Scrum-based projects,” Symmetry-Basel, vol. 11, no. 8, pp. 1–22, 2019, http://dx.doi.org/10.3390/sym11081032.
- 4. D. T. Hulett, Integrated cost-schedule risk analysis, 1st ed. London: Routledge, 2011. Accessed: Jun. 06, 2022. [Online]. Available: https://www.routledge.com/Integrated-Cost-Schedule-Risk-Analysis/Hulett/p/book/9780566091667
- 5. M. A. Ajam, “Leading Megaprojects : A Tailored Approach,” Leading Megaprojects, Jan. 2020, http://dx.doi.org/10.1201/9781003029281.
- 6. I. Dikmen and T. Hartmann, “Seeing the risk picture: Visualization of project risk information,” in EG-ICE 2020 Workshop on Intelligent Computing in Engineering, Proceedings, 2020.
- 7. D. Streeb, M. El-Assady, D. A. Keim, and M. Chen, “Why Visualize? Untangling a Large Network of Arguments,” IEEE Trans Vis Comput Graph, vol. 27, no. 3, pp. 2220–2236, 2021, http://dx.doi.org/10.1109/TVCG.2019.2940026.
- 8. G.-P. Bonneau et al., “Overview and state-of-the-art of uncertainty visualization,” Math Vis, vol. 37, pp. 3–27, 2014, http://dx.doi.org/10.1007/978-1-4471-6497-5_1.
- 9. C. Ware, Information Visualization. Elsevier, 2013. http://dx.doi.org/10.1016/C2009-0-62432-6.
- 10. R. Mazza, Introduction to information visualization. 2009. http://dx.doi.org/10.1007/978-1-84800-219-7.
- 11. D. Kuchta, J. Grobelny, R. Michalski, and J. Schneider, “Vector and Triangular Representations of Project Estimation Uncertainty: Effect of Gender on Usability,” Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12747 LNCS, pp. 473–485, Jun. 2021, http://dx.doi.org/10.1007/978-3-030-77980-1_36.
- 12. J. Schneider, D. Kuchta, and R. Michalski, “A vector visualization of uncertainty complementing the traditional fuzzy approach with applications in project management,” Appl Soft Comput, vol. 137, p. 110155, Apr. 2023, http://dx.doi.org/10.1016/j.asoc.2023.110155.
- 13. S. Chanas and P. Zieliński, “Critical path analysis in the network with fuzzy activity times,” Fuzzy Sets Syst, vol. 122, no. 2, pp. 195–204, Sep. 2001, http://dx.doi.org/10.1016/S0165-0114(00)00076-2.
- 14. D. Kuchta, “Use of fuzzy numbers in project risk (criticality) assessment,” International Journal of Project Management, vol. 19, no. 5, pp. 305–310, Jul. 2001, http://dx.doi.org/10.1016/S0263-7863(00)00022-3.
- 15. S. Chanas and J. Kamburowski, “The use of fuzzy variables in pert,” Fuzzy Sets Syst, vol. 5, no. 1, pp. 11–19, Jan. 1981, http://dx.doi.org/10.1016/0165-0114(81)90030-0.
- 16. B. Gładysz, “Fuzzy-probabilistic PERT,” Ann Oper Res, vol. 258, no. 2, pp. 437–452, Nov. 2017, http://dx.doi.org/10.1007/S10479-016-2315-0/TABLES/3.
- 17. J. W. Chinneck, “PERT for Project Planning and Scheduling,” in Practical Optimization: a Gentle Introduction, Ottawa, Canada, 2016, pp. 1–11. Accessed: Feb. 24, 2023. [Online]. Available: https://www.optimization101.org/
- 18. M. F. Shipley, A. de Korvin, and K. Omer, “BIFPET methodology versus PERT in project management: fuzzy probability instead of the beta distribution,” Journal of Engineering and Technology Management, vol. 14, no. 1, pp. 49–65, Mar. 1997, http://dx.doi.org/10.1016/S0923-4748(97)00001-5.
- 19. O. Pavlačka and J. Talašová, “Fuzzy vectors as a tool for modeling uncertain multidimensional quantities,” Fuzzy Sets Syst, vol. 161, no. 11, pp. 1585–1603, Jun. 2010, http://dx.doi.org/10.1016/J.FSS.2009.12.008.
- 20. O. Pavlačka, “Modeling uncertain variables of the weighted average operation by fuzzy vectors,” Inf Sci (N Y), vol. 181, no. 22, pp. 4969–4992, Nov. 2011, http://dx.doi.org/10.1016/J.INS.2011.06.022.
- 21. M. Arana-Jiménez, A. Rufián-Lizana, Y. Chalco-Cano, and H. Román-Flores, “Generalized convexity in fuzzy vector optimization through a linear ordering,” Inf Sci (N Y), vol. 312, pp. 13–24, Aug. 2015, http://dx.doi.org/10.1016/J.INS.2015.03.045.
- 22. J. Schneider and R. Urban, “Lévy Subordinators in Cones of Fuzzy Sets,” J Theor Probab, vol. 32, no. 4, pp. 1909–1924, Dec. 2019, http://dx.doi.org/10.1007/S10959-018-0853-X/METRICS.
- 23. J. Schneider and R. Urban, “A Proof of Donsker’s Invariance Principle Based on Support Functions of Fuzzy Random Vectors,” https://doi.org/10.1142/S0218488518500022, vol. 26, no. 1, pp. 27–42, Jan. 2018, http://dx.doi.org/10.1142/S0218488518500022.
- 24. S. Zaleski and R. Michalski, “Success Factors in Sustainable Management of IT Service Projects: Exploratory Factor Analysis,” Sustainability, vol. 13, no. 8, p. 4457, Apr. 2021, http://dx.doi.org/10.3390/SU13084457.
- 25. J. Schneider, D. Kuchta, and R. Michalski, “A vector visualization of uncertainty complementing the traditional fuzzy approach with applications in project management,” Appl Soft Comput, p. 110155, Feb. 2023, http://dx.doi.org/10.1016/J.ASOC.2023.110155.
- 26. W. W. Koczkodaj, “Statistically Accurate Evidence of Improved Error Rate by Pairwise Comparisons,” Percept Mot Skills, vol. 82, no. 1, pp. 43–48, Dec. 1996, http://dx.doi.org/10.2466/pms.1996.82.1.43.
- 27. W. W. Koczkodaj, “Testing the accuracy enhancement of pairwise comparisons by a Monte Carlo experiment,” J Stat Plan Inference, vol. 69, no. 1, pp. 21–31, Jun. 1998, http://dx.doi.org/10.1016/S0378-3758(97)00131-6.
- 28. R. Michalski, “Examining users’ preferences towards vertical graphical toolbars in simple search and point tasks,” Comput Human Behav, vol. 27, no. 6, pp. 2308–2321, Nov. 2011, http://dx.doi.org/10.1016/j.chb.2011.07.010.
- 29. R. Michalski, “The influence of color grouping on users’ visual search behavior and preferences,” Displays, vol. 35, no. 4, 2014, http://dx.doi.org/10.1016/j.displa.2014.05.007.
- 30. J. Grobelny and R. Michalski, “The role of background color, interletter spacing, and font size on preferences in the digital presentation of a product,” Comput Human Behav, vol. 43, pp. 85–100, Feb. 2015, http://dx.doi.org/10.1016/J.CHB.2014.10.036.
- 31. J. Grobelny and R. Michalski, “Various approaches to a human preference analysis in a digital signage display design,” Human Factors and Ergonomics in Manufacturing & Service Industries, vol. 21, no. 6, pp. 529–542, Nov. 2011, http://dx.doi.org/10.1002/HFM.20295.
- 32. M. Płonka, J. Grobelny, and R. Michalski, “Conjoint Analysis Models of Digital Packaging Information Features in Customer Decision-Making,” Int J Inf Technol Decis Mak, Nov. 2022, http://dx.doi.org/10.1142/S0219622022500766.
- 33. T. L. Saaty, “A scaling method for priorities in hierarchical structures,” J Math Psychol, vol. 15, no. 3, pp. 234–281, Jun. 1977, http://dx.doi.org/10.1016/0022-2496(77)90033-5.
Uwagi
1. Main Track Short Papers
2. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-da7ee7dc-e6c0-40ea-b8f8-f76448a918d6