Journal of Statistical Physics

, Volume 151, Issue 3–4, pp 567–606 | Cite as

Crises and Collective Socio-Economic Phenomena: Simple Models and Challenges



Financial and economic history is strewn with bubbles and crashes, booms and busts, crises and upheavals of all sorts. Understanding the origin of these events is arguably one of the most important problems in economic theory. In this paper, we review recent efforts to include heterogeneities and interactions in models of decision. We argue that the so-called Random Field Ising model (rfim) provides a unifying framework to account for many collective socio-economic phenomena that lead to sudden ruptures and crises. We discuss different models that can capture potentially destabilizing self-referential feedback loops, induced either by herding, i.e. reference to peers, or trending, i.e. reference to the past, and that account for some of the phenomenology missing in the standard models. We discuss some empirically testable predictions of these models, for example robust signatures of rfim-like herding effects, or the logarithmic decay of spatial correlations of voting patterns. One of the most striking result, inspired by statistical physics methods, is that Adam Smith’s invisible hand can fail badly at solving simple coordination problems. We also insist on the issue of time-scales, that can be extremely long in some cases, and prevent socially optimal equilibria from being reached. As a theoretical challenge, the study of so-called “detailed-balance” violating decision rules is needed to decide whether conclusions based on current models (that all assume detailed-balance) are indeed robust and generic.


Random field Ising model Collective phenomena Crises Avalanches 


  1. 1.
    MacKay, C.: Memoirs of Extraordinary Delusions and the Madness of Crowds (1852). Reprinted by L.C. Page, Boston (1932) Google Scholar
  2. 2.
    Keynes, J.M.: The General Theory of Employment, Interest and Money (in particular, Chap. 12). McMillan, London (1936) Google Scholar
  3. 3.
    Minsky, H.: John Maynard Keynes. McGraw-Hill, New York (2008) Google Scholar
  4. 4.
    Minsky, H.: Stabilizing an Unstable Economy McGraw-Hill, New York (2008) Google Scholar
  5. 5.
    Soros, G.: The New Paradigm for Financial Markets: The Credit Crisis of 2008 and What it Means. PublicAffairs, New York (2008) Google Scholar
  6. 6.
    Akerlof, G., Shiller, R.: Animal Spirits. Princeton University Press, Princeton (2009) Google Scholar
  7. 7.
    Kirman, A.: Complex Economics: Individual and Collective Rationality. Routledge, London (2010) Google Scholar
  8. 8.
    Sornette, D.: Endogenous versus exogenous origins of crises. In: Albeverio, S., Jentsch, V., Kantz, H. (eds.) Extreme Events in Nature and Society. Springer, Heidelberg (2005) Google Scholar
  9. 9.
    Sornette, D.: Why Stocks Markets Crash. Critical Events in Complex Financial Systems. Princeton University Press, Princeton (2004) Google Scholar
  10. 10.
    Bouchaud, J.-P.: The endogenous dynamics of markets: price impact, feedback loops and instabilities. In: Berd, A. (ed.) Lessons from the 2008 Crisis. Risk Books, Incisive Media, London (2011) Google Scholar
  11. 11.
    Rodgers, E.: Diffusion of Innovation, 6th edn. Free Press, New York (2003) Google Scholar
  12. 12.
    Kirman, A.: What or whom does the representative individual represent? J. Econ. Perspect. 6, 117 (1992) Google Scholar
  13. 13.
    Goldenfeld, N.: Lectures on Phase Transitions and the Renormalization Group. Addison Wesley, Reading (1992) Google Scholar
  14. 14.
    Sethna, J.P.: Statistical Mechanics: Entropy, Order Parameters, and Complexity. Oxford University Press, London (2006) MATHGoogle Scholar
  15. 15.
    Schelling, T.: Micromotives and Macrobehaviour. Norton, New York (1978) Google Scholar
  16. 16.
    Schelling, T.: Dynamic models of segregation. J. Math. Soc., 1, 143 (1971) CrossRefGoogle Scholar
  17. 17.
    Granovetter, M.: Threshold models of collective behaviour. Am. J. Sociol. 83, 1420 (1978) CrossRefGoogle Scholar
  18. 18.
    Granovetter, M., Soong, R.: Threshold models of diffusion and collective behaviour. J. Math. Sociol. 9, 1165 (1983) CrossRefGoogle Scholar
  19. 19.
    Weidlich, W.: The statistical description of polarisation phenomena in society. Br. J. Math. Stat. Psychol. 24, 251 (1971) MATHCrossRefGoogle Scholar
  20. 20.
    Galam, S., Gefen, Y., Shapir, Y.: Sociophysics: a mean behavior model for the process of strike. J. Math. Sociol. 9, 13 (1982) CrossRefGoogle Scholar
  21. 21.
    Föllmer, H.: Random economies with many interacting agents. J. Math. Econ. 1, 51 (1974) MATHCrossRefGoogle Scholar
  22. 22.
    Anderson, P.W., Arrow, K., Pine, D.: The Economy as an Evolving Complex System I. Addison-Wesley, Reading (1992) Google Scholar
  23. 23.
    Arthur, W.B., Durlauf, S., Lane, D.: The Economy as an Evolving Complex System II. Addison-Wesley, Reading (1997) Google Scholar
  24. 24.
    Kirman, A.: Ants, rationality and recruitment. Q. J. Econ. 108, 137 (1993) CrossRefGoogle Scholar
  25. 25.
    Durlauf, S.: Statistical mechanics approaches to socioeconomic behavior. In: Arthur, W.B., Durlauf, S., Lane, D. (eds.) The Economy as an Evolving Complex System II. Addison-Wesley, Reading (1997) Google Scholar
  26. 26.
    Durlauf, S.: Path dependence in aggregate output. Ind. Corp. Change 1, 149 (1994) CrossRefGoogle Scholar
  27. 27.
    Brock, W., Hommes, C.: Models of complexity in economics and finance. In: Hey, C., et al. (eds.) System Dynamics in Economic and Financial Models. Wiley, New York (1997) Google Scholar
  28. 28.
    Brock, W., Durlauf, S.: Discrete choice with social interactions. Rev. Econ. Stud. 68, 235 (2001) MathSciNetMATHCrossRefGoogle Scholar
  29. 29.
    Becker, G.S.: A note on restaurant pricing and other examples of social influences on price. J. Polit. Econ. 99, 1109 (1991) CrossRefGoogle Scholar
  30. 30.
    Becker, G.S., Murphy, K.: Social Economics. Market Behavior in a Social Environment. The Belknap Press/Harvard University Press, Cambridge (2000) Google Scholar
  31. 31.
    Durlauf, S.N., Peyton Young, H.: Social Dynamics. Brookings Institution Press/MIT Press, London (2001) MATHGoogle Scholar
  32. 32.
    Bikhchandani, S., Hirshleifer, D., Welch, I.: A theory of fads, fashions, custom and cultural changes as informational cascades. J. Polit. Econ. 100, 992 (1992) CrossRefGoogle Scholar
  33. 33.
    Chamley, Ch.: Rational Herds. Cambridge University Press, Cambridge (2004) Google Scholar
  34. 34.
    Orléan, A.: Bayesian interactions and collective dynamics of opinions. J. Econ. Behav. Organ. 28, 257 (1995) CrossRefGoogle Scholar
  35. 35.
    Challet, D., Marsili, M., Zhang, Y.C.: Minority Games. Oxford University Press, London (2005) MATHGoogle Scholar
  36. 36.
    Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591 (2009) ADSCrossRefGoogle Scholar
  37. 37.
    Buchanan, M.: The Social Atom. Bloomsbury Press, New York (2007) Google Scholar
  38. 38.
    Ball, P.: Why Society is a Complex Matter. Springer, Berlin (2012) CrossRefGoogle Scholar
  39. 39.
    Mazloumian, A., Eom, Y.-H., Helbing, D., Lozano, S., Fortunato, S.: How citation boosts promote scientific paradigm shifts and Nobel prizes. PLoS ONE 6(5), e18975 (2011) ADSCrossRefGoogle Scholar
  40. 40.
    Sethna, J.P., Dahmen, K.A., Kartha, S., Krumhans, J.A., Roberts, B.W., Shore, J.D.: Hysteresis and hierarchies: dynamics of disorder-driven first-order phase transformations. Phys. Rev. Lett. 70, 3347 (1993) ADSCrossRefGoogle Scholar
  41. 41.
    Perkovic, O., Dahmen, K., Sethna, J.P.: Avalanches, Barkhausen noise, and plain old criticality. Phys. Rev. Lett. 75, 4528 (1995) ADSCrossRefGoogle Scholar
  42. 42.
    Sethna, J., Dahmen, K., Myers, C.: Crackling noise. Nature 410, 242 (2001) ADSCrossRefGoogle Scholar
  43. 43.
    Galam, S., Moscovici, S.: Towards a theory of collective phenomena: consensus and attitude changes in groups. Eur. J. Soc. Psychol. 21, 49 (1991) CrossRefGoogle Scholar
  44. 44.
    Bouchaud, J.P.: Power-laws in economics and finance: some ideas from physics. Quant. Finance 1, 105 (2001) CrossRefGoogle Scholar
  45. 45.
    Nadal, J.-P., Phan, D., Gordon, M.B., Vannimenus, J.: Multiple equilibria in a monopoly market with heterogeneous agents and externalities. Quant. Finance 5, 557 (2006) MathSciNetCrossRefGoogle Scholar
  46. 46.
    Gordon, M.B., Nadal, J.-P., Phan, D., Vannimenus, J.: Seller’s dilemma due to social interactions between customers. Physica A 356, 628 (2005) ADSCrossRefGoogle Scholar
  47. 47.
    Gordon, M.B., Nadal, J.-P., Phan, D., Semeshenko, V.: Discrete choices under social influence: generic properties. Math. Models Methods Appl. Sci. 19(Suppl. 1), 1441 (2009) MathSciNetMATHCrossRefGoogle Scholar
  48. 48.
    Michard, Q., Bouchaud, J.-P.: Theory of collective opinion shifts: from smooth trends to abrupt swings. Eur. Phys. J. B 47, 151 (2005) ADSCrossRefGoogle Scholar
  49. 49.
    Harras, G., Tessone, C.J., Sornette, D.: Noise-induced volatility of collective dynamics. Phys. Rev. E 85, 011150 (2012) ADSCrossRefGoogle Scholar
  50. 50.
    Molins, J., Vives, R.: Long range Ising model for credit risk modeling. AIP Conf. Proc. 779, 156 (2005) ADSCrossRefGoogle Scholar
  51. 51.
    Anand, K., Kirman, A., Marsili, M.: Epidemics of rules, rational negligence and market crashes. Eur. J. Financ. (2011) Google Scholar
  52. 52.
    Lorenz, J., Battiston, S., Schweitzer, F.: Systemic risk in a unifying framework for cascading processes on networks. Eur. Phys. J. B 71, 441 (2009) MathSciNetADSMATHCrossRefGoogle Scholar
  53. 53.
    Sieczka, P., Sornette, D., Holyst, J.: The Lehman Brothers effect and bankruptcy cascades. Eur. Phys. J. B 82, 257 (2011) ADSCrossRefGoogle Scholar
  54. 54.
    Arthur, W.B.: Complexity in economic and financial markets. Complexity 1, 20 (1955) Google Scholar
  55. 55.
    Hardin, G.: The tragedy of the commons. Science 162, 1243 (1968) ADSCrossRefGoogle Scholar
  56. 56.
    Mézard, M., Montanari, A.: Information, Physics and Computation. Oxford University Press, London (2009) MATHCrossRefGoogle Scholar
  57. 57.
    Anderson, S.P., De Palma, A., Thisse, J.F.: Discrete Choice Theory of Product Differentiation. MIT Press, New York (1992) MATHGoogle Scholar
  58. 58.
    Dhar, D., Shukla, P., Sethna, J.P.: Distribution of avalanche sizes in the hysteretic response of random field Ising model on a Bethe lattice at zero temperature. J. Phys. A, Math. Gen. 30, 5259 (1997) MathSciNetADSMATHCrossRefGoogle Scholar
  59. 59.
    Sabhapandit, S., Shukla, P.: Dhar, D.: Zero-temperature hysteresis in the random-field Ising model on a Bethe lattice. J. Stat. Phys. 98, 103 (2000) MathSciNetMATHCrossRefGoogle Scholar
  60. 60.
    Weidlich, W.: Physics and social science—the approach of synergetics. Phys. Rep. 204, 1–163 (1991) MathSciNetADSCrossRefGoogle Scholar
  61. 61.
    Nadeau, R., Cloutier, E., Guay, J.-H.: New evidence about the existence of a bandwagon effect in the opinion formation process. Int. Polit. Sci. Rev. 14, 203 (1993) CrossRefGoogle Scholar
  62. 62.
    Kay, B.J.: Polls and the bandwagon effect on the electoral process. Can. Parliam. Rev. 14, 203 (1993/97) Google Scholar
  63. 63.
    Hohnisch, M., Pittnauer, S., Solomon, S., Stauffer, D.: Socioeconomic interaction and swings in business confidence indicators. Physica A 345, 646 (2005) ADSGoogle Scholar
  64. 64.
    Lux, T.: Rational forecasts or social opinion dynamics? Identification of interaction effects in a business climate survey. J. Econ. Behav. Organ. 72, 638 (2009) CrossRefGoogle Scholar
  65. 65.
    Bouchaud, J.-P., Farmer, J.D., Lillo, F.: How markets slowly digest changes in supply and demand. In: Handbook of Financial Markets: Dynamics and Evolution. North-Holland/Elsevier, Amsterdam (2009) Google Scholar
  66. 66.
    Tòth, B., Lempérière, Y., Deremble, C., De Lataillade, J., Kockelkoren, J., Bouchaud, J.-P.: Anomalous price impact and the critical nature of liquidity in financial markets. Phys. Rev. X 1, 021006 (2011) CrossRefGoogle Scholar
  67. 67.
    Bass, F.M.: A new product growth model for consumer durables. Manag. Sci. 15, 215 (1969) MATHCrossRefGoogle Scholar
  68. 68.
    Young, H.P.: Innovation diffusion in heterogeneous populations: contagion, social influence, and social learning. Am. Econ. Rev. 99, 1899 (2009) CrossRefGoogle Scholar
  69. 69.
    Frontera, C., Vives, E.: Computer studies of the 2D random field Ising model at T=0. Comput. Phys. Commun. 121, 188 (1999) ADSCrossRefGoogle Scholar
  70. 70.
    Dahmen, K., Sethna, J.P.: Hysteresis, avalanches, and disorder induced critical scaling: a renormalization group approach. Phys. Rev. B 53, 14872 (1996) ADSCrossRefGoogle Scholar
  71. 71.
    Corral, A., Font-Clos, F.: Branching processes, criticality, and self-organization: application to natural hazards. arXiv:1207.2589
  72. 72.
    Pradhan, S., Hansen, A., Chakrabarti, B.: Failure processes in elastic fiber bundles. Rev. Mod. Phys. 82, 499 (2010) ADSCrossRefGoogle Scholar
  73. 73.
    da Silveira, R.: An introduction to breakdown phenomena in disordered systems. Am. J. Phys. 67, 1177 (1999) ADSCrossRefGoogle Scholar
  74. 74.
    Sornette, D.: Mean-field solution of a block-spring model of earthquakes. J. Phys. I France 2, 2089 (1992) CrossRefGoogle Scholar
  75. 75.
    Sornette, D.: Irreversible mean-field model of the critical behavior of charge-density waves below the threshold for sliding. Phys. Lett. A 176, 360 (1993) MathSciNetADSCrossRefGoogle Scholar
  76. 76.
    Krzakala, F., Ricci-Tersenghi, F., Zdeborova, L.: Elusive spin-glass phase in the random field Ising model. Phys. Rev. Lett. 104, 207208 (2010) ADSCrossRefGoogle Scholar
  77. 77.
    Galla, T., Farmer, D.: Complex dynamics in learning complicated games. arXiv:1109.4250
  78. 78.
    Lucas, A., Lee, C.H.: Multistable binary decision making on networks. arXiv:1210.6044
  79. 79.
    Raafat, R.M., Chater, N., Frith, C.: Herding in humans. Trends Cogn. Sci. 13, 420 (2009) CrossRefGoogle Scholar
  80. 80.
    Baddeley, M.: Herding, social influence and economic decision-making: socio-psychological and neuroscientific analyses. Philos. Trans. R. Soc. Lond. B, Biol. Sci. 365, 281 (2010) CrossRefGoogle Scholar
  81. 81.
    Salganik, M.J., Dodds, P.S., Watts, D.J.: Experimental study of inequality and unpredictability in an artificial cultural market. Science 311, 854 (2006) ADSCrossRefGoogle Scholar
  82. 82.
    Guedj, O., Bouchaud, J.P.: Experts earning forecasts, bias, herding and gossamer information. Int. J. Theor. Appl. Finance 8, 933 (2005) MathSciNetMATHCrossRefGoogle Scholar
  83. 83.
    Lorenz, J., Rauhut, H., Schweitzer, F., Helbing, D.: How social influence can undermine the wisdom of crowd.
  84. 84.
    Reinhart, C.M., Rogoff, K.S.: This Time is Different. Princeton University Press, Princeton (2009) Google Scholar
  85. 85.
    Gigerenzer, G., Goldstein, D.: Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650 (1996) CrossRefGoogle Scholar
  86. 86.
    Gigerenzer, G., Todd, P.M.: Simple Heuristics that Make Us Smart. Oxford University Press, London (1999) Google Scholar
  87. 87.
    Manski, C.F.: Identification Problems in Social Sciences. Harvard University Press, Cambridge (1995) Google Scholar
  88. 88.
    Bongaarts, J., Watkins, S.C.: Social interactions and contemporary fertility transitions. Popul. Dev. Rev. 22, 639 (1996) CrossRefGoogle Scholar
  89. 89.
    Durlauf, S., Walker, J.: Social interactions and fertility transitions. In: Casterline, J. (ed.) Diffusion Processes and Fertility Transition: Selected Perspectives. National Academy Press, Washington (2001) Google Scholar
  90. 90.
    Glaeser, E.L., Sacerdote, B., Scheinkman, J.A.: Crime and social interactions. Q. J. Econ. 111, 507 (1996) CrossRefGoogle Scholar
  91. 91.
    Challet, D., Krause, A.: What questions to ask in order to validate an agent-based model. Unilever report (2006) Google Scholar
  92. 92.
    Anderson, L.R., Holt, C.A.: Information cascades in the laboratory. Am. Econ. Rev. 87, 847 (1997) Google Scholar
  93. 93.
    Alevy, E., Haigh, M.S., List, J.A.: Information cascades: evidence from a field experiment with financial market professionals (October 2005) Google Scholar
  94. 94.
    Card, D., Mass, A., Rothstein, J.: Tipping and the dynamics of segregation in neighborhoods and schools. Q. J. Econ. 123, 177 (2008) CrossRefGoogle Scholar
  95. 95.
    Blume, L.E., Brock, W.A., Durlauf, S.N., Ioannides, Y.: Identification of social interactions. In: Benhabib, J., Bisin, A., Jackson, M. (eds.) Handbook of Social Economics. North-Holland, Amsterdam (2011) Google Scholar
  96. 96.
    Hosking, G.: The credit crunch and the importance of trust. History and Policy Paper No. 77 (2008). Available at
  97. 97.
    Anand, K., Gai, P., Marsili, M.: The rise and fall of trust networks. Progress in Artificial Economics. Lect. Notes Econ. Math. Syst. 645, 77 (2010) CrossRefGoogle Scholar
  98. 98.
    Cont, R., Bouchaud, J.P.: Herd behaviour and aggregate fluctuations in financial markets. Macroecon. Dyn. 4, 139 (2000) CrossRefGoogle Scholar
  99. 99.
    Curty, Ph., Marsili, M.: Phase coexistence in a forecasting game. J. Stat. Mech. 2006, P03013 (2006) MathSciNetCrossRefGoogle Scholar
  100. 100.
    Harras, G., Sornette, D.: How to grow a bubble: a model of myopic adapting agents. J. Econ. Behav. Organ. 80, 137 (2011) CrossRefGoogle Scholar
  101. 101.
    Borghesi, C., Bouchaud, J.-P.: Spatial correlations in vote statistics: a diffusive field model for decision-making. Eur. Phys. J. B 75, 395 (2010) ADSMATHCrossRefGoogle Scholar
  102. 102.
    Borghesi, C., Raynal, J.C., Bouchaud, J.-P.: Election turnout statistics in many countries: similarities, differences, and a diffusive field model for decision-making. PLoS ONE 7(5), e36289 (2012) ADSCrossRefGoogle Scholar
  103. 103.
    Schweitzer, F., Holyst, J.A.: Modelling collective opinion formation by means of active Brownian particles. Eur. Phys. J. B 15, 723 (2000) ADSCrossRefGoogle Scholar
  104. 104.
    Schweitzer, F.: Coordination of decisions in a spatial model of Brownian agents. In: Economics with Heterogeneous Interacting Agents (WEHIA). Springer, Berlin (2002) Google Scholar
  105. 105.
    Borghesi, C., Bouchaud, J.-P.: Of songs and men: a model for multiple choice with herding. Qual. Quant. 41, 557 (2007) CrossRefGoogle Scholar
  106. 106.
    Raffaelli, G., Marsili, M.: Statistical mechanics model for the emergence of consensus. Phys. Rev. E 72, 016114 (2005) ADSCrossRefGoogle Scholar
  107. 107.
    Sornette, D., Zajdenweber, D.: The economic return of research: the Pareto law and its implications. Eur. Phys. J. B 8, 653 (2000). and refs. therein ADSCrossRefGoogle Scholar
  108. 108.
    Redner, S.: How popular is your paper? An empirical study of the citation distribution. Eur. Phys. J. B 4, 131 (1998) ADSCrossRefGoogle Scholar
  109. 109.
    McFadden, D.: Conditional logit analysis of qualitative choice behavior. In: Zarembka, P. (ed.) Frontiers in Econometrics, pp. 105–142. Academic Press, New York (1974) Google Scholar
  110. 110.
    Nadal, J.-P., Weisbuch, G., Chenevez, O., Kirman, A.: A formal approach to market organisation: choice functions, mean-field approximation and maximum entropy principle. In: Lesourne, J., Orléan, A. (eds.) Advances in Self-Organization and Evolutionary Economics, pp. 149–159. Economica, London (1998) Google Scholar
  111. 111.
    Marsili, M.: On the multinomial logit model. Physica A 269, 9 (1999) MathSciNetADSCrossRefGoogle Scholar
  112. 112.
    Thurstone, L.L.: The prediction of choice. Psychometrica 10, 237 (1945) MathSciNetCrossRefGoogle Scholar
  113. 113.
    Grauwin, S., Bertin, E., Lemoy, R., Jensen, P.: Competition between collective and individual dynamics. Proc. Natl. Acad. Sci. USA 106, 20622 (2009) ADSCrossRefGoogle Scholar
  114. 114.
    Grauwin, S., Goffette-Nagota, F., Jensen, P.: Dynamic models of residential segregation: an analytical solution. J. Public Econ. 96, 124 (2012) CrossRefGoogle Scholar
  115. 115.
    Thaler, R.H., Sunstein, C.R.: Nudge. Yale University Press, New Haven (2007) Google Scholar
  116. 116.
    Jona-Lasinio, G.: From fluctuations in hydrodynamics to nonequilibrium thermodynamics. arXiv:1003.4164
  117. 117.
    Cugliandolo, L.F., Kurchan, J., Le Doussal, P., Peliti, L.: Glassy behaviour in disordered systems with nonrelaxational dynamics. Phys. Rev. Lett. 78, 350 (1997) and refs. therein ADSCrossRefGoogle Scholar
  118. 118.
    Bradde, S., Biroli, G.: The generalized Arrhenius law in out of equilibrium systems. arXiv:1204.6027
  119. 119.
    Agliari, E., Barra, A., Burioni, R., Camboni, F., Contucci, P.: Effective interactions in group competition with strategic diffusive dynamics. arXiv:0905.3813
  120. 120.
    Parisi, G.: Asymmetric neural networks and the process of learning. J. Phys. A 19, L675 (1986) MathSciNetADSCrossRefGoogle Scholar
  121. 121.
    Crisanti, A., Sompolinsky, H.: Dynamics of spin systems with randomly asymmetric bonds: Langevin dynamics and a spherical model. Phys. Rev. A 36, 4922 (1987) MathSciNetADSCrossRefGoogle Scholar
  122. 122.
    Yukalov, V.I., Sornette, D.: Theory of behavioral decision biases of social agents. Working paper of ETH Zurich. arXiv:1202.4918 (2012) and references therein
  123. 123.
    Kirman, A.P., Vriend, N.J.: Learning to be loyal. A study of the Marseille fish market; interaction and market structure. Lect. Notes Econ. Math. Syst. 484, 33 (2000) CrossRefGoogle Scholar
  124. 124.
    Weisbuch, G., Kirman, A., Herreiner, D.: Market organisation and trading relationships. Econ. J. 110, 411 (2000) CrossRefGoogle Scholar
  125. 125.
    Thouless, D.J.: Long-range order in one-dimensional Ising systems. Phys. Rev. 187, 732 (1969) ADSCrossRefGoogle Scholar
  126. 126.
    Dyson, F.J.: An Ising ferromagnet with discontinuous long-range order. Commun. Math. Phys. 21, 269 (1971) MathSciNetADSCrossRefGoogle Scholar
  127. 127.
    Anderson, P.W., Yuval, G., Hamann, D.R.: Exact results in the Kondo problem. Phys. Rev. B 1, 4464 (1970) ADSCrossRefGoogle Scholar
  128. 128.
    Anderson, P.W., Yuval, G.: Some numerical results on the Kondo problem and the inverse square one-dimensional Ising model. J. Phys. C 4, 607 (1971) ADSCrossRefGoogle Scholar
  129. 129.
    Caccioli, F., Franz, S., Marsili, M.: Ising model with memory: coarsening and persistence properties. J. Stat. Mech. 2008, P07006 (2008) CrossRefGoogle Scholar
  130. 130.
    Barabási, A.-L.: The origin of bursts and heavy tails in humans dynamics. Nature 435, 207 (2005) ADSCrossRefGoogle Scholar
  131. 131.
    Sornette, D., Deschâtres, F., Gilbert, T., Ageon, Y.: Endogeneous vs exogeneous shocks in complex systems: an empirical test using book sales ranking. Phys. Rev. Lett. 93, 228701 (2004) ADSCrossRefGoogle Scholar
  132. 132.
    Chicheportiche, R., Bouchaud, J.-P.: The fine structure of volatility feedback. arXiv:1206.2153, and references therein
  133. 133.
    Woodford, M.: Learning to believe in sunspots. Econometrica 58, 277 (1990) MathSciNetMATHCrossRefGoogle Scholar
  134. 134.
    Orléan, A.: Le pouvoir de la finance. Odile Jacob, Paris (1999) Google Scholar
  135. 135.
    Wyart, M., Bouchaud, J.-P.: Self-referential behaviour, overreaction and conventions in financial markets. J. Econ. Behav. Organ. 63, 1 (2007) CrossRefGoogle Scholar
  136. 136.
    De Bondt, W., Thaler, R.: Does the market overreact? J. Finance 40, 793 (1985) CrossRefGoogle Scholar
  137. 137.
    Shiller, R.J.: Irrational Exuberance, pp. 186–189. Princeton University Press, Princeton (2000) Google Scholar
  138. 138.
    Raffaelli, G., Marsili, M.: Dynamic instability in a phenomenological model of correlated assets. J. Stat. Mech. 2008, L08001 (2008) Google Scholar
  139. 139.
    Hommes, C., Sonnemans, J., Tuinstra, J., van de Velden, H.: Expectations and bubbles in asset pricing experiments. J. Econ. Behav. Organ. 67, 116 (2008) CrossRefGoogle Scholar
  140. 140.
    Batista, J., Challet, D., Bouchaud, J.-P.: in preparation Google Scholar
  141. 141.
    Bouchaud, J.-P., Cont, R.: A Langevin approach to stock market fluctuations and crashes. Eur. Phys. J. B 6, 543 (1998) ADSCrossRefGoogle Scholar
  142. 142.
    Peters, R.D., Le Berre, M., Pomeau, Y.: Prediction of catastrophes: an experimental model. Phys. Rev. E 86, 026207 (2012) ADSCrossRefGoogle Scholar
  143. 143.
    Dahmen, K., Ben-Zion, Y.: Physics of Jerky motion in slowly driven magnetic and earthquake fault systems. In: Extreme Environmental Events 2011, pp. 680–696 Google Scholar
  144. 144.
    Lux, T., Marchesi, M.: Volatility clustering in financial markets: a micro-simulation of interacting agents. Int. J. Theor. Appl. Finance 3, 675 (2000) MathSciNetMATHCrossRefGoogle Scholar
  145. 145.
    Giardina, I., Bouchaud, J.-P.: Bubbles, crashes and intermittency in agent based market models. Eur. Phys. J. B 31, 421 (2003) MathSciNetADSCrossRefGoogle Scholar
  146. 146.
    Hommes, C.: Heterogeneous Agent Models in Economics and Finance. Handbook of Computational Economics, 2 (2006) Google Scholar
  147. 147.
    Samanidou, E., Zschischang, E., Stauffer, D., Lux, T.: In: Schweitzer, F. (ed.) Microscopic Models for Economic Dynamics. Lecture Notes in Physics. Springer, Berlin (2002) Google Scholar
  148. 148.
    Cristelli, M., Pietronero, L., Zaccaria, A.: Critical overview of agent-based models for economics. arXiv:1101.1847
  149. 149.
    Chakraborti, A., Muni Toke, I., Patriarca, M., Abergel, F.: Econophysics review: II. Agent-based models. Quant. Finance 11, 1013 (2011) MathSciNetCrossRefGoogle Scholar
  150. 150.
    Ferguson, N.: The Ascent of Money: A Financial History of the World. Penguin, Baltimore (2009) Google Scholar
  151. 151.
    Plerou, V., Gopikrishnan, P., Amaral, L.A., Meyer, M., Stanley, H.E.: Scaling of the distribution of price fluctuations of individual companies. Phys. Rev. E 60, 6519 (1999) ADSCrossRefGoogle Scholar
  152. 152.
    Cutler, D.M., Poterba, J.M., Summers, L.H.: What moves stock prices? J. Portf. Manag. 15, 412 (1989) CrossRefGoogle Scholar
  153. 153.
    Fair, R.C.: Events that shook the market. J. Bus. 75, 713 (2002) CrossRefGoogle Scholar
  154. 154.
    Joulin, A., Lefevre, A., Grunberg, D., Bouchaud, J.-P.: Stock price jumps: news and volume play a minor role. Wilmott 46, 1 (2008) Google Scholar
  155. 155.
    Kirman, A.: The economic crisis is a crisis for economic theory. CESifo Econ. Stud. 56, 498 (2010) CrossRefGoogle Scholar
  156. 156.
    Gatti, D., Gaffeo, E., Gallegati, M., Giulioni, G., Palestrini, A.: Emergent macroeconomics: an agent-based approach to business fluctuations. Springer, Berlin (2008) Google Scholar
  157. 157.
    Barrat, J.L., Feigelman, M., Kurchan, J., Dalibard, J.: Slow relaxations and non-equilibrium dynamics in condensed matter. Les Houches Session LXXVII. Springer, Berlin (2003) Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Capital Fund ManagementParisFrance
  2. 2.Ecole PolytechniquePalaiseauFrance

Personalised recommendations