Advertisement

Selecting Information in Financial Markets Herding and Opinion Swings in a Heterogeneous Mimetic Rational Agent-Based Model

  • Aymeric Vié
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

As expectations are driven by information, its selection is central in explaining common knowledge building and unraveling in financial markets. This paper addresses this information selection problem by proposing imitation as a key mechanism to explain opinion dynamics. Behavioral and cognitive approaches are combined to design mimetic rational agents able to infer and imitate each other’s choices and strategies in opinion making process. Model simulations tend to reproduce stylized facts of financial markets such as opinion swings, innovation diffusion, social differentiation and existence of positive feedback loops. The influence of imitation reliability and information precision on opinion dynamics is discussed. The results shed light on two competing aspects of imitation behavior: building collective consensus and favoring innovation diffusion. The role of contrarian and individualistic attitudes in triggering large-scale changes is highlighted. From the results, some policy recommendations to reach better financial markets stability through opinion dynamics management are finally presented.

Keywords

Agent-based computational economics Metamimetic chains Mimetic rationality 

References

  1. 1.
    Alfarano, S., Lux, T., Wagner, F.: Estimation of agent-based models: the case of an asymmetric herding model. Comput. Econ. 26(1), 19–49 (2005)CrossRefGoogle Scholar
  2. 2.
    Arthur, B.W.: Complexity and the economy. Oxford University Press, Oxford (2014)Google Scholar
  3. 3.
    Arthur, B.W., Holland, J.H., Lebaron, B., Palmer, R.G., Tayler, P.: Asset Pricing under Endogenous Expectations in an Artificial Stock Market (1996)Google Scholar
  4. 4.
    Assenza, T., Brock, W.A., Hommes, C.H.: Animal spirits, heterogeneous expectations, and the amplification and duration of crises. Econ. Inq. 55(1), 542–564 (2017)CrossRefGoogle Scholar
  5. 5.
    Barucci, E., Tolotti, M.: The dynamics of social interaction with agents’ heterogeneity (2009)Google Scholar
  6. 6.
    Brock, W.A., Durlauf, S.N.: Discrete choice with social interactions. Rev. Econ. Stud. 68(2), 235–260 (2001)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Brock, W.A., Hommes, C.H.: Heterogeneous beliefs and routes to chaos in a simple asset pricing model. J. Econ. Dyn. Control 22(8–9), 1235–1274 (1998)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Brock, W.A., Hommes, C.H.: Rational animal spirits. In: Herings, P.J.J., van der Laan, G., Talman, A.J.J. (eds.) The Theory of Markets, North-Holland, Amsterdam, pp. 109–137 (1999)Google Scholar
  9. 9.
    Challet, D., Marsili, M., Zhang, Y.C.: Minority games: interacting agents in financial markets. OUP Catalogue (2013)Google Scholar
  10. 10.
    Chavalarias, D.: Metamimetic games: modeling metadynamics in social cognition. J. Artif. Soc. Soc. Simul. 9(2), 5 (2006). http://jasss.soc.surrey.ac.uk/9/2/5.htmlGoogle Scholar
  11. 11.
    Chiarella, C., He, X.-Z.: Heterogeneous beliefs, risk and learning in a simple asset pricing model. Comput. Econ. 19(1), 95–132 (2002)CrossRefGoogle Scholar
  12. 12.
    Cont, R., Bouchaud, J.P.: Herd behavior and aggregate fluctuations in financial markets. Macroecon. Dyn. 4(2), 170–196 (2000)CrossRefGoogle Scholar
  13. 13.
    Conte, R., Paolucci, M.: Intelligent social learning. J. Artif. Soc. Soc. Simul. 4(1), U61–U82 (2001). http://www.soc.surrey.ac.uk/JASSS/4/1/3.htmlGoogle Scholar
  14. 14.
    Daudé, E.: Contributions of multi-agent systems for diffusion processes studies. Cybergeo: Eur. J. Geogr. 255, 1–16 (2004)Google Scholar
  15. 15.
    Dosi, G., Napoletano, M., Roventini, A., Stiglitz, J., Treibich, T.: Rational Heuristics? Expectations and behaviors in Evolving Economies with Heterogeneous interacting agents (2017)Google Scholar
  16. 16.
    Frank, H.: Natural selection, rational economic behavior and alternative outcomes of the evolutionary process. J. Socio-Econ. 32–6(12), 601–622 (2003)CrossRefGoogle Scholar
  17. 17.
    Gaunersdorfer, A.: Endogenous fluctuations in a simple asset pricing model with heterogeneous agents. J. Econ. Dyn. Control 24, 799–831 (2000)CrossRefGoogle Scholar
  18. 18.
    Harras, G., Sornette, D.: How to grow a bubble: a model of myopic adapting agents. J. Econ. Behav. Organ. 80(1), 137–152 (2011)CrossRefGoogle Scholar
  19. 19.
    Hommes, C.H.: Financial markets as nonlinear adaptive evolutionary systems (2001)Google Scholar
  20. 20.
    Hommes, C.H.: Heterogeneous agent models in economics and finance. In: Tesfatsion, L., Judd, K.L. (eds.) Handbook of Computational Economics, vol. 2, pp. 1109–1186. Elsevier (2006). Chap. 23Google Scholar
  21. 21.
    Kaizoji, T., Bornholdt, S., Fujiwara, Y.: Dynamics of price and trading volume in a spin model of stock markets with heterogeneous agents. Phys. A: Stat. Mech. Appl. 316(1), 441–452 (2002)CrossRefGoogle Scholar
  22. 22.
    Kirman, A.: Ants, rationality, and recruitment. Q. J. Econ. 108(1), 137–156 (1993)MathSciNetCrossRefGoogle Scholar
  23. 23.
    Kirman, A.P., Teyssiere, G.: Micro-economic models for long memory in the volatility of financial time series, In: Herings, P.J.J., Van der Laan, G., Talman, A.J.J. (eds.) The Theory of Markets, North Holland, Amsterdam, pp. 109–137 (2002)Google Scholar
  24. 24.
    Kirman, A., Zimmermann, J.B. (eds.) Economics with Heterogeneous Interacting Agents, vol. 503. Springer Science and Business Media (2012)Google Scholar
  25. 25.
    Kristoufek, L., Vosvrda, M.: Herding, minority game, market clearing and efficient markets in a simple spin model framework. Commun. Nonlinear Sci. Numer. Simul. 54, 148–155 (2017)ADSMathSciNetCrossRefGoogle Scholar
  26. 26.
    Lux, T.: Stochastic behavioral asset-pricing models and the stylized facts (2009)Google Scholar
  27. 27.
    Lux, T., Marchesi, M.: Scaling and criticality in a stochastic multiagent model of a financial market. Nature 397, 498–500 (1999)ADSCrossRefGoogle Scholar
  28. 28.
    Makarewicz, T.: Contrarian behavior, information networks and heterogeneous expectations in an asset pricing model. Comput. Econ. 50(2), 231–279 (2017)CrossRefGoogle Scholar
  29. 29.
    Orléan, A.: Bayesian interactions and collective dynamics of opinion - herd behavior and mimetic contagion. J. Econ. Behav. Organ. 28, 257–274 (1995)CrossRefGoogle Scholar
  30. 30.
    Sornette, D., Zhou, W.X.: Importance of positive feedbacks and overconfidence in a self-fulfilling Ising model of financial markets. Phys. A: Stat. Mech. Appl. 370(2), 704–726 (2006)CrossRefGoogle Scholar
  31. 31.
    Tsakas, N.: Naive learning in social networks: Imitating the most successful neighbor (2012)Google Scholar
  32. 32.
    Wilensky, U.: NetLogo (1999)Google Scholar
  33. 33.
    Zhou, W.X., Sornette, D.: Self-organizing Ising model of financial markets. Eur. Phys. J. B 55(2), 175–181 (2007)ADSMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Sciences PoSaint-Germain-en-LayeFrance
  2. 2.University of MilanMilanItaly

Personalised recommendations