Abstract
Based on evolutionary game theory, this paper presents a model that allows to reproduce different patterns of change of the main paradigm of a scientific community. One of these patterns is the classical scientific revolution of Thomas Kuhn (The Structure of Scientific Revolutions. University of Chicago Press, Chicago 1962), which completely replaces an old paradigm by a new one. Depending on factors like the acceptance rate of extra-paradigmatic works by the reviewers of scientific journals, there are however also other forms of change, which may e.g. lead to the coexistence of an old and a new paradigm. After analysing the different types of paradigm-changes and the conditions of their occurrence by means of EXCEL based simulation runs, the article explores the applicability of the model to a particular case: the spread of agent based modelling at the expense of the older systems dynamics approach. For the years between 1993 and 2012 the model presented in this article reproduces the observed bibliometric data remarkably well: it thus seems to be empirically confirmed.
Keywords
This is a substantially enlarged and improved version of an article in German language, published by the same author: G. Mueller, Die Krise der wissenschaftlichen Routine. In: Verhandlungen des 37. Kongresses der Deutschen Gesellschaft für Soziologie. http://www. publikationen.soziologie.de. Bochum, 2015.
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- 1.
- 2.
By definition A e = 0 and E o = 0 are the lowest possible values of these two parameters. Similarly, since A e ≤ A i = 1 and E o ≤ E n = 1, A e and E o cannot exceed the value 1.
- 3.
P n + P o = (F n \( \raisebox{2pt}{$\scriptstyle*$} \)S n ) / (F n \( \raisebox{2pt}{$\scriptstyle*$} \)S n + F o \( \raisebox{2pt}{$\scriptstyle*$} \)S o ) + (F o \( \raisebox{2pt}{$\scriptstyle*$} \)S o ) / (F n \( \raisebox{2pt}{$\scriptstyle*$} \)S n + F o \( \raisebox{2pt}{$\scriptstyle*$} \)S o )= (F n \( \raisebox{2pt}{$\scriptstyle*$} \)S n + F o \( \raisebox{2pt}{$\scriptstyle*$} \)S o ) / (F n \( \raisebox{2pt}{$\scriptstyle*$} \)S n + F o \( \raisebox{2pt}{$\scriptstyle*$} \)S o ) = 1
- 4.
Model-fit = Square root of (Sum of squares between observed and simulated P n /20) = 0.0041
- 5.
As explained earlier in Sect. 3.1, A e = 1 is a relative and not an absolute acceptance rate.
References
Kuhn, T.: The Structure of Scientific Revolutions. University of Chicago Press, Chicago (1962)
Tracy, J., et al.: Tracking trends in psychological science. In: Dalton, T., Evans, R. (eds.) The Life Cycle of Psychological Ideas. Kluwer, New York (2004). Chapter 5
Edmonds, B., et al.: Simulating the Social Processes of Science. J. Artif. Soc. Soc. Simulat. http://jasss.soc.surrey.ac.uk/14/4/14.html (2011)
Sobkowicz, P.: Simulations of opinion changes in scientific communities. Scientometrics 87, 221–232 (2011)
Sterman, J.: The growth of knowledge: testing a theory of scientific revolutions with a formal model. Technol. Forecast. Soc. Change 28, 93–122 (1985)
Sterman, J., Wittenberg, J.: Path dependence, competition, and succession in the dynamics of scientific revolution. Organ. Sci. 10, 322–341 (1999)
Gilbert, N., Troitzsch, K.: Simulation for the Social Scientist. Open University Press, Maidenhead (2011)
Maynard Smith, J.: Evolution and the Theory of Games. Cambridge University Press, Cambridge (1993)
Webb, J.: Game Theory: Decisions, Interaction and Evolution. Springer, London (2007)
Weibull, J.: Evolutionary Game Theory. MIT Press, Cambridge, MA (1996)
Axelrod, R.: The Evolution of Cooperation. Penguin, London (1990)
Hanauske, M.: Evolutionary game theory and complex networks of scientific information. In: Scharnhorst, A., et al. (eds.) Models of Science Dynamics. Springer, Berlin (2012). Chapter 5
Mueller, G.: Universities as producers of evolutionarily stable signs of excellence for academic labor markets? Semiotica 175, 429–450 (2009)
Mueller, G.: The dynamics and evolutionary stability of cultures of corruption: theoretical and empirical analyses. Adv. Complex Syst. 15(6) (2012)
Bornmann, L., Daniel, H.-D.: The luck of the referee draw: the effect of exchanging reviews. Learned Publ. 22, 117–125 (2009)
Daniel, H.-D.: Guardians of Science: Fairness and Reliability of Peer Review. VCH Verlagsgesellschaft, Weinheim (1993)
Shatz, D.: Peer Review: A Critical Inquiry. Rowman & Littlefield, Lanham (2004)
Gilbert, N.: Agent-Based Models. Sage, Los Angeles (2007)
Forrester, J.: Industrial Dynamics. MIT Press, Cambridge, MA (1961)
Schelling, T.: Dynamic models of segregation. J. Math. Sociol. 1, 143–186 (1971)
Scholar Google: Title-word entries “Agent based”, “System dynamics”, “Systems dynamics”. http://scholar.google.de/. Accessed 30 Oct 2014
Mueller, G.: Die Krise der wissenschaftlichen Routine: Computer-Simulationen zu Kuhns “Structure of Scientific Revolutions.” In: Lessenich, S. (ed.) Routinen der Krise — Krise der Routinen. Verhandlungen des 37. Kongresses der Deutschen Gesellschaft für Soziologie in Trier 2014. http://www.publikationen.soziologie.de/. Bochum (2015)
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Mueller, G.P. (2017). Simulating Thomas Kuhn’s Scientific Revolutions: The Example of the Paradigm Change from Systems Dynamics to Agent Based Modelling. In: Jager, W., Verbrugge, R., Flache, A., de Roo, G., Hoogduin, L., Hemelrijk, C. (eds) Advances in Social Simulation 2015. Advances in Intelligent Systems and Computing, vol 528. Springer, Cham. https://doi.org/10.1007/978-3-319-47253-9_25
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