Cybersemiotic Approach to the Theory of Scientific Discoveries

   Relevance. Since the 20s of the XXI century, the cultural era of neo-modernity began, associated with the expansion of the influence of artificial intelligence technologies on society, the economy and scientific and technological progress. It is relevant to develop a theory of scientific discoveries, which will offer new models for managing scientific discoveries and civilizational risks, taking into account digitalization and changes in human thinking.
   Purpose. Within the framework of this research, two goals are set ‒ to propose a new "useful" theory of scientific discoveries, which will take into account the factors of changes in the psychology of people's thinking and the potential for automation of scientific discoveries, and to conduct a philosophical and methodological analysis of the proposed theory and its impact on the management of scientific discoveries and scientific and technological progress.
   Objectives: to prepare an overview of approaches to the theory of scientific discoveries; to propose a typology of approaches based on the analysis of thinking artifacts; to formulate a new approach and a "useful" theory of scientific discoveries; to conduct a philosophical and methodological analysis.
   Methodology. Content analysis, comparative approach, structural and functional analysis, historical and philosophical approach.

    Results: hypothesis about the meta-task of scientific discoveries in the form of the development of a quantum hypergraph; theory of synthetic scientific discoveries based on a cybersemiotic approach.
   Conclusions. It is necessary to reconsider attempts to explain scientific discoveries without taking into account changes in people's thinking and automation. The cybersemiotic approach to the theory of scientific discoveries opens up new models for managing scientific discoveries based on data, rather than bureaucracy, fashion or social capital. Digitalization of scientific discoveries opens up the opportunity to master a new type of mental operator – a quantum hypergraph, which is a prerequisite for the emergence of synthetic noosphere technologies and a trigger for changing the type of civilizational model based on overcoming the polarization of people's thinking.

1. Lakatos I. Musgrave A., Falsification and the Methodology of Scientific Research Programmes. Lakatos I., eds.Criticism and the Growth of Knowledge. Cambridge, Cambridge University Press, 1970.

2. Laudan L., Donovan A., Laudan R., Barker P., Brown H., Leplin J. Scientific Change ‒ Philosophical Models and Historical Research. Synthese, 1986, vol. 69(2), pp. 141-223.

3. Fuchs S. A Sociological Theory of Scientific Change. University of North Carolina Press, 1993.

4. Radder H. Philosophy and history of science: Beyond the Kuhnian paradigm. Studies in History and Philosophy of Science, 1997, vol. 28(4), pp. 633-655.

5. Simon H. A., Langley P. W., Bradshaw G. L. Scientific discovery as problem-solving. Synthese, 1981, vol. 47, pp. 1–27.

6. Pearson K. The Problem of the Random Walk. Nature, 1905, vol. 72, p. 294.

7. Sandstrom P. E. Scholars as subsistence foragers. Bulletin of the American Society for Information Science, 1999, vol. 25(3), pp. 17‒20.

8. Pirolli P. Information Foraging Theory: Adaptive Interaction with Information. Oxford, Oxford University Press, 2007. 226 p.

9. Goffman W., Harmon G. Mathematical approach to the prediction of scientific discovery. Nature, 1971, vol. 229, pp. 103-104.

10. Goffman W., Newill V. A. Generalisation of epidemic theory: an application to the transmission of ideas. Nature, 1964, vol. 204, pp. 225-228.

11. Stepin V. S. K probleme struktury i genezisa nauchnoi teorii [To the Problem of the Structure and Genesis of Scientific Theory]. Filosofiya, metodologiya, nauka [Philosophy, Methodology and Science]. Moscow, Nauka Publ., 1972, pp. 158-185.

12. Mullins N. C., Hargens L. L., Hecht P. K., Kick E. L. The group structure of co-citation clusters: A comparative study. American Sociological Review, 1977, vol. 42(4), pp. 552-562.

13. Dorigo M., Gambardella L. M. Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1997, vol. 1(1), pp. 53-66.

14. Burt R. S. Structural holes and good ideas. American Journal of Sociology, 2004, vol. 110(2), pp. 349-399.

15. Börner K., Maru J. T., Goldstone R. L. The simultaneous evolution of author and paper networks. PNAS, 2004, vol. 101, pp. 5266-5273.

16. Ausloos M., Lambiotte R. Drastic events make evolving networks. The European Physical Journal B ‒ Condensed Matter and Complex Systems, 2007, vol. 57(1), pp. 89-94.

17. Kuhn T. S. The Structure of Scientific Revolutions. University of Chicago Press, 1970. 209 p.

18. Davis M. S. That's Interesting! Towards a Phenomenology of Sociology and a Sociology of Phenomenology. Phil. Soc. Sci., 1971, vol. 1, pp. 309-344.

19. Brush S. G. Dynamics of theory change: The role of predictions. The Proceedings of the Biennial meeting of the Philosophy of Science Association. Cambridge, Published online by Cambridge Umiversity Press, 2022.

20. Mayer R. E., Davidson J. E. The search for insight: Grappling with Gestalt Psychology's unanswered questions. Sternberg R. J., eds. The Nature of Insight. Cambridge, MA, The MIT Press Publ., 1995, pp. 3-32.

21. Perkins D. N. Insight in minds and genes. Sternberg R. J., eds. The Nature of Insight. Cambridge, MA, MIT Press Publ., 1995, pp. 495-534.

22. Heinze T., Bauer G. Characterizing creative scientists in nano-S&T: Productivity, multidisciplinarity, and network brokerage in a longitudinal perspective. Scientometrics, 2007, vol. 70(3), pp. 811-830.

23. Kostoff R. N. Where is the Discovery in Literature-Based Discovery? Information Science and Knowledge Management, 2008, vol. 15.

24. Shneiderman B. Creating creativity: user interfaces for supporting innovation. ACM Transactions on Computer-Human Interaction, 2000, vol. 7, pp. 114-138.

25. Human-Computer Interaction in the New Millennium; ed. by J. M. Carroll. New York, ACM Press Publ., 2002, pp. 235-258.

26. Budanov V. G. Postneklassika: filosofiya, nauka, kul'tura [Postnonclassics: philosophy, science, culture]. St. Petersburg, Mir Publ., 2009, pp. 361-396.

27. Arshinov V. I., Svirsky Ya. I. Slozhnostnyi mir i ego nablyudatel' [The complex world and its observer]. Filosofiya nauki i tekhniki = Philosophy of Science and Technology, 2016, vol. 21, no. 1, pp. 78-91.

28. The Turing al scientist grand challenge. Available at: https://www.turing.ac.uk/research/research-projects/turing-ai-scientist-grand-challenge. (accessed 14.08.2022)