Clio—detail from The Allegory of Painting by Johannes Vermeer

Cliodynamics (/ˌkldˈnæmɪks/) is a transdisciplinary area of research integrating cultural evolution, economic history/cliometrics, macrosociology, the mathematical modeling of historical processes during the longue durée, and the construction and analysis of historical databases.[1] Cliodynamics treats history as science. Its practitioners develop theories that explain such dynamical processes as the rise and fall of empires, population booms and busts, spread and disappearance of religions.[2][3] These theories are translated into mathematical models. Finally, model predictions are tested against data. Thus, building and analyzing massive databases of historical and archaeological information is one of the most important goals of cliodynamics.[4]


The word cliodynamics is composed of clio- and -dynamics. In Greek mythology, Clio is the muse of history. Dynamics, most broadly, is the study of how and why phenomena change with time.[5]


The term was originally coined by Peter Turchin in 2003,[6] and can be traced to the work of such figures as Ibn Khaldun[7] Alexandre Deulofeu, Jack Goldstone, Sergey Kapitsa, Randall Collins, John Komlos, and Andrey Korotayev.

Mathematical modeling of historical dynamics

Many historical processes are dynamic (a dynamic process is one that changes with time). Populations increase and decline, economies expand and contract, states grow and collapse. A very common approach, which has proved its worth in innumerable applications (particularly, but not exclusively, in the natural sciences), consists of taking a holistic phenomenon and splitting it up into separate parts that are assumed to interact with each other. This is the dynamical systems approach, because the whole phenomenon is represented as a system consisting of several elements (or subsystems) that interact and change dynamically; that is, over time. In the dynamical systems approach, one sets out explicitly with mathematical formulae how different subsystems interact with each other. This mathematical description is the model of the system, and one can use a variety of methods to study the dynamics predicted by the model, as well as attempt to test the model by comparing its predictions with observed empirical, dynamic evidence. Cliodynamics is the application of this same approach to the social sciences in general and to the study of historical dynamics in particular.

Cliodynamics practitioners apply mathematical models to explain macrohistorical patterns – things like the rise of empires, social discontent, civil wars, and state collapse.[4][8][9] Although the focus is usually on the dynamics of large conglomerates of people, the approach of cliodynamics does not preclude the inclusion of human agency in its explanatory theories. Such questions can be explored with agent-based computer simulations.

Databases and data sources

Cliodynamics relies on large bodies of evidence to test competing theories on a wide range of historical processes. This typically involves building massive stores of evidence.[10] The rise of digital history and various research technologies have allowed huge databases to be constructed in recent years. Some prominent databases utilized by cliodynamics practitioners include the following:


As of 2016, the main directions of academic study in cliodynamics are:

  • The coevolutionary model of social complexity and warfare, based on the theoretical framework of Cultural Multilevel Selection[16][17][18][19][20]
  • The study of revolutions and rebellions[21][16][17]
  • Structural-demographic theory and secular cycles[22][23][24][25][26][27]
  • Explanations of the global distribution of languages benefitted from the empirical finding that the geographic area in which a language is spoken is more closely associated with the political complexity of the speakers than with all other variables under analysis.[28]
  • mathematical modeling of the long-term ("millennial") trends of World-systems analysis,[29][30][31]
  • structural-demographic models of the Modern Age revolutions, including the Arab revolutions of 2011.[32]
  • The analysis of vast quantities of historical newspaper content has been pioneered by,[33][34] which showed how periodic structures can be automatically discovered in historical newspapers. A similar analysis was performed on social media, again revealing strongly periodic structures.[35]

There are several established venues of peer reviewed cliodynamics research:

  • Cliodynamics: The Journal of Quantitative History and Cultural Evolution[36]
    • peer-reviewed web-based (open-access) journal that publishes on the transdisciplinary area of cliodynamics. It seeks to integrate historical models with data to facilitate theoretical progress.
    • The first issue was published in December 2010. Cliodynamics is a member of Scopus and the Directory of Open Access Journals (DOAJ).
  • The University of Hertfordshire's Cliodynamics Lab[37]
    • first lab in the world dedicated explicitly to the new research area of Cliodynamics. The Cliodynamics Lab is based at the University of Hertfordshire and is directed by Pieter François, who founded the Lab in 2015.
  • Santa Fe Institute[38]
    • private, not-for-profit research and education center where leading scientists grapple with some of the most compelling and complex problems of our time. The institute supports work in complex modeling of networks and dynamical systems. One of the areas of SFI research is Cliodynamics (see History as Science).
    • In the past the Institute has sponsored a series of conversations and meetings on theoretical history (see, for example, An Inquiry into History, Big History, and Metahistory).


Critics of the Cliodynamic approach often argue that the complex social formations of the past cannot and should not be reduced to quantifiable, analyzable 'data points', for doing so overlooks each historical society's peculiar circumstances and dynamics.[39][40][41] Many historians and social scientists contend that there are no generalizable causal factors that can explain large numbers of cases, but that historical investigation should focus on the unique trajectories of each case, highlighting commonalities in outcomes where they exist. As Zhao notes, "most historians believe that the importance of any mechanisms in history changes, and more important, there is no time-invariant structure that can organize all the historical mechanisms into a system".[39][40] Cliodynamicists, on the other hand, contend that there are large-scale, macrohistorical patterns that can explain the historical dynamics of the majority of known cases, and that these patterns can be uncovered through systematic, mathematical analysis. They argue that the ability of cliodynamics research to expose these patterns and to explain historical events demonstrates the feasibility of the approach.[1]


Isaac Asimov employed a fictional version of this discipline, what he called psychohistory, as a major plot device in his Foundation series of science fiction novels.[42]

See also


  1. 1 2 Turchin 2008.
  2. Sussan 2013.
  3. Schrodt 2005.
  4. 1 2 Spinney 2012.
  5. Parry 2013.
  6. Orf 2013.
  7. Tainter 2004, p. 488.
  8. Seabright 2004, p. 806-7.
  9. Keen & Owen 2017.
  10. Spinney 2016.
  11. Turchin et al. 2015.
  12. Kirby et al. 2016.
  13. Peregrine 2003.
  14. "eHRAF Archaeology". Human Relations Area Files.
  15. "eHRAF World Cultures". Human Relations Area Files.
  16. 1 2 Turchin 2003.
  17. 1 2 Turchin 2005.
  18. Turchin 2009.
  19. Turchin 2011.
  20. Koyama 2016.
  21. Goldstone 1991.
  22. Turchin & Nefedov 2009.
  23. Korotayev, Malkov & Khaltourina 2006b.
  24. Korotayev & Khaltourina 2006.
  25. Greby 2016.
  26. Zeigler 2010.
  27. Graber 2008.
  28. Currie & Mace 2009.
  29. Tsirel 2004.
  30. Korotayev, Malkov & Khaltourina 2006a.
  31. Korotayev 2006, p. 44-62 etc..
  32. Korotayev & Zinkina 2011.
  33. Dzogang et al. 2016.
  34. Burkhart 2016.
  35. Dzogang, Lansdall-Welfare & Cristianini 2016.
  36. Cliodynamics: The Journal of Quantitative History and Cultural Evolution
  37. The University of Hertfordshire's Cliodynamics Lab
  38. Santa Fe Institute
  39. 1 2 Zhao 2006, p. 309–310.
  40. 1 2 Lange 2012.
  41. Tainter 2004, p. 488–489.
  42. Finley, Klint. 2013. "Mathematicians Predict The Future With Data from the Past." Wired.
  43. Turchin 2015.


  • Burkhart, Richard H. 2016. "Applied Mathematics and Political Crises". Siam News.
  • Currie, Thomas E.; Mace, Ruth (2009). "Political complexity predicts the spread of ethnolinguistic groups". PNAS. 106 (18): 7339–7344. doi:10.1073/pnas.0804698106. PMC 2670878. PMID 19380740.
  • Dzogang, Fabon; Lansdall-Welfare, Thomas; FindMyPast Newspaper Team; Cristianini, Nello (2016-11-08). "Discovering Periodic Patterns in Historical News". PLOS ONE. 11 (11): e0165736. doi:10.1371/journal.pone.0165736. ISSN 1932-6203. PMC 5100883. PMID 27824911.
  • Dzogang F, Lansdall-Welfare T, Cristianini N (2016). "Seasonal Fluctuations in Collective Mood Revealed by Wikipedia Searches and Twitter Posts" (PDF). Proceedings of the 2016 IEEE international conference on data mining workshop (SENTIRE), Barcelona. p. 12–15.
  • Finley, Klint. 2013. "Mathematicians Predict The Future With Data from the Past." Wired.
  • Goldstone J. 1991. Revolution and Rebellion in the Early Modern World. Berkeley, California: University of California Press.
  • Graber, Robert B. (2008). "Review of Andrey Korotayev, Artemy Malkov, and Darkia Khaltourina, Introduction to Social Macrodynamics (Three Volumes)". Journal of Social Evolution and History. 7 (2).
  • Greby, James (2016). "'Cliodynamics' Research Proves American Freaks Out Every 50 Years". Inverse.
  • Keen, Steven, and Charles Owen. 2017."The Value of Everything: E120. Professor Steve Keen Interview." The Future of Money (segment starts at 47:18)
  • Kirby, Kathryn R.; Gray, Russell D.; Greenhill, Simon J.; Jordan, Fiona M.; Gomes-Ng, Stephanie; Bibiko, Hans-Jörg; Blasi, Damián E.; Botero, Carlos A.; Bowern, Claire; Ember, Carol R.; Leehr, Dan; Low, Bobbi S.; McCarter, Joe; Divale, William (2016). "D-PLACE: A Global Database of Cultural, Linguistic and Environmental Diversity". PLoS ONE. 11 (7). doi:10.1371/journal.pone.0158391.
  • Komlos J., Nefedov S. 2002. Compact Macromodel of Pre-Industrial Population Growth. Historical Methods. (35): 92–94.
  • Korotayev, Andrey V. (2006). "The World System urbanization dynamics". In Turchin, Peter; Grinin, Leonid Efimovich; Korotayev, Andrey V.; de Munck, Victor C. History & mathematics: Historical dynamics and development of complex societies. History & mathematics. Moscow: KomKniga/URSS. p. 44-62. ISBN 978-5-484-01002-8.
  • Korotayev, Andrey V.; Malkov, Artemy Sergeevich; Khaltourina, Daria (2006a). Introduction to Social Macrodynamics: Compact Macromodels of the World System Growth. Moscow: URSS. ISBN 978-5-484-00414-0.
  • Korotayev, Andrey V.; Malkov, Artemy Sergeevich; Khaltourina, Daria (2006b). Introduction to social macrodynamics: secular cycles and millennial trends. Moscow: URSS. ISBN 978-5-484-00559-8.
  • Korotayev, Andrey V.; Khaltourina, Daria (2006). Introduction to Social Macrodynamics: Secular Cycles and Millennial Trends in Africa. Moscow: URSS. ISBN 978-5-484-00560-4. (Excerpts) (Publisher's page)
  • Korotayev, A.; Zinkina, J. (2011). "Egyptian Revolution: A Demographic Structural Analysis". Entelequia. Revista Interdisciplinar. 13: 139–169.
  • Korotayev A. et al., A Trap At The Escape From The Trap? Demographic-Structural Factors of Political Instability in Modern Africa and West Asia. Cliodynamics 2/2 (2011): 1-28.
  • Koyama, Mark (2016). "Review of Ultra Society: how 10,000 years of war made humans the greatest cooperators on earth by Peter Turchin" (PDF). Journal of Bioeconomics. Springer Nature. 18 (3): 239–242. doi:10.1007/s10818-016-9234-7. ISSN 1387-6996.
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