can history become a real science? peter turchin university of connecticut talk presented at santa...
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Can History Become a Real Science?
Peter TurchinUniversity of Connecticut
Talk presented at Santa Fe, March 2007
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
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
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"
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
Year
1950 1960 1970 1980 1990
Larc
h B
udm
oth
dens
ity
0.01
0.1
1
10
100
1000
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)
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
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
England: the effect of population pressure on real wages
Year
1200 1300 1400 1500 1600 1700 1800
Var
iabl
es, l
og-s
cale
, arb
itrar
y co
nst.
"Misery index" = inverse real wagePopulation pressure = N/K
Instability Index (log-transformed)
-0.2 -0.1 0.0 0.1 0.2
Com
poun
d an
nual
gro
wth
rat
e
0.0
0.2
0.4
0.6
0.8
England: 1540-1870. Demographic data from Wrigley et al 1997 Instability data from quantification of narrative sources
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
Iran: Bulliet (1979) Conversion to Islam
Century CE
7 8 9 10
Pro
port
ion
conv
erte
d
0
1
datamodel
Spain: Bulliet (1979)
Century C.E.
7 8 9 10 11
Pro
port
ion
conv
erte
d
0
1
datamodel
Mormonism: Stark (1984) The rise of a new world faith
1840 1860 1880 1900 1920 1940 1960 1980
Pro
port
ion
of W
orld
Pop
ulat
ion
Con
vert
ed
0.0000
0.0002
0.0004
0.0006
0.0008
0.0010datamodel
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
M
Egypt
Axum
FatimAlmorav
Almohad
Mali
Mam
Hsnu
Juan
Turk
UigTufan
Khazar
Hsi
Khorezm
Kara-Kh
Mongol
GoldenH
ChagataiTimur
ShangHanTang
Liang
Liao
Sung
Jur
Ming
ManchuRom
HunsFrank Kiev
Lith-Pol
Osman
Russia
Srivi
Khmer
Maur
Kushan
GuptaHarsha
Delhi
Mughal
Mar
AssyrMed
AchSas
SeleParth
CaliphSelj
Sam BuyGhazn
AyyIl-Kh
Byz
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
Unification Period Ethnicity From Capital
Shang 1766–1122 BCE
?? NC (Huang He) Anyang (Huang-He)
W. Zhou 1122 –771 BCE Frontier Han(“Western Barbarians”)
NW (Wei River Valley)
Loyang (Huang He)
Qin 221–206 BCE Frontier Han NW (Wei River Valley)
Xianyang(Wei)
Han 202 BCE–220 Han NW (looks like their base was at the confluence of Wei and Huang)
Chang’an(Wei)
N. Wei(partial, N)
386–534 To-ba (Turkic) NW Loyang (Huang He)
Sui 581–618 Han NW (Wei River Valley)
Chang’an(Wei)
Tang 618–907 Han (ruling family of Turkish descent)
NW (Wei RV?) Chang’an(Wei)
Liao(partial, N)
907–1125 Khitan (Altaic?)
NE Beijing
N. Sung(partial, w/o N)
960–1127 Han NC (from lower Huang He area around Kaifeng)
Kaifeng(Huang He)
Jin(partial, N)
1115–1234 Jurchen (Tungus)
NE Beijing
Yuan 1206–1368 Mongol NW (Mongolia) Beijing
Ming 1368–1644 Han CS (from Nanjing area): the only unification not from N
Beijing
Qing 1644–1911 Manchu (Tungus)
NE Beijing
Communist 1949– Han NW (Long March to Wei River Valley; unification from there)
Beijing
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)
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?
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
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
Two Kinds of Sciences(Randall Collins, The Sociology of
Philosophies)"Rapid Discovery" "Traditional"
Rapid rate of knowledge production
Slow rate of knowledge production
Consensus Dissensus
Priority disputes; simultaneous discovery frequent
Disputes focus on clashing ideologies
Nobody reads the founders
The founders are constantly re-examined and re-appraised
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?
Sources of data
• Archaeological• Skeletal material• Coin hoards• Quantification of narrative sources• and many, many other
Novgorod the Great
Novgorod: Time distribution of birchbark documents
1000 1100 1200 1300 1400 1500
Doc
umen
ts p
er d
ecad
e
0
10
20
30
40
50
60
Nerev
1000 1100 1200 1300 1400 1500
Nor
mal
ized
dat
a
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
shoeslocksclothamber
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
Average height: skeletal material from Europe (Koepke et al 2005)
Birth Century
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
Hei
ght i
n cm
(in
vers
e sc
ale)
168
169
170
171
172
173
Principate
Dominate
Carolingian
Early Medieval
LateMedieval
EarlyModern
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
Figure 4. Graph of standardized, smoothed population (N, black) superimposed on smoothed warfare frequency (W, red). (Kohler et al. 2006)
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
Year, BCE
-200 -150 -100 -50 0
Hoa
rds
per
deca
de
0
10
20
30
Coin Hoards: Republican Rome, 230-0 BCE (Michael Crawford)
Year, BCE
-200 -150 -100 -50 0
Inst
ab
ility
In
de
x
0
2
4
6
8
10
Instability in Republican Rome, from narrative sources
Year
-220 -200 -180 -160 -140 -120 -100 -80 -60 -40 -20 0
Coi
n ho
ards
0
10
20
30
Inst
abili
ty I
ndex
0
2
4
6
8
10HoardsInstability
Coin Hoards and the Instability Index
Bohemia, Moravia, and Silesia
Year
1300 1350 1400 1450 1500 1550 1600 1650 1700 1750 1800 1850
Hoa
rds
per
deca
de
0
50
100
150
200Hussite Wars (1420-36)
Thirty-Year War (1618-48)
Hoards in NW Germany
1000 1100 1200 1300 1400 1500 1600 1700 1800
Hoa
rds
per
half-
cent
ury
0
10
20
30
40
50
W. WestfalenE. WestfalenPfalz/SaarNordrhein
Welf/Hohen-staufencivil wars
LateMedievalCrisis
Crisis of XVIIc
Quantifying sociopolitical instability from narrative
sources• Sociopolitical instability:
– state collapse, peasant uprisings, civil wars, and other instances of major internal disturbances
• Construct an annual index by noting which years had an instability event, and which years did not (either 0 or 1)
• A decadal index = the number of instability years per decade (varies between 0 & 10)
years Description
1138-53 Anarchy (civil war between Stephen and Matilda/Henry)
1173-4 Uprising of Henry the Younger. Rebellion of several English earls.
1215-7 Civil war (Magna Carta)
1232 Revolt against papal collectors
1233 Richard Marshal rebelled and was murdered in Ireland
1263-7 Civil war: barons against the king
1315 Civil disorders during supremacy of Lancaster (1314-22)
1321-2 Civil war. Baron uprising in the western counties.
1326-7 Rebellion of Isabella and Mortimer. Edward II deposed, murdered in prison
1330 Edward III led the baronial opposition to Mortimer (hanged, 1330)
1381 Peasants' Revolt
1387-8 Insurrection of the “Lords Appelant”
1391 Coup d’etat of Richard II
1397-9 Events leading to the deposition of Richard II (1399).
1400-8 Glyn Dwr rebellion
1414 A Lollard plot against the king's life
1448-51 Domestic disorders
1450 Jack Cade's rebellion
years Description
1455-6 The Wars of Roses: 1st phase
1460-5 The Wars of Roses: 2nd phase
1467-71 The Wars of Roses: 3rd phase
1483-5 The Wars of Roses: 4th phase
1495 Rebellion of Perkin Warbeck
1497 Insurrection in Cornwall
1536-7 Pilgrimage of Grace
1549 Kett’s rebellion
1554 Wyatt’s rebellion
1569 Rebellion of catholic lords of the North
1639-40 The Bishops’ Wars
1642-7 Civil War
1648-51 Second Civil War
1655 Penruddock rising in Salisbury
1660 Monk’s coup; restoration of James II
1666 Revolt of Scottish Covenanters
1679 Revolt of Scottish Covenanters
1685 Monmouth and Argyll rebellions
1687-92 Glorious Revoultion, with intervention by France
1715-6 Jacobite rebellion in Scotland
1745-6 Scottish rising (Jacobite pretender)
England
1100 1200 1300 1400 1500 1600 1700 1800
Inst
abili
ty I
ndex
0
5
10
Events per decadeSmoothed, h =25 y
Rome: Sorokin's index of internal war
Year
-500 -400 -300 -200 -100 0
So
cio
po
litic
al i
nst
ab
ility
ind
ex
0
50
100
150
200
250
"Wheels within Wheels"
• Two kinds of oscillations superimposed:
• Secular cycles– periods = 200−300 y, or ~10
generations (second-order dynamics)
• "Fathers-and-sons cycles"– periods = 40−60 y, or ~2 generations
(first-order dynamics)
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
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