Virtually Science: An Agent-based Model of the Rise and Fall of Scientific Research Programs

Is there more to ‘good science’ than explaining novel facts? Social interaction within the scientific community plays a pivotal role in defining acceptable research. This paper develops an agent-based computational model of scientific communities to explore the connection between research outcomes and the socio-cultural environment they are constructed in. Agent-to-agent interaction is added to the notion of scientific research programs (SRPs) developed by Lakatos (1969, 1970) as an important factor guiding the practice of researchers. Early in scientific inquiry, when there are many novel facts to explain, the research community is fragmented and researchers prefer to use their own talents (or innovation) when conducting research. Over time, consensus emerges in the form of a dominate SRP which utilizes intra-disciplinary approaches (or adaptation). This result illustrates the rise and fall of scientific paradigms as described by Kuhn (1970). The history of Keynesian macroeconomic theory provides colloquial support for the simulation model.PublishedBlackhouse, R. E. (2004), ‘The methodology of scientific research programs’, in The Elger Companion to Economics and Philosophy, eds. J. B. Davies, A. Marciano, and J. Runde, Edward Elgar: Northampton, 181-196. Blaug, M. (1992),. The Methodology of Economics, 2nd ed., Cambridge University Press: Cambridge. Eastman, T. E and Mahootian, F. (2009), ‘Complementary frameworks of scientific inquiry: Hypothetico-deductive, hypothetico-inductive and observational-inductive’, World Futures, 65(1), 61-75. Edmonds, B. (2007), ‘Artificial science: A simulation to study the social processes of science’, in Social Simulation: Technologies, Advances and New Discoveries, eds. B. Edmonds, K. G. Troitzsch, and C. H. Iglesias, IGI Publishing: Hershey, 61-67. Epstein, J. M. (1999), ‘Agent-based computational models and generative social science’, Complexity, 4 (5), 41-60. Gilbert, N. (1997), ‘A simulation of the structure of academic science’, Sociological Research Online, 2, URL http://www.socresonline.org.uk/2/2/3.html. Hands, D. W. (1994), ‘The sociology of scientific knowledge’, in New Directions in Economic Methodology, ed. R. E. Blackhouse, Routledge: New York, 75-106 Hands, D. W. (2004), ‘Constructivism: The social construction of scientific knowledge’ in The Elger Companion to Economics and Philosophy, eds. J. B. Davies, A. Marciano, and J. Runde, Edward Elgar: Northampton, 197-212. Hands, D. W. (2008), ‘Popper and Lakatos in economic methodology’, in The Philosophy of Economics: An Anthology, 3rd ed., ed. D. M. Hausman, Cambridge University Press: Cambridge, 188-203. Hodgson, G. M. (1998), ‘The approach of institutional economics’, The Journal of Economic Literature, 36(1), 166-192. Jalladeau, J. and Kuhn, W. E. (1978), ‘Research program versus paradigm in the development of economics’, Journal of Economic Issues, 12(3), 583-608. Keesing, R. M. (1974), ‘Theories of culture’, Annual Review of Anthropology, 3, 79-97. Kuhn, T. (1970), The Structure of Scientific Revolutions, 2nd ed., The University of Chicago Press: Chicago. Lakatos, I. (1969), ‘Criticism and the methodology of scientific research programmes’, Proceedings of the Aristotelian Society, 69, 149-186. Lakatos, I. (1970), ‘Falsification and the methodology of scientific research programmes’, in Criticism and the Growth of Knowledge, eds. I. Lakatos and A. Musgrave, The Cambridge University Press: Cambridge, 91-196. Landreth, H. and Colander, D. C. (2002), History of Economic Thought, 4th ed., Houghton Mifflin: Boston. Macy, M. W. and Willer, R. (2002), ‘From factors to actors: Computational sociology and agent-based modelling’, Annual Review of Sociology, 28, 143-166. Mankiw, N.G. (2006), ‘The macroeconomist as scientist and engineer’, The Journal of Economic Perspectives, 20(4): 29-46. McCloskey, D. N. (1983), ‘The rhetoric of economics’, The Journal of Economic Literature, 21(2): 481-517. Morgan, M. S. (2009), ‘Models, stories and the economic world’, The Journal of Economic Literature, 8(3), 361-384. Nelson, R. N. and Winter, S. G. (1982), An Evolutionary Theory of Economic Change, Harvard University Press: Harvard. Peirce, C. S. (1967), ‘Note on the theory of the economy of research’, Operations Research, 15(4), 643-648. Shapere, D. (1964), ‘The structure of scientific revolutions’, The Philosophical Review, 73(3), 383-394. Sun, R. and Naveh, I. (2009), ‘Cognitive simulation of academic science’, Proceedings of the 2009 International Joint Conference on Neural Networks, 418-424. Tesfatsion, L. (2002), ‘Agent-based computational economics: Growing economies from the bottom up’, Artificial Life, 8(1), 55-82