UDC: 165
https://doi.org/10.25198/2077-7175-2026-5-89


viewing

INTELLECTUAL CONFLICT IN THE ECONOMICS

N. D. Astashova
National Research Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
e-mail: nadya.astashova@mail.ru

Abstract. The article discusses the current problems of the conflict between different strategies of economic thinking. The purpose of the work is to analyze the intellectual disagreements that arise as a result of the conflict between different approaches and strategies of thinking in crisis situations. The researcher faces the task of systematically analyzing the current trends in the development of economic science in situations of uncertainty. The method of critical analysis is aimed at rethinking the established scientific concepts and identifying the uniqueness of the current stage of development of economic science. The study examines the concepts of innovative, intellectual, and experimental economics, as well as the problems of using mathematical models and algorithms. The article provides a comparative analysis of the principles of precaution and proactivity using the example of an economic crisis. The author concludes that it is necessary to rethink the position associated with understanding the economy as a rigid system of attitudes and views. The complexity of the current economic system is such that crises inevitably occur in various parts of the system, so it is impossible to say with certainty that there is no crisis in any part of the system. It is necessary to acknowledge that the concept of economic stability is nothing more than an illusion that people tend to comfort themselves with. However, crises are an important indicator that the previous set of scientific views and methods is no longer sufficient to meet new challenges, and alternative solutions must be developed. In this situation, the analysis of economic problems becomes increasingly complex, and different approaches to their study compete with each other. In particular, we can observe the interplay between thinking strategies that focus on experimentation and high-risk decision-making, and strategies that assume a stable environment where the behavior of economic agents tends to conform to established expectations. Thus, it can be argued that the fact that economic knowledge is developing in an ambiguous and internally contradictory way does not mean that science and its tools are incapable of solving pressing problems. In modern economic thought, there is a tendency towards the development of comprehensive solutions that allow for the integration of diverse economic strategies. The results are highly innovative, as they produce isolated subject areas and research traditions through the lens of economic analysis and social epistemology.

Key words: social epistemology, crisis of science, thinking strategies, proactivity, precaution, economics.

Cite as: Astashova, N. D. (2026) [Intellectual conflict in the economics]. Intellekt. Innovacii. Investicii [Intellect. Innovations. Investments]. Vol. 5, pp. 89–99. – https://doi.org/10.25198/2077-7175-2026-5-89.


References

  1. Abramov, E. V., Moroz, T. A. (2022) [Mathematical Foundations of Decision-Making under Uncertainty]. Vestnik VIEPP [Bulletin of VIEPP].Vol. 2, pp. 36–41. (In Russ.).
  2. Adzhemoglu, D., Robinson, D. A. (2015) Pochemu odni strany bogatye, a drugie bednye. Proishozhdenie vlasti, procvetaniya i nishchety [Why some countries are rich and others are poor. The origin of Power, prosperity, and poverty]. Moscow: AST, 692 p. (In Russ., transl. from Engl.).
  3. Kasavin, I. T. (2000) [Space and Time: in search of the «natural ontology» of Knowledge]. Obshhestvennye nauki i sovremennost [Social sciences and modernity]. Vol. 1, pp. 90–99. (In Russ.).
  4. Kolpakov, V. A. (2007) [The crisis of economics as a loss of its vital importance]. Epistemologiya i filosofiya nauki [Epistemology and philosophy of science]. Vol. 12. No. 2, pp. 203–211. (In Russ.).
  5. Kun, T. (2009) Struktura nauchnyh revolyucij [The structure of scientific revolutions]. Moscow: AST, 317 p. (In Russ., transl. from Engl.).
  6. North, D., Wallis, J., Weingast, B. (2011) Nasiliye i sotsial'nyye poryadki. Kontseptual'nyye ramki dlya inter-pretatsii pis'mennoy istorii chelovechestva [Violence and Social Orders: A Conceptual Framework for Interpreting Recorded Human History]. Moscow: Gaidar Institute Press, 480 p. (In Russ., transl.from Egl.).
  7. Chepyuk, O. R. (2019) [The economic crisis as a phenomenon of subjectivity: the experience of socio-philosophical analysis]. Kant [Kant]. Vol. 1(30), pp. 231–238. (In Russ.).
  8. Sheler, M. (2011) Struktura nauchnyh Problemy sociologii znaniya. [Problems of the sociology of knowledge]. Moscow: Institute for General Humanitarian Research, 320 p. (In Russ., transl. from German).
  9. Fleck, L. (1980) Entstehung und Entwicklung einer wissenschaftlichen Tatsache: Einf. in d. Lehre von Denkstil u. Denkkollektiv. Frankfurt am Main: Suhrkamp, 189 p. (In German).
  10. Fleck, L. (1986) Scientific Observation and Perception in General. In: R.S. Cohen and Th. Schnelle (eds.), Cognition and Fact: Materials on Ludwik Fleck (Boston Studies in the Philosophy and History of Science Book 87). Springer Dordrecht, pp. 59–78. – https://doi.org/10.1007/978-94-009-4498-5. (In Eng.).
  11. Fuller, S. (2013) The Proactionary Principle. Between No Caution and Precaution. Breakthrough Institute. Available at: https://thebreakthrough.org/articles/the-proactionary-principle (accessed: 04.11.2025) (In Eng.).
  12. Fuller, S., Lipińska, V. (2014) The Proactionary Imperative: A Foundation for Transhumanism. Basingstoke: Palgrave Macmillan, 168 p. – https://doi.org/10.1057/9781137302922. (In Eng.).
  13. Gupta, S., et al. (2023) Take Expert Advice Judiciously: Combining Groupwise Calibrated Model Probabilities with Expert Predictions. ECAI 2023. Front. Artif. Intell. Appl. Vol. 372, pp. 956–963. – https://doi.org/10.3233/FAIA230366. (In Eng.).
  14. Janeway, W. H. (2012) Doing capitalism in the innovation economy: markets, speculation and the state. Cambridge University Press, 329 p. – https://doi.org/10.1017/CBO9781139381550. (In Eng.).
  15. Keynes, J. M. (1937) The General Theory of Employment. Quarterly Journal of Economics, February 1937. E. Johnson and D. Moggridge (eds.). The Collected Writings of John Maynard Keynes. Vol. 14. Cambridge University Press and Macmillan for the Royal Economic Society, pp. 112–113. (In Eng.).
  16. Kindleberger, C. P., Aliber, R. Z. (2009) Manias, Panics and Crashes: A History of MacKenzie D. Material Markets: How Economic Agents are Constructed. Oxford: Oxford University Press, 228 p. (In Eng.).
  17. MacKenzie, D. (2009) Material Markets: How Economic Agents are Constructed. Oxford: Oxford University Press, 228 p.
  18. McCloskey, D. N. (2016) Max U versus Humanomics: a critique of neoinstitutionalism. Journal of Institutional Economics. Vol. 12. No. 1, pp. 1–27. – https://doi.org/10.1017/S1744137415000053. (In Eng.).
  19. Minsky, H. P. (1992) The Financial Instability Hypothesis. The Jerome Levy Economics Institute Working Paper. Vol. 74, pp. 1–11. (In Eng.).
  20. Mokyr, J. (2016) A culture of growth: the origins of the modern economy. Princeton University Press, 400 p. (In Eng.).
  21. Muniesa, F., Callon, M. (2007) Economic Experiments and the Construction of Markets. In: MacKenzie D., Muniesa F., Siu L. et al. Do Economists Make Markets?: On the Performativity of Economics. Princeton: Princeton University Press, pp. 163–189. (In Eng.).
  22. Sen, A. (2005) On ethics and economics. Malden, MA ; Oxford, UK : Blackwell Publishing, 131 p. (In Eng.).
  23. Singh, S., Jain, S., Jha, S. S. (2023). On Subset Selection of Multiple Humans To Improve Human-AI Team Accuracy. In Proc. of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023), London, United Kingdom, May 29 –June 2, pp. 317–325. (In Eng.).
  24. Straitouri, E., et al. (2023) Improving Expert Predictions with Conformal Prediction. Proceedings of the 40th International Conference on Machine Learning, pp. 32633–32653. – https://doi.org/10.48550/arXiv.2201.12006. (In Eng.).
  25. The precautionary principle: Definitions, applications and governance. – Available at: https://www.europarl.europa.eu/thinktank/et/document/EPRS_IDA(2015)573876 (accessed: 03.11.2025) (In Eng.).
  26. Zhang, Z., Wells, K., Carneiro, G. (2023) Learning to Complement with Multiple Humans (LECOMH): Integrating Multi-rater and Noisy-Label Learning into Human-AI Collaboration. – https://doi.org/10.48550/arXiv.2311.13172. (In Eng.).

In order for us to provide you with high-quality services, we use cookies that are stored on your computer (location information; ip-address; type, language, OS and browser version; device type and screen resolution; source from which you came to site the user; what pages the user opens and what buttons the user clicks; the same information is used to process statistical data on the use of the site through the Yandex.Metrika Internet service). By clicking the "I agree" button, you confirm that you are informed about the use of cookies on our website. You can disable cookies in your browser settings.