Журнал: Управление наукой: теория и практикаТамбовцев В. Л.Исследовательское поведение: ограниченно рациональное производство рационального научного знания

Журнал: Управление наукой: теория и практика

Тамбовцев В. Л.

Исследовательское поведение: ограниченно рациональное производство рационального научного знания

DOI: https://doi.org/10.19181/smtp.2023.5.1.11
Тамбовцев Виталий Леонидович
МГУ им. М. В. Ломоносова, Москва, Россия
Доктор экономических наук, профессор

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Тамбовцев В. Л. Исследовательское поведение: ограниченно рациональное производство рационального научного знания // Управление наукой: теория и практика. 2023. Том. 5. № 1. С. 185-203.
DOI: https://doi.org/10.19181/smtp.2023.5.1.11. EDN: SCMTFF

Рубрика:

Культурно-исторический контекст и стратегии научно-технологического развития

Аннотация:

Люди сильно разнятся между собой по познавательным способностям, однако у всех них эти способности ограничены, начиная от возможностей восприятия окружающей реальности и кончая осуществлением математических расчётов и логических выводов из сделанных посылок. Если полностью рациональный индивид не только обладает полной информацией о мире, но и неограниченными возможностями совершать расчёты и делать логические выводы, то реальные люди, включая профессиональных исследователей, лишь ограниченно рациональны. Тем не менее научные знания, производимые учёными, близки к полностью рациональным. В статье рассматриваются компоненты ограниченной рациональности и те механизмы внутри науки, которые позволяют совершать такой переход. Ведущая роль среди этих механизмов принадлежит научной коммуникации, одной из функций которой является коррекция непроизвольных и неосознаваемых ошибок, совершаемых ограниченно рациональными исследователями. Показано, что выполнение этой функции сталкивается с определёнными сложностями, которые важно исследовать для улучшения процесса корректировки ошибок.

Литература:

  • 1. Newton-Smith, W. H. (1981). The Rationality of Science. London: Routledge.
  • 2. Merton, R. K. (1942). Science and technology in a democratic order. Journal of Legal and Political Sociology. Vol. 1. P. 115–126.
  • 3. Simon, H. A. (1955). A behavioral model of rational choice. Quarterly Journal of Economics. Vol. 69, no. 1. P. 99–118.
  • 4. Sent, E.-M. (2018). Rationality and bounded rationality: you can’t have one without the other. European Journal of the History of Economic Thought. Vol. 25, is. 6. P. 1370–1386.
  • 5. Bazerman, M. H. and Messick, D. M. (1998). On the power of a clear definition of rationality. Business Ethics Quarterly, Vol. 8, is. 3. P. 477–480.
  • 6. Simon, H. A. (1956). A Comparison of Game Theory and Learning Theory. Psychometrika. Vol. 21, is. 3. P. 267–272.
  • 7. Boudon, R. (1989). Subjective Rationality and the Explanation of Social Behavior. Rationality and Society. Vol. 1, is 2. P. 173–196.
  • 8. Ryall, M. D. (2003). Subjective Rationality, Self-Confirming Equilibrium, and Corporate Strategy. Management Science. Vol. 49, no. 7. P. 936–949.
  • 9. Gilboa, I., Maccheroni, F., Marinacci, M. and Schmeidler, D. (2010). Objective and subjective rationality in a multiple prior model. Econometrica. Vol. 78, no. 2. P. 755–770.
  • 10. Loewenstein, G. (1996). Out of Control: Visceral Influences on Behavior. Organizational Behavior and Human Decision Processes. Vol. 65, no. 3. P. 272–292.
  • 11. Kahneman, D. and Tversky, A. (1984). Choices, values, and frames. American Psychologist. Vol. 39, no. 4. P. 341–350.
  • 12. Thaler, R. H. (1991). Quasi Rational Economics. New York: Russell Sage Found.
  • 13. Kahneman, D. (2003). Maps of Bounded Rationality: Psychology for Behavioral Economics. American Economic Review. Vol. 93, is. 5. P. 1449–1475.
  • 14. Jones, B. D. (1999). Bounded rationality. Annual Review of Political Science. Vol. 2, is. 1. P. 297–321.
  • 15. Coase, R. (1992). The Institutional Structure of Production. American Economic Review. Vol. 82, is. 4. P. 713–719.
  • 16. Polya, G. (1954). Mathematics and Plausible Reasoning. Vol. I&II. Princeton, NJ: Princeton University Press.
  • 17. Hertwig, R. and Pachur, T. (2015). Heuristics, History of. In: Wright J. (Ed.) International Encyclopedia of the Social & Behavioral Sciences, 2nd ed., Vol. 10. P. 879–835. Oxford: Elsevier.
  • 18. Reber, A. S. (1992). The cognitive unconscious: An evolutionary perspective. Consciousness and Cognition. Vol. 1, is. 2. P. 93–133.
  • 19. Greenwald, A. G. and Ganaji, M. R. (1995). Implicit Social Cognition: Attitudes, Self-Esteem, and Stereotypes. Psychological Review. Vol. 102, is. 1. P. 4–27.
  • 20. Casarett, D. (2016). The Science of Choosing Wisely – Overcoming the Therapeutic Illusion. New England Journal of Medicine. Vol. 374, no. 13. P. 1203–1205.
  • 21. Lieder, F. and Griffiths, T. L. (2020). Resource-rational analysis: Understanding human cognition as the optimal use of limited computational resources. Behavioral and Brain Sciences. Vol. 43, article e1; DOI 10.1017/S0140525X1900061X.
  • 22. Hahna, M., Futrell, R., Levy, R. and Gibson, E. (2022). A resource-rational model of human processing of recursive linguistic structure. PNAS: Psychological and Cognitive Sciences. Vol. 119, no. 43, article e2122602119.
  • 23. Tversky, A. and Kahneman, D. (1974). Judgment under Uncertainty: Heuristics and Biases. Science. New Series. Vol. 185, no. 4157. P. 1124–1131.
  • 24. Taylor, R. N. (1975). Psychological determinants of bounded rationality: Implications for decision-making strategies. Decision Sciences. Vol. 6, is. 3. P. 409–429.
  • 25. Caverni, J.-P., Fabre, J.-M. and Gonzalez, M. (1990). Cognitive Biases: Their Contribution for Understanding Human Cognitive Processes. Advances in Psychology. Vol. 68. P. 7–12.
  • 26. Byyny, R. L. (2017). Cognitive bias: Recognizing and managing our unconscious biases. The Pharos. No. Winter. P. 2–6.
  • 27. Johnson, D. and Levin, S. (2009). The tragedy of cognition: psychological biases and environmental inaction. Current Science. Vol. 97, no. 11. P. 1593–1603.
  • 28. Van Vugt, M., Griskevicius, V. and Schultz, P. W. (2014). Naturally Green: Harnessing Stone Age Psychological Biases to Foster Environmental Behavior. Social Issues and Policy Review. Vol. 8, is. 1. P. 1–32.
  • 29. Haselton, M. G., Bryant, G. A., Wilke, A., Frederick, D. A., Galperin, A., Frankenhuis, W. E. and Moore, T. (2009). Adaptive rationality: An evolutionary perspective on cognitive bias. Socia1 Cognition. Vol. 27, no. 5. P. 733–763.
  • 30. Jussim, L. (2012). Social perception and social reality: Why accuracy dominates bias and self-fulfilling prophecy. New York: Oxford University Press.
  • 31. Jussim, L. (2017). Pr?cis of Social Perception and Social Reality: Why accuracy dominates bias and self-fulfilling prophecy. Behavioral and Brain Sciences. Vol. 40, article e1 DOI:10.1017/S0140525X1500062X.
  • 32. Kahneman, D. and Tversky, A. (1996). On the Reality of Cognitive Illusions. Psychological Review. Vol. 103, no. 3. P. 582–591.
  • 33. Samuelson, W., Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty. Vol. 1, is.1. P. 7–59.
  • 34. Barberis, N., Shleifer, A. and Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics. Vol. 49, is. 3. P. 307–343.
  • 35. Gifford, R. (2011). The dragons of inaction: Psychological barriers that limit climate change mitigation and adaptation. American Psychologist. Vol. 66, no. 4. P. 290–302.
  • 36. Chu, J. S. G. and Evans, J. A. (2021). Slowed canonical progress in large fields of science. Proceedings of the National Academy of Sciences (PNAS). Vol. 118, no. 41, article e2021636118.
  • 37. Gigerenzer, G. (1991). How to make cognitive illusions disappear: Beyond “heuristics and biases”. In: Stroebe W. & Hewstone M. (Eds.). European Review of Social Psychology. (Vol. 2. P. 83–115). Chichester, UK: Wiley.
  • 38. Gigerenzer, G and Brighton, H. (2009). Homo heuristicus: Why biased minds make better inferences. Topics in Cognitive Science. Vol. 1, is. 1. P. 107–143.
  • 39. Grandori, A. and Cholakova, M. (2013). Unbounding bounded rationality: Heuristics as the logic of economic discovery. International Journal of Organization Theory & Behavior. Vol. 16, no. 3. P. 368–392.
  • 40. Gigerenzer, G. (2008). Why heuristics work. Perspectives on Psychological Science. Vol. 3, is.1. P. 20–29.
  • 41. Gigerenzer, G. and Gaissmaier, W. (2011). Heuristic Decision Making. Annual Review of Psychology, Vol. 62. P. 451–82.
  • 42. Goldman, A. (1986). Epistemology and Cognition. Cambridge, MA: Harvard University Press.
  • 43. Wible, J. R. (1997). Towards an evolutionary conception of rationality in science and economics. In: Wible, J. R. The Economics of Science: Methodology and Epistemology as if Economics Really Mattered. London: Routledge. P. 190–202.
  • 44. Liebenberg, L. (2021). The Origin of Science: The Evolutionary Roots of Scientific Reasoning and its Implications for Tracking Science. 2nd ed. Cape Town: CyberTracker.
  • 45. Feyerabend, P. (1975). Against Method: Outline of an Anarchistic Theory of Knowledge. London: Verso.
  • 46. Bergstr?m, L. (1980). Some Remarks Concerning Rationality in Science. In: Hilpinen R. (Ed.) Rationality in Science Dordrecht: Springer. P. 1–11.
  • 47. Szollosi, A. and Newell, B. R. (2020). People as intuitive scientists: Reconsidering statistical explanations of decision making. Trends in Cognitive Sciences. Vol. 24, is. 12. P. 1008–1018.
  • 48. Viktoruk, E. N. and Chernyeva, A. S. (2010). Understanding Horizons in Methodology of Socially-Humanitarian Cognition. Journal of Siberian Federal University: Humanities & Social Sciences. Vol. 5, no. 3. P. 776–784.
  • 49. Turk-Browne, N. B., Junge, J. A. and Scholl, B. J. (2005). The Automaticity of Visual Statistical Learning. Journal of Experimental Psychology: General. Vol. 134, no. 4. P. 552–564.
  • 50. Bandura, A. (1977). Social Learning Theory. Englewood Cliffs, NJ: Prentice Hall.
  • 51. Becker, G. S. (1976). The economic approach to human behavior. In: Becker, G. S. The Economic Approach to Human Behavior Chicago: University of Chicago Press. P. 3–14.
  • 52. Merton, R. K. (1942). A note on science and democracy. Journal of Legal and Political Sociology. Vol. 1. P. 115–126.
  • 53. Ioannidis, J. P. A. (2012). Why Science Is Not Necessarily Self-Correcting. Perspectives on Psychological Science. Vol. 7, is. 6. P. 645–654.
  • 54. Stroebe, W., Postmes, T. and Spears, R. (2012). Scientific Misconduct and the Myth of Self-Correction in Science. Perspectives on Psychological Science. Vol. 7, no. 6. P. 670–688.
  • 55. Allchin, D. (2015). Correcting the “self-correcting” mythos of science. Filosofia e Hist?ria da Biologia. Vol. 10, is. 1. P. 19–35.
  • 56. Romero, F. (2016). Can the Behavioral Sciences Self-Correct? A Social Epistemic Study. Studies In History and Philosophy of Science Part A. Vol. 60, is.1. P. 55–69.
  • 57. De Vries, R., Anderson, M. S. and Martinson, B. C. (2006). Normal Misbehavior: Scientists Talk about the Ethics of Research. Journal of Empirical Research on Human Research Ethics. Vol. 1, is.1. P. 43–50.
  • 58. Necker, S. (2014). Scientific misbehavior in economics. Research Policy. Vol. 43, is. 10. P. 1747–1759.
  • 59. Hesselmann, F., Graf, V., Schmidt, M. and Reinhart, M. (2017). The visibility of scientific misconduct: A review of the literature on retracted journal articles. Current Sociology Review. Vol. 65, no. 6. P. 814–845.
  • 60. Bruner, J. P. and Holman, B. (2019). Self-correction in science: Meta-analysis, bias and social structure. Studies in History and Philosophy of Science. Part A. Vol. 78. P. 93–97.
  • 61. Tourish, D., Craig, R. (2020). Research Misconduct in Business and Management Studies: Causes, Consequences and Possible Remedies. Journal of Management Inquiry. Vol. 29, is. 2. P. 174–187.
  • 62. Chubin, D. E. (1985). Misconduct in Research: An Issue of Science Policy and Practice. Minerva. Vol. 23, no. 2. P. 175–202.
  • 63. Biagioli, M., Kenney, M., Martin, B. and Walsh, J. P. (2019). Academic misconduct, misrepresentation and gaming: A reassessment. Research Policy. Vol. 48, is. 2. P. 401–413.
  • 64. Ioannidis, J. P. (2005). Why most published research findings are false. PLoS Medicine. Vol. 2, is. 8, article e124; DOI: 10.1371/journal.pmed.0020124.
  • 65. Wilholt, T. (2009). Bias and values in scientific research. Studies in History and Philosophy of Science. Vol. 40, is.1. P. 92–101.
  • 66. Ditto, P. H. (2009). Passion, reason, and necessity: A quantity-of-processing view of motivated reasoning. In: Bayne, T. & Fern?ndez, J. (Eds.). Delusion and Self-Deception: Affective and Motivational Influences on Belief Formation New York: Psychology Press. P. 23–53.
  • 67. Berggren, N., Jordahl, H. and Stern, C. (2009). The political opinions of Swedish social scientists. Finnish Economical Papers. Vol. 22, no. 2. P. 75–88.
  • 68. Charlton, B. G. (2009). Clever sillies: Why high IQ people tend to be deficient in common sense. Medical Hypotheses. Vol. 73, no. 6. P. 867–70.
  • 69. Woodley, M. A. (2010). Are high-IQ individuals deficient in common sense? A critical examination of the ‘clever sillies’ hypothesis. Intelligence. Vol. 38. P. 471–80.
  • 70. Franco, A., Malhotra, N. and Simonovits, G. (2014). Publication bias in the social sciences: Unlocking the file drawer. Science. Vol. 345, no. 6203. P. 1502–1505.
  • 71. Fanelli, D., Costas, R. and Ioannidis, J. P. A. (2017). Meta-assessment of bias in science. Proceedings of the National Academy of Sciences. Vol. 114, no. 14. P. 3714–3719.
  • 72. Peterson, E. L. (2019). Can scientific knowledge sift the wheat from the tares? A brief history of bias (and fears about bias) in science. In: McCain, K. & Kampourakis, K. (Eds.). What is Scientific Knowledge? An Introduction to Contemporary Epistemology of Science London: Routledge. P. 195–211.
  • 73. May, J. (2021). Bias in Science: Natural and Social. Synthese. Vol. 199, is. 1–2. P. 3345–3366.
  • 74. Nickerson, R. S. (1998). Confirmation Bias: A Ubiquitous Phenomenon in Many Guises. Review of General Psychology. Vol. 2, no. 2. P. 175–220.
  • 75. Kappes, A., Harvey, A. H., Lohrenz, T., Montague, P. R. and Sharot, T. (2020). Confirmation bias in the utilization of others' opinion strength. Nature Neuroscience. Vol. 23, is. 1. P. 130–137.
  • 76. Schumm, W. R. (2021). Confirmation bias and methodology in social science: An editorial. Marriage & Family Review. Vol. 57, is. 4. P. 285–293.
  • 77. McSweeney, B. (2021). Fooling ourselves and others: confirmation bias and the trustworthiness of qualitative research – Part 1 (the threats). Journal of Organizational Change Management. Vol. 34, no. 5. P. 1063–1075.
  • 78. Fine, M. A. (2022). Distinctions between Scientific Misconduct and Bias in Social Science: Avoidable versus Unavoidable Deviations from Best Practices in Research. Marriage & Family Review. Vol. 58, is. 1. P. 89–100.
  • 79. Moser, S. (2013). Confirmation Bias: The Pitfall of Forensic Science. Themis: Research Journal of Justice Studies and Forensic Science. Vol. 1, is. 1. P. 71–80.
  • 80. Jenkins, H. M. and Ward, W. C. (1965). Judgment of contingency between responses and outcomes. Psychological Monographs. Vol. 79, is. 1. P. 1–17.
  • 81. Shanks, D. R. and Dickinson, A. (1987). Associative accounts of causality judgment. In: Bower, G. H. (Ed.). The Psychology of Learning and Motivation San Diego, CA: Academic Press. P. 229–261.
  • 82. Matute, H., Blanco, F., Yarritu, I., D?az-Lago, M., Vadillo, M. A. and Barberia, I. (2015). Illusions of causality: How they bias our everyday thinking and how they could be reduced. Frontiers in Psychology. Vol. 6. article 888. DOI: 10.3389/fpsyg.2015.00888.
  • 83. Moshman, D. (1990). Rationality as a Goal of Education. Educational Psychology Review. Vol. 2, no. 4. P. 335–364.
  • 84. Park, P. S. (2022). The evolution of cognitive biases in human learning. Journal of Theoretical Biology. Vol. 541, article 111031.
  • 85. Merton, R. K. (1968). The Matthew Effect in Science. Science. Vol. 159, no. 3810. P. 56–63.
  • 86. Matute, H., Yarritu, I. and Vadillo, M. A. (2011). Illusions of causality at the heart of pseudoscience. British Journal of Psychology. Vol. 102, no. 3. P. 392–405.
  • 87. Torres, M. N., Barberia, I. and Rodr?guez-Ferreiro, J. (2020). Causal illusion as a cognitive basis of pseudoscientific beliefs. British Journal of Psychology. Vol. 111, no. 4. P. 840–852.
  • 88. Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science. Vol. 43, is. 9. P. 628–638.
  • 89. Hamilton, D. P. (1990). Publishing by – and for? – the Numbers. Science. Vol. 250, no. 4986. P. 1331–1332.
  • 90. Hamilton, D. P. (1991). Research Papers: Who’s Uncited Now. Science. Vol. 251, no. 4989. P. 25.
  • 91. Schwartz, C. A. (1997). The rise and fall of uncitedness. College & Research Libraries. Vol. 58, no. 1. P. 19–29.
  • 92. Van Noorden, R. (2017). The science that’s never been cited. Nature. Vol. 552, no. 7684. P. 162–164.
  • 93. Camacho-Minano, M. and Nunez-Nickel, M. (2009). The multilayered nature of reference selection. Journal of the American Society for Information Science and Technology. Vol. 60, is. 4. P. 754–777.
  • 94. MacRoberts, M. H. and MacRoberts, B. R. (2010). Problems of citation analysis: A study of uncited and seldom?cited influences. Journal of the American Society for Information Science and Technology. Vol. 61, is. 1. P. 1–12.

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