Skip to main content
SpringerLink
Log in

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

  • Published:
Journal of Statistical Physics Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Notes

  1. As a recent anecdotal, but pittoresque piece of evidence: the explosion of “love-locks” on the Pont des Arts in Paris since 2008.

  2. The converse is in fact also true: discontinuous behaviour at the agent level may end up being smoothed out at the macro level!

  3. See also the earlier insightful paper by Becker [29].

  4. The paper Discrete choice with social interactions by Brock and Durlauf [28] has 1034 Google scholar citations at the time of writing.

  5. Think for example of the tax-evasion culture in some countries, that has recently become an acute issue.

  6. Any other i-dependent value could have been chosen, since this simply amounts to shifting the value of idiosyncratic field f i .

  7. The distinction between the two interpretations will be discussed again in Sect. 6.1.

  8. It is interesting to notice that in Kirman’s ant recruitment model [24], one rather has P(N +→N ++1)=P(N +→N +−1)=μϕ(1−ϕ) because change of opinions are supposed to happen during two-body “encounters” where one of the two convinces the second one to change his mind. In the present setting, encounters are not necessary.

  9. Note that there our choice of J differs by a factor 2 from the usual convention.

  10. In agreement with the above convention, f i large means that agent i is more prone to join, i.e. his threshold is lower.

  11. These effects, strictly speaking, disappear in finite dimensional lattices because of nucleation. But the time scales associated with nucleation may be so large that these metastable states still have a real existence.

  12. The exponent −3/2 in the avalanche size distribution is the same as the one appearing in the first return time distribution of a one-dimensional random walk. Indeed, the critical branching process can be mapped onto that problem, see [72, 74, 75] for more elaborations on this point.

  13. This universality was also noted and emphasized in [46, 47].

  14. For other recent examples of agent based models with an exponential number of equilibria, see [77, 78].

  15. Peyton Young in [31] contrasts instrumental conformism, when it is beneficial to do what others do, to informational conformism, when we conform to what others do because it conveys information on best actions. The problem, we believe, is that often nobody really has any idea of what’s going on, so the “information” provided by the others actions is close to zero.

  16. The rfim is actually well known to have a rather wide critical region, as emphasized in [41].

  17. We emphasize again here that if J=0, or if the variations were due to different speeds v in different countries, one should observe κ=1. The way h and w are extracted from data is detailed in [48].

  18. See also [92, 93] for experiments on information cascades and [94] for some empirical evidence of tipping points in urban segregation phenomena. We also refer the reader to [95] for a review on “econometric” approaches to social phenomena.

  19. I personally know people who cashed their money just after Lehman’s default, and left their bank with a plastic bag full of bank notes.

  20. In the following expression, 〈⋯〉 denotes an averaging over all positions \(\vec{R}\).

  21. Note that here we neglect the immediate influence of others in the actual decision to vote, i.e. the JS i S j interaction term. See the discussion of this point in [101].

  22. Taking the relevant inter-town distance to be ∼10 km and the time for opinions to get closer to be a few months, one can estimate D to be of the order of magnitude of a few hundreds km2 per year.

  23. Other arguments, inspired by statistical physics, could go as follows [111]: imagine a choice consisting in two sub-choices concerning issues in disjoint sets \(\mathcal{A},\mathcal{B}\). These two sub-choices are furthermore assumed to be independent, i.e. the choice of an alternative α in \(\mathcal{A}\) does not impact the utility of any of the alternatives b in \(\mathcal{B}\), and vice-versa. The utilities are therefore additive in that case: U α⊕b =U α +U b . Since the sub-choices are independent, one should also have P α⊕b =P α ×P b . Looking for probabilities that depend on the total utility of the choice therefore selects the exponential form. One could also try to replicate the usual canonical construction of the Boltzmann weight, by arguing that a choice is never in isolation but interacts with many other choices, while the agent is only interested in the total utility. Sub-optimal choices (corresponding to a finite β) are allowed because they only have a small contribution to the total utility. However, these arguments are somewhat ad-hoc.

  24. One should of course keep in mind that the time needed to reach full equilibrium might be very large, or even infinite if there is a phase transition and ergodicity breaking—something that requires the number of states to be infinite.

  25. We neglect here an entropic term, which is small when β→∞, see [113, 114] for details.

  26. This is similar to reinforcement learning rules, see e.g. [35, 77] for some references in the present context.

  27. More rigorous work is needed on this whole issue; in particular on whether the existence of a transition in the Ising model is sufficient to ensure loyalty formation.

  28. Interesting situations could occur when these two effects are in conflict, for example when overcrowding or saturation effects prevent full condensation.

  29. Whereas the impact of individual orders in strongly concave, the impact of the aggregate order imbalance is, to a good approximation, linear. See [65, 66] for a long discussion of this issue.

  30. In fact, the rfim has also been related to the physics of earthquakes and material creep, see [42, 143]. It would be interesting to make a detailed connection with the work of Ref. [142].

  31. This is one of the ambitions of the CRISIS project, see: http://www.crisis-economics.eu/home.

References

  1. MacKay, C.: Memoirs of Extraordinary Delusions and the Madness of Crowds (1852). Reprinted by L.C. Page, Boston (1932)

    Google Scholar 

  2. Keynes, J.M.: The General Theory of Employment, Interest and Money (in particular, Chap. 12). McMillan, London (1936)

    Google Scholar 

  3. Minsky, H.: John Maynard Keynes. McGraw-Hill, New York (2008)

    Google Scholar 

  4. Minsky, H.: Stabilizing an Unstable Economy McGraw-Hill, New York (2008)

    Google Scholar 

  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. Akerlof, G., Shiller, R.: Animal Spirits. Princeton University Press, Princeton (2009)

    Google Scholar 

  7. Kirman, A.: Complex Economics: Individual and Collective Rationality. Routledge, London (2010)

    Google Scholar 

  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. Sornette, D.: Why Stocks Markets Crash. Critical Events in Complex Financial Systems. Princeton University Press, Princeton (2004)

    Google Scholar 

  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. Rodgers, E.: Diffusion of Innovation, 6th edn. Free Press, New York (2003)

    Google Scholar 

  12. Kirman, A.: What or whom does the representative individual represent? J. Econ. Perspect. 6, 117 (1992)

    Google Scholar 

  13. Goldenfeld, N.: Lectures on Phase Transitions and the Renormalization Group. Addison Wesley, Reading (1992)

    Google Scholar 

  14. Sethna, J.P.: Statistical Mechanics: Entropy, Order Parameters, and Complexity. Oxford University Press, London (2006)

    MATH  Google Scholar 

  15. Schelling, T.: Micromotives and Macrobehaviour. Norton, New York (1978)

    Google Scholar 

  16. Schelling, T.: Dynamic models of segregation. J. Math. Soc., 1, 143 (1971)

    Article  Google Scholar 

  17. Granovetter, M.: Threshold models of collective behaviour. Am. J. Sociol. 83, 1420 (1978)

    Article  Google Scholar 

  18. Granovetter, M., Soong, R.: Threshold models of diffusion and collective behaviour. J. Math. Sociol. 9, 1165 (1983)

    Article  Google Scholar 

  19. Weidlich, W.: The statistical description of polarisation phenomena in society. Br. J. Math. Stat. Psychol. 24, 251 (1971)

    Article  MATH  Google Scholar 

  20. Galam, S., Gefen, Y., Shapir, Y.: Sociophysics: a mean behavior model for the process of strike. J. Math. Sociol. 9, 13 (1982)

    Article  Google Scholar 

  21. Föllmer, H.: Random economies with many interacting agents. J. Math. Econ. 1, 51 (1974)

    Article  MATH  Google Scholar 

  22. Anderson, P.W., Arrow, K., Pine, D.: The Economy as an Evolving Complex System I. Addison-Wesley, Reading (1992)

    Google Scholar 

  23. Arthur, W.B., Durlauf, S., Lane, D.: The Economy as an Evolving Complex System II. Addison-Wesley, Reading (1997)

    Google Scholar 

  24. Kirman, A.: Ants, rationality and recruitment. Q. J. Econ. 108, 137 (1993)

    Article  Google Scholar 

  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. Durlauf, S.: Path dependence in aggregate output. Ind. Corp. Change 1, 149 (1994)

    Article  Google Scholar 

  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. Brock, W., Durlauf, S.: Discrete choice with social interactions. Rev. Econ. Stud. 68, 235 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  29. Becker, G.S.: A note on restaurant pricing and other examples of social influences on price. J. Polit. Econ. 99, 1109 (1991)

    Article  Google Scholar 

  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. Durlauf, S.N., Peyton Young, H.: Social Dynamics. Brookings Institution Press/MIT Press, London (2001)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  33. Chamley, Ch.: Rational Herds. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  34. Orléan, A.: Bayesian interactions and collective dynamics of opinions. J. Econ. Behav. Organ. 28, 257 (1995)

    Article  Google Scholar 

  35. Challet, D., Marsili, M., Zhang, Y.C.: Minority Games. Oxford University Press, London (2005)

    MATH  Google Scholar 

  36. Castellano, C., Fortunato, S., Loreto, V.: Statistical physics of social dynamics. Rev. Mod. Phys. 81, 591 (2009)

    Article  ADS  Google Scholar 

  37. Buchanan, M.: The Social Atom. Bloomsbury Press, New York (2007)

    Google Scholar 

  38. Ball, P.: Why Society is a Complex Matter. Springer, Berlin (2012)

    Book  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  41. Perkovic, O., Dahmen, K., Sethna, J.P.: Avalanches, Barkhausen noise, and plain old criticality. Phys. Rev. Lett. 75, 4528 (1995)

    Article  ADS  Google Scholar 

  42. Sethna, J., Dahmen, K., Myers, C.: Crackling noise. Nature 410, 242 (2001)

    Article  ADS  Google Scholar 

  43. Galam, S., Moscovici, S.: Towards a theory of collective phenomena: consensus and attitude changes in groups. Eur. J. Soc. Psychol. 21, 49 (1991)

    Article  Google Scholar 

  44. Bouchaud, J.P.: Power-laws in economics and finance: some ideas from physics. Quant. Finance 1, 105 (2001)

    Article  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  48. Michard, Q., Bouchaud, J.-P.: Theory of collective opinion shifts: from smooth trends to abrupt swings. Eur. Phys. J. B 47, 151 (2005)

    Article  ADS  Google Scholar 

  49. Harras, G., Tessone, C.J., Sornette, D.: Noise-induced volatility of collective dynamics. Phys. Rev. E 85, 011150 (2012)

    Article  ADS  Google Scholar 

  50. Molins, J., Vives, R.: Long range Ising model for credit risk modeling. AIP Conf. Proc. 779, 156 (2005)

    Article  ADS  Google Scholar 

  51. Anand, K., Kirman, A., Marsili, M.: Epidemics of rules, rational negligence and market crashes. Eur. J. Financ. (2011)

  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)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  53. Sieczka, P., Sornette, D., Holyst, J.: The Lehman Brothers effect and bankruptcy cascades. Eur. Phys. J. B 82, 257 (2011)

    Article  ADS  Google Scholar 

  54. Arthur, W.B.: Complexity in economic and financial markets. Complexity 1, 20 (1955)

    Google Scholar 

  55. Hardin, G.: The tragedy of the commons. Science 162, 1243 (1968)

    Article  ADS  Google Scholar 

  56. Mézard, M., Montanari, A.: Information, Physics and Computation. Oxford University Press, London (2009)

    Book  MATH  Google Scholar 

  57. Anderson, S.P., De Palma, A., Thisse, J.F.: Discrete Choice Theory of Product Differentiation. MIT Press, New York (1992)

    MATH  Google Scholar 

  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)

    Article  MathSciNet  ADS  MATH  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  60. Weidlich, W.: Physics and social science—the approach of synergetics. Phys. Rep. 204, 1–163 (1991)

    Article  MathSciNet  ADS  Google Scholar 

  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)

    Article  Google Scholar 

  62. Kay, B.J.: Polls and the bandwagon effect on the electoral process. Can. Parliam. Rev. 14, 203 (1993/97)

    Google Scholar 

  63. Hohnisch, M., Pittnauer, S., Solomon, S., Stauffer, D.: Socioeconomic interaction and swings in business confidence indicators. Physica A 345, 646 (2005)

    ADS  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  67. Bass, F.M.: A new product growth model for consumer durables. Manag. Sci. 15, 215 (1969)

    Article  MATH  Google Scholar 

  68. Young, H.P.: Innovation diffusion in heterogeneous populations: contagion, social influence, and social learning. Am. Econ. Rev. 99, 1899 (2009)

    Article  Google Scholar 

  69. Frontera, C., Vives, E.: Computer studies of the 2D random field Ising model at T=0. Comput. Phys. Commun. 121, 188 (1999)

    Article  ADS  Google Scholar 

  70. Dahmen, K., Sethna, J.P.: Hysteresis, avalanches, and disorder induced critical scaling: a renormalization group approach. Phys. Rev. B 53, 14872 (1996)

    Article  ADS  Google Scholar 

  71. Corral, A., Font-Clos, F.: Branching processes, criticality, and self-organization: application to natural hazards. arXiv:1207.2589

  72. Pradhan, S., Hansen, A., Chakrabarti, B.: Failure processes in elastic fiber bundles. Rev. Mod. Phys. 82, 499 (2010)

    Article  ADS  Google Scholar 

  73. da Silveira, R.: An introduction to breakdown phenomena in disordered systems. Am. J. Phys. 67, 1177 (1999)

    Article  ADS  Google Scholar 

  74. Sornette, D.: Mean-field solution of a block-spring model of earthquakes. J. Phys. I France 2, 2089 (1992)

    Article  Google Scholar 

  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)

    Article  MathSciNet  ADS  Google Scholar 

  76. Krzakala, F., Ricci-Tersenghi, F., Zdeborova, L.: Elusive spin-glass phase in the random field Ising model. Phys. Rev. Lett. 104, 207208 (2010)

    Article  ADS  Google Scholar 

  77. Galla, T., Farmer, D.: Complex dynamics in learning complicated games. arXiv:1109.4250

  78. Lucas, A., Lee, C.H.: Multistable binary decision making on networks. arXiv:1210.6044

  79. Raafat, R.M., Chater, N., Frith, C.: Herding in humans. Trends Cogn. Sci. 13, 420 (2009)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  82. Guedj, O., Bouchaud, J.P.: Experts earning forecasts, bias, herding and gossamer information. Int. J. Theor. Appl. Finance 8, 933 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  83. Lorenz, J., Rauhut, H., Schweitzer, F., Helbing, D.: How social influence can undermine the wisdom of crowd. http://www.pnas.org/cgi/doi/10.1073/pnas.1008636108

  84. Reinhart, C.M., Rogoff, K.S.: This Time is Different. Princeton University Press, Princeton (2009)

    Google Scholar 

  85. Gigerenzer, G., Goldstein, D.: Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103, 650 (1996)

    Article  Google Scholar 

  86. Gigerenzer, G., Todd, P.M.: Simple Heuristics that Make Us Smart. Oxford University Press, London (1999)

    Google Scholar 

  87. Manski, C.F.: Identification Problems in Social Sciences. Harvard University Press, Cambridge (1995)

    Google Scholar 

  88. Bongaarts, J., Watkins, S.C.: Social interactions and contemporary fertility transitions. Popul. Dev. Rev. 22, 639 (1996)

    Article  Google Scholar 

  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. Glaeser, E.L., Sacerdote, B., Scheinkman, J.A.: Crime and social interactions. Q. J. Econ. 111, 507 (1996)

    Article  Google Scholar 

  91. Challet, D., Krause, A.: What questions to ask in order to validate an agent-based model. Unilever report (2006)

  92. Anderson, L.R., Holt, C.A.: Information cascades in the laboratory. Am. Econ. Rev. 87, 847 (1997)

    Google Scholar 

  93. Alevy, E., Haigh, M.S., List, J.A.: Information cascades: evidence from a field experiment with financial market professionals (October 2005)

  94. Card, D., Mass, A., Rothstein, J.: Tipping and the dynamics of segregation in neighborhoods and schools. Q. J. Econ. 123, 177 (2008)

    Article  Google Scholar 

  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. Hosking, G.: The credit crunch and the importance of trust. History and Policy Paper No. 77 (2008). Available at http://www.historyandpolicy.org/papers/policy-paper-77.html

  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)

    Article  Google Scholar 

  98. Cont, R., Bouchaud, J.P.: Herd behaviour and aggregate fluctuations in financial markets. Macroecon. Dyn. 4, 139 (2000)

    Article  Google Scholar 

  99. Curty, Ph., Marsili, M.: Phase coexistence in a forecasting game. J. Stat. Mech. 2006, P03013 (2006)

    Article  MathSciNet  Google Scholar 

  100. Harras, G., Sornette, D.: How to grow a bubble: a model of myopic adapting agents. J. Econ. Behav. Organ. 80, 137 (2011)

    Article  Google Scholar 

  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)

    Article  ADS  MATH  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  103. Schweitzer, F., Holyst, J.A.: Modelling collective opinion formation by means of active Brownian particles. Eur. Phys. J. B 15, 723 (2000)

    Article  ADS  Google Scholar 

  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. Borghesi, C., Bouchaud, J.-P.: Of songs and men: a model for multiple choice with herding. Qual. Quant. 41, 557 (2007)

    Article  Google Scholar 

  106. Raffaelli, G., Marsili, M.: Statistical mechanics model for the emergence of consensus. Phys. Rev. E 72, 016114 (2005)

    Article  ADS  Google Scholar 

  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

    Article  ADS  Google Scholar 

  108. Redner, S.: How popular is your paper? An empirical study of the citation distribution. Eur. Phys. J. B 4, 131 (1998)

    Article  ADS  Google Scholar 

  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. 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. Marsili, M.: On the multinomial logit model. Physica A 269, 9 (1999)

    Article  MathSciNet  ADS  Google Scholar 

  112. Thurstone, L.L.: The prediction of choice. Psychometrica 10, 237 (1945)

    Article  MathSciNet  Google Scholar 

  113. Grauwin, S., Bertin, E., Lemoy, R., Jensen, P.: Competition between collective and individual dynamics. Proc. Natl. Acad. Sci. USA 106, 20622 (2009)

    Article  ADS  Google Scholar 

  114. Grauwin, S., Goffette-Nagota, F., Jensen, P.: Dynamic models of residential segregation: an analytical solution. J. Public Econ. 96, 124 (2012)

    Article  Google Scholar 

  115. Thaler, R.H., Sunstein, C.R.: Nudge. Yale University Press, New Haven (2007)

    Google Scholar 

  116. Jona-Lasinio, G.: From fluctuations in hydrodynamics to nonequilibrium thermodynamics. arXiv:1003.4164

  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

    Article  ADS  Google Scholar 

  118. Bradde, S., Biroli, G.: The generalized Arrhenius law in out of equilibrium systems. arXiv:1204.6027

  119. Agliari, E., Barra, A., Burioni, R., Camboni, F., Contucci, P.: Effective interactions in group competition with strategic diffusive dynamics. arXiv:0905.3813

  120. Parisi, G.: Asymmetric neural networks and the process of learning. J. Phys. A 19, L675 (1986)

    Article  MathSciNet  ADS  Google Scholar 

  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)

    Article  MathSciNet  ADS  Google Scholar 

  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. 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)

    Article  Google Scholar 

  124. Weisbuch, G., Kirman, A., Herreiner, D.: Market organisation and trading relationships. Econ. J. 110, 411 (2000)

    Article  Google Scholar 

  125. Thouless, D.J.: Long-range order in one-dimensional Ising systems. Phys. Rev. 187, 732 (1969)

    Article  ADS  Google Scholar 

  126. Dyson, F.J.: An Ising ferromagnet with discontinuous long-range order. Commun. Math. Phys. 21, 269 (1971)

    Article  MathSciNet  ADS  Google Scholar 

  127. Anderson, P.W., Yuval, G., Hamann, D.R.: Exact results in the Kondo problem. Phys. Rev. B 1, 4464 (1970)

    Article  ADS  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  129. Caccioli, F., Franz, S., Marsili, M.: Ising model with memory: coarsening and persistence properties. J. Stat. Mech. 2008, P07006 (2008)

    Article  Google Scholar 

  130. Barabási, A.-L.: The origin of bursts and heavy tails in humans dynamics. Nature 435, 207 (2005)

    Article  ADS  Google Scholar 

  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)

    Article  ADS  Google Scholar 

  132. Chicheportiche, R., Bouchaud, J.-P.: The fine structure of volatility feedback. arXiv:1206.2153, and references therein

  133. Woodford, M.: Learning to believe in sunspots. Econometrica 58, 277 (1990)

    Article  MathSciNet  MATH  Google Scholar 

  134. Orléan, A.: Le pouvoir de la finance. Odile Jacob, Paris (1999)

    Google Scholar 

  135. Wyart, M., Bouchaud, J.-P.: Self-referential behaviour, overreaction and conventions in financial markets. J. Econ. Behav. Organ. 63, 1 (2007)

    Article  Google Scholar 

  136. De Bondt, W., Thaler, R.: Does the market overreact? J. Finance 40, 793 (1985)

    Article  Google Scholar 

  137. Shiller, R.J.: Irrational Exuberance, pp. 186–189. Princeton University Press, Princeton (2000)

    Google Scholar 

  138. Raffaelli, G., Marsili, M.: Dynamic instability in a phenomenological model of correlated assets. J. Stat. Mech. 2008, L08001 (2008)

    Google Scholar 

  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)

    Article  Google Scholar 

  140. Batista, J., Challet, D., Bouchaud, J.-P.: in preparation

  141. Bouchaud, J.-P., Cont, R.: A Langevin approach to stock market fluctuations and crashes. Eur. Phys. J. B 6, 543 (1998)

    Article  ADS  Google Scholar 

  142. Peters, R.D., Le Berre, M., Pomeau, Y.: Prediction of catastrophes: an experimental model. Phys. Rev. E 86, 026207 (2012)

    Article  ADS  Google Scholar 

  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

  144. Lux, T., Marchesi, M.: Volatility clustering in financial markets: a micro-simulation of interacting agents. Int. J. Theor. Appl. Finance 3, 675 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  145. Giardina, I., Bouchaud, J.-P.: Bubbles, crashes and intermittency in agent based market models. Eur. Phys. J. B 31, 421 (2003)

    Article  MathSciNet  ADS  Google Scholar 

  146. Hommes, C.: Heterogeneous Agent Models in Economics and Finance. Handbook of Computational Economics, 2 (2006)

    Google Scholar 

  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. Cristelli, M., Pietronero, L., Zaccaria, A.: Critical overview of agent-based models for economics. arXiv:1101.1847

  149. Chakraborti, A., Muni Toke, I., Patriarca, M., Abergel, F.: Econophysics review: II. Agent-based models. Quant. Finance 11, 1013 (2011)

    Article  MathSciNet  Google Scholar 

  150. Ferguson, N.: The Ascent of Money: A Financial History of the World. Penguin, Baltimore (2009)

    Google Scholar 

  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)

    Article  ADS  Google Scholar 

  152. Cutler, D.M., Poterba, J.M., Summers, L.H.: What moves stock prices? J. Portf. Manag. 15, 412 (1989)

    Article  Google Scholar 

  153. Fair, R.C.: Events that shook the market. J. Bus. 75, 713 (2002)

    Article  Google Scholar 

  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. Kirman, A.: The economic crisis is a crisis for economic theory. CESifo Econ. Stud. 56, 498 (2010)

    Article  Google Scholar 

  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. 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 

Download references

Acknowledgements

I thank J. Batista, S. Battiston, E. Beinhocker, E. Bertin, G. Biroli, Ch. Borghesi, M. Buchanan, D. Challet, R. Chicheportiche, R. Cont, P. Contucci, D. Farmer, P. Jensen, S. Grauwin, C. Hommes, A. Kirman, J. Kurchan, F. Lillo, M. Marsili, M. Mézard, Q. Michard, J.P. Nadal, A. Orléan, L. Papaxanthos, M. Potters, J. Sethna, M. Tarzia, S. Wolf, M. Wyart, F. Zamponi and Y.C. Zhang for very useful conversations on these subjects over the years, and S. Fortunato and S. Redner for giving me the opportunity to gather my thoughts on the subject. I thank in particular J. Batista, G. Biroli, D. Challet, A. Kirman, M. Marsili, D. Sornette, S. Wolf and the referees for carefully reading the manuscript and giving useful advice to improve it. This work is part of the European project CRISIS. I would like to dedicate this paper to Alan Kirman, whose work has been extremely inspiring to me.

Author information

Authors and Affiliations

  1. Capital Fund Management, 23 rue de l’Université, 75007, Paris, France

    Jean-Philippe Bouchaud

  2. Ecole Polytechnique, Route de Saclay, 91120, Palaiseau, France

    Jean-Philippe Bouchaud

Authors
  1. Jean-Philippe Bouchaud
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jean-Philippe Bouchaud.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bouchaud, JP. Crises and Collective Socio-Economic Phenomena: Simple Models and Challenges. J Stat Phys 151, 567–606 (2013). https://doi.org/10.1007/s10955-012-0687-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10955-012-0687-3

Keywords

Search

Navigation