Abstract
Every scientific or intellectual movement rests on central premises and assumptions that shape its philosophy. The purpose of this study is to review a brief account of the main philosophical bases of grey systems theory (GST) and the paradigm governing its principles. So, the recent studies on the philosophical foundations of GST have been reviewed and tried to pay attention to some key ambiguities in the previous studies and give more and clearer explanations in this paper. Also, this paper tries to fill the gap among the previous studies and to provide a purposeful connection between them by expressing two key concepts: complete information and imbalance knowledge. Primarily, the study addresses the theoretical foundations of uncertainty and the concept of greyness. Next, it focuses on the notion of “complete information” and challenges to it. Then, it reviews such processes as perception, cognition, and understanding, as well as their dynamic nature. It explains how knowledge is produced through understanding and interpreting information/data and the dynamics governing the whole process. Also, the study describes any dataset, no matter how large it may be, will remain incomplete, imperfect, and grey, so humans only rely on incomplete datasets to interpret the world. As such, information and knowledge are always grey and uncertain because they are basically contingent on subjective understandings and interpretations and imperfect inputs and data. Finally, as a key development, this study also demonstrates that human grey knowledge remains imbalanced across different disciplines and spheres. In the end, a brief overview of the philosophical paradigm of GST is also provided. GST is depicted as an anti-realistic, anti-positivistic, and non-deterministic approach, which is inherently pluralistic and ideographic. According to GST principles, dynamicity and change are essential parts of human narratives of the world and systems, and human knowledge is constantly reproduced through collecting new information. As a result, knowledge, theories, narratives, and scientific laws dynamically change. Given this premise, one could argue that GST is considerably compatible with the postulates of post-modern thinking.
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References
Ahir, H., Bloom, N., & Furceri, D. (2022). The World Uncertainty Index (No. w29763; p. w29763). National Bureau of Economic Research. https://doi.org/10.3386/w29763
Albert, H. (2015). Karl Popper, critical rationalism, and the Positivist dispute. Journal of Classical Sociology, 15(2), https://doi.org/10.1177/1468795X14567829.
Anderson, J. N. (2019). David Hume. P&R Publishing.
Andrew, A. M. (2011). Why the world is grey. Grey Systems: Theory and Application, 1(2), https://doi.org/10.1108/20439371111163738.
Ashcraft, M. H., & Radvansky, G. A. (2014). Cognition (Sixth edition). Pearson Education.
BAGGINI, J. (2022). GREAT GUIDE: what david hume can teach us about being human and living well. PRINCETON UNIV PRESS.
Baron, M. (2018). Probability and Statistics for Computer Scientists (0 ed.). Chapman and Hall/CRC. https://doi.org/10.1201/9781420011425
Baskarada, S., & Koronios, A. (2013). Data, information, knowledge, Wisdom (DIKW): a semiotic theoretical and empirical exploration of the Hierarchy and its quality dimension. Australasian Journal of Information Systems, 18(1), https://doi.org/10.3127/ajis.v18i1.748.
Bird, A. (2014). Thomas Kuhn (0 ed.). Routledge. https://doi.org/10.4324/9781315710839
Bloom, A., & Kirsch, A. (2016). The Republic of Plato (Paperback third edition). Basic Books.
Bock, P. (2020). The Limits of Knowledge. In Getting It Right (pp. 115–162). Elsevier. https://doi.org/10.1016/B978-0-12-816165-4.50011-1
Boulding, K. E. (1956). General Systems Theory—The Skeleton of Science. Management Science, 2(3), 197–208. https://doi.org/10.1287/mnsc.2.3.197.
Box, G., & Draper, N. (1987). Empirical model-building and response surfaces. John Wiley & Sons Inc.
Burrell, G., & Morgan, G. (2016). Sociological paradigms and organisational analysis: elements of the sociology of corporate life. Routledge.
Bykvist, K. (2017). Moral uncertainty. Philosophy Compass, 12(3), https://doi.org/10.1111/phc3.12408. Article 3.
Camelia, D. (2015a). Grey systems theory in economics – a historical applications review. Grey Systems: Theory and Application, 5(2), 263–276. https://doi.org/10.1108/GS-05-2015-0018.
Camelia, D. (2015b). Grey systems theory in economics – bibliometric analysis and applications’ overview. Grey Systems: Theory and Application, 5(2), 244–262. https://doi.org/10.1108/GS-03-2015-0005.
Chen, N., & Xie, N. (2020). Uncertainty representation and information measurement of grey numbers. Grey Systems: Theory and Application, 10(4), https://doi.org/10.1108/GS-01-2020-0009. Article 4.
Cilliers, P. (2005). Knowledge, limits and boundaries. Futures, 37(7), 605–613. https://doi.org/10.1016/j.futures.2004.11.001.
Cupers, K., & Miessen, M. (2018). Spaces of Uncertainty. In K. Cupers & M. Miessen (Eds.), Spaces of Uncertainty—Berlin revisited (pp. 14–19). De Gruyter. https://doi.org/10.1515/9783035614404-002
Curtler, H. M. (2016). Rediscovering Values: Coming to Terms with Postmodernism: Coming to Terms with Postmodernism (0 ed.). Routledge. https://doi.org/10.4324/9781315503530
de Beer, C. S. (2015). An acritical philosophy of information. In Information Science as an Interscience (pp. 1–10). Elsevier. https://doi.org/10.1016/B978-0-08-100140-0.00001-8
Deng, Y. (2020). Uncertainty measure in evidence theory. Science China Information Sciences, 63(11), 210201. https://doi.org/10.1007/s11432-020-3006-9.
DeWitt, R. (2018). Worldviews: An introduction to the history and philosophy of science (Third edition). Wiley.
Docherty, T. (2016). Postmodernism (0 ed.). Routledge. https://doi.org/10.4324/9781315504612
Enke, B., & Graeber, T. (2019). Cognitive Uncertainty (No. w26518; p. w26518). National Bureau of Economic Research. https://doi.org/10.3386/w26518
Eskov, V. M., Eskov, V. V., Gavrilenko, T. V., & Zimin, M. I. (2014). Uncertainty in the quantum mechanics and biophysics of complex systems. Moscow University Physics Bulletin, 69(5), 406–411. https://doi.org/10.3103/S002713491405004X.
Feyerabend, P. (2010). Against method (4th ed). Verso.
Floridi, L. (2019). The logic of information: A theory of philosophy as conceptual design (First edition). Oxford University Press.
Furner, J. (2017). Philosophy of data: why? Education for Information, 33(1), 55–70. https://doi.org/10.3233/EFI-170986.
Gaarder, J. (1996). Sophie’s world: a novel about the history of philosophy. Phoenix.
Gallagher, K. T. (2020). The philosophy of knowledge. Fordham University Press. https://doi.org/10.1515/9780823296521.
Ghomshei, M. M., Meech, J. A., & Naderi, R. (2008). Fuzzy Logic in a Postmodern Era. In Forging New Frontiers: Fuzzy Pioneers II (Vol. 218, pp. 363–376). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-73185-6_18
Goldstein, E. B., & Brockmole, J. R. (2017). Sensation and perception (Tenth edition, student edition). Cengage Learning.
Greco, J. (2022). Transmission of knowledge. Cambridge Univ Press.
Grimm, S. R. (Ed.). (2017). Explaining understanding: new perspectives from epistemology and philosophy of science. Routledge,Taylor & Francis Group.
Gutierrez-Basulto, V., Jung, J. C., Lutz, C., & Schröder, L. (2017). Probabilistic description logics for subjective uncertainty. Journal of Artificial Intelligence Research, 58, 1–66. https://doi.org/10.1613/jair.5222.
Hester, P. T., Adams, K. MacG. (2013). Thinking systemically about Complex Systems. Procedia Computer Science, 20, 312–317. https://doi.org/10.1016/j.procs.2013.09.278.
Hirsh, S., Bates, M., & Dalrymple, P. (2012). From vision to reality: The emerging information professional: From Vision to Reality: The Emerging Information Professional. Proceedings of the American Society for Information Science and Technology, 49(1), Article 1. https://doi.org/10.1002/meet.14504901226
Hou, F., Triantaphyllou, E., & Yanase, J. (2022). Knowledge, ignorance, and uncertainty: an investigation from the perspective of some differential equations. Expert Systems with Applications, 191, 116325. https://doi.org/10.1016/j.eswa.2021.116325.
Jaeger, G. (2014). What in the (quantum) world is macroscopic? American Journal of Physics, 82(9), https://doi.org/10.1119/1.4878358. Article 9.
Jarvis, S. H. (2020). Hybrid Time Theory: Cosmology and Quantum Gravity (I). https://doi.org/10.13140/RG.2.2.20045.79847/2
Javanmardi, E., Javanmardi, S., Xie, N., & Chaoqing, Y. (2021). Financial Performance Evaluation ofAutomotive Companies on Tehran Stock Exchange Using Absolute GRA and TOPSIS Models.The Journal of Grey System, 33(3), Article 3.
Javanmardi, E., & Liu, S. (2020). Exploring the human cognitive capacity in understanding Systems: a Grey Systems Theory Perspective. Foundations of Science, 25(3), https://doi.org/10.1007/s10699-019-09618-3.
Javanmardi, E., Liu, S., & Xie, N. (2020a). Exploring Grey Systems Theory-Based methods and applications in sustainability studies: a systematic. Review Approach Sustainability, 12(11), https://doi.org/10.3390/su12114437.
Javanmardi, E., Liu, S., & Xie, N. (2020b). Exploring the philosophical paradigm of Grey Systems Theory as a Postmodern Theory. Foundations of Science, 25(4), https://doi.org/10.1007/s10699-019-09640-5. Article 4.
Javanmardi, E., Liu, S., & Xie, N. (2021). Exploring the philosophical foundations of Grey Systems Theory: subjective processes, information extraction and knowledge formation. Foundations of Science, 26(2), https://doi.org/10.1007/s10699-020-09690-0.
Javanmardi, E., Nadaffard, A., Karimi, N., Feylizadeh, M., & Javanmardi, S. (2022). Diagnosis and prediction of failures in maintenance systems using fuzzy inference and Z-number method. Journal of Intelligent & Fuzzy Systems, 1–15. https://doi.org/10.3233/JIFS-212116.
Javanmardi & Liu. (2019). Exploring Grey Systems Theory-Based Methods and Applications in Analyzing Socio-Economic Systems. Sustainability, 11(15), https://doi.org/10.3390/su11154192.
Jifa, G., & Lingling, Z. (2014). Data, DIKW, Big Data and Data Science. Procedia Computer Science, 31, 814–821. https://doi.org/10.1016/j.procs.2014.05.332.
John Locke collection (2015). First Rate Publishers.
Klein, P. (2007). Human knowledge and the infinite progress of reasoning. Philosophical Studies, 134(1), 1–17. https://doi.org/10.1007/s11098-006-9012-9.
Köhn, J. (2017). Uncertainty in Economics. Springer International Publishing. https://doi.org/10.1007/978-3-319-55351-1.
Langford, G. O. (2012). Engineering systems integration: Theory, metrics, and methods (1st edition). CRC Press.
Lemert, C. C. (2015). Postmodernism is Not What You Think (0 ed.). Routledge. https://doi.org/10.4324/9781315632650
Liu, S. (2021). Negative grey relational model and measurement of the reverse incentive effect of fields medal. Grey Systems: Theory and Application. https://doi.org/10.1108/GS-10-2021-0148.
Liu, S., Fang, Z., Xie, N., & Yang, Y. (2018). Explanation of terms of Grey models for decision-making. Grey Systems: Theory and Application, 8(4), https://doi.org/10.1108/GS-10-2018-081.
Liu, S., Forrest, J., & Yang, Y. (2017). Grey Data Analysis: Methods, Models and Applications (1st ed. 2017). Springer Singapore: Imprint: Springer. https://doi.org/10.1007/978-981-10-1841-1
Liu, S., Rui, H., Fang, Z., Yang, Y., & Forrest, J. (2016). Explanation of terms of grey numbers and its operations. Grey Systems: Theory and Application, 6(3), https://doi.org/10.1108/GS-09-2016-0031.
Liu, S., Sheng, K., & Forrest, J. (2012). On uncertain systems and uncertain models. Kybernetes, 41(5/6), https://doi.org/10.1108/03684921211243211. Article 5/6.
Liu, S., Xie, N., Yang, Y., & Forrest, J. (2016). Explanation of terms of sequence operators and grey data mining. Grey Systems: Theory and Application, 6(3), https://doi.org/10.1108/GS-09-2016-0032.
Liu, S., Yang, Y., Xie, N., & Forrest, J. (2016). New progress of Grey System Theory in the new millennium. Grey Systems: Theory and Application, 6(1), https://doi.org/10.1108/GS-09-2015-0054.
Longino, H. E. (2002). The fate of knowledge. Princeton University Press. https://doi.org/10.1515/9780691187013.
Lottholz, P. (2016). Exploring the Boundaries of Knowledge via Hybridity. Journal of Intervention and Statebuilding, 10(1), 136–142. https://doi.org/10.1080/17502977.2015.1095204.
Loucks, D. P., & van Beek, E. (2017). An Introduction to Probability, Statistics, and Uncertainty. In D. P. Loucks & E. van Beek, Water Resource Systems Planning and Management (pp. 213–300). Springer International Publishing. https://doi.org/10.1007/978-3-319-44234-1_6
Magee, B. (2016). The story of philosophy (Revised edition). Dorling Kindersley Publishing.
Malcolm, N. (2020). Thought and knowledge: essays. Cornell University Press. https://doi.org/10.7591/9781501738760.
Mather, G. (2016). Foundations of sensation and perception. Psychology Press. https://doi.org/10.4324/9781315672236. 0 ed.
Maxwell, N. (2017). Karl Popper, Science and Enlightenment. UCL Press. https://doi.org/10.2307/j.ctt1vxm8p6.
McIntosh, C., & Press, C. (Eds.). (2013). Cambridge advanced learner’s dictionary: With CD-ROM (Fourth edition). Cambridge University Press.
Mierzwiak, R. (2019). Characteristics of selected approaches of uncertainty modelling in the context of Management Sciences. Humanities and Social Sciences Quarterly. https://doi.org/10.7862/rz.2019.hss.7.
Mierzwiak, R., Nowak, M., & Xie, N. (2020). A new approach to the degree of greyness. Grey Systems: Theory and Application, 11(2), https://doi.org/10.1108/GS-11-2019-0048. Article 2.
Mierzwiak, R., Xie, N., & Nowak, M. (2018). New axiomatic approach to the concept of grey information. Grey Systems: Theory and Application, 8(2), https://doi.org/10.1108/GS-12-2017-0041. Article 2.
Minati, G. (2016). Knowledge to manage the knowledge society: the Concept of Theoretical Incompleteness. Systems, 4(3), 26. https://doi.org/10.3390/systems4030026.
Minati, G. (2017). The past, Present and possible future for Systems. International Journal of Systems and Society, 4(1), 1–9. https://doi.org/10.4018/IJSS.2017010101.
Mittelstrass, J. (2002). The Limits of Science and the Limitations of Knowledge. ALLEA Biennial Yearbook 2002, 11–26.
Moslehian, M. S. (2005). Postmodern view of humanistic mathematics. Resonance, 10(11), https://doi.org/10.1007/BF02837651.
Nguyen, V. L., Destercke, S., & Hüllermeier, E. (2019). Epistemic Uncertainty Sampling. In P. Kralj Novak, T. Šmuc, & S. Džeroski (Eds.), Discovery Science (Vol. 11828, pp. 72–86). Springer International Publishing. https://doi.org/10.1007/978-3-030-33778-0_7
Nowak, M., Mierzwiak, R., Wojciechowski, H., & Delcea, C. (2020). Grey portfolio analysis method. Grey Systems: Theory and Application, 10(4), 439–454. https://doi.org/10.1108/GS-11-2019-0049.
Olcaysoy Okten, I., Gollwitzer, A., & Oettingen, G. (2022). When knowledge is blinding: the dangers of being certain about the future during uncertain societal events. Personality and Individual Differences, 195, 111606. https://doi.org/10.1016/j.paid.2022.111606.
Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341–356. https://doi.org/10.1007/BF01001956.
Prigogine, I., & Stengers, I. (1997). The end of certainty: Time, chaos, and the new laws of nature (1st Free Press ed). Free Press.
Reisberg, D. (2019). Cognition: exploring the science of the mind. W. W. Norton & Company. Seventh Edition.
Rosenberg, A., & McIntyre, L. (2019). Philosophy of Science: A Contemporary Introduction (4th ed.). Routledge. https://doi.org/10.4324/9780429447266
RUSSELL, B. (2022). MY PHILOSOPHICAL DEVELOPMENT. ROUTLEDGE.
Sardar, Z. (2020). The smog of ignorance: knowledge and wisdom in postnormal times. Futures, 120, 102554. https://doi.org/10.1016/j.futures.2020.102554.
Schertz, K. E., & Berman, M. G. (2019). Understanding Nature and its cognitive benefits. Current Directions in Psychological Science, 28(5), 496–502. https://doi.org/10.1177/0963721419854100.
Silverman, H. J. (Ed.). (2017). Postmodernism: Philosophy and the Arts (1st ed.). Routledge. https://doi.org/10.4324/9781315112299
Smart, B. (2016). Postmodernity (0 ed.). Routledge. https://doi.org/10.4324/9780203822715
Sterman, J. D. (2009). Business dynamics: Systems thinking and modeling for a complex world (Nachdr.). Irwin/McGraw-Hill.
Vargas, M. R., & Department of Philosophy, Florida State University. (2018). Reflectivism, Skepticism, and values. Social Theory and Practice, 44(2), https://doi.org/10.5840/soctheorpract201844234.
Williams, M. (2017). Skepticism. In J. Greco, & E. Sosa (Eds.), The blackwell guide to Epistemology (pp. 33–69). Blackwell Publishing Ltd. https://doi.org/10.1002/9781405164863.ch1.
Wu, K., & Brenner, J. (2017). Philosophy of information: Revolution in Philosophy. Towards an informational metaphilosophy of Science. Philosophies, 2(4), 22. https://doi.org/10.3390/philosophies2040022.
Wuppuluri, S., & Ghirardi, G. (Eds.). (2017). Space, Time and the limits of Human understanding. Springer International Publishing. https://doi.org/10.1007/978-3-319-44418-5.
Xie, N. (2017). Explanations about grey information and framework of grey system modeling. Grey Systems: Theory and Application, 7(2), https://doi.org/10.1108/GS-05-2017-0012.
Xie, N. (2018). Interval grey number based project scheduling model and algorithm. Grey Systems: Theory and Application, 8(1), https://doi.org/10.1108/GS-11-2017-0035.
Yackinous, W. S. (2015a). More Views on Systems Thinking. In Understanding Complex Ecosystem Dynamics (pp. 21–46). Elsevier. https://doi.org/10.1016/B978-0-12-802031-9.00002-4
Yackinous, W. S. (2015b). Understanding complex ecosystem dynamics: a systems and engineering perspective. Elsevier / Academic Press.
Yang, Y., Liu, S., & Xie, N. (2019). Uncertainty and grey data analytics. Marine Economics and Management, 2(2), https://doi.org/10.1108/MAEM-08-2019-0006.
Yazdanbakhsh, O., & Dick, S. (2018). A systematic review of complex fuzzy sets and logic. Fuzzy Sets and Systems, 338, 1–22. https://doi.org/10.1016/j.fss.2017.01.010.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Zaliwski, A. S. (2011). Information – is it subjective or objective? TripleC: communication, capitalism. & Critique Open Access Journal for a Global Sustainable Information Society, 9(1), 77–92. https://doi.org/10.31269/triplec.v9i1.250.
Zhang, M., Selic, B., Ali, S., Yue, T., Okariz, O., & Norgren, R. (2016). Understanding Uncertainty in Cyber-Physical Systems: A Conceptual Model. In A. Wąsowski & H. Lönn (Eds.), Modelling Foundations and Applications (Vol. 9764, pp. 247–264). Springer International Publishing. https://doi.org/10.1007/978-3-319-42061-5_16
Acknowledgements
This work was supported by a long-term project of national major talent plan of China (YQR20024), projects of the National Natural Science Foundation of China (72071111). It is also supported by a joint project of both the NSFC and the RS of the UK (71811530338), a project of Intelligence Introduction base of the Ministry of Science and Technology (G2021181014L). At the same time, the authors would like to acknowledge the partial support of the Fundamental Research Funds for the Central Universities of China (NC2019003).
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Javanmardi, E., Liu, S. & Xie, N. A Developmental Review of the Philosophical and Conceptual Foundations of Grey Systems Theory. Found Sci (2022). https://doi.org/10.1007/s10699-022-09886-6
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DOI: https://doi.org/10.1007/s10699-022-09886-6