Logo PTI Logo FedCSIS

Proceedings of the 18th Conference on Computer Science and Intelligence Systems

Annals of Computer Science and Information Systems, Volume 35

On some concept lattice of social choice functions

, ,

DOI: http://dx.doi.org/10.15439/2023F3892

Citation: Proceedings of the 18th Conference on Computer Science and Intelligence Systems, M. Ganzha, L. Maciaszek, M. Paprzycki, D. Ślęzak (eds). ACSIS, Vol. 35, pages 11971203 ()

Full text

Abstract. Social choice functions or voting procedures are one of the crucial issues in the domain of political sciences. They map individuals' preferences over a set of candidates to some subset (possibly one-element) of the candidates who can be thought as the winners of an election procedure. The paper is aimed at applications of formal concept analysis methods to study of social choice functions. We will construct concept lattices over selected set of social choice functions applied for political elections using as attributes some properties investigated in political sciences. We will discuss issues connected with reducibility of both objects and attributes, irreducibility of object concepts as well as attribute concepts and attribute implications. We will discuss also the shape of the constructed concept lattice of social choice functions which in some part is very regular from the perspective of the lattice theory.

References

  1. Bundesant für Bevölkerungsschutz und Katastrophenhilfe: Feuerwehr - Dienstvorschrift 100 Führung und Leitung im Einsatz: Fuhrungssystem, FwDV 100 Stand: 10 März 1999.
  2. S. Burris and S. Sankappanavar, “A Course in Universal Algebra”. The Millennium Edition updated in 2012 is freely available on-line at the web page University of Waterloo, https://www.math.uwaterloo.ca/~snburris/htdocs/UALG/univ-algebra2012.pdf.
  3. M. Fedrizzi, J. Kacprzyk, and H. Nurmi, “How different are social choice functions: a rough sets approach”, Quality & Quantity: International Journal of Methodology, vol. 30(1), 1996, pp. 87–99.
  4. P. C.Fishburn, The Theory of Social Choice Functions, Princeton University Press, Princeton, 1973.
  5. P. C. Fishburn, “Social choice functions”, Society for Industrial and Applied Mathematics Review vol. 16(1), 1974, pp. 63–90.
  6. B. Ganter and R. Wille, Formal Concept Analysis: Mathematical Foundations, Springer, Heidelberg, 1999.
  7. A. Graeger, U. Cimolino, H. de Vries, and J. Sümersen, Einsatzund Abschnittsleitung: Das Einsatz-Füchrungs-System (EFS), Ecomed Sicherheit, 2009.
  8. D. I. Ignatov and L. Kwuida, “On Shapley value interpretability in concept-based learning with formal concept analysis”, Annals of Mathematics and Artificial Intelligence vol. 99, 2022, pp. 1197–1222.
  9. J. Kacprzyk, “Group decision making with a fuzzy majority”, Fuzzy Sets and Systems vol. 18, 1986, pp. 105—118.
  10. J. Kacprzyk, M. Fedrizzi, and H. Nurmi, “Group decision making and consensus under fuzzy preferences and fuzzy majority”, Fuzzy Sets Systems vol. 49 1992, pp. 21–31.
  11. J. Kacprzyk, J. Merigó, H. Nurmi, and S. Zadrożny, “Multi-agent systems and voting: how similar are voting procedures”, in 19th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems IPMU 2020, Springer, Cham, 2020, pp. 172–184.
  12. J. Kacprzyk, H. Nurmi, and S. Zadrożny, “Reason vs. rationality: from rankings to tournaments in individual choice”, in Transaction on Computational Collective Intelligence, LNCS, vol. 10480, 27, 2017, pp. 28–39.
  13. J. Kacprzyk, H. Nurmi, and S. Zadrożny, “Towards a comprehensive similarity analysis of voting procedures using rough sets and similarity measures”, in: Rough Sets and Intelligent Systems - Professor Zdzislaw Pawlak in Memoriam, vol. 1, Springer, 2013, pp. 359–380.
  14. J. Kacprzyk and S. Zadrożny, “Towards a general and unified characterization of individual and collective choice functions under fuzzy and nonfuzzy preferences and majority via the ordered weighted average operators” International Journal of Intelligent Systems vol. 24, 2009, pp. 4–26.
  15. J. Kacprzyk and S. Zadrożny, “Towards human consistent data driven decision support systems using verbalization of data mining results via linguistic data summaries”, Bulletin of the Polish Academy of Sciences: Technical Sciences, vol. 58(3), 2010, pp. 359–370.
  16. J. S. Kelly, Social choice theory, Springer, Berlin, 1988.
  17. A. Krasuski and P. Wasilewski, Outlier Detection by Interaction with Domain Experts. Fundamenta Informaticae vol. 127(1-5), 2013, pp. 529–544.
  18. H. Nurmi, Comparing Voting Systems, D. Reidel, Dordrecht, 1987.
  19. H. Nurmi, Voting Paradoxes and How to Deal With Them, Springer, Heidelberg, 1999.
  20. H. Nurmi, “The choice of voting rules based on preferences over criteria”, in Outlooks and Insights on Group Decision and Negotiation, Springer, Cham, 2015, pp. 241–252.
  21. H. Nurmi and J. Kacprzyk, “On fuzzy tournaments and their solution concepts in group decision making”, European Journal of Operational Research, vol. 51(2), 1991, pp. 223–232.
  22. H. Nurmi, J. Kacprzyk and M. Fedrizzi, “Probabilistic, fuzzy and rough concepts in social choice”, European Journal of Operational Research, vol. 95, 1996, 264–277.
  23. H. Nurmi, J. Kacprzyk, and S. Zadrożny, “Voting systems in Theory and Practice”, in Colllective Decisions: Theory, Algorithms and Decision Support Systems, Studies in Systems, Decision and Control, vol. 392, Springer, Cham, 2022, pp.3–16.
  24. Z. Pawlak, “Information Systems – theoretical foundations”, Information systems, 6, 1981, pp. 205–218.
  25. Z. Pawlak, “Rough sets”, International Journal of Computing and Information Sciences 18, 1982, pp. 341–356.
  26. Z. Pawlak, Rough sets. Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht, 1991.
  27. A. Revenko and S. O. Kuznetsov, “Attribute Exploration of Properties of Functions on Ordered Sets”, in 7th International Conference on Concept Lattices and Their Applications, 2010, pp. 313–324.
  28. A. Revenko and S. O. Kuznetsov, “Attribute Exploration of Properties of Functions on Sets”, Fundamenta Informaticae vol. 115(4), 2012, pp. 377-394.
  29. G. Stumme, “Conceptual knowledge discovery and data mining with formal concept analysis”, Tutorial slides at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases ECML/PKDD’2002, https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=f9a8f4529aca992903823150dda77d9a89d193a2, 2002.
  30. P. Wasilewski, “Concept lattices vs. Approximation spaces”, in 10th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, Lecture Notes in Artificial Intelligence 3641, 2005, pp. 114–123.
  31. P. Wasilewski, “Algebras of Definable Sets vs. Concept Lattices”, Fundamenta Informaticae, 167(3), 2019, pp. 235–256.
  32. R. Wille, “Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts”, in: Ordered Sets. NATO Advanced Study Institutes Series, vol. 83, Reidel, Dordrecht, 1982, pp. 445–470.