Abstract
For the last three decades, physicists have been moving beyond the boundaries of their discipline, using their methods to study various problems usually instigated by economists. This trend labeled ‘econophysics’ can be seen as a hybrid area of knowledge that exists between economics and physics. Econophysics did not spring from nowhere—the existing literature agrees that econophysics emerged in the 1990s and historical studies on the field mainly deal with what happened during that decade. This article aims at investigating what happened before the 1990s by clarifying the epistemic background that might have paved the way to the emergence of econophysics. This historical exploration led me to highlight the active role played by the Santa Fe Institute by promoting interdisciplinary research on complexity in 1980s. Precisely, by defining three research themes on economic complexity, the SFI defined a research agenda and a way of extending physics\biology to economics. This article offers a possible archeology of econophysics to clarify what could have contributed to the development of a particular episteme in the 1980s easing the advent of econophysics in the 1990s.
Similar content being viewed by others
Notes
Pareto (1897) was one of the first authors to study this relationship when he investigated the repartition of wealth in the population (he noticed that many people seem to have a low wealth whereas few ones have a huge wealth).
Kerr served as assistant director of the FBI (from 1997 to 2001), as director of research for the CIA (from 2001 to 2005) and as Principal Deputy Director of U.S. National Intelligence from October 2007 to January 2009. He is currently a member of the board of Iridium Communications.
The initial name of the SFI was the Rio Grande Institute because the label “Santa Fe Institute” belonged to an existing organization that helped alcoholics and drugs addicted people. When this institution became defunct a few months later, the final name of the research institute was “Santa Fe Institute”.
In the chapter of his memoires, Cowan commented on how he contacted people he met at WHSC.
The study on complexity probably started with May (1972) and his analysis of stability of complex systems.
Wolfram (1984, p. 186) wrote “Through computers, many complex systems are for the first time becoming amenable to scientific investigation. The revolution associated with the introduction in science may well be a fundamental as, say, the revolution in biology with the introduction of the telescope”. Wolfram (2002) wrote a book in which he discussed the possibility to have a “new kind of science” [the title of his book] “which refers to the idea that the universe, and everything in it, can be explained by simple [computerized] programs” (Mitchell 2009, p. 157). Wolfram attended the first meeting where the Institute was founded and he has always been an active member of this community.
According to Walrop (1992), Arthur mainly found his inspiration in his non-academic experience (as demographer for the Population Council in Bangladesh) in order to develop his concept of adaptive system, whereas Holland derived his idea of adaptive system from the studies on what computer scientists called “perceptron” in the 1950s (a perceptron was an algorithm that was able to classify or recognize specific output).
Although Estoup (1916) was the first scientist to discuss this linearity in relation to words.
Interestingly, Zipf observed this linearity in different languages.
Although modern probability theory was properly created in the 1930s, in particular through the works of Kolmogorov, it was not until the 1950s that Kolmogorov’s axioms became the dominant paradigm in this discipline thanks to the popularizing works of Doob (1953) and Feller (1957). These two writers had a major influence on the construction of modern probability theory, particularly through their two main books published in the early 1950s, which proved, on the basis of the framework laid down by Kolmogorov, all results obtained prior to the 1950s, thereby enabling them to be accepted and integrated into the discipline’s theoretical corpus (Shafer and Vovk 2001, p. 60).
See Stanley (1971) for a review of theoretical literature related to the scaling laws in the 1960s.
See Walrop (1992) for further information on the role played by the computer at the SFI.
See Schinckus (2018) for more details on the link between the SFI, econophyscis and biology.
These first publications also aimed to clarify the difference between chaos theory and complexity era (see Mitchell 2009).
Let us mention that Citicorp is still a funding body of the Santa Fe Institute.
The first one was organized few months (September 1987) after the financial support provided by Citicorp. It is worth mentioning that on the 21 contributors, 6 were working in a department of Economics, 12 in a department of physics, 1 in a Food Research Institute, 1 in a department of computer sciences and 1 in a school of Medicine (See Anderson et al. 1988).
Rosser (1999) identified three predecessors of complexity: Cybernetics, Catastrophe theory and Chaos theory, which all proposed a specific framework for dealing with non-linear dynamics. Within the complexity framework, this non-linear dynamics is combined with emergent properties. See Rosser (1999) for further details about these issues and their links with complexity.
It is worth mentioning here that power laws can be used for describing other things than a complex system and that a complex system can also be described through other lenses than a power law—this section simply highlights that the high number of works in statistical econophysics tend to characterize complex system through a power law in line with Bak’s methodological suggestions.
References
Anderson, P., Arrow, K., & Pines, D. (1988). Foreword. In P. Anderson, K. Arrow, & D. Pines (Eds.), The economy as an evolving complex system (p. xiii). Boston: Addison-Wesley.
Arthur B. (2014). A small group of Santa Fe researchers changed economic thinking. http://www.biourbanism.org/small-group-santa-fe-researchers-changed-economic-thinking/. Accessed 8 October.
Arthur, B., et al. (1997). The economy as an evolving complex system II. Boston: Addison-Wesley.
Ausloos, M. (1998). The money games physicists play. Europhysics News, 29(2), 70–72.
Ausloos, M. (2010). Econophysics in Belgium. The first (?) 15 years. Science and Culture, 76, 293–298.
Bachelier, L. (1900). Théorie de la spéculation reproduced in. Annales de l’Ecole Normale Supérieure, 3ème série 17(January), 21–86. Reprint, 1995, J. Gabay, Paris.
Bak, P. (1994). Introduction to self-criticality. In G. Cowan, D. Pines, & D. Meltzer (Eds.), Complexity: Metaphors, models, and reality (pp. 476–482). Santa Fe: Santa Fe Institute.
Bak, P., Tang, C., & Wiesenfeld, K. (1987). Self-organized criticality: an explanation of 1/f noise. Physical Review Letters, 59(4), 381–384.
Blume, L., & Durlauf, S. (2006). The economy as an evolving complex system III. New York: Oxford University Press.
Bouchaud, J.-P. (2002). An introduction to statistical finance. Physica A, 313, 238–251.
Brody, S. (1945). Bioenergetics and growth. New York: Reinhold Publishing.
Cassidy, D. (2011). A short history of physics in the American Century. Cambridge: Harvard University Press.
Chakrabarti, B., Chakraborti, A., Chakravarty, S., & Chatterjee, A. (2013). Econophysics of income and wealth distributions. Cambridge: Cambridge University Press.
Chopard, B., & Droz, M. (2005). Cellular automata modeling in physical systems. Cambridge: Cambridge University Press.
Cowan, G. (2010). Manhattan project to the Santa Fe Institute: The memoirs of George A. Cowan. Santa Fe: University of New Mexico Press.
Dash, K. C. (2019). The story of econophysics. Cambridge: Cambridge Scholars.
Domb, C., & Hunter, D. (1965). On the critical behaviour of ferromagnets. Proceeding of the Physical Society, 86, 1147.
Doob, J. (1953). Stochastic process. New York: Wiley.
Epstein, J. (2006). Generative social science: Studies in agent-based computational modeling. Princeton: Princeton University Press.
Epstein, J., & Axtell, R. (1996). Growing artificial societies: Social science from bottom up. Cambridge: MIT Press.
Erickson, P., Judy, L., Daston, L., Lemov, R., Sturm, Th, & Gordin, M. (2014). How reason almost lost its mind: The Strange Career of Cold War Rationality. Chicago: Chicago University Press.
Estoup, J. B. (1916). Gammes Sténographique. Paris: Institut Sténographique.
Feller, W. (1957). An introduction to probability theory and its applications. New York: Wiley.
Feng, L., Li, B., Podobnik, B., Preis, T., & Stanley, E. (2012). Linking agent-based models and stochastic models of financial markets. Proceedings of the National Academy of Sciences of the United States of America, 110, 8388–8392.
Fredkin, E. (2003). An introduction to digital philosophy. International Journal of Theoretical Physics, 42(2), 189–247.
Galison, P. (1997). Image & logic: A material culture of microphysics. Chicago: The University of Chicago Press.
Gardner, M. (1970). The fantastic combinations of John. Conway’s new solitaire game ”life. Scientific American, 223, 120–123.
Gell-Mann, M. (1984). The concept of the Institute. In D. Pines (Ed.), Emerging synthesis in science (pp. 1–15). Reading, MA: Addison-Wesley.
Gingras, Y., & Schinckus, C. (2012). The Institutionalization of Econophysics in the shadow of physics. Journal of the History of Economics Thought, 34, 109–130.
Hedlund, G. (1969). Endomorphisms and automorphisms of the shift dynamical system. Mathematical Systems Theory, 3, 51–59.
Holland, J. (1986). A mathematical framework for studying learning in classifier systems. In D. Farmer, et al. (Eds.), Evolution, games and learning (pp. 307–317). Amsterdam: North-Holland.
Hughes, R. (1999). The Ising model, computer simulation, and universal physics. In Mary S. Morgan & Margaret Morrison (Eds.), Models as mediators: Perspectives on natural and social science (pp. 97–145). Cambridge: Cambridge University Press.
Jaynes, E. (1957). Information theory and statistical mechanics. Physical Review, 106, 620.
Jovanovic, F., & Schinckus, C. (2013a). The history of econophysics as a new approach in modern financial theory. History Of Political Economy, 45(3), 443–474.
Jovanovic, F., & Schinckus, C. (2013b). Econophysics: A new challenge for financial economics? Journal of the History of Economic Thought, 35(3), 319–352.
Jovanovic, F., & Schinckus, C. (2017). Financial economics and econophysics: An emerging dialogue. New York: Oxford University Press.
Kadanoff, L. (1966). Scaling laws for Ising models near Tc. Physics, 2, 263–272.
Kaiser, D. (2012). Booms, busts, and the world of ideas: Enrollment pressures and the challenge of specialization. Osiris, 27, 276–302.
Kaufmman, S. (1984). Emergent properties in random complex automata. Physica D: Nonlinear Phenomena, 10, 145–156.
Kleiber, M. (1932). Body size and metabolism. Hilgardia, 6, 315–351.
Kolmogorov, A. (1941). Dissipation of energy in isotropic turbulence. Doklady Akademii Nauk SSSR, 32, 19–21.
Kolmogorov, A. (1942). Equations of turbulent motion in an incompressible fluid. Izvestiya Akademii Nauk SSSR Seriya Fizika, 6, 56–58.
lachinski, A. (1997). Irreducible Semi-Autonomous Adaptive Combat (ISAAC): An artificial-life approach to land warfare. Center for Naval Analyses Research Memorandum, CRM 97-61.10
Levin, L. (1973). Universal search problems. Problemy Peredaci Informacii, 9, 115–116. Translated in Problems of Information Transmission 9, 265– 266.
Lindgren, K., & Nordahl, M. (1994). Cooperation and community structure in artificial ecosystems. Artificial Life, 1, 15–37.
Majorana, E. (1942). Il valore delle leggi statistiche nella fisica e nelle scienze sociali. Scientia, 36, 58–66.
Mantegna, R. (1991). Levy Walks and enhanced diffusion in Milan stock exchange. Physica A, 179(1), 232–242.
Mantegna, R., & Stanley, E. (1999). An introduction to econophysics. Cambridge: Cambridge University Press.
Mardia, K., & Jupp, P. (2000). Directional statistics. Chichester: Wiley.
May, R. M. (1972). Will a large complex system be stable? Nature, 18(238), 413–414.
McCauley, J. L. (2006). Response to “Worrying trends in econophysics”. Physica A, 371(1), 601–609.
Milokowski, M. (2007). Is computationalism trivial? In S. Stuart & G. D. Crnkovic (Eds.), Computation, information, cognition: The nexus and the liminal (pp. 236–246). Newcastle: Cambridge Scholars Publishing.
Mirowski, P. (1996). Do you know the way to Santa Fe? Or, Political Economy Gets More Complex. In S. Pressman (Ed.), Interaction in political economy: Malvern after ten years. New York: Routledge.
Mirowski, P. (2002). Machine dreams. Cambridge: Cambridge University Press.
Mitchell, M. (2009). Complexity: A guided tour. Oxford: Oxford University Press.
Moore, E. (1962). Machine models of self-reproduction. Proceedings of Symposia in Applied Mathematics, 14, 17–33.
Morgan, M. (1990). The history of econometric ideas. Cambridge: Cambridge University Press.
Müller, V. (2010). Pancomputationalism: Theory or metaphor? In R. Hagengruber (Ed.), Philosophy’s relevance in information science (p. 7). Berlin: Springer.
Myhill, J. (1963). The converse of Moore’s Garden-of-Eden theorem. Proceedings of the American Mathematical Society, 14, 685–686.
Nagel, K., & Rasmussen, S. (1994). Traffic at the Edge of Chaos. In R. Brooks (Ed.), Artificial life IV. Cambridge: MIT Press.
O’Sullivan, D., & Haklay, M. (2000). Agent-based models and individualism: is the world agent-based? Environment and Planning, 32, 1409–1425.
Pang, T. (2006). An introduction to computational physics. Cambridge: Cambridge University Press.
Pareto, V. (1897). Cours d’Economie Politique. Lausanne: University of Lausanne.
Pines, D. (1988). Introduction and overview. In P. Anderson, K. Arrow, & D. Pines (Eds.), The economy as an evolving complex system (pp. 1–3). Santa Fe: Westfield Press.
Pines, D. (2014). An institution without fiefdoms—the origins of SFI. https://www.santafe.edu/about/history. Accessed 8 October.
Prietula, M., Carley, K., & Gasser, L. (Eds.). (1998). Simulating organizations: Computational models of institutions and groups. Menlo Park, CA: AAAI Press.
Rickles, D. (2007). Econophysics for philosophers. Studies in History and Philosophy of Modern Physics, 38(4), 948–978.
Rosser, B. (1999). On the complexities of complex economic dynamics. Journal of Economic Perspectives, 13(4), 169–192.
Rosser, B. (2010). Is a transdisciplinary perspective on economic complexity possible? Journal of Economic Behavior & Organization, 75(1), 3–11.
Rowe, F. (2008). The origin of autonomous agents by natural selection. Bio-systems, 91(2), 355–373.
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.
Schelling, T. (1978). Micromotives and macrobehavior. New York: Norton.
Schiff, J. (2011). Cellular automata: A discrete view of the world. London: Wiley.
Schinckus, C. (2011). What can econophysics contribute to financial economics? International Review of Economics, 58(2), 147–163.
Schinckus, C. (2013a). Between complexity of modelling and modelling of complexity: An essay on econophysics. Physica A, 392, 3654–3665.
Schinckus, C. (2013b). Econophysics, a new step in the evolution of physical sciences (on invitation). Contemporary Physics, 54(1), 17–32.
Schinckus, C. (2018). From DNA to economics: analogies in econobiology. Review of Contemporary Philosophy, 17(1), 31–42.
Shafer, G., & Vovk, V. (2001). Probability and finance: It’s only a game!. New York: Wiley.
Sharma, B. G., Agrawal, S., Sharma, M., Bisen, D., Sharma, R. (2011). Econophysics: A brief review of historical development, present status and future trends. Working paper. https://arxiv.org/pdf/1108.0977.pdf.
Stanley, H. E. (1971). Introduction to phase transitions and critical phenomena. London: Oxford University Press.
Stanley, H. E., Afanasyev, V., Nunes, Amaral L., Buldyrev, S., Goldberger, A., Havlin, S., et al. (1996). Anomalous fluctuations in the dynamics of complex systems: from DNA and physiology to econophysics. Physica A, 224(1), 302–321.
Stauffer, D. (2000). Econophysics—A new area for computational statistical physics. International Journal of Modern Physics C, 11(6), 1081–1088.
Toffoli, T. (1977). Computation and construction universality of reversible cellular automata. Journal of Computer and System Science, 15, 213–219.
Tusset, G. (2018). From Galileo to modern economics the Italian Origins Of Econophysics. London: Palgrage Macmillan.
Walrop, M. (1992). Complexity: The emerging science at the edge of order and chaos. New York: Simon & Schuster editions.
Wolfram, S. (1984). Universality and complexity in cellular automata. Physica D: Nonlinear Phenomena, 10, 1–35.
Wolfram, S. (2002). A new kind of science. New York: Wolfram Media.
Zhang, Y. C. (1998). Evolving models of financial markets. Europhysics News, 29(2), 51–54.
Zipf, G. (1935). The psycho-biology of language. Mifflin: Houghton.
Zuse, K. (1969). Rechnender Raum. Braunschweig: Friedrich Vieweg & Sohn.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The corresponding author states that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Schinckus, C. The Santa Fe Institute and Econophysics: A Possible Genealogy?. Found Sci 26, 925–945 (2021). https://doi.org/10.1007/s10699-020-09714-9
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10699-020-09714-9