El juego de la vida: una interpretación basada en el contagio de la euforia durante la formación de burbujas especulativas (The game of life: an interpretation based in the contagion of euphoria during the formation of speculative bubbles)
DOI:
https://doi.org/10.24275/uam/azc/dcsh/ae/2019v34n86/GomezPalabras clave:
Burbujas especulativas, Autómatas celulares, Contagio, Sistemas compls.Resumen
Estudios experimentales han mostrado evidencia de que la dinámica en los mercados accionarios propicia que ciertos agentes alienten altas expectativas sobre un activo, tal y como se entiende que ocurre con una burbuja especulativa. Por ello modelar el contagio de la euforia (expectativas) puede ayudar a identificar escenarios de comportamiento dentro de un ambiente complejo. El objetivo de la investigación es realizar simulaciones por computadora con autómatas celulares (ACs) para emular el contagio de la euforia durante la formación de una burbuja especulativa. En específico, se propone El juego de la vida, de Conway como un ejemplo para simular dinámicas complejas, donde el desplazamiento de los individuos es un elemento esencial. En las conclusiones se señala que bajo esta propuesta sólo se requiere de pocos inversionistas y de una ubicación estratégica para contagiar a un número importante de participantes en el mercado.
Clasificación JEL: G17, N22, D01
Abstract
Experimental studies have shown evidence that the dynamics in the stock markets generate that some agents encourage high expectations about an asset, as it is understood that it happens in a speculative bubble. Therefore, modeling the contagion of euphoria (expectations) can help to identify behavioral scenarios in a complex environment. The purpose of this research is to perform computer simulations using cellular automata (ACs) to emulate the contagion of euphoria during the formation of a speculative bubble. Specifically, The Game of Life by Conway is proposed as an example to simulate complex dynamics where the displacement of individuals is an essential element. The conclusions indicate that under this proposal only a few investors and a strategic location are required to infect a significant number of market participants.
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Ang, J. S., Diavatopoulos, D. and Schwarz, T. V. (2010). “The creation and control of speculative bubbles in a laboratory setting”, in Lee et al. (Eds.), Handbook of Quantitative Finance and Risk Management, Springer Science, pp. 137-164. DOI: 10.1007/978-0387-77117-5_9.
Baynes, T. M. (2009). “Complexity in urban development and management: Historical overview and opportunities”, Journal of Industrial Ecology, vol.13, no. 2, pp. 214- 227. DOI: 10.1111/j.1530-9290.2009.00123.x
Bedau, M. A. (2007). “Artificial Life”, in M. Mohan and C. Stephens (Eds.), Handbook of the Philosophy of Science, vol. 3, Philosophy of biology, pp. 595-613. Amsterdam: North Holland. Bikhchandani S., and Sharma S. (2001). “Herd behavior in financial markets”, International Monetary Fund Staff Papers, vol. 47, no. 3, pp. 279-310.
Black F. and Scholes, M. (1973). “The pricing of options and corporate liabilities”, The Journal of Political Economy, vol. 81, no. 3, pp. 637-654. Blaug, M. (2001). Teoría económica en retrospección, México: Fondo de Cultura Económica.
Boccara, N. (1994). “Automata network models of interacting populations”, in E. Goles and S. Martínez, (Eds.) Cellular automata, dynamical systems and neural networks, Springer, pp. 23-78.
De la Reza, G. A. (2010). Sistemas complejos, perspectivas de una teoría general, Barcelona: Anthropos.
Duarte, J. B., Talero, L. H. y Sierra K. J. (2007). “Evaluation of the effect of investor psychology on an artificial stock market through its degree of efficiency”, Contaduría y Administración, vol. 62, no.4, pp. 1361-1376.
Emmeche, C. (1998). Vida simulada en el ordenador: la nueva ciencia de la inteligencia artificial, Barcelona: Gedisa.
Fogel, D. (2006). Evolutionary computation. Toward a new philosophy of machine intelligence. IEEE press series on Computational Intelligence and John Wiley & Sons. DOI:10.1002/0471749214.
Gaylor, R. J., y Wellin, P. R. (1995). Computer simulations with Mathematica, explorations in complex physical and biological systems, New York: Springer-Verlag.
Gravner, J. (2003). “Growth phenomena in cellular automata”, in D. Griffeath y C. Moore (Eds.), New constructions in cellular automata, New York: Oxford University Press, pp. 161-181.
Iraola, M. A. y Santos, M. S. (2016). “Speculative bubbles”, in G. Jones (Ed.), Banking Crises, London: Palgrave Macmillan, pp. 316-320. https://doi.org/10.1057/9781137553799_30
Kowalska-Styczen, A. (2009). “Simulation model of consumer decision making”, Operations Research & Decisions, Vol. 19, no. 4, pp. 47-60.
Lechuga, J. (2013). “El calentamiento financiero global”, Análisis Económico, vol.28, núm. 68, pp. 79-102.
McNelis, P. D. (2005). Neural networks in finance: Gaining predictive edge in the market, San Diego: Elsevier Academic Press.
Ohgai, A., Igarashi, M. y Watanabe, K (2001). “Estimating changing distributions of population and employees by cellular automata”, in S. Bandini y T. Worsch (Eds.), Theoretical and Practical Issues on Cellular Automata, Proceeding of the fourth International Conference on cellular automata of Research an Industry, London: Springer, pp. 99-107. doi: 10.1007/978-1-4471-0709-5
Page, S. E. (2011). Diversity and complexity, Princeton, NJ: Princeton University Press.
Radner, R. (1979). “Rational expectations equilibrium generic existence and the information revealed by prices”, Econometrica, vol. 47, no. 3, pp. 655-678. DOI:0012-9682(197905)47:3<655:reegea>2.0.CO;2-X
Sell, F. (2001). Contagion in financial Markets, Northampton, MA: Edward Elgar.
Semet, Y., Gelly, S., Schoenauer M., and Sebag, M. (2004). “Artificial agent and speculative bubbles”, Computational Finance and its Applications, Southampton, U.K.: wit Press, pp. 35-44. DOI:10.2495/CF040041
Simon, H. (2006). Las ciencias de lo artificial, Granada: Comares.
Valenzuela, M. L. M., Aguilera, B. F., Soria L. J.A., and Molero, M. E. (2008). “Creation and evolution of development scenarios for metropolitan patters”, in M. Paegelow and M. T. Camacho (Eds.), Modelling environmental dynamics: Advances in geomatics solutions, Berlin: Springer-Verlag, pp. 339- 362. DOI:10.1007%2F978-3-540-68498-5
Weaver, W. (1948). “Science and complexity”, American Scientist, vol, 36, no. 4, pp. 536-544.
Zeigler, B., Praehofer, H., y Kim, T. G. (2000). Theory of modelling and simulation, integrating discrete event and continues complex dynamic systems, New York:Academic Press.
