Tempo and Mode in Cultural Macroevolution

Abstract

Evolutionary scientists studying social and cultural evolution have proposed a multitude of mechanisms by which cultural change can be effected. In this article we discuss two influential ideas from the theory of biological evolution that can inform this debate: the contrast between the micro- and macro-evolution, and the distinction between the tempo and mode of evolution. We add the empirical depth to these ideas by summarizing recent results from the analyses of data on past societies in Seshat: Global History Databank. Our review of these results suggests that the tempo (rates of change, including their acceleration and deceleration) of cultural macroevolution is characterized by periods of apparent stasis interspersed by rapid change. Furthermore, when we focus on large-scale changes in cultural traits of whole groups, the most important macroevolutionary mode involves inter-polity interactions, including competition and warfare, but also cultural exchange and selective imitation; mechanisms that are key components of cultural multilevel selection (CMLS) theory.

Full text

Tempo and Mode in Cultural Macroevolution
Peter Turchin
1,2
and Sergey Gavrilets
3
Abstract
Evolutionary scientists studying social and cultural evolution have proposed a multitude of mechanisms by which cultural change
can be effected. In this article we discuss two influential ideas from the theory of biological evolution that can inform this debate:
the contrast between the micro- and macro-evolution, and the distinction between the tempo and mode of evolution. We add
the empirical depth to these ideas by summarizing recent results from the analyses of data on past societies in Seshat: Global
History Databank. Our review of these results suggests that the tempo (rates of change, including their acceleration and decel-
eration) of cultural macroevolution is characterized by periods of apparent stasis interspersed by rapid change. Furthermore,
when we focus on large-scale changes in cultural traits of whole groups, the most important macroevolutionary mode involves
inter-polity interactions, including competition and warfare, but also cultural exchange and selective imitation; mechanisms that
are key components of cultural multilevel selection (CMLS) theory.
Keywords
cultural macroevolution, historical data, evolution of complex societies, inter-polity interactions, warfare, major evolutionary
transitions
Date Received November 23, 2021; Accepted November 28, 2021
Introduction
The rise of politically centralized societies and states in human
history is one of the central questions in archaeology, anthropol-
ogy, and other social sciences (Blanton & Fargher, 2008;
Flannery & Marcus, 2012; Johnson & Earle, 2000;
Sanderson, 1999; Turchin, 2022). For the past century and a
half, the studies of these phenomena have greatly benefited
by drawing insights from the theory of biological evolution
(Carneiro, 2003; Currie & Mace, 2011). Over the last couple
of decades, cultural evolution theory has emerged as a powerful
tool for the analysis of structure and dynamics of human socie-
ties (Richerson & Christiansen, 2013). In this new discipline
culture is defined as socially transmitted information, such as
ideas, skills, attitudes, and norms, that affects people’s behav-
iour, practices, and actions. New cultural traits appear as a
result of innovation, driven by intentional design, blind trial
and error, or, simply, transmission error. They spread by imita-
tion, teaching, and other kinds of learning. Cultural traits can
also go extinct. Broadly speaking, cultural evolution is the
change of culture over time (Richerson & Christiansen,
2013). Cultural evolution has adopted many methods and
insights of biological evolution theory (Boyd & Richerson,
1985; Cavalli-Sforza & Feldman, 1981; Lumsden & Wilson,
1981). Both theories share the focus on variation, selection,
and inheritance as the key evolutionary processes; emphasize
population (group) level processes; and utilize dynamic
approaches with deep similarities in mathematical models
describing changes in the frequencies of genes or cultural
traits (but there are also important differences, Richerson
et al., 2021; Smolla et al., 2021).
Two particularly influential ideas coming from the theory of
biological evolution concern the distinctions between micro-
and macroevolution and between the tempo and mode of evolu-
tion. In evolutionary biology, microevolution means genetic
and phenotypic changes within populations while macroevolu-
tion is changes above the species level. In spite of this distinc-
tion, it is generally understood that microevolutionary processes
1
Complexity Science Hub Vienna
2
University of Connecticut, Storrs, CT, USA
3
Department of Ecology and Evolutionary Biology, Department of
Mathematics, Center for the Dynamics of Social Complexity, University of
Tennessee, Knoxville, TN, USA
Corresponding Author:
Peter Turchin, University of Connecticut, 75 N Eagleville Rd, Storrs, CT, USA.
Email: peter.turchin@uconn.edu
Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License
(https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission
provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-s age).
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Evolutionary Psychology
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are sufficient in themselves to explain the patterns of macroevo-
lution including those studied by paleontologists (Simpson,
1944). A similar distinction is often made in cultural evolution
(Duffy, 1980; Mesoudi, 2011). Cultural microevolution, thus,
can be defined as the change in the frequency of cultural vari-
ants within a population (Cavalli-Sforza & Feldman, 1981).
Cultural macroevolution, in contrast, is large-scale changes in
cultural traits of whole groups (Eldredge, 2009). Some group-
level traits reduce directly to individual traits, but others are
emergent properties of groups, not reducible to individuals.
The distinction between the tempo and mode of evolution
was first made in one of the foundational books of the
Modern Synthesis by the paleontologist G.G. Simpson
(1944). The “tempo”has to do with “evolutionary rates. . .,
their acceleration and deceleration, the conditions of exception-
ally slow or rapid evolutions, and phenomena suggestive of
inertia and momentum.”The “mode”involves “the study of
the way, manner, or pattern of evolution, a study in which
tempo is a basic factor, but which embraces considerably
more than tempo”. These ideas have had a profound impact
on evolutionary biology and paleontology (Fitch & Ayala,
1994; Gould & Eldredge, 1977). The main modes (or mecha-
nisms) in evolutionary biology are natural and sexual selection,
mutation, recombination, migration, and genetic drift. The main
modes as seen by paleontologists are speciation, phyletic evolu-
tion, and quantum evolution (Gould, 1994; Simpson, 1944).
Evolutionary scientists studying social and cultural evolu-
tion have proposed a multitude of mechanisms by which cul-
tural change can be effected: random change, biased
transmission, and natural selection on cultural traits (Boyd &
Richerson, 1992); selective imitation and selective migration
(Richerson et al., 2016); cultural group selection (Richerson
et al., 2016; Turchin, 2006); genetic group selection (Bowles
et al., 2003); self-interested design (Gavrilets & Duwal
Shrestha, 2021; Singh et al., 2017); evoked culture (Krasnow
& Delton, 2016; Tooby & Cosmides, 1989); cognitive niche
(Pinker, 2010; Tooby & deVore, 1987; Whiten & Erdal,
2012); and demographic swamping (Henrich, 2004). Which
ones are more important is a controversial topic (Cofnas,
2018; Gavrilets & Duwal Shrestha, 2021; Krasnow & Delton,
2016 Richerson et al., 2016; Singh et al., 2017; Smith, 2020).
Although theoretical archaeology and cultural evolution
largely developed in separate streams and some social scientists
still debate the usefulness of evolutionary theories in explaining
macrosocial change (Manning, 2020; Marcus, 2008; Spencer,
2019; Turner & Machalek, 2018), there are many ideas that
cross-cut the two disciplines. For example, following Morton
Fried (1967) archaeologists make distinction between “pristine”
and “secondary”states, with selective imitation playing a large
role in the latter. A particularly interesting theoretical frame-
work is “peer polity interaction”(Renfrew & Cherry, 1986),
which evokes the mechanism of cultural group selection.
Renfrew, in particular, distinguishes such mechanisms of
change as warfare, competitive emulation, symbolic entrain-
ment, and the transmission of innovation, which have obvious
parallels in the cultural evolutionary theory.
The discipline of cultural evolution has an enormous poten-
tial to throw light on Neolithic Revolution, the rise of cities and
states, the spread of world religions and other ideologies (for
example, those associated with the Enlightenment), and the
recent dramatic improvements in the quality of life experienced
by people living in many parts of the globe. These changes can
be viewed as “major evolutionary transitions”in human history
(c.f. with major evolutionary transitions in biology, Maynard
Smith and Szathmáry, 1995). Cultural evolution is part of
what might be considered as the third wave of evolutionary
thinking in anthropology and other social sciences. The first
wave, “classical social evolution”of the nineteenth century, is
associated with such figures as Herbert Spencer, Lewis Henry
Morgan, and Edward B. Tylor. The second wave, or
“neo-evolutionism”of such anthropologists as V. Gordon
Childe, Leslie White, Elman Service, Marshall Sahlins, Julian
Steward, and sociologist Talcott Parsons was introduced by
the publication of Childe’sMan Makes Himself in 1936 and
peaked during the 1960s and 1970s. The third wave, in addition
to cultural evolution, includes such approaches as evolutionary
psychology (Tooby & Cosmides, 1989). Note, however, that
cultural evolutionists and evolutionary psychologists disagree
about certain foundational issues, for example, the role of cul-
tural group selection in explaining human cooperation and
culture change (Chudek et al., 2013; Krasnow & Delton,
2016; Richerson et al., 2016).
Here we use the Tempo/Mode framework to guide our think-
ing about the evolution of cultural traits that have transformed
human societies over the past 10,000 years—political centrali-
zation, specialized governance institutions, and the social
scale on which humans interact and cooperate. These are mac-
roevolutionary group-level characteristics because, for
example, it doesn’t make sense to speak of individuals as “cen-
tralized”or not (of course, individuals within the same group
can vary in their attitudes and norms, e.g. willingness to
submit to the authority). Our work greatly extends earlier
studies of cultural macroevolution which focused on state for-
mation (Spencer, 1990) and coevolution of social and political
traits in Austronesian-speaking societies of Island South-East
Asia and the Pacific (Currie & Mace, 2011).
Tempo: Rates of Change
During the Holocene—roughly, the past 10,000 years—the
social scale at which humans interact and cooperate increased
by six orders of magnitude, from societies of hundreds (or a
few thousand) to hundreds of millions and even billions. A par-
ticular form of political organization, the state, arose in
mid-Holocene, eventually becoming the dominant form of
social organization over the world. Other dimensions of
change include increasingly productive economies, widespread
adoption of writing and literacy, but also deeper inequalities and
entrenched class hierarchies (Flannery & Marcus, 2012;
Johnson & Earle, 2000; Kradin, 2021; Sanderson, 1999).
The pattern of evolutionary change was not monotonic.
Evolutionary archaeologists view social evolution—the
2Evolutionary Psychology

origins and development of new forms of social organization—
as a process in which long, stable periods were interrupted by
brief periods of rapid change (Marcus, 2008; Marcus &
Flannery, 1996; Redmond & Spencer, 2012). Borrowing a
concept from biological evolution, Charles Spencer (2019) pro-
posed that the transitions from autonomous village societies to
chiefdoms, and then from chiefdoms to the states, can be con-
ceptualized as a shift from one peak to another on an adaptive
landscape (Gavrilets, 2004; Wright, 1932).
We can now add empirical depth to these ideas thanks to the
massive data on past societies in Seshat: Global History
Database (François et al., 2016; Turchin et al., 2015). In an
analysis of 51 variables reflecting such characteristics of
human societies as social scale, economy, features of gover-
nance, and information systems for 414 societies from 30
regions around the world, we showed that these different char-
acteristics show strong relationships with each other and that a
single principal component (PC1) captures around three-
quarters of the observed variation (Turchin et al., 2018).
When we plot this measure of social complexity against time,
we observe a variety of patterns of change, including gradual
increase, no change, and decline (Figure 1). However, the
overall statistical pattern is that of periods of apparent stasis
interspersed by rapid change (Figure 2). As a result, the distri-
bution of rates of change is very non-Gaussian, with more than
70% of frequencies clustering at 0, and very large changes (both
increases and decreases) much more frequent than would be
observed under the assumption of normality.
Mode: Qualitative Patterns and Mechanisms
of Change
In the Introduction we listed a great variety of ideas about the
mode(s) of social evolution, proposed by cultural evolutionists,
evolutionary psychologists, and evolutionary anthropologists.
In this section we use the Seshat sample to relate these ideas
to data. Our focus is on the transition from centralized societies
without internally specialized administration (“chiefdoms”)to
societies with internally specialized governance structures
(“states”).
For political centralization, we use the Seshat measure of
hierarchy that focuses on the length of chains of command.
Hier averages the number of levels in military, administrative,
and settlement hierarchies (the last one is a particularly useful
measure for archaeologically known societies). We use the tran-
sition in Hier from 2 to 3 as the threshold, because for archae-
ologically known societies (in which centralization threshold
tends to be crossed) our primary source of information about
hierarchy levels is the settlement hierarchy. A settlement hierar-
chy with two levels could correspond to a chiefly seat with sub-
ordinate villages or, alternatively, it could be a result of smaller
and larger independent polities (single settlements) coexisting
in a landscape. A three-tier hierarchy, thus, is a more secure
indicator of a politically centralized society (typically, a
complex chiefdom).
The Seshat measure of internally specialized administrative
organization is Gov, which aggregates eleven binary variables.
The first four variables code for presence/absence of profes-
sional military officers, soldiers, religious specialists, and
administrative specialists (bureaucrats). Two variables code
for bureaucracy characteristics: presence/absence of an exami-
nation system and of merit promotion. The next variable, spe-
cialized government buildings, is particularly useful for
societies known only from their archaeological record. The
Figure 1. Trajectories of social complexity in 10 world regions
(out of 30 total). (A) Africa and east Asia. Broken lines indicate
95% confidence intervals. (B) Southwest Asia, south Asia, Europe,
and Central Asia. (C) Southeast Asia, North America, South
America, and Oceania. PC1 has been rescaled to fall between 0
(low complexity) and 10 (high complexity) to aid interpretation.
Source: Figure 3 in (Turchin et al., 2018).
Turchin and Gavrilets 3

Figure 2. Blue bars: frequency distribution of rates of change in PC1 per century in the seshat sample of past polities. Red curve: Gaussian
distribution with the same mean and variance as the data.
Figure 3. Evolution of largest territorial polities over the past 5,000 years. Brown curve: average territory of the three largest polities. Tan
shading: mean ±SD.
4Evolutionary Psychology

final four variables code for the characteristics of the legal
system: formal legal code, professional judges, professional
advocates, and specialized buildings used for legal purposes
(courts). Gov is scaled to be between 0 and 1, and we use the
mid-point, Gov =0.5 as the threshold.
Using this operationalization of chiefdom and state, we ask
two questions. First, what was the mode of evolutionary
change that resulted in a region crossing the Gov =0.5 thresh-
old? Second (and related to Tempo), how much time elapses
between crossing the hierarchy and governance thresholds?
Patterns of Change
The most frequent mode of an NGA crossing the governance
threshold is by being annexed by another, larger and more
complex state (19 cases in Table 1). In retrospect, this result
should not be surprising. As states became the most successful
form of sovereign political organization, they spread over and
eventually occupied all inhabitable areas on the Earth. This
process was mostly a result of conquest and territorial annexa-
tion, and this is reflected in Table 1.
The next largest category is secondary state formation (11
cases in Table 1). Here we follow the established tradition in
archaeology that distinguishes between “pristine”(primary,
first-generation) and secondary states (Claessen, 2016;
Flannery & Marcus, 2012; Fried, 1967; Service, 1975). The
list of pristine states varies among authorities, but they are
usually considered to include Mesopotamia, Egypt, China,
Mesoamerica, Peru, and India (Spencer, 2010).
First-generation states are relatively rare, and this is reflected
in the Seshat sample (5 cases in Table 1).
As we might expect, primary state formation tends to take
more time. The mean (±SD) time between centralization and
crossing the governance threshold for primary states is 1300
(±570) years, while for secondary states it is 370 (±420)
years. Secondary states benefit from selective imitation of suc-
cessful “ultrasocial institutions”(Turchin et al., 2013), which
enable cooperation at the level of large-scale human groups.
For example, state formation in Southeast Asia (represented
in the Seshat Sample by Cambodian Basin and Central Java)
was very rapid due to the importation of world religions,
writing, and governance institutions from South Asia.
Mechanisms of Change
The most general mode of evolutionary change, which results in
transitions to states in the Seshat sample, thus, is interpolity inter-
actions: competition (including extreme forms, such as warfare,
conquest, and cultural assimilation) and exchange (with an
emphasis on its informational dimensions). The operation of
this general mechanism is most obvious when a polity loses in
competition with a more successful one, resulting in annexation
(which is the most common mode of transition to statehood in
the Seshat sample). Secondary state formation also involves
selective imitation (by definition) as well as interpolity conflict.
Despite the quantitative difference between their rates of
evolution, as we saw in the previous section, qualitatively the
modes of evolutionary change do not differ between the
primary and secondary state formation cases. After all, “pristine”
states also did not arise in splendid isolation, as is often noted by
evolutionary archaeologists. Morton Fried (1967), who first made
a distinction between primary and secondary states, argued that
the transition from a stratified society (a chiefdom) to a pristine
state also occurred in an environment occupied by other similar
polities, which developed together as a result of such interactions
as competition, war, trade, and cultural exchange. As we men-
tioned above, later this concept was formalized as the “peer-polity
interactions”(Renfrew & Cherry, 1986). In their review of the
rise of early states, Flannery and Marcus (2012) agreed: “in the
four cases we examined, not one kingdom was the offspring of
a rank society that simply got bigger. …Instead, all four king-
doms arose through the forced unification of competing rank
societies.”
Other transitions insocial scaleand complexity of human soci-
eties may have also involved the same general mechanism. For
example, in our previous research we used agent-based simula-
tions to model the rise and spread of “megaempires”—states
that controlled territories of millions of square kilometres and
populations of tens of millions (Turchin et al., 2013). The
central premise of the model was that costly institutions that
enabled large human groups to function without splitting up
(ultrasocial institutions) evolved as a result of intense competition
between societies—warfare. Warfare intensity, in turn, depended
on the spread of historically attested military technologies (e.g.,
chariots and cavalry) and on geographic factors (e.g., rugged
landscape). The model-predicted pattern of spread of large-scale
societies within a realistic landscape of the Afroeurasian land-
mass between 1500 BCE and 1500 CE was very similar to the
observed one, with the model explaining 65% of variance in
the data. A subsequent spatially explicit statistical analysis con-
firmed that large-scale societies developed more commonly in
regions where warfare was more intense (as proxied by distance
from the Eurasian steppe), thus creating a stronger selection pres-
sure for societies to scale up (Currie et al., 2020). This analysis
also identified an additional factor: transitions to megaempires
were more likely in regions where agriculture has been practiced
for longer (thus providing more time for the norms and institu-
tions that facilitate large-scale organization to emerge).
Whereas our previous studies focused on Afroeurasia during
the Ancient and Medieval eras, the most recent analysis was
global (sampling, in addition, sub-Saharan Africa, the
Americas, and the Oceania) and extended temporal coverage
back to the Neolithic (where data allowed). We used a
general dynamical model, based on the theoretical framework
of cultural macroevolution, and Seshat data to test 17 potential
predictor variables proxying mechanisms suggested by major
theories of sociopolitical complexity (Turchin et al., 2021c).
Not limiting itself to testing the effects of each potential predic-
tor in isolation, this analysis tested >100,000 possible combina-
tions of these predictors with three response variables capturing
different aspects of social complexity (social scale, hierarchy
levels, and the sophistication of governance). We found that
Turchin and Gavrilets 5

the best-fitting models indicate a strong causal role played by a
combination of increasing agricultural productivity, antiquity of
agriculture, and invention/adoption of military technologies
(Turchin et al., 2021c), thus confirming previous studies that
had a more limited geographic and temporal scope. Other
classes of predictors (proxying functionalist and internal con-
flict theories) do not appear to play a significant causal role in
propelling advances in such aspects of social complexity as
social scale, hierarchical complexity, or governance sophistica-
tion. Furthermore, our analysis found strong evidence for non-
linear autoregressive terms that stabilize evolutionary dynamics
around equilibria set by the values of predictors (agriculture and
warfare). These results suggest that periods of rapid change are
induced by such technological advances as the joint spread of
iron metallurgy and horse riding during the first millennium
BCE, or by the gunpowder revolution of the mid-second millen-
nium. Rapid directional change is then followed by a period of
stabilizing selection, until another technological revolution ele-
vates the equilibrial level again.
A pattern of apparent stasis interspersed by periods of rapid
change, predicted by this model, is consistent with the statistical
results in Figure 2. Such a “punctuated equilibrium”pattern is
even more apparent when we focus on the evolution of largest
and most complex societies worldwide. For example, Figure 3
shows how one aspect of social scale, the territory controlled
by largest polities, evolved over the past five thousand years.
The most recent rapid growth phase followed 1500; it was pre-
ceded by other such periods starting in 500 BCE, 2000 BCE,
and 2700 BCE. In between, there was little change in the
maximum polity territory. The longest period of no systematic
change was during the nearly two millennia between 300 BCE
and 1500 CE. Empires rose and fell and the list of three largest
polities was constantly updated, but the areas of these polities
continued fluctuating around the 3 million square kilometre level.
Furthermore, periods of rapid change are clearly associated
with major technological “revolutions”, especially those trig-
gered by novel military technologies. The most recent military
revolution in Figure 3 is the well-known one that originated in
Table 1. Modes of Transition to Statehood Observed in the Seshat Sample.
NGA Chiefdom State Time Polity Mode
Southern Mesopotamia −4200 −2300 1900 Akkadian Empire Primary
Upper Egypt −3100 −2600 500 Old Kingdom of Egypt Primary
Middle Yellow River Valley −3000 −1200 1800 Shang Dynasty of China Primary
Basin of Mexico −400 700 1100 Epiclassic Basin of Mexico Primary
Cuzco 100 1300 1200 Inca Empire Primary
Galilee −2000 −2000 0 Canaan Secondary
Konya Plain −2000 −1600 400 Old Kingdom of Hatti Secondary
Crete −1900 −700 1200 Archaic Crete Secondary
Yemeni Coastal Plain −800 200 1000 Aksum Secondary
Latium −700 0 700 Roman Empire Secondary
Middle Ganga −600 −300 300 Mauryan Empire Secondary
Cambodian Basin 300 300 0 Funan Secondary
Kansai 300 600 300 Asuka Japan Secondary
Central Java 800 800 0 Medang Kingdom Secondary
Niger Inland Delta 1000 1000 0 Ghana Empire Secondary
Iceland 1100 1300 200 Kingdom of Norway Secondary
Susiana −3800 −2200 1600 Akkadian Empire Annexation
Sogdiana −900 −500 400 Achaemenid Empire Annexation
Paris Basin −700 0 700 Roman Empire Annexation
Kachi Plain −500 −500 0 Achaemenid Empire Annexation
Deccan −300 −300 0 Mauryan Empire Annexation
Valley of Oaxaca −300 1600 1900 Habsburg Empire Annexation
Orkhon Valley −200 1000 1200 Khitan Empire Annexation
Cahokia 1100 1800 700 United States Annexation
North Colombia 1500 1600 100 Habsburg Empire Annexation
Ghanaian Coast 1600 1900 300 British Empire Annexation
Finger Lakes 1600 1800 200 United States Annexation
Lowland Andes 1600 1600 0 Habsburg Empire Annexation
Big Island Hawaii 1700 1900 200 United States Annexation
Southern China Hills 1700 1700 0 Qing Dynasty of China Annexation
Oro PNG 1800 1900 100 British Empire Annexation
Lena River Valley 1800 1800 0 Russian Empire Annexation
Chuuk Islands 1900 1900 0 Spanish Empire Annexation
Garo Hills 1900 1900 0 British Empire Annexation
Kapuasi Basin 1900 1900 0 British Empire Annexation
6Evolutionary Psychology

Western Europe during the fifteenth centuries. The key innova-
tion was the arrival in Europe of gunpowder and cannon, which
were invented in China much earlier (Chase, 2003). The turning
point when cannon became the game-changer was the 1450s. In
1453 the French ended the Hundred Years War by expelling the
English from the continent (apart from their last foothold of
Calais). This was accomplished by a compact French army
using siege artillery against the castles held by the English.
Three years later, in 1456, cannons were the key to the success-
ful conquest of Constantinople by the Ottomans. Other military
innovations rapidly followed: hand-held firearms, field artillery,
new fortifications (star-forts, or trace italienne), and new
methods of drilling soldiers and battlefield tactics. Equally
important were the contemporary advances in ship building
and sailing (Cipolla, 1965). The new ocean-going ships,
armed with cannon, enabled European exploration and con-
quest of far-flung territories. Because the sailing ship was so
important in transforming local European developments into
what became a global event, one of us has proposed that we
refer to this period of rapid evolution as the “Gunboat
Revolution”(Turchin, 2009).
Several military historians and historical sociologists have
advanced the argument that the Gunboat Revolution trans-
formed the scale of war and led to an increase in the authority
of the state (Mann, 1986; Parker, 1996; Roberts, 1956; Tilly,
1990). As Charles Tilly famously stated, “War Made the
State and the State Made War”(Tilly, 1990). This thesis was
not universally accepted (Black, 1995; Duffy, 1980); but cur-
rently most of the debate focuses on the particulars of how dif-
ferent aspects of social change, driven by intense interpolity
competition, played out in different European states. A recent
review concluded that the core argument has stood up well to
time (Kaspersen & Strandsbjerg, 2017).
Our research on the Cavalry Revolution provides another
well-documented example of how a novel military technology
can intensify interstate competition and result in rapid evolution
of social scale and complexity. Here’s how we see the sequence
of events. Around 1,000 BCE, nomadic herders in the steppes
north of the Black Sea improved the bit and bridle to the point
where it allowed effective control of horses when riding.
Shortly thereafter, thousands of metal bits suddenly appear and
spread within the Eurasian steppes and regions south of them
(Drews, 2004). The steppe pastoralists combined this technology
with a powerful recurved bow, which could be used from the
horseback, and iron metallurgy that gave arrowheads greater pen-
etrating power. Horse archers became the “weapon of mass
destruction”of the Ancient World (Turchin, 2009). In response
to this threat from the steppe, the agrarian societies, which did
not have access to plentiful supply of horses, were forced to
build large infantry armies, develop new armour, such as the
hoplite panoply (Drews, 2004), and new projectile weapons,
such as the crossbow which was used in China from the fourth
century BCE (Hui, 2005). They were further impelled to mobilize
more of their populations towards collective efforts to build and
maintain defenses, to produce and distribute enough goods to
keep the large armies supplied, and to develop increasingly
complex administrative systems to manage all of these moving
parts. Ideological innovations—leading to major world religions,
such as Zoroastrianism and Buddhism, as well as later
Christianity and Islam—helped to unite larger and more disparate
populations for such collective efforts (Bellah, 2011; Turchin
et al., 2021a).
Remarkably, although the new forms of horse-based warfare
spread to different parts of the Eurasian continent at different
times, the time lag between this development and the first
appearance of mega-empires was always 300–400 years
(Table 2). Apparently, this time period was necessary for the
selective regime of intense interpolity warfare to generate cul-
tural macroevolutionary change. The Gunboat Revolution
required a similar period of time to unfold. Whereas effective
gunpowder weapons and new sailing techniques appeared in
Western Europe in the fifteenth century, it was only between
1750 and 1850 when European empires had achieved world
dominance (Morris, 2010).
Given that our research has provided strong empirical
support for the idea that cascades of technological advances,
especially in the military sphere, are an important driver of cul-
tural macroevolution in the long run, it is legitimate to ask, what
drives the evolution of technological cascades? A recent study
by the Seshat project found that world population size, connec-
tivity between geographical areas of innovation and adoption,
and critical enabling technological advances, such as iron met-
allurgy and horse riding are strong predictors of change in mil-
itary technology, whereas state-level factors such as polity
population, territorial size, or governance sophistication play
no major role (Turchin et al., 2021b). What is interesting
about this result is that it again confirms the key role of interso-
cietal interactions: competition and exchange.
Conclusions
We started this article with a list of theories proposed by evolu-
tionary scientists to account for cultural change. Our review of
recent analyses using the Seshat Databank suggests that when
we focus on cultural macroevolution—large-scale changes in cul-
tural traits of whole groups—the most important evolutionary
mode involves inter-polity interactions, including competition
and warfare, but also cultural exchange and selective imitation.
These mechanisms, of course, are key components of cultural
Table 2. Relative Timing of First Appearance of Iron Metallurgy,
Cavalry, and Largest Mega-Empires (Defined as States Controlling
More Than 3 mln km
2
). The Dates are Rounded to the Nearest
Century Mark, Negative Dates are BCE. “Time lag”=Time Between
the Arrival of Cavalry in a Region and the Rise of a Mega-Empire There.
Eurasian Region Central Southern Eastern Western
Iron metallurgy −900 −1400 −300 −800
Cavalry −900 −600 −400 −400
Territory ≥3 mln km
2
−500 −300 −100 0
Time lag 400 300 300 400
Mega-empire identity Achaemenid Maurya Eastern Han Rome
Turchin and Gavrilets 7

multilevel selection (CMLS) theory (Richerson et al., 2016). On
the other hand, mechanisms of cultural change, proposed by evo-
lutionary psychologists, such as evoked culture and cognitive
niche, do not appear to offer a productive research agenda for cul-
tural macroevolution (although they may have utility for cultural
microevolution). We note that there are clear parallels between
the pattern of apparent stasis interspersed by rapid change we
observed in our data and punctuated equilibrium in paleontology
(Eldredge, 2009; Eldredge & Gould, 1972; Gould, 1994; Gould
& Eldredge, 1977). However the underlying mechanisms we
have identified here and those in biological macroevolution
(Eldredge et al., 2005) are different.
As we noted earlier, social scientists investigating specific
evolutionary transitions in human history, often propose theo-
ries that are very similar in spirit to CMLS. The core argument
of CMLS is that competition between societies, taking the form
of warfare, imposes a selection regime that weeds out dysfunc-
tional, poorly organized, and internally uncooperative polities,
while favouring those with larger populations and effective
institutions. This mechanism is implicitly, and often explicitly,
evoked by processual archaeologists studying the evolution of
first states and by military historians and historical sociologists
studying the rise of the modern state, as we reviewed above.
Our analysis operated above the level of individuals and
groups and did not consider the question of how societies
managed to successfully organize collective actions underlying
major social transitions. Behavioural economics, cultural evolu-
tion theory, and evolutionary psychology have identified a
number of possible mechanisms including rewards and punish-
ment, social norms, genetic or cultural relatedness, social institu-
tions and inculcation and propaganda which can lead to the
success of large-scale collective actions (Gavrilets & Richerson,
2017, 2021; McElreath & Boyd, 2007; Olson, 1965; Richerson
& Boyd, 2005; Singh et al., 2017). Interestingly, the importance
of collective solidarity for the emergence and persistence of states
and empires was already clear to the Middle Ages Arab sociolo-
gist, philosopher, and historian Ibn Khaldun who introduced the
notion of Asabiyyah to explain the cyclic nature of dynasties in
the Medieval Maghreb. Although there are some parallels
between the notion of Asabiyyah and modern notions of social
identity (Tajfel, 1981; Tajfel & Turner, 1979), identity fusion
(Whitehouse et al., 2017) and tight and loose cultures (Chua
et al., 2019; Gelfand et al., 2011 Harrington & Gelfand, 2014;),
much more work is needed on the psychological aspects of
groups solidarity, as well as historical data measuring it.
We emphasize that our conclusions have to be tentative.
Cultural evolution is a very young scientific discipline, and
we are currently at the beginnings of its theoretical-empirical
synthesis. There is a lot of work remaining, including refining
theories and models, and especially more data for testing theo-
retical predictions.
Acknowledgments
We thank L. Betzig for the invitation to contribute to this special issue.
PT was supported by the program “Complexity Science”of the
Austrian Research Promotion Agency FFG under grant #873927. SG
was supported by the U. S. Army Research Office grants
W911NF-14-1-0637 and W911NF-18-1-0138, the Office of Naval
Research grant W911NF-17-1-0150, and the Air Force Office of
Scientific Research grant FA9550-21-1-0217.
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to
the research, authorship, and/or publication of this article.
Funding
The author(s) disclosed receipt of the following financial support for
the research, authorship, and/or publication of this article: This work
was supported by the U. S. Army Research Office (grant number
grants W911NF-14-1-0637 and W911NF-18-1-0138, grant #873927).
ORCID iDs
Peter Turchin https://orcid.org/0000-0002-1292-8100
Sergey Gavrilets https://orcid.org/0000-0003-1581-4018
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