Can History Become a Real Science?

1. Can History Become a Real Science? Peter Turchin University of Connecticut Talk presented at Santa Fe, March 2007

2. Main Points of the Talk • Most historians, philosophers, and the lay public believe that there are no general laws of history • I argue that the presence of strong empirical regularites implies the operation of general laws • These laws can be discovered • there are much greater amounts of quantitative data on historical processes than might be expected • but data sets are short and noisy

3. The Focus of the Talk is • not on past accomplishments • too early for that! • but on future directions • what I intend to work on during the next ~5 years

5. No General Laws of History? • Historical processes are too complex and too different from physical or biological ones (Karl Popper) • Any explanation of the course of events is specific to there and then (the great majority of historians) • "There are no general laws in history, apart from those imagined by their proponents" • "History is just one damn thing after another"

7. Ecosystems vs. Social Systems • Both are very complex and heterogenous • Organisms have a kind of free will • Insects, for example, are even less predictable than people • At the micro level, ecosystems are a complete "mess" • Yet, very clear patterns emerge at the macro level, such as population cycles • and there are laws of nature underlying these patterns

9. Narrowing the focus: cliodynamics • Large human collectives (≥105 ind) • Long time scales: • a time step ≈ a human generation (20-30 y) • dynamics on multi-decadal and centennial scales • A key role for mathematical models • Quantitative variables, time-series data • Patterns at a macro scale, but mechanisms at the individual level (There are other promising directions: social evolution, micro-scale ABS, etc)

10. Cliodynamics vs. Cliometrics • Cliodynamics: from Clio (the muse of history) and dynamics (the study of temporally varying processes) • an explicit math component (models) • Cliometrics: in general, quantification in history • statistical, not mechanism-oriented; lacks explicit theory-building approaches • Synergism between the two approaches

11. Strong empirical patterns I • Secular cycles: second-order dynamics • The demographic-structural theory: a rapidly maturing theoretical framework for explaining secular cycles • verbal propositions translated into a suite of mathematical models • model predictions tested empirically for a variety of agrarian states • strong effect of population pressure on real wages (Malthusian mechanism) • strong effect of sociopolitical instability on population growth

13. England: 1540-1870. Demographic data from Wrigley et al 1997 Instability data from quantification of narrative sources

14. Strong empirical patterns II:Religious Conversion • Dynamics of many cases are well described by the logistic growth model • Conversion to Islam • Iran • Spain • Christianity • The Church of Latter-Day Saints (Mormonism) - see Turchin 2003. Hist. Dynamics. Ch. 6

18. Strong empirical patterns III: Spatial distribution of "imperiogenesis" • Database: largest territorial polities • excluding modern sea-based empires • Source: Taagepera, supplemented • Cut-off point: area ≥ 1 Mm2 at peak • More than 60 such polities are known • only 1 (Inca) outside Afroeurasia

19. Lith-Pol Russia Frank GoldenH Kiev Huns Khazar Mongol Manchu Khorezm Chagatai Byz Juan Liao Rom Jur Timur Osman Kara-Kh Hsi Turk Parth Assyr Liang Sele Selj Med Ghazn Hsnu Shang Almorav Caliph Sam Buy Ayy Uig Han Tang Sas Kushan Fatim Il-Kh Mam Tufan Mughal Ming Ach Sung Maur Delhi Gupta Egypt Harsha Almohad Mar Mali Axum Khmer Srivi M

21. Largest territorial polities tend to arise at interfaces between settled and nomadic societies • Not a strict "law", but rather a statistical correlation • Several "hotspots" of imperiogenesis and upsweeps in max. territorial size • Mesopotamia and Iran • Northern India • Northern China

24. The East Asian Imperiogenesis Hotspot: Empirical Patterns • 14 unifications of China from the Shang to Communist eras (some partial) • (E.N. Anderson, supplemented) • Summary: • 8 unifications from NW (usually, Wei RV) • 3 unifications from NE (Liao, Manchuria) • 2 unifications from NC (Huang He) • 1 unification from SC (Nanjing)

25. The broad context:The puzzle of human ultrasociality • Evolution of cooperation in small groups (~102 ind) by group selection is essentially understood • D.S. Wilson, Boyd, Richerson, Bowles • Asabiya (Ibn Khaldun): capacity for collective action • But how did huge groups of 106−108 cooperating individuals arise?

26. The "Mirror Empires" Model • A steppe frontier between settled agriculturalists and nomadic pastoralists • Starting point: small-scale polities on both sides of the frontier • Pastoralists enjoy preponderance of military power; need the products of agriculture

31. Outcome • An agrarian empire and a nomadic imperial confederation arise simultaneously in a mirror fashion • The process occurs in a series of steps of increasing territorial size and social complexity • A positive feedback loop (self-feeding process) • Runaway territorial growth is eventually stopped by space or logistic limits

32. Two Kinds of Sciences(Randall Collins, The Sociology of Philosophies)

33. Can cliodynamics become a "rapid discovery science"? • In the end, this is an empirical issue: "the proof is in the pudding" • We have to generate a constant flow of new results • We need to propose and defend candidates for general laws • So what about the data?

34. Sources of data • Archaeological • Skeletal material • Coin hoards • Quantification of narrative sources • and many, many other

35. Novgorod the Great

40. Stature as a proxy for population density • Abundance of data (106 skeletons) • Human height is a very sensitive indicator of nutrition conditions • a proxy for population pressure • But temporal resolution is poor • Radiocarbon dating errors are ~ 50 y • However, given the abundance of data, it should be possible to use statistical methods for error reduction

42. Skeletons, cont. • Can be used to score the intensity of interpersonal violence, and thus, indirectly, sociopolitical instability • Example: the study of Tim Kohler et al in the American Southwest

43. Figure 4. Graph of standardized, smoothed population (N, black) superimposed on smoothed warfare frequency (W, red). (Kohler et al. 2006)

45. Coin hoards • Abundant in many historical eras; datable • Frequency of hoards (per decade) reflects conditions of internal disorder: • people bury hoards in times of danger • most emergency hoards are recovered, except when the owner is unable to do so • Caveat: • hoard incidence reflects not only internal disorder, but also catastrophic external invasions

46. Coin Hoards: Republican Rome, 230-0 BCE (Michael Crawford)

47. Instability in Republican Rome, from narrative sources

48. Coin Hoards and the Instability Index