by Matthew Oldham
Arthur, W. B. (2013). Complexity Economics: A Different Framework for Economic Thought. SFI WORKING PAPER, 2013-04–012.
Friedman, M. (1953). The Methodology of Positive Economics. In Essays in Positive Economics (Vol. 3). Chicago, Ill.: University of Chicago Press.
Kirman, A. P. (1992). Whom or What Does the Representative Individual Represent? Journal of Economic Perspectives, 6(2), 117–136. http://doi.org/10.1257/jep.6.2.117
Schumpeter, J. A. (1942). Capitalism, Socialism and Democracy. New York: Harper and Row.
Simon, H. A. (1955). A Behavioral Model of Rational Choice. The Quarterly Journal of Economics, 69(1), 99. http://doi.org/10.2307/1884852
Tesfatsion, L., & Judd, K. L. (Eds.). (2006). Handbook of Computational Economics. Amsterdam Boston: Elsevier.
Van Valen, L. (1973). A New Evolutionary Law. Evolutionary Theory, 1, 1–30.
Cont, R. (2007). Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models. In Long Memory in Economics (pp. 289–309). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-540-34625-8_10
Farmer, J. D., Gallegati, M., Hommes, C., Kirman, A., Ormerod, P., Cincotti, S., … Helbing, D. (2012). A complex systems approach to constructing better models for managing financial markets and the economy. The European Physical Journal Special Topics, 214(1), 295–324. https://doi.org/10.1140/epjst/e2012-01696-9
Friedman, M. (1953). The Methodology of Positive Economics. In Essays in positive economics (Vol. 3). Chicago, Ill.: University of Chicago Press.
Johnson, N. F., Jefferies, P., & Hui, P. M. (2003). Financial Market Complexity. Oxford ; New York: Oxford University Press.
Lux, T., & Alfarano, S. (2016). Financial Power Laws: Empirical Evidence, Models, and Mechanisms. Chaos, Solitons & Fractals, 88, 3–18. https://doi.org/10.1016/j.chaos.2016.01.020
Mandelbrot, B. (1963). The Variation of Certain Speculative Prices. The Journal of Business, 36(4), 394. https://doi.org/10.1086/294632
Sornette, D. (2014). Physics and financial economics (1776–2014): puzzles, Ising and agent-based models. Reports on Progress in Physics, 77(6), 62001. https://doi.org/10.1088/0034-4885/77/6/062001
Scogings, C., & Hawick, K. (2012). An agent-based model of the Battle of Isandlwana (pp. 1–12). IEEE. https://doi.org/10.1109/WSC.2012.6465043
Trautteur, G., & Virgilio, R. (2003). An agent-based computational model for the Battle of Trafalgar: a comparison between analytical and simulative methods of research (pp. 377–382). IEEE Comput. Soc. https://doi.org/10.1109/ENABL.2003.1231440
Bar-Eli, M., Avugos, S., & Raab, M. (2006). Twenty Years of “Hot Hand” Research: Review and Critique. Psychology of Sport and Exercise, 7(6), 525–553. https://doi.org/10.1016/j.psychsport.2006.03.001
Clauset, A., Kogan, M., & Redner, S. (2015). Safe Leads and Lead Changes in Competitive Team Sports. Physical Review E, 91(6). https://doi.org/10.1103/PhysRevE.91.062815
Gabel, A., & Redner, S. (2012). Random Walk Picture of Basketball Scoring. Journal of Quantitative Analysis in Sports, 8(1). https://doi.org/10.1515/1559-0410.1416
Martín-González, J. M., de Saá Guerra, Y., García-Manso, J. M., Arriaza, E., & Valverde-Estévez, T. (2016). The Poisson Model Limits in NBA Basketball: Complexity in Team Sports. Physica A: Statistical Mechanics and Its Applications, 464, 182–190. https://doi.org/10.1016/j.physa.2016.07.028
McGarry, T., Anderson, D. I., Wallace, S. A., Hughes, M. D., & Franks, I. M. (2002). Sport Competition as a Dynamical Self-organizing System. Journal of Sports Sciences, 20(10), 771–781. https://doi.org/10.1080/026404102320675620
Merritt, S., & Clauset, A. (2014). Scoring dynamics across professional team sports: tempo, balance and predictability. EPJ Data Science, 3(1). https://doi.org/10.1140/epjds29