Complex Systems in Finance and Econometrics

2011 Edition
| Editors: Robert A. Meyers (Editor-in-Chief)

Extreme Events in Socio-economic and Political Complex Systems, Predictability of

  • Vladimir Keilis-Borok
  • Alexandre Soloviev
  • Allan Lichtman
Reference work entry

Article Outline


Definition of the Subject


Common Elements of Data Analyzes


US Economic Recessions


Homicide Surges

Summary: Findings and Emerging Possibilities



False Alarm Extreme Event Prediction Algorithm Presidential Election Prediction Target 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Primary Literature

  1. 1.
    Allègre CJ, Le Mouël J-L, Ha Duyen C, Narteau C (1995) Scaling organization of fracture tectonics (SOFT) and earthquake mechanism. Phys Earth Planet Inter 92:215–233Google Scholar
  2. 2.
    Armstrong JS, Cuzan AG (2005) Index methods for forecasting: An application to american presidential elections. Foresight Int J Appl Forecast 3:10–13Google Scholar
  3. 3.
    Blanter EM, Shnirman MG, Le Mouël JL, Allègre CJ (1997) Scaling laws in blocks dynamics and dynamic self‐organized criticality. Phys Earth Planet Inter 99:295–307Google Scholar
  4. 4.
    Bongard MM, Vaintsveig MI, Guberman SA, Izvekova ML, Smirnov MS (1966) The use of self‐learning prog in the detection of oil containing layers. Geol Geofiz 6:96–105Google Scholar
  5. 5.
    Burridge R, Knopoff L (1967) Model and theoretical seismicity. Bull Seismol Soc Am 57:341–360Google Scholar
  6. 6.
    Carlson SM (1998) Uniform crime reports: Monthly weapon‐specific crime and arrest time series 1975–1993 (National, State, 12-City Data), ICPSR 6792 Inter‐university Consortium for Political and Social Research. Ann ArborGoogle Scholar
  7. 7.
    Farmer JD, Sidorowich J (1987) Predicting chaotic time series. Phys Rev Lett 59:845CrossRefGoogle Scholar
  8. 8.
    Gabrielov A, Keilis‐Borok V, Zaliapin I, Newman WI (2000) Critical transitions in colliding cascades. Phys Rev E 62:237–249Google Scholar
  9. 9.
    Gabrielov A, Keilis‐Borok V, Zaliapin I (2007) Predictability of extreme events in a branching diffusion model. arXiv:0708.1542Google Scholar
  10. 10.
    Gabrielov AM, Zaliapin IV, Newman WI, Keilis‐Borok VI (2000) Colliding cascade model for earthquake prediction. Geophys J Int 143(2):427–437Google Scholar
  11. 11.
    Gelfand IM, Guberman SA, Keilis‐Borok VI, Knopoff L, Press F, Ranzman IY, Rotwain IM, Sadovsky AM (1976) Pattern recognition applied to earthquake epicenters in California. Phys Earth Planet Inter 11:227–283Google Scholar
  12. 12.
    Gell-Mann M (1994) The quark and the jaguar: Adventures in the simple and the complex. Freeman, New YorkGoogle Scholar
  13. 13.
    Crutchfield JP, Farmer JD, Packard NH, Shaw RS (1986) Chaos Sci Am 255:46–57Google Scholar
  14. 14.
    Gvishiani AD, Kosobokov VG (1981) On found of the pattern recognition results applied to earthquake‐prone areas. Izvestiya Acad Sci USSR. Phys Earth 2:21–36Google Scholar
  15. 15.
    Holland JH (1995) Hidden order: How adaptation builds complexity. Addison, ReadingGoogle Scholar
  16. 16.
    IMF (1997) International monetary fund, international financial statistics. CD-ROMGoogle Scholar
  17. 17.
    Kadanoff LP (1976) Scaling, universality and operator algebras. In: Domb C, Green MS (eds) Phase transitions and critical phenomena, vol 5a. Academic, London, pp 1–34Google Scholar
  18. 18.
    Keilis‐Borok VI, Lichtman AJ (1993) The self‐organization of American society in presidential and senatorial elections. In: Kravtsov YA (ed) Limits of predictability. Springer, Berlin, pp 223–237Google Scholar
  19. 19.
    Keilis‐Borok VI, Press F (1980) On seismological applications of pattern recognition. In: Allègre CJ (ed) Source mechanism and earthquake prediction applications. Editions du centre national du la recherché scientifique, Paris, pp 51–60Google Scholar
  20. 20.
    Keilis‐Borok VI, Soloviev AA (eds) (2003) Nonlinear dynamics of the lithosphere and earthquake prediction. Springer, BerlinGoogle Scholar
  21. 21.
    Keilis‐Borok V, Soloviev A (2007) Pattern recognition methods and algorithms. Ninth workshop on non‐linear dynamics and earthquake prediction, Trieste ICTP 1864-11Google Scholar
  22. 22.
    Keilis‐Borok VI, Sorondo MS (2000) (eds) Science for survival and sustainable development. The proceedings of the study-week of the Pontifical Academy of Sciences, 12–16 March 1999. Pontificiae Academiae Scientiarvm Scripta Varia, Vatican CityGoogle Scholar
  23. 23.
    Keilis‐Borok V, Stock JH, Soloviev A, Mikhalev P (2000) Pre‐recession pattern of six economic indicators in the USA. J Forecast 19:65–80Google Scholar
  24. 24.
    Keilis‐Borok VI, Gascon DJ, Soloviev AA, Intriligator MD, Pichardo R, Winberg FE (2003) On predictability of homicide surges in megacities. In: Beer T, Ismail‐Zadeh A (eds) Risk science and sustainability. Kluwer, Dordrecht (NATO Sci Ser II Math, Phys Chem 112), pp 91–110Google Scholar
  25. 25.
    Keilis‐Borok VI, Soloviev AA, Allègre CB, Sobolevskii AN, Intriligator MD (2005) Patterns of macroeconomic indicators preceding the unemployment rise in Western Europe and the USA. Pattern Recogn 38(3):423–435Google Scholar
  26. 26.
    Keilis‐Borok V, Soloviev A, Gabrielov A, Zaliapin I (2007) Change of scaling before extreme events in complex systems. In: Proceedings of the plenary session on “predictability in science: Accuracy and limitations”, Pontificiae Academiae Scientiarvm Scripta Varia, Vatican CityGoogle Scholar
  27. 27.
    Kravtsov YA (ed) (1993) Limits of predictability. Springer, BerlinGoogle Scholar
  28. 28.
    Lichtman AJ, Keilis‐Borok VI (1989) Aggregate‐level analysis and prediction of midterm senatorial elections in the United States, 1974–1986. Proc Natl Acad Sci USA 86(24):10176–10180Google Scholar
  29. 29.
    Lichtman AJ (1996) The keys to the White House. Madison Books, LanhamGoogle Scholar
  30. 31.
    Lichtman AJ (2005) The keys to the White House: Forecast for 2008. Foresight Int J Appl Forecast 3:5–9Google Scholar
  31. 30.
    Lichtman AJ (2008) The keys to the White House, 2008 edn. Rowman/Littlefield, LanhamGoogle Scholar
  32. 32.
    Ma Z, Fu Z, Zhang Y, Wang C, Zhang G, Liu D (1990) Earthquake prediction: Nine major earthquakes in china. Springer, New YorkGoogle Scholar
  33. 33.
    Mason IB (2003) Binary events. In: Jolliffe IT, Stephenson DB (eds) Forecast verification. A practitioner's guide in atmospheric science. Wiley, Chichester, pp 37–76Google Scholar
  34. 34.
    Molchan GM (1990) Strategies in strong earthquake prediction. Phys Earth Planet Inter 61:84–98CrossRefGoogle Scholar
  35. 35.
    Molchan GM (1991) Structure of optimal strategies of earthquake prediction. Tectonophysics 193:267–276CrossRefGoogle Scholar
  36. 36.
    Molchan GM (1994) Models for optimization of earthquake prediction. In: Chowdhury DK (ed) Computational seismology and geodynamics, vol 1. Am Geophys Un, Washington, pp 1–10Google Scholar
  37. 37.
    Molchan GM (1997) Earthquake prediction as a decision‐making problem. Pure Appl Geophys 149:233–237CrossRefGoogle Scholar
  38. 38.
    Molchan GM (2003) Earthquake prediction strategies: A theoretical analysis. In: Keilis‐Borok VI, Soloviev AA (eds) Nonlinear dynamics of the lithosphere and earthquake prediction. Springer, Berlin, pp 209–237Google Scholar
  39. 39.
    Molchan G, Keilis‐Borok V (2008) Earthquake prediction: Probabilistic aspect. Geophys J Int 173(3):1012–1017Google Scholar
  40. 40.
  41. 41.
  42. 42.
    Newman W, Gabrielov A, Turcotte DL (eds) (1994) Nonlinear dynamics and predictability of geophysical phenomena. Am Geophys Un, Int Un Geodesy Geophys, WashingtonGoogle Scholar
  43. 43.
    OECD (1997) Main economic indicators: Historical statistics 1960–1996. Paris, CD-ROMGoogle Scholar
  44. 44.
    Press F, Briggs P (1975) Chandler wobble, earthquakes, rotation and geomagnetic changes. Nature 256:270–273, LondonCrossRefGoogle Scholar
  45. 45.
    Press F, Briggs P (1977) Pattern recognition applied to uranium prospecting. Nature 268:125–127CrossRefGoogle Scholar
  46. 46.
    Press F, Allen C (1995) Patterns of seismic release in the southern California region. J Geophys Res 100(B4):6421–6430CrossRefGoogle Scholar
  47. 47.
    Soloviev A (2007) Application of the pattern recognition techniques to earthquake‐prone areas determination. Ninth workshop on non‐linear dynamics and earthquake prediction, Trieste ICTP 1864-9Google Scholar
  48. 48.
    Stock JH, Watson MW (1989) New indexes of leading and coincident economic indicators. NBER Macroecon Ann 4:351–394CrossRefGoogle Scholar
  49. 49.
    Stock JH, Watson MW (1993) A procedure for predicting recessions with leading indicators. In: Stock JH, Watson MW (eds) Business cycles, indicators, and forecasting (NBER Studies in Business Cycles, vol 28), pp 95–156Google Scholar
  50. 50.
    Tukey JW (1977) Exploratory data analysis. Addison‐wesley series in behavioral science: Quantitative methods. Addison, ReadingGoogle Scholar
  51. 51.
    Turcotte DL, Newman WI, Gabrielov A (2000) A statistical physics approach to earthquakes. In: Geocomplexity and the physics of earthquakes. Am Geophys Un, WashingtonGoogle Scholar
  52. 52.
    Zaliapin I, Keilis‐Borok V, Ghil M (2003) A Boolean delay model of colliding cascades, II: Prediction of critical transitions. J Stat Phys 111(3–4):839–861Google Scholar

Books and Reviews

  1. 53.
    Bongard MM (1967) The problem of recognition. Nauka, MoscowGoogle Scholar
  2. 54.
    Brito DL, Intriligator MD, Worth ER (1998) In: Eliassson G, Green C (eds) Microfoundations of economic growth: A Schumpeterian perspective. University of Michigan Press, Ann ArborGoogle Scholar
  3. 55.
    Bui Trong L (2003) Risk of collective youth violence in french suburbs. A clinical scale of evaluation, an alert system. In: Beer T, Ismail‐Zadeh A (eds) Risk science and sustainability. Kluwer, Dordrecht (NATO Sci Ser II Math Phys Chem 112)Google Scholar
  4. 56.
    Engle RF, McFadden DL (1994) (eds) Handbook of econometrics, vol 4. North‐Holland, AmsterdamGoogle Scholar
  5. 57.
    Klein PA, Niemira MP (1994) Forecasting financial and economic cycles. Wiley, New YorkGoogle Scholar
  6. 58.
    Messner SF (1983) Regional differences in the economic correlates of the urban homicide rate. Criminology 21:477–488CrossRefGoogle Scholar
  7. 59.
    Mitchell WC (1951) What happens during business cycles: A progress report. NBER, New YorkGoogle Scholar
  8. 60.
    Mitchell WC, Burns AF (1946) Measuring business cycles. NBER, New YorkGoogle Scholar
  9. 61.
    Moore GH (ed) (1961) Business cycle indicators. NBER, New YorkGoogle Scholar
  10. 62.
    Mostaghimi M, Rezayat F (1996) Probability forecast of a downturn in US economy using classical statistical theory. Empir Econ 21:255–279CrossRefGoogle Scholar
  11. 63.
    Watson MW (1994) In: Engle RF, McFadden DL (eds) Handbook of econometrics, vol IV. North‐Holland, AmsterdamGoogle Scholar

Copyright information

© Springer-Verlag 2009

Authors and Affiliations

  • Vladimir Keilis-Borok
    • 1
    • 2
  • Alexandre Soloviev
    • 2
    • 3
  • Allan Lichtman
    • 4
  1. 1.Institute of Geophysics and Planetary Physics and Department of Earth and Space SciencesUniversity of CaliforniaLos AngelesUSA
  2. 2.International Institute of Earthquake Prediction Theory and Mathematical GeophysicsRussian Academy of ScienceMoscowRussia
  3. 3.Abdus Salam International Centre for Theoretical PhysicsTriesteItaly
  4. 4.American UniversityWashington D.C.USA