Skip to main content
SpringerLink
Log in

Three-way Decision, Three-World Conception, and Explainable AI

  • Conference paper
  • First Online:
Rough Sets (IJCRS 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13633))

Included in the following conference series:

Abstract

Three-way decision is about thinking, problem-solving, and computing in threes or through triads. By dividing a whole into three parts, by focusing on only three things, or by considering three basic ingredients, we may build a theory, a model, or a method that is simple-to-understand, easy-to-remember, and practical-to-use. This philosophy and practice of triadic thinking appears everywhere. In particular, there are a number of three-world or tri-world models in different fields and disciplines, where a complex system, a complicated issue, or an intricate concept is explained and understood in terms of three interrelated worlds, with each world enclosing a group of elements or representing a particular view. The main objective of this paper is to review and re-interpret various three-world conceptions through the lens of three-way decision. Three-world conceptions offer more insights into three-way decision with new viewpoints, methods, and modes. They can be used to construct easy-to-understand explanations in explainable artificial intelligence (XAI).

Y. Yao : I would like to express my thanks to Professor Duoqian Miao and Professor JingTao Yao for organizing the IRSS President’s forum and for encouraging me to write this paper. I am grateful to the reviewers for their encouraging and constructive comments. This work was partially supported by a Discovery Grant from NSERC, Canada.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 69.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 89.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    The two types presented here are related to the distinction, suggested by Achinstein [1], of an “explaining act” and an explanation as a “product” of an explaining act. Ruben [23] made a similar distinction through “process and product.” The first type is more about an explanation itself. The second type relies on an understanding of an “explaining act” that includes both the formulation and the communication of an explanation.

  2. 2.

    This example will be further examined in the later part of the paper. For an actual application, we may point at the earlier expert system MYCIN that uses the What-Why-How triad, in which an explanation subsystem focuses mainly on Why and How questions to justify the decision of the system or to educate the user [32]. The triad is equally useful for enhancing human intelligence and guiding human behavior [46]. For example, the Golden Circle leadership model, introduced by Sinek [29], is based on the Why-How-What triad, which advises that every organization and everyone of us should know the three most important things: why we do (i.e., purpose and goals), how we do, and what we do. The same Why-How-What triad was used by Clear [6] in his three-level model of behavior change, focusing on what we believes, what we do, and what we get.

  3. 3.

    https://sloanreview.mit.edu/audio-series/three-big-points/, accessed May 20, 2022.

  4. 4.

    https://www.nature.com/articles/d41586-019-01362-9, accessed May 20, 2022.

  5. 5.

    As an example, we may take a look at the many different interpretations and explanations of a Chinese classic, “I Ching” (The Book of Changes). “I Ching” has shaped every aspects of Chinese ways of seeing, knowing, and living (for example, culture, art, politics, science, etc.) throughout the Chinese history. Many scholars have interpreted and explained, and are continually searching for new interpretations and explanations, this classic text from many different angles. The notion of SMV space may shed a new light by organizing some of the existing interpretations and explanations at the three levels: (1) images and numbers at the S (Symbols) level, (2) meaning and principles at the M (Meaning) level, and (3) living and practice, according to its meaning and principles, at the V (Value) level. Although this organization may not be hundred percent appropriate or accurate, it does provide a good enough approximation in terms of the text itself, the meaning of the text, and the value of the text.

  6. 6.

    A few important issues regarding AI and human-machine relations are relevant to the discussion here, such as alignment and control. Christian [5] argued that artificial intelligence systems, in particular machine learning, need to be aligned with human values. Russell [24] pointed out that advances in AI may pose a potential risk to the human race by out of control superhuman AI. Future AI research must ensure that machines remain beneficial to humans and we humans must retain “absolute power over machines that are more powerful than us.” By living together in the three worlds of SMV, namely, symbols/data, meaning/knowledge, value/wisdom, humans and machines may coexist in harmony.

References

  1. Achinstein, P.: The Nature of Explanation. Oxford University Press, New York (1983)

    Google Scholar 

  2. Adadi, A., Berrada, M.: Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018)

    Article  Google Scholar 

  3. Assagioli, R.: The Balancing and Synthesis of the Opposites. Psychosynthesis Research Foundation, New York (1972)

    Google Scholar 

  4. Boer, C.: Thinking in threes: how we human love patterns. Kindle Edition (2014)

    Google Scholar 

  5. Christian, B.: The Alignment Problem: Machine Learning and Human Values. W.W. Norton & Company, New York (2020)

    Google Scholar 

  6. Clear, J.: Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones. Avery, New York (2018)

    Google Scholar 

  7. Cowan, N.: The magical number 4 in short-term memory: a reconsideration of mental storage capacity. Behav. Brain Sci. 24, 87–185 (2000)

    Article  Google Scholar 

  8. Deakin, M.A.B.: The Name of the Number. ACER Press, Camberwell, Victoria (2007)

    Google Scholar 

  9. Dundes, A.: The number three in American culture. In: Dundes, A. (ed.) Every Man His Way: Readings in Cultural Anthropology, pp. 401–424. Prentice-Hall, Englewood Cliffs (1968)

    Google Scholar 

  10. Gallo, C.: Talk Like TED: The 9 Public-Speaking Secrets of the World’s Top Minds. Sy. Martin’s Press, New York (2014)

    Google Scholar 

  11. Gu, J.F., Zhu, Z.C.: Knowing Wuli, sensing Shili, caring for Renli: methodology of the WSR approach. Syst. Pract. Action Res. 13, 11–20 (2000)

    Article  Google Scholar 

  12. Gunning, D., Stefik, M., Choi, J., Miller, T., Stumpf, S., Yang, G.Z.: XAI - Explainable artificial intelligence. Sci. Robot. 4, 7120 (2019)

    Article  Google Scholar 

  13. Harteveld, C.: Triadic Game Design: Balancing Reality. Meaning and Play. Springer-Verlag, London (2011). https://doi.org/10.1007/978-1-84996-157-8

  14. Kaufman, E.L., Lord, M.W., Reese, T.W., Volkmann, J.: The discrimination of visual number. Am. J. Psychol. 62, 498–525 (1949)

    Article  Google Scholar 

  15. Logan, D., King, J., Fischer-Wright, H.: Tribal Leadership: Leveraging Natural Groups to Build a Thriving Organization. Harper Business, New York (2011)

    Google Scholar 

  16. Miller, G.A.: The magical number seven, plus or minus two: some limits on our capacity for processing information. Psychol. Rev. 63, 81–97 (1956)

    Article  Google Scholar 

  17. Mouton, J.: Understanding Social Research. Van Schaik Publishers, Hatfield, Pretoria (1996)

    Google Scholar 

  18. Pawlak, Z.: Rough sets. Int. J. Comput. Inf. Sci. 11, 341–356 (1982)

    Article  MATH  Google Scholar 

  19. Pawlak, Z.: Rough Sets. Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1991)

    Google Scholar 

  20. Pogliani, L., Klein, D.J., Balaban, A.T.: Does science also prefer a ternary pattern? Int. J. Math. Educ. Sci. Technol. 37, 379–399 (2006)

    Article  MathSciNet  Google Scholar 

  21. Popper, K..: In Search of a Better World: Lectures and Essays from Thirty Years. Routledge, New York (1994)

    Google Scholar 

  22. Radej, B., Golobič, M.: Complex Society: In the Middle of a Middle World. Vernon Press, Wilmington, Delaware (2021)

    Google Scholar 

  23. Ruben, D.H.: Explaining Explanation. Routledge, New York (1990)

    Google Scholar 

  24. Russell, S.J.: Human Compatible: Artificial Intelligence and the Problem of Control. Viking, New York (2019)

    Google Scholar 

  25. Schneider, M.S.: A Beginner’s Guide to Constructing the Universe: The Mathematical Archetypes of Nature, Art, and Science. Harper, New York (1994)

    Google Scholar 

  26. Schopenhauer, A.: The World as Will and Idea, Vol. I, II, III, 7th Edition. Kegan Paul, Trench, Trübner & Co., London (1909)

    Google Scholar 

  27. Shannon, C.E., Weaver, W.: The Mathematical Theory of Communication. The University of Illinois Press, Urbana (1949)

    MATH  Google Scholar 

  28. Shaw, V.N.: Three Worlds of Collective Human Experience: Individual Life, Social Change, and Human Evolution. LNCS (LNAI), Springer, Cham (2019). https://doi.org/10.1007/978-3-319-98195-6_16

  29. Sinek, S.: Start With Why: How Great Leaders Inspire Everyone to Take Action. Portfolio/Penguin, New York (2009)

    Google Scholar 

  30. Stern, H.W.. In support of a triadic conception of reality. https://harrisstern.medium.com/in-support-of-a-triadic-conception-of-reality-38a784229e9d Accessed 9 June 2022

  31. Tall, D.: How Humans Learn to Think Mathematically: Exploring the Three Worlds of Mathematics. Cambridge University Press, New York (2013)

    Book  Google Scholar 

  32. Van Melle, W.: MYCIN: a knowledge-based consultation program for infectious disease diagnosis. Int. J. Man-Mach. Stud. 10, 313–322 (1978)

    Article  Google Scholar 

  33. Watson, P.: Ideas: A History, from Fire to Freud. Weidenfeld & Nicolson, London (2005)

    Google Scholar 

  34. Webel, C.P.: The World as Idea: A Conceptual History. Routledge, New York (2022)

    Google Scholar 

  35. Wei, W.J., Miao, D.Q., Li, Y.X.: A bibliometric profile of research on rough sets. In: IJCRS 2019, LNCS, vol. 11499, pp. 534–548 (2019). https://doi.org/10.1007/978-3-030-22815-6_41

  36. Weinberg, S.: To Explain the World: The Discovery of Modern Science. Harper Perennial, New York (2015)

    MATH  Google Scholar 

  37. Yang, B., Li, J.: Complex network analysis of three-way decision researches. Int. J. Mach. Learn. Cybern. 11(5), 973–987 (2020). https://doi.org/10.1007/s13042-020-01082-x

    Article  Google Scholar 

  38. Yao, J.T.: The impact of rough set conferences. In: IJCRS 2019, LNCS, vol. 11499, pp. 383–394 (2019). https://doi.org/10.1007/978-3-030-22815-6_30

  39. Yao, Y.Y.: Three-way decision: an interpretation of rules in rough set theory. In: RSKT 2009, LNCS, vol. 5589, pp. 642–649 (2009). https://doi.org/10.1007/978-3-642-02962-2_81

  40. Yao, Y.Y.: Three-way decisions with probabilistic rough sets. Inf. Sci. 180, 341–353 (2010)

    Article  MathSciNet  Google Scholar 

  41. Yao, Y.Y.: An outline of a theory of three-way decisions. In: RSCTC 2012, LNCS, vol. 7413, pp. 1–17 (2012). https://doi.org/10.1007/978-3-642-32115-3_1

  42. Yao, Y.Y.: Three-way decisions and cognitive computing. Cogn. Comput. 8, 543–554 (2016)

    Article  Google Scholar 

  43. Yao, Y.Y.: Three-way decision and granular computing. Int. J. Approximate Reasoning 103, 107–123 (2018)

    Article  MATH  Google Scholar 

  44. Yao, Y.Y.: Tri-level thinking: models of three-way decision. Int. J. Mach. Learn. Cybern. 11, 947–959 (2020)

    Article  Google Scholar 

  45. Yao, Y.Y.: Three-way granular computing, rough sets, and formal concept analysis. Int. J. Approximate Reasoning 116, 106–125 (2020)

    Article  MathSciNet  MATH  Google Scholar 

  46. Yao, Y.: The geometry of three-way decision. Appl. Intell. 51(9), 6298–6325 (2021). https://doi.org/10.1007/s10489-020-02142-z

    Article  Google Scholar 

  47. Yao, Y.Y.: Set-theoretic models of three-way decision. Granular Comput. 6, 133–148 (2021)

    Article  Google Scholar 

  48. Yao, Y.Y.: Symbols-Meaning-Value (SMV) space as a basis for a conceptual model of data science. Int. J. Approximate Reasoning 144, 113–128 (2022)

    Article  MathSciNet  MATH  Google Scholar 

  49. Yao, Y.Y.: Human-machine co-intelligence through symbiosis in the SMV space. Appl. Intell. 1–21 (2022). https://doi.org/10.1007/s10489-022-03574-5

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, University of Regina, Regina, SK, Canada, S4S 0A2

    Yiyu Yao

Authors
  1. Yiyu Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiyu Yao .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    JingTao Yao

  2. Iwate Prefectural University, Takizawa, Iwate, Japan

    Hamido Fujita

  3. Shanghai University, Shanghai, China

    Xiaodong Yue

  4. Tongji University, Shanghai, China

    Duoqian Miao

  5. University of Kansas, Lawrence, KS, USA

    Jerzy Grzymala-Busse

  6. Soochow University, Suzhou, Jiangsu, China

    Fanzhang Li

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Yao, Y. (2022). Three-way Decision, Three-World Conception, and Explainable AI. In: Yao, J., Fujita, H., Yue, X., Miao, D., Grzymala-Busse, J., Li, F. (eds) Rough Sets. IJCRS 2022. Lecture Notes in Computer Science(), vol 13633. Springer, Cham. https://doi.org/10.1007/978-3-031-21244-4_4

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-21244-4_4

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21243-7

  • Online ISBN: 978-3-031-21244-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation