Modeling the social organization of science

Alexander, J.M., Himmelreich, J., & Thomson, C. (2015). Epistemic landscapes, optimal search, and the division of cognitive labor. Philosophy of Science, 82(3), 424–453.

Axtell, R.L., Epstein, J.M., Dean, J.S., Gumerman, G.J., Swedlund, A.C., Harburger, J., Chakravarty, S., Hammond, R., Parker, J., & Parker, M. (2002). Population growth and collapse in a multiagent model of the kayenta anasazi in long house valley. Proceedings of the National Academy of Sciences, 99(3), 7275–7279.

Bala, V., & Goyal, S. (1998). Learning from neighbours. Review of Economic Studies, 65, 595–621.

Bearman, P., Moody, J., & Stovel, K. (2004). Chains of affection: the structure of adolescent romantic and sexual networks. American Journal of Sociology, 110, 44–91.

Betz, G (2011). Prediction. In Jarvie, I., & Zamora-Bonilla, J. (Eds.) Handbook of philosophy of social science (pp. 645–664). London: Sage.

Börner, K, Boyack, K W, Milojevic, S, & Morris, S (2012). An introduction to modeling science: basic model types, key definitions, and a general framework for the comparison of process models. In Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) Models of science dynamics, encounters between complexity theory and information sciences (pp. 3–22). Berlin: Springer.

Brock, W.A., & Durlauf, S.N. (1999). A formal model of theory choice in science. Economic Theory, 14, 113–30.

Cartwright, N., Shomar, T., & Suárez, M. (1995). The tool box of science: tools for the building of models with a superconductivity example. Poznan Studies in the Philosophy of the Sciences and the Humanities, 44, 137–149.

De Langhe, R. (2014). A unified model of the division of cognitive labor. Philosophy of Science, 81(3), 444–459.

De Langhe, R., & Greiff, M. (2009). Standards and the distribution of cognitive labor. The Logic Journal of the IGPL, 18, 278–293.

Edmonds, B., Gilbert, N., Ahrweiler, P., & Scharnhorst, A. (2011). Simulating the social processes of science. Journal of Artificial Societies and Social Simulation, 14(4), 14.

Epstein, B. (2011). Agent-based modeling and the fallacies of individualism. In Humphreys, P., & Imbert, C. (Eds.) Models, simulations, and representations (pp. 115–144). New York: Routledge.

Epstein, J.M. (1999). Agent-based computational models and generative social science. Complexity, 4(5), 41–60.

Eubank, S., Guclu, H., Anil Kumar, V.S., Marathe, M.V., Srinivasan, A., Toroczkai, Z., & Wang, N. (2004). Modelling disease outbreaks in realistic urban social networks. Nature, 429, 180–184.

Franses, P.H. (2002). A concise introduction to econometrics: an intuitive guide. Cambridge: Cambridge University Press.

Friedman, M. (1953). Essays in positive economics. Chicago: University of Chicago Press.

Gell-Mann, M. (1995). What is complexity? Complexity, 1(1), 16–19.

Gibbard, A., & Varian, H.R. (1978). Economic models. The Journal of Philosophy, 75(11), 664–677.

Giere, R.N. (1988). Explaining science: a cognitive approach. Chicago: University of Chicago Press.

Gilbert, N. (2008). Agent-based models. London: Sage Publications Inc.

Gilbert, N., & Troitzsch, K.G. (2005). Simulation for the social scientist. New York: McGraw-Hill.

Goldman, A.I., & Shaked, M. (1991). An economic model of scientific activity and truth acquisition. Philosophical Studies: An International Journal for Philosophy in the Analytic Tradition, 63(1), 31–55.

Grim, P., & et al. (2013). How simulations fail. Synthese, 190, 2367–2390.

Hobijn, B., & Franses, P.H. (2000). Asymptotically perfect and relative convergence of productivity. Journal of Applied Econometrics, 15, 59–81.

Hobijn, B., & Franses, P.H. (2001). Are living standards converging? Structural Change and Economic Dynamics, 12, 171–200.

Kennan, J., & Walker, J. (2011). The effect of expected income on individual migration decisions. Econometrica, 79(1), 211–251.

Kitcher, P. (1990). The division of cognitive labor. The Journal of Philosophy, 87(1), 5–22.

MacLeod, M., & Nersessian, N. (2013). Building simulations from the ground up, modeling and theory in systems biology. Philosophy of Science, 80(4), 533–556.

Mainzer, K. (2007). Thinking in complexity: the computational dynamics of matter, mind and mankind. Berlin: Springer.

Mali, F, Kronegger, L, Doreian, P, & Ferligoj, A (2012). Dynamic scientific co-authorship networks Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.), Springer.

Mäki, U. (1992). On the method of isolation in economics. Poznan Studies in the Philosophy of the Sciences and the Humanities, 26, 19–54.

Miller, J.H., & Page, S.E. (2007). Complex adaptive systems: an introduction to computational models of social life. Princeton and Oxford: Princeton University Press.

Morgan, M.S., & Morrison, M. (Eds.) (1999). Models as mediators: perspectives on natural and social science. Cambridge: Cambridge University Press.

Muldoon, R., & Weisberg, M. (2011). Robustness and idealization in models of cognitive labor. Synthese, 183, 161–174.

Oberkampf, W.L., & Roy, C.J. (2010). Verification and validation in scientific computing. Cambridge: Cambridge University Press.

Radicchi, F., Fortunato, S., & Vespignani, A. (2012). Citation networks. In Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) Models of science dynamics: encounters between complexity theory and information science (pp. 233–257): Springer.

Reiss, J. (2012). Idealization and the aims of economics: three cheers for instrumentalism. Economics and Philosophy, 28(03), 363–383.

Rosenberg, A. (1993). Scientific innovation and the limits of social scientific prediction. Synthese, 97, 161–182.

Rosenstock, S., O’Connor, C., & Burner, J. (forthcoming). In Epistemic Networks, is Less Connectivity Really More?Philosophy of Science.

Rudd, P.A. (2000). An introduction to classical econometric theory. Oxford: Oxford University Press.

Schelling, T. (1969). Models of segregation. American Economic Review, 59 (2), 488–493.

Schelling, T. (1971). Dynamic models of segregation. Journal of Mathematical Sociology, 1(2), 143–186.

Sent, E.-M. (1997). An economist’s glance at goldman’s economics. Philosophy of Science, 64, 139–148.

Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) (2012). Models of science dynamics encounters between complexity theory and information sciences. Berlin: Springer.

Strevens, M. (2003). The role of the priority rule in science. The Journal of Philosophy, 100, 55–79.

Thoma, J. (2015). The epistemic division of labor revisited. Philosophy of Science, 82(3), 454–472.

Van den Besselaar, P., Börner, K., & Scharnhorst, A. (2012). Science policy and the challenges for modelling science. In Scharnhorst, A., Börner, K., & van den Besselaar, P. (Eds.) Models of science dynamics: encounters between complexity theory and information science (pp. 261–266): Springer.

Van Dijk, D., Franses, P.H., & Paap, R. (2002). A nonlinear long memory model, with an application to US unemployment. Journal of Econometrics, 110, 135–165.

Weisberg, M. (2013). Simulation and similarity: using models to understand the world. Oxford: Oxford University Press.

Weisberg, M., & Muldoon, R. (2009). Epistemic landscapes and the division of cognitive labor. Philosophy of Science, 76(2), 225–252.

Winsberg, E. (2006). Models of success versus the success of models: reliability without truth. Synthese, 152, 1–19.

Winsberg, E. (2010). Science in the age of computer simulation. Chicago: The University of Chicago Press.

Wooldridge, J.M. (2009). Introductory econometrics: a modern approach. South-Western Cengage Learning: Mason (OH).

Ylikoski, P. (1995). The invisible hand and science. Science Studies, 8, 32–43.

Zollman, K. (2007). The communication structure of epistemic communities. Philosophy of Science, 74(5), 574–587.

Zollman, K. (2010). The epistemic benefit of transient diversity. Erkenntnis, 72, 17–35.