Academia.eduAcademia.edu

Outline

Avoiding the Worst Decisions: A Simulation and Experiment

2023, Mathematics

https://doi.org/10.3390/MATH11051165

Abstract

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY

References (36)

  1. Hertwig, R.; Grüne-Yanoff, T. Nudging and boosting: Steering or empowering good decisions. Perspect. Psychol. Sci. 2017, 12, 973-986. [CrossRef] [PubMed]
  2. Summerfield, C.; Tsetsos, K. Do humans make good decisions? Trends Cogn. Sci. 2015, 19, 27-34. [CrossRef] [PubMed]
  3. Summerfield, C.; Tsetsos, K. Rationality and efficiency in human decision-making. In The Cognitive Neurosciences VII; Gazzaniga, M., Ed.; MIT Press: Cambridge, MA, USA, 2020; pp. 427-438.
  4. Takemura, K. Behavioral Decision Theory: Psychological and Mathematical Descriptions of Human Choice Behavior, 2nd ed.; Springer: Tokyo, Japan, 2021.
  5. Takemura, K. Escaping from Bad Decisions: A Behavioral Decision-Theoretic Perspective; Academic Press: New York, NY, USA, 2021.
  6. Gigerenzer, G.; Todd, P.M.; ABC Research Group. Simple Heuristics That Make Us Smart; Oxford University Press: New York, NY, USA, 1999.
  7. Janis, I.L. Victims of Groupthink; Houghton Mifflin: Boston, MA, USA, 1972.
  8. Janis, I.L. Groupthink: Psychological Studies of Policy Decisions and Fiascoes, 2nd ed.; Houghton Mifflins: Boston, MA, USA, 1982.
  9. Bault, N.; Rusconi, E. The art of influencing consumer choices: A reflection on recent advances in decision neuroscience. Front. Psychol. 2020, 10, 3009. [CrossRef] [PubMed]
  10. Trueblood, J.S.; Brown, S.D.; Heathcote, A. The multiattribute linear ballistic accumulator model of context effects in multialterna- tive choice. Psychol. Rev. 2014, 121, 179. [CrossRef]
  11. Tsetsos, K.; Chater, N.; Usher, M. Salience driven value integration explains decision biases and preference reversal. Proc. Natl. Acad. Sci. USA 2012, 109, 9659-9664. [CrossRef]
  12. Tsetsos, K.; Moran, R.; Moreland, J.; Chater, N.; Usher, M.; Summerfield, C. Economic irrationality is optimal during noisy decision making. Proc. Natl. Acad. Sci. USA 2016, 113, 3102-3107. [CrossRef]
  13. Brandstätter, E.; Gigerenzer, G.; Hertwig, R. The priority heuristic: Making choices without trade-offs. Psychol. Rev. 2006, 113, 409.
  14. Gigerenzer, G.; Reb, J.; Luan, S. Smart heuristics for individuals, teams, and organizations. Annu. Rev. Organ. Psychol. Organ. Behav. 2022, 9, 171-198. [CrossRef]
  15. Martignon, L.; Hoffrage, U. Fast, frugal, and fit: Simple heuristics for paired comparison. Theory Decis. 2002, 52, 29-71. [CrossRef]
  16. Miettinen, K. Nonlinear Multiobjective Optimization; Kluwer Academic Publishers: Alphen am Rhein, The Netherlands, 1999.
  17. Bettman, J.R. Information Processing Theory of Consumer Choice; Addison-Wesley Pub. Co.: Boston, MA, USA, 1979.
  18. Payne, J.W.; Bettman, J.R.; Johnson, E.J. The Adaptive Decision Maker; Cambridge University Press: Cambridge, UK, 1993.
  19. Takemura, K.; Haraguchi, R.; Tamari, Y. Cognitive effort accuracy of decision strategies in multi-attribute decision-making process: A behavioral decision theoretic approach using computer simulation technique. Cogn. Stud. 2015, 22, 368-387.
  20. Kohli, R.; Jedidi, K. Representation and inference of lexicographic preference models and their variants. Mark. Sci. 2007, 26, 380-399. [CrossRef]
  21. Matsumoto, M.; Nishimura, T. Mersenne twister: A 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. Model. Comput. Simul. TOMACS 1998, 8, 3-30. [CrossRef]
  22. Takemura, K. Psychology of Decision Making: Investigation of Its Process; Fukumura Shuppan: Tokyo, Japan, 1996.
  23. Schwartz, B.; Ward, A.; Monterosso, J.; Lyubomirsky, S.; White, K.; Lehman, D.R. Maximizing versus satisficing: Happiness is a matter of choice. J. Personal. Soc. Psychol. 2002, 83, 1178. [CrossRef]
  24. Klein, N.; O'Brien, E. People use less information than they think to make up their minds. Proc. Natl. Acad. Sci. USA 2018, 115, 13222-13227. [CrossRef]
  25. Denoeux, T. Maximum likelihood estimation from uncertain data in the belief function framework. IEEE Trans. Knowl. Data Eng. 2011, 25, 119-130. [CrossRef]
  26. Bryson, N.; Mobolurin, A. A qualitative discriminant approach for generating quantitative belief functions. IEEE Trans. Knowl. Data Eng. 1998, 10, 345-348. [CrossRef]
  27. Feng, F.; Cho, J.; Pedrycz, W.; Fujita, H.; Herawan, T. Soft set based association rule mining. Knowl.-Based Syst. 2016, 111, 268-282.
  28. Ye, J.; Zhan, J.; Ding, W.; Fujita, H. A novel fuzzy rough set model with fuzzy neighborhood operators. Inf. Sci. 2021, 544, 266-297.
  29. Liu, P.; Zhang, X.; Pedrycz, W. A consensus model for hesitant fuzzy linguistic group decision-making in the framework of Dempster-Shafer evidence theory. Knowl.-Based Syst. 2021, 212, 106559. [CrossRef]
  30. Calzada, A.; Liu, J.; Wang, H.; Kashyap, A. A new dynamic rule activation method for extended belief rule-based systems. IEEE Trans. Knowl. Data Eng. 2014, 27, 880-894. [CrossRef]
  31. Deng, X.; Deng, Y. D-AHP method with different credibility of information. Soft Comput. 2019, 23, 683-691. [CrossRef]
  32. Deng, Y. Random permutation set. Int. J. Comput. Commun. Control 2022, 17. [CrossRef]
  33. Xiao, F. EFMCDM: Evidential fuzzy multicriteria decision making based on belief entropy. IEEE Trans. Fuzzy Syst. 2019, 28, 1477-1491. [CrossRef]
  34. Xiao, F. Generalized quantum evidence theory. Appl. Intell. 2022, 1-16. [CrossRef]
  35. Xiao, F.; Wen, J.; Pedrycz, W. Generalized divergence-based decision making method with an application to pattern classification. IEEE Trans. Knowl. Data Eng. 2022, 1. [CrossRef]
  36. Disclaimer/Publisher's Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.