Limits of scientific explanation (I)

Main Article Content

Marcin Gorazda

Abstract

The purpose of the paper is to challenge one of the most important assumptions of the neo-positivists, namely the unity of science. The idea that all of the sciences, both natural and social, should have the same structure and should deploy similar methods is, after Grobler, called naturalism. I try to argue for anti-naturalism. An interesting example seems to be economics. It does not, however, demonstrate the success, similar to that achieved by natural sciences. Certain naturalistic explanations for this lack of success are reviewed and criticized in the paper. Firstly, complexity: at the beginning of this naturalistic argument, one encounters the problem of definition. Up to nine different notions of complexity are proposed and only a few of them are practically quantitative. Secondly, mathematics: in the natural sciences we explore mathematical theories in order to capture the regularities in the investigated phenomena and to include them in the corresponding equations. However, even if we do not have a perfectly corresponding mathematical model, regularities themselves can be observed. Wherever we do not have a good theory expressed in terms of exact mathematical equations, we should at least be able to judge the existence or non-existence of certain regularities on the basis of linear (statistical) or non-linear methods. Those methods, some of them extremely sophisticated, are being extensively applied in economics and in econometrics (the so called quantitative methods). The results are disappointing.

The anti-naturalistic argumentation of Grobler is dealt with separately. Grobler names three anti-naturalistic arguments: complexity (as mentioned above), the free will of humans (which the author did not find interesting enough) and, finally, the reasoning which is called, ”inherent two-way interdependence”. Grobler maintains that we are able to work out a meta-theory which shall include both predictions and the possible impact of those predictions on the theory’s object. This proposal is rejected in the paper.

Article Details

How to Cite
Gorazda, M. (2012). Limits of scientific explanation (I). Philosophical Problems in Science (Zagadnienia Filozoficzne W Nauce), (51), 41–75. Retrieved from https://zfn.edu.pl/index.php/zfn/article/view/86
Section
Articles

References

Backhouse, R. E. (2002). The Penguin History of Economics. London: Penguin Books.

Backhouse, R. E. (2010). The Puzzle of Modern Economics: Science or Ideology? Cambridge: Cambridge University Press.

Comte, A. (1961). Metoda pozytywna w szesnastu wykładach. (W. Wojciechowska, Trans.) Warszawa: Państwowe Wydaniwctwo Naukowe.

Friedman, M. (2008). The Methodology of Positive Economics. In D. M. Hausman (Ed.), The Philosophy of Economics. An Anthology (pp. 145-178). Cambridge: Cambridge University Press.

Goldberg, M. D., & Frydman, R. (2009). Ekonomia wiedzy niedoskonałej. (M. Krawczyk, Trans.) Warszawa: Wydawnictwo Krytyki Politycznej.

Gorazda, M. (2009). Przyczynek do krytyki statystyczno-relewantnego modelu wyjaśniania naukowego,. Zagadnienia Filozoficzne w Nauce (45), p. 145.

Grobler, A. (2006). Metodologia nauk. Kraków: Aureus.

Gul, F., & Pesendorfer, W. (2001). Temptation and self-control. Econometrica , 69 (6), 1403-35.

Hayek, F. A. (1937, February). Economics and Knowledge. Economica IV , pp. 33-54.

Hayek, F. A. (1967). The Theory of Complex Phenomena. In F. A. Hayek, Studies in Philosophy, Politics and Economics (pp. 22-42). London: Routledge & Kegan Paul.

Jajuga, K., & Papla, D. (1997). Teoria chaosu w analizie finansowych szeregów czasowych - aspekty teoretyczne i badania empiryczne. Dynamiczne modele ekonometryczne. V Ogólnopolskie Seminarium Naukowe. Toruń: Katedra Ekonometrii i Statystyki UMK w Toruniu.

Jevons, W. S. (2011, 02 18). The Theory of Political Economy. Retrieved 02 18, 2011, from Library of Economics and Liberty: http://www.econlib.org/library/YPDBooks/Jevons/jvnPE.html

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decisions under risk. Econometrica (47), 313-327.

Kułakowski, K. (2010, 11 07). Automaty komórkowe. Retrieved 11 07, 2011, from Wydział Fizyki i Informatyki Stosowanej AGH: http://www.ftj.agh.edu.pl/~kulakowski/ac/

Landreth, H., & Colander, D. C. (2005). Historia myśli ekonomicznej. (A. Szeworski, Trans.) Warszawa: Wydawnictwoi Naukowe PWN.

Mainzer, K. (2007). Poznawanie złożoności. Obliczeniowa dynamika materii, umysłu i ludzkości. (r. M. Hetmański, Trans.) Lublin: Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej.

Marciszewski, W. (2004). Nierozstrzygalność i algorytmiczna niedostępność w naukach społecznych. Filozofia Nauki , 12 (3-4), pp. 5-31.

Marciszewski, W. (2010, 09 06). Niewymierność i Nieobliczalność a Sztuczna Inteligencja. Przyczynek do problemu jedności świata i jedności nauki. Retrieved 09 06, 2010, from Calculemus: http://www.calculemus.org/publ-WM/2003/niewym.html

Marshall, A. (2000). Principles of Economics. Retrieved 02 22, 2011, from Library of Economics and Liberty: http://www.econlib.org/library/Marshall/marP.html

Miśkiewicz, M. (2007). Zastosowanie wykładników Lapunowa do prognozowania zjawisk ekonomicznych opisanych za pomocą szeregów czasowych. Prace naukowe Akademii Ekonomicznej we Wrocławiu. Zastosowanie Metod Ilościowych (1189), pp. 211-223.

Mitchell, M. (2009). Complexity. A Guided Tour. Oxford New York: Oxford University Press.

Olszewski, A. (2009). Teza Churcha. Kontekst historyczno-filozoficzny. Kraków: Universitas.

Plucińska, A., & Pluciński, E. (2000). Probabilistyka. Rachunek prawdopodobieństwa. Statystyka matematyczna. Procesy stochastyczne. Warszawa: Wydawnictwa Naukowo-Techniczne.

Ross, D., & Kincaid, H. (2009). The Oxford Handbook of Philosophy of Economics. Oxford: Oxford University Press.

Smith, V. (1994). Economics in the Laboratory. Journal of Economic Perspective, vol. 8 (Winter 1994): 113-31 , 8, 113-31.