“Spiraling Coupled Human and Biotic Systems:

Microbial and Sociocultural Coevolution Since the Bronze Age”


Christopher Chase-Dunn (University of California-Riverside, Sociology)

Joel L. Sachs (University of California-Riverside, Biology)

Peter Turchin (University of Connecticut, Ecology and Evolutionary Biology)

Robert Hanneman (University of California-Riverside, Sociology



Dynamics of Coupled Natural and Human Systems (CNH)


Submitted to NSF CNH December 9, 2010

Start date: July 1, 2011. 48 months. End date: June 31, 2015


v. 12-6-10, 11950 words


Project Summary: This proposed four-year project will develop and test new dynamical models of human sociocultural evolution that are coupled with the evolution of human-associated microbial populations (pathogens, commensals and mutualists). It is well-known that complex human societies co-evolve with microbes, but we know much less about how and why that process occurs. This project examines the linkages over a millennial (6000 year) time scale. It will complement other research projects with longer and shorter temporal scales. It was during the past 6,000 years that human populations became connected across long distances and a substantial proportion came to live under highly crowded conditions in cities -- factors that promoted the emergence of more lethal and virulent pathogens. But how has the expansion of human-associated microbes affected the larger population of microbes? And what parts of human sociocultural and biological evolution have been due to the interaction with microbes? This project will develop original simulation models of these interactions and will test some of the implications of these models by building a database focused on nine world regions designed for this purpose.

Intellectual Merit: Human sociocultural evolution is somewhat similar to, but also importantly different from, biological evolution. This project focuses on the relationships between the emergence of complexity and hierarchy in human polities and the evolution of microbial populations. We examine how the expansion of the use of domesticated plants, animals and certain domesticated microbes (yeasts, etc.) combined with the emergence of greater human population density, transformations of landscapes and increased long-distance interaction to more strongly link and to speed up sociocultural, biotic and human biological evolution. The coevolution of humans and microbes has constituted both a spiraling competitive arms race and a symbiotic expansion co-adaptive feedback loops that have altered the fundamental nature of world regions and the whole Earthly biome. The proposed project uses an interdisciplinary critical Coupled Human and Natural Systems approach to draw upon knowledge from the social, ecological and biological sciences. The Co-PIs are specialists in evolutionary biology, ecology and sociology.

Broader Impacts: Microbes both cause diseases and provide important natural services for humans. Historically humans have influenced the evolution of microbes by modifying landscapes, using domesticated microbes (yeasts and certain bacteria) in the processing of foods, and by providing an expanded population of spatially concentrated megafauna (both humans themselves and farm animals) that serve as both exploitable resources for pathogens and habitat opportunities for commensals and mutualist microbes. In turn, microbes are constantly changing humans both biologically and culturally. An apparent speed-up in the rate human genetic evolution may be largely due to an “arms race” between microbial pathogens and the developing abilities of their hosts to resist virulent pathogens. Humans have also invented institutions that are designed to protect against infectious diseases and to harness the energies of mutualist microbes.  Better understanding of the coevolution of human social structures, human biological evolution and microbial populations will have important implications for public health policies and for improved public awareness of the important links between human and microbial systems. Better understanding of historical domestication of microbes will also provide useful insights about the increasing human manipulation of biotic evolution by means of biotechnology. Interdisciplinary communities of researchers functioning at the interface of the natural and cultural sciences (biology, ecology, sociology, geography, history, mathematics, etc.) as well as policy-makers stand to benefit from insights gained through these studies.  The project’s Biome and Anthrome World Regions (BAWR) data set will be of use to other researchers and educators investigating the patterned changes in world history.

 Project Description

This project will produce three formal models:

1.   The dynamics of the evolution of microbes in world regions over the last 6000 years; and

2.   the evolution of sociocultural systems in world regions since the emergence of cities and states, and

3. a combined model of the coevolution of microbes and sociocultural systems over the last 6000 years.

These models will be developed as computer simulations, but we will also distill the major feedbacks contained in the models into simpler formulations that can be investigated analytically. Although each of these components has been well-investigated, there is a paucity of studies that integrate these models in a coherent and transparent manner.  How have the expansion of human habitation and the development of complex polities affected the ecology and evolution of human-associated microbes and otherl microbial populations?

            The synthesis of the critical elements of these models and the identification of patterns that emerge from the human/microbial coevolution in the context of world history is unique to our project.  In addition the project will generate an historical quantitative data-set designed to test these models. The data-set will contain quantitative estimates of the values of many of the variables in the models for nine world regions over the past six thousand years – the Biome and Anthrome World Regions (BAWR dataset). Implications of the models will then be tested with the quantitative data.


Christopher Chase-Dunn:  SES-0350819 “The Social Foundations of Global Conflict and Cooperation:  Waves of Globalization and Global Elite Integration, 19th to 21st Century” (with Thomas E. Reifer) Amount: $152,312 PERIOD: April 1, 2004 through May 31, 2006. 

Christopher Chase-Dunn and Peter Turchin: NSF-HSD SES-0527720  Award type: HSD-AOC. “Global state formation”. Amount: $450,000 PERIOD: October 1, 2005- August 30, 2009. This project studied the growth/decline phases and upward sweeps of cities and states.  It has produced nine published journal articles and eleven book chapters. Four more journal articles are under submission.

Joel L. Sachs. NSF - DEB 0816663  “The evolution and origins of uncooperative rhizobia”  Amount $250,000 PERIOD July 1 2008 -  June 30, 2011.


This project employs a critical version of the Couple Human and Natural Systems (CHANS) approach to evolutionary human and natural ecology (Liu et al 2007). Our approach to CHANS is critical because we are wary of the assumptions of equilibrium and resilience that are often implicit in functionalist ecology and sociology (e.g. Gotts 2007). But we agree whole-heartedly that the systematic and historical linkages between natural and human systems need to be scientifically understood. The usual cautions against teleology and non-scientific assumptions about “progress” need to be reaffirmed.

          In everyday language the word evolution is usually assumed to mean biological evolution – changes in the genetic make-up populations by means of natural selection. We also use the word evolution in connection with the analysis of patterned changes in human institutions, organizations and polities. Human polities have evolved from small nomadic hunter-gatherer bands to large states and empires with complex divisions of labor and great hierarchies, and to the modern nation-state and the contemporary global political economy. The main differences between sociocultural evolution and biological evolution are:

·                         the nature of what evolves (population gene pools vs. institutions, organizations and technology, and

·                         the emergence of symbolically-mediated reflexive innovation in sociocultural evolution.

Within sociocultural evolution human polities have become larger, more complex and more hierarchical as institutions, organizations and technologies emerged that made it possible for humans to live in larger and larger settlements. Moreover, sociocultural evolution is generally much faster than biological evolution because innovations can be communicated across polity boundaries much more quickly than adaptive genes can alter the gene pool of a population. An exception to this is the case of rapidly reproducing life forms such as most microbes. Bacteria not only reproduce quickly, but they also share genetic information by passing genes from one cell to another. The relative temporalities of innovation and adaptation are important for understanding coupling and coevolution.  The term coevolution will be understood here to include both competitive and cooperative developmental processes. An arms race between parasites and hosts is competition, but it also drives co-adaptation because those species that can increase fitness by means of symbiosis have an advantage. Both competitive “arms races” and co-adaptive development are important forms of coevolution. [1]

           We employ the demographic-structural (Turchin and Nefadov 2009) and comparative world-systems perspectives on human sociocultural evolution (Chase-Dunn and Hall 1997). We also consider human genetic change during the last 6000 years because it has been an important factor in the connection between microbes and culture. Many social scientists have assumed that human biological evolution slowed or stopped because it was supplanted by sociocultural adaptation. Sociocultural institutions are often assumed to protect humans from the forces of natural selection. While we agree that human institutions have been designed to do this, they have often failed. And sociocultural institutions themselves affect which humans survive and reproduce as well as the fitness of domesticated microbes, plants and animals.

Analysis of new genome sequence data has revealed that humans have undergone a dramatic acceleration of genetic change in the last several thousand years. The new source of data is the Human HapMap project, which has compiled and made public millions of genetic mutations across four diverse human populations (Altshuler et al. 2005). Genetic loci in the human genome that have undergone recent adaptive evolution include genes for skin pigmentation (Williamson et al. 2007), hair morphology (Mou et al. 2008) and lactose tolerance (Bersaglieri et al. 2004) to name a few. Major changes in population size, biogeographic distribution and diet of human populations are alleged to have driven these adaptive evolutionary changes (Hawks et al. 2007; Pickrell et al. 2009). We hypothesize that much of this speed-up of human biological evolution has been a response to the coevolution of microbes and to the effects of sociocultural evolution.

            This proposed project will occupy a relatively unpopulated niche between very long-run CHANS studies of the relationships between humans and microbes (e.g. Aufderheide et al 2004; Buikstra and Wilbur 2005; that use archaeological evidence, and studies that use the gigantic amounts of temporally and geographically fine statistical data available for 20th and 21st century decades, especially on mortality and morbidity due to epidemic diseases. We are developing theoretical models and a data set that focus on the millennial time scale during which humans developed cities and states that produced documents, but not systematic population statistics – the last six thousand years before the First World War (1912 CE).  Our models are designed to apply to geographical regions that are composed of ecological biomes and expanding human-modified landscapes (called “anthromes”) (Ellis et al 2010). And one of the nine world regions we will study is the whole globe as a single “region” between 1700 CE and 1912 CE.

MODEL DEVELOPMENT: We will develop three system simulation models: a model of genetic and ecological change of microbes; a model of human sociocultural evolution; and a single dynamic feedback model that links sociocultural and microbial evolution.

In order to keep the reader’s attention we will present the linkage model first, later followed by the microbial and sociocultural models.

1.    Coupled Microbial and Human Sociocultural Evolution

After developing separate systems models for sociocultural and biotic evolution (see below and Figure 1) the project will link them by theorizing the main interactions between humans and microbes within regions on a millennial time scale.  We will also consider the ways in which human genetic change are likely to mediate between sociocultural evolution and the biological evolution of microbes.  Microorganisms are vital to humans and the environment, as they participate in the global carbon and nitrogen cycles, as well as fulfilling vital roles in virtually all ecosystems, such as recycling organic waste and metabolizing compounds that other organisms cannot. Microbes also have a central role as symbionts to all animals and plants.

Major changes in human civilization, including the invention and growth of agriculture, the domestication of animals, urbanization, industrialization and the recent scientific revolution can explain major patterns in the appearance, spread and disappearance human infectious diseases (Blaser 2006). Human and microbes co-evolve, in some cases so intimately that historical human migrations can be inferred by reconstructing the recent evolutionary history of human-associated viruses and bacteria (Wirth et al. 2005). Moreover, evolution in our indigenous microbial populations (gut flora, etc). has driven some of the recent changes in human traits, including increases in mean height (Beard & Blaser 2002) and weight (Blaser 2006).

         The connections between humans and microbes are both intentional and unintentional, and the unintentional connections include pathogenic exploitation, symbiosis, mutualism and commensalism.  Intentional use of microbes by humans during the time period of our study includes uses in food, science and warfare.[2] Microorganisms have long been used in baking, brewing, winemaking, pickling and other food-making processes. They have also been used to control the fermentation process in the production of cultured dairy products such as yogurt and cheese. The cultures not only provide flavor and aroma, but they also inhibit the reproduction of other undesirable organisms. Microbes have also been used as subjects of, and tools for, science since the invention of the microscope. During historical warfare diseased corpses were thrown into castles during sieges using catapults. And smallpox-contaminated blankets were given to indigenous peoples to hasten their demise. 

            It has been mentioned above that the relative rates of evolutionary change are important for understanding coevolution. The rate of sociocultural evolution has speeded up, and it has always been faster than the rate of biological evolution of megafauna.  But microbes reproduce faster and so they evolve much faster than humans do. Bacteria can also evolve rapidly because of their ability to pass genetic material from individual to individual. They do not need to rely solely on the inheritance of traits from parent to offspring. Adaptive mutations can be transferred directly to others.

            The process of domestication is one of the most important linkages between sociocultural and microbial evolution.  Human hunter-gatherer bands domesticated the dog at least 12,000 years ago.  The domestication of plants for horticulture began about 11,000 years ago in the Levant, in the same region of the Levant where the first year-round settlements of diversified foragers had emerged one or two millenia before. This was then followed by the domestication of other animals (rabbits, chickens, pigs, goats, cattle, equines).  The use of yeasts for baking and brewing emerged along with the intensive collecting and planting of grains.

Domesticated plant and animal species often live close to humans. The concentration and expansion of these forms of domesticated life has constituted an opportunity for microbes. And sometimes the microbes that thrive in such contexts, especially those that use animal domesticates, can move from the animals to the humans (zoonosis).  As population densities have risen with sociocultural evolution, more and different mutualist and parasitic microbes have become endemic in human and human domesticated populations.  Microbial parasitism of humans is well studied, but only recently has the extent of mutualism and commensalism become widely known. The number of humans has risen exponentially, providing huge resources for those mutualist and parasitic microbes that inhabit humans themselves or human artifacts such as domesticated animals, cities, cropland, or bodies of water that are created or polluted by humans.

Infectious diseases that drive human morbidity and mortality have been important throughout human biological evolution, as they are for other megafauna. But since the birth of cities, states and agriculture the emergence of pathogenic microbes has speeded up and they have become more virulent. Increasingly pathogenic microbes have developed particular characteristics such that they produce short-term, acute illnesses that are transmitted quickly and efficiently. These characteristics would not have been favored in populations of microbes that infected isolated, sparse human hunter-gatherer populations. Modern pathogens have evolved along with the emergence of the environment of a dense, crowded, and large-size human population where there are lots of hosts available for infection. 

Humans, crops and domesticated species have all experienced great increases compared to the species that have disappeared in the wake of habitat loss. So it is likely that humans are favoring microbes/microbial traits that are adapted to large populations and to highly interconnected populations. Both of these factors favor pathogenicity and virulence among human-associated microbes (Alizon, Hurford, and Van Baalen   2009; Greger 2007).

Most microbes are thought to be specialized on a single host or a small subset of hosts. Hence, one major effect of human expansion has been that some microbial species have greatly increased their population sizes (microbes that live on humans, crops, domesticated animals, weeds, etc). Many other microbial species have gone extinct (along with their hosts). The favored microbes are different from those that have gone extinct with respect to their level of dependence on human-generated resources. But they may also be different in other respects. Are the characteristics of the microbes that have expanded (using humans and human resources) systematically different from the ones that have gone extinct? Have human-associated parasites and mutualists, on the average, become somewhat simpler in terms of the size of their genomes because they have shed functions that have come to be carried out by their hosts? There are examples of both increased and decreased genomic complexity among the human-associated microbes, but is there an overall trend toward less genomic complexity?  And has the whole universe of microbes become less diverse because of the expansion of human-related microbes and the extinction of microbes living in ecologies that have been transformed by humans? As humans have simplified the ecologies of regions by transforming wild biomes into monocrop anthromes, have the emergent microbe strains become super-tough, as are many of the human-associated non-domesticates in the macro-sphere (e.g. starlings, rats, house mice and dandelions)?

And has the overall biomass of microbes increased as humans have expanded opportunities for some? Humans have increased the total amount of energy available because we have harvested ancient sunlight in the form of fossil fuels and we have added a lot of water to habitats that were formerly dry through irrigation.  Or have the expansions been counter-balanced by the extinctions such that the microbial biomass remains about the same size that it was before the human expansion? Is the microbial biomass significantly larger than it was 6000 years ago?

The microbial evolutionary rate has almost certainly sped up because of the interaction with humans. There is strong evidence of an evolutionary arms-races that has occurred between microbes and hosts in which each is selected to evolve faster than the other in order to maximize fitness (Bersaglieri et al 2004; Holden et al 2004). And this arms race with microbes may be one of the main factors behind an apparent speed-up in human genetic change over the last few thousand years (Hawks et al. 2007). And this spiral may have directly or indirectly contributed to the acceleration of human sociocultural evolution and vice versa.

Obviously killer diseases like the fourteenth century plague epidemic across Eurasia selected for resistance very quickly (McNeill 1976; Abu-Lughod 1989).  The population structure was quite different in 1360 than it had been in 1340 because the mortality was around 30% in many areas. Massive, rapid selection by waves of smallpox, cholera, dysentery, and etc. has also constituted a strong selective pressure. But we are talking a very specific form of selection --relative resistance to infectious diseases. This is not necessarily "human evolution" in the long-term sense of bigger brains. Cochrane and Harpending (2009) claim that cognitive and emotional capabilities have also evolved recently and that these differences account for the uneven development of the peoples of different regions. Though we are not able to do original research on human genetic change, we can consider the extent to which the patterns of development that we find within and between regions are consistent with different models of human genetic change.   

Just as environmental psychology explains the architecture of human habitats by taking into account the psychological needs of individuals to be protected from too much exposure to other humans (Fletcher 1995), so can our knowledge of disease transmission be used for explaining the architecture of monuments, residential buildings and the emergence of highly structured and planned settlements.  Human social hierarchies symbolize power with monuments and the production of order, but elites are also responsible for protecting non-elites from pestilence as well as other things. Elites that fail to do these things expose themselves to resistance and attack.

The structure of human settlements has come to include investment in and centralized planning of water systems that have been designed to provide clean water to elites, and increasingly to non-elites. And the same may be said of sewer systems that are designed to reduce pollution. There were substantial public health measures in most of the cities of the ancient world—aqueducts, sewers, organized street and canal cleaning, regulation of garbage disposal, hospitals, and planned drainage systems. It is true that public health institutions only dramatically reduced morbidity and mortality in the 19th century. Many ancient cities were considered to be demographic sinks where people go to die because of the high rates of infectious diseases for infants, children and recently arrived migrants who had not been exposed to these diseases as children in their home places. But elites built elaborate water and sewage systems for themselves and their families, and occasionally for the use of the general public in the Bronze Age. Elites also protected themselves from exposure to diseases by limiting interaction with non-elites, controlling access to certain central areas (temples and palaces) within cities and escaping to rural redoubts when epidemics arrived. There we also efforts to obtain intelligence from connected ports and to quarantine ships from places known to be experiencing epidemics. Thus well before the 19th century epidemic diseases provided selection pressures in favor of those polities that could reduce exposure to epidemic diseases. The invention and diffusion of urban planning, gridded streets, square-walled cities, organized water provision and organized sewage disposal partly resulted from the pressure of pathogens on human populations.

The topologies of human interaction networks have important implications for the coevolution with pathogens.  Before the emergence of cities the human population lived in widely-separated and infrequently interacting dense, but small clusters.  In a small and densely clustered human population, a pathogen that is too deadly will quickly destroy the critical mass of humans it needs to sustain itself.  Since these populations interact infrequently, the probability that such a pathogen could spread widely is small.  As human populations expanded, a power-law (log-normal) distribution of settlement sizes emerged along with “small-world” interaction network patterns.  In such a situation a disease can emerge that is more deadly because it can move among population clusters to sustain itself.  We hypothesize that pathogens evolved toward more destructive forms once large cities emerged and city systems became organized into small world network topologies (indicated by a log-normal distribution of city and town sizes).The developmental pattern of city size distributions is for a large capital city to emerge based on the ability of an emergent state to gather taxes and tribute from a wide area, and then for middle-sized towns and cities to emerge as the economy becomes more complex and integrated (Rozman 1973).

The density and structure of populations that will produce epidemic spread have a critical region between some lower and upper bound that depends on the infectiveness and deadliness of the pathogen [see, for example, the discussion in Watts (2003: Chaps 5-8)].  As human population densities increased, and settlement size distributions became more hierarchical, and then more log-normal, the selection pressures for pathogen attributes also changed. Those pathogens that did not evolve to seize the new opportunities were destined to remain local, if endemic. Others became capable of attaining the necessary levels of infectiveness and mortality to be suited to the structure of the expanded and denser human population.

The “big-city” hypothesis claims that the mortality rate from infectious diseases should be higher in the largest cities. At some point human settlements became large enough to act as perennial reservoirs of infection, sustaining endemic populations of lethal pathogens. William H. McNeill’s (1977) path-breaking world historical study of Plagues and Peoples during the last two millennia theorized that urban residents in crowded and unsanitary districts had suffered high mortality in childhood but that the survivors acquired a degree of immunity. So the main pools of those susceptible to infection were the young and also recent immigrants from less dense rural areas or smaller settlements. McNeill also surmised that the mortality rates in the cities would be high, especially among the young, but also fairly stable since the pool of susceptibles to epidemics was limited. Rural regions and towns were thought to be too thinly populated for endemic lethal infections to persist and so they would experience lower background mortality rates, but they would also have less immunity and would be more vulnerable to epidemic episodes. McNeill’s characterization of the disease selection regime was inspired by the threshold theorems of infectious disease modelers that relate the size and spacing of epidemics to the densities of susceptible hosts (e.g. Kermack and McKendrick 1927; Black 1966).

Cliff, Haggett and Smallman-Raynor (1998) used mortality data from 100 cities around the world that had been gathered by the U.S. public health officials from 1888 to 1912 to test the big city hypothesis. They examined overall mortality rates and the rates of six different infectious diseases. Their results show the largest cities in both the developed and the less developed world had lower mortality rates than did smaller cities. They also found that overall mortality rates did not decline during the period they studied. It is possible however, that McNeill’s characterization of the selection regime of pathogenic diseases was correct for the period he was studying, before the industrial revolution and the emergence of modern public health measures. We will use our data on world regions to test some of the implications of McNeill’s model.

Biotic evolution and the genesis and dynamics of epidemic diseases:

Our project will study the causes of biotic genetic and population change on a millennial time scale (the last 6000 years) by modifying existing system models that explain population growth and the evolution of new types of microbes using relationships with resources, selection pressures, population pressures on resources and migration into new niches. Our study of microbial evolution will also follow the bioinformatics research on microbial metagenome sequencing and phylogenetic analysis.

            A general microbial model will be developed using well-understood models of host-associated microbes. While most of these models do not deal directly with the evolution of the characteristics of general microbial populations, they can be adapted for our more general purpose.

The basic population growth model for host-associated microbes is as follows:

Ro =   (bvN) / (d + v + c(v))     (Anderson & May 1982;  adapted by Frank 1996)

Ro       Parasite fitness (rate of population expansion)

b          Transmission rate of disease (with contact between infected, uninfected infected individuals.

N         Host population size

d          Host mortality rate (disease free)

c          Host recovery rate

v          Parasite virulence (increase in host morbidity, mortality)

       The general conclusions that can be drawn from parasite models are as follows (Frank 1996) :

·         Parasite fitness is increased when there is a high transmission rate among hosts.

·         Parasite fitness is increased when host population size is large.

·         There are often tradeoffs between virulence and transmission rates to new hosts.

·         Parasite virulence increases when transmission rate to uninfected hosts is high.

·         Parasite virulence decreases when transmission rate to uninfected hosts is low, or when transmission occurs between parent and offspring (the later case can cause a shift to mutualism).

·         Parasite virulence increases when hosts are infected by multiple parasite strains (because parasites that use up host resources quickly will outcompete other genotypes within the host).

Modeling the dynamics of epidemic diseases:

The pathogens that produce epidemic diseases spread and evolve in interactions with human and animal populations in response to climate and environmental changes. We employ a community ecology approach to understanding the development of pathogenic microbes.  Epidemiological thresholds (e.g. R0)[3] are used to indicate when a pathogenic disease can invade an otherwise disease-free population.  Since information on the actual occurrence of specific diseases is generally unavailable, one approach is to determine when specific environmental conditions and social situations occur to give rise to various modes of transmission and mortality levels. Probabilities will be assigned to the incidence of various pathogens according to these conditions.  Occurrence of diseases that meet the epidemiological thresholds can be determined stochastically. If R0 is too high, the disease goes extinct. Otherwise it spreads through the population.  As human populations grow, fights wars, and changes the climate (anthropogenic climate change), they hit one threshold after another. The size and intensity of epidemics can also be estimated stochastically from the power law distribution.

Generic epidemiologic models (e.g. SIR, SIS, SIRS; see Anderson & May, 1991; Hethcote, 2000; Diekmann & Heesterbeek, 2000) will be used to develop ecological models according to conditions. Mechanisms and structures that need to be considered, are varying seasonal effects across disease types and and different vectors that affect transmission. The time scales of different diseases are important. Fast cycles (1-3 years, e.g. measles) are buffered by the much slower rates of change of human populations and so for our long-term models they can be averaged over decades. Political instability and warfare (movements of armies and refugees), migration, transients (traders and other mobile groups), have also been important factors in the spread of pathogens and the occurrences of epidemics. This will be a side model on the interaction of population mobility and disease dynamics that will help us to estimate the thresholds for the shift from endemic background diseases to epidemics.

Epidemics will be modeled as a function of population density and mobility, instability (internal conflict), warfare and climate change. Climate and environment impact not only human diseases directly but also zoonosis as well as agricultural and livestock pathogenic diseases, and there is synergy between well-being and disease resistance and recovery. 

We will also study endemic diseases (e.g. TB, dysentery, STD’s, etc.). Some of the short-term cyclical epidemics can be considered endemic when averaged over decades. Shifting levels of endemic diseases can also be affected by climate, demographics and population mobility on a slightly longer time scale.

Disease dynamics are one area where the fast time scale makes it possible to reduce the complexity of models.  Rather than including all of the above explicitly in the higher level model, more detailed spatially-explicit side models can be used to produce parameters and relations that are included as forcing functions that summarize overall mortality indexed by specific conditions and demographic factors (age, class, etc.).  The differences in disease dynamics between rural and urban areas may require maintaining distinct spatially explicit epidemiologic building blocks.  But information about the population density can be input to one summary function to capture the essential features.

Pestilence and disease dynamics of domestic crop and animal populations will also be modeled as above and will be a feedback to the human population when they destroy the harvest, cull the livestock, or serve as a reservoir for human diseases.

The demographic-structural and ecological world-systems evolution model

 Our models of sociocultural evolution include feedbacks among population growth, political instability, and epidemics within polities and competition and cooperation between interacting polities.  We conceptualize the dynamical effects, including feedbacks, between population, popular well-being, internal and external warfare, disease, climate, state formation and collapse, the rise and fall of empires, the growth and decline of cities and the evolution of institutions and organizations that permit polities and world-systems to become larger and more complex.  We also include the spatial and architectural organization of cities, the expansion of the use of domesticated plants, animals and microbes, changes in land-use, the building of irrigation and transportation systems, trade routes and links among cities and food processing activities that use fermentation.

a) The single polity level: three primary factors that interact endogenously

i). Population dynamics: logistic with K (carrying capacity) a function of technology and climate. For some regions and historical periods we have the data to derive numerical values of parameters (for example, for Western Europe during the medieval and early modern periods, see Turchin and Nefadov 2009: Appendix to Chapter 3).

ii). Political instability and internal war: In the demographic-structural model population pressure on resources produces conditions that cause state collapse and civil war. Important intervening variables are elite overexpansion and state fiscal troubles. We can model the effects of population pressure by introducing time lags.  For example elites are likely to grow fastest when resource surplus is at a maximum. But elite power and income are at a maximum when non-elites (labor) are near carrying capacity. There will be an oversupply of labor and so profits (going to elites) rise as costs drop. Thus social pressures on each group lag the changes in economic fortune that in turn lag behind the changes in population.  The changes in the different classes follow one another and class resentments build until mechanisms are instituted (or occur exogenously) to dissipate, or suppress the situation (see Table 1), or it blows up. When a situation reaches a critical point, the distribution of resources is unacceptable and social solidarity (asabiya[4]) across classes or groups has disappeared. Then internal conflict (civil war or revolution) usually results. This adds to the population death rate and further degrades the environmental carrying capacity ( K).

iii). Endemic and epidemic diseases (discussed above).

b). There are two partially exogenous factors that impact upon demographic structural cycles within a single polity:

i). Climate change acts as a driving function in the models.  For Central Asia, data from modern times on climate, and it’s relation to water availability, agriculture and herds, human population fluctuations and carrying capacity can be modeled and then applied to deduce populations and carrying capacity in the past (Kradin 2000).  Similar relationships can be drawn for other areas taking account of changes technology, agricultural practices and public health & medicine where possible. Although climate changes are often exogenous, there are some local anthropogenic effects on microclimate that can affect carrying capacity -- overgrazing, forest clearing and resulting erosion or desertification affect rainfall and temperature.

ii). External war: Warfare is partly exogenous, though it may be affected by climate change, both exogenous and anthropogenic. And elites that are threatened ofter go to war to try to get resources or just to divert attention from internal problems. And failure in warfare is a well-known cause of state collapse.  Especially in the Chinese case, where external war was largely due to incursions by Central Asian nomads, there could be interesting climatic effects. For example a period of favorable climate followed by a few bad years may encourage military innovation and invention, particularly when states on the metaethnic frontier[5] are (or are perceived as) weak. In addition internal wars, driven by fighting over scarce resources, can speed up this process. For example lessons learned from the Peloponnesian and the Corinthian Wars led to developments which eventually allowed Alexander to conquer the Persian Empire, central Asia, and north-western India. The French revolution produced the superior military tactics of Napoleon. And tribal fighting in Mongolia produced the armies and organizational skills of Genghis Khan. Like Climate change, there are internal causes of the likelihood of a polity to go to war. Elites under pressure often find external scapegoats and this can lead to interpolity conflict. While it is sometimes exogenous when dealing with a single state, external war becomes endogenous when modeling all players involved in the conflict (see below)

  c). Here are the main feedback loops postulated by the demographic-structural model. All have been formalized as a system of differential equations:

i).Population growth results in oversupply of labor and higher demand for food.
ii). Elites, who employ labor and own lands that produce food, benefit from these 
              scissor effects[6].
iii). Elite numbers and appetites (consumption levels) expand. 
Numbers expand as a result of:
 (a) Heightened elite reproduction and 
 (b) Enhanced upward mobility by non-elites
iv).            Eventually, growth of elite numbers and consumption levels leads to elite overproduction.
    v). Elite overproduction puts a strain on state resources. Intra-elite competition and factionalization result.
vi). All these factors cause increase in sociopolitical instability, which eventually results in an outbreak of civil war.
vii).Instability causes population decline and more importantly, eventually takes care of elite overproduction. Population starts growing and a new cycle ensues.

The multipolity (world-system) level adds system-wide interpolity warfare, trade and synchronized cycles of growth and decline across distant regions due to the spread of epidemics and similar climate changes. Circumscription emerges when migration fills the landscape up with inhabitants. This increases interpolity warfare which acts as a demographic regulator. Interpolity trade also emerges and some societies specialize in long-distance trade. We will develop both compartmental and spatially explicit models that represent the interpolity interactions.

 The iteration model of world-systems evolution (Chase-Dunn and Hall 1997: Chapter 6) depicts the processes that cause polities to become larger, more complex and hierarchical.  It is called an iteration model because its overall structure is a positive feedback loop that explains the long-term growing scale of human societies and world-systems.[7]  But within the overall positive feedback loop there is a smaller negative feedback loop that comprises the human demographic regulator based on resource extraction and interpolity warfare and leads to resource scarcity and environmental degradation (Fletcher et al 2010). [See Figure 1 below].

Trade leads not only to exchange of resources, goods and services but also to the exchange of ideas and innovations (e.g. McNeill and McNeill 2003). Interpolity trade not spatially binds regional world-systems and is important for generating innovations at network nodes that are an important cause of systemic evolution. Political-military interaction among states also generates a cyclical rise and fall of empires, and occasional upward sweeps of empire size (Alvarez et al 2010).  Epidemics and non-anthropogenic climate change are included in the model as well.  We note that anthropogenic climate change (e.g. due to deforestation, etc.) is part of more general category of environmental degradation.  Chase-Dunn and Hall (1997) note that it is semiperipheral polities out on the edge of old core regions (newly founded states or city-states specializing in trade) that are often the agents of systemic transformation.[8]

Long periods of intense conflict within and between societies lower the resistance to empire formation.  A semiperipheral marcher state can “roll up the system” under such circumstances.  Thus did the Neo-Assyrians, the Achaemenid Persians, Alexander, the Romans, the Islamic Caliphates and the Aztecs produce the core-wide empires that constituted the great upward sweeps of empire size (Alvarez et al 2010.

During the Bronze and Iron Ages expansions of the tributary empires, a new niche emerged for states that specialized in the carrying trade among the empires and adjacent regions.  These semiperipheral capitalist city-states were usually “thalassocratic” entities that used naval power to protect sea-going trade (e.g. Dilmun, the Phoenician city-states, Venice, Genoa, Malacca), but Assur on the Tigris, the “Old Assyrian city-state and its colonies,” was a land-based example of this phenomenon that relied mainly upon donkey caravans for transportation (Larsen 1976).  The semiperipheral capitalist city-states did not typically conquer other states to construct large empires, but their trading and production activities promoted regional commerce and the emergence of markets within and between the tributary states.

The expansion of trading and communication networks facilitated the growth of empires and vice versa.  The emergence of agriculture, mining and manufacturing production of surpluses for trade gave conquerors an incentive to expand state control into distant areas.  And the apparatus and infrastructure of the empire was itself often a boon to trade.  The specialized trading states promoted the production of trade surpluses, bringing peoples into commerce over wide regions, and thus they helped to create the conditions for the emergence of larger empires.

The emergence of states and markets directly articulated population pressures to produce new technologies and organizational forms. But these adaptive institutional mechanisms have are eventually overwhelmed as population and resource pressures continue to increase, causing the whole system to head back toward the Malthusian corrections of the nasty bottom.


Figure 1: Conceptual Diagram of Causes Within and Between Three Realms

An innovation of the proposed research is that, where humans are part of evolution of microbial populations, the impacts of human factors are also dynamic -- the human biology is, itself changing, thus impacting the role of humans in microbial evolution.  Human socio-cultural activity (population sizes, densities, interaction patterns) also affect microbial dynamics by modifying the environmental selection factors that operate.  Human biological and sociocultural evolution are both largely, but not entirely exogeneous to microbial evolution.

Similarly, human biological evolution and sociocultural evolution dynamics have some formal theory and model development. Models of these dynamics, however, usually treat sociocultural institutions and microbial populations as stable (if they treat them, at all).  Interactions with microbes is one factor impacting human evolution; socio-cultural patterns affect patterns of interaction and reproduction among humans, and hence the direction and pace of human evolution.  Again, density, scale, hierarchy, and network structure in human populations may impact human evolution by affecting the mixing rates and also fitness of offspring -- modifying human biological evolution.

General models of multi-population dynamics and, to varying degrees, population evolution are somewhat developed in each of the three main spheres shown in Figure 1.  But most existing models in each of the three main spheres usually assume that the others are not relevant, or static.  However, each sphere shapes each of the others by modifying reproduction dynamics, and selection on the basis of fitness of each of the others.  Formal models of these types of dynamics are, to date, much less developed. Our project will not develop new formal models of human genetic change, nor will we be able to incorporate data on recent genetic change into our project data set. But we will stay abreast of the growing literature on recent human genetic change in order to consider its relevance for the results of our research.


The Biomes and Anthroms World Regions Data Set

The following sections describe the structure and format for a geo-chronological dataset that will focus on the biotic, landscape, food processing and consumption, demographic, political, climate change and epidemiological aspects of settlements and polities in several world regions over the past six millennia.  The methods developed by Ellis et al (2010) for estimating the size and location of biomes (wild areas) and anthroms (landscapes that have been significantly modified by humans) will be used to map our world regions over the time periods covered for each. The main purpose of this dataset will be to determine the dynamical effects, including feedbacks, between microbes, population, popular well-being, internal and external warfare, disease, climate, and growth/decline phases of polities and settlements and the evolution of larger and more complex human polities.

The Biomes and Anthromes in World Regions (BAWR) dataset will be established and maintained by the Social and Biological Historical Evolution Research Working Group at the Institute for Research on World-Systems (IROWS) at the University of California-Riverside in collaboration with colleagues at other universities. The dataset will be edited by Kirk Lawrence (kirk.lawrence@email.ucr.edu). This dataset will be made available for public usage.  The dataset will link with the World Historical Dataverse at the University of Pittsburgh http://www.dataverse.pitt.edu/

Dataset Format : The proposed dataset will use CSV datafiles that will be stored on IROWS web site[9] at the University of California-Riverside. Though a relational database that links settlements, polities and regions with time periods is very useful, this project will present the public version of the data in spreadsheet format that is more broadly useable by other researchers, and can be easily transferred into relational databases if so desired.

Dataset Structure: The BAWR dataset will include historical and prehistoric quantitative estimates of several demographic, political, climate and epidemiological characteristics of settlements and polities. The characteristics will be grouped into several world regions.  Additional world regions can be added if quantitative estimates of the main variables are located.  An effort will be made to use the same or similar metrics across world regions, but in some cases this may not be possible.

World Regions and the Central Political-Military Network

Nine world regions will be studied: Mesopotamia, Egypt, West Asia-Mediterranean, Europe, South Asia, Central Asia, East Asia, Japan, and the whole Earth as a region from 1700 CE to 1912 CE.[10] Each region’s data will be contained in a single Excel worksheet, with a row for each year of data.  Although the spatial boundaries of each of these world regions will be held constant over time, there will be another spreadsheet that incorporates them as a group as they coalesce in the Central Political-Military Network (PMN)[11], defined by David Wilkinson(1987) as the regional system that was created when the Mesopotamian and Egyptian PMNs merged around 1500 BCE. The characteristics of individual settlements and polities within each of these world regions will be coded. We will focus on the largest settlements and polities in each region and on the size distributions of large and small settlements and polities and on important smaller settlements and polities, such as city-states.


The main variables around which the BAWR dataset will be constructed are the population sizes of settlements and the territorial sizes of polities.  Every effort will be made to use the same kinds of measurements of variables across the different world regions in order to maximize comparability for cross-region comparisons.  Each world region will have a work sheet for settlements and separate work sheets for polities, climate change, landscapes, diet and epidemic diseases.

The general framework of the variables for each world region is as follows:

The BAWR variables:

I.      Settlements

(1)      Year (a single year, BCE indicated by negative sign, e.g. -3250 = 3250 BCE)

(2)      Period (period of years, e.g. -3250)

(3)      Region (e.g. Mesopotamia) or PMN (e.g. Central System)

(4)      Settlement Name  (e.g. New York)                        

(5)      Alternative Names (e.g. New Amsterdam)

(6)      Built-up Area of the city (hectares)

(7)      Areal and Population Sizes of residential area (house or hearth counts) 

(8)      Population size estimate of the whole settlement

(9)      Longitude of settlement center

(10)    Latitude of settlement center

(11)            Internal conflict

(12)            Involvement in warfare

(13)         Epidemic disease

(14)   Urban planning: rectangular walls, street grid

(15)   Organized drainage or sewer system

(16)   Organized fresh water system: aqueduct, public and restricted wells, etc.

(17)   Extensive use of fermentation in food processing

II.    Polities

(1)      Year  (a single year, BCE indicated by negative sign, e.g. -3250) 

(2)      Period (period of years, e.g.  -3250 to -3225)

(3)      Region (e.g. Mesopotamia) or PMN (e.g. Central System)

(4)      Name of polity                    

(5)      Alternative names

(6)      Territorial size of polity (square megameters)

(7)      Total population size estimate

(8)      Capital city:

(9)      Longitude of center of capital city

(10)    Latitude of center of capital city

(11)    Internal political unrest

(12)    Involvement in warfare with other polities  

(13)    Use of pathogenic agents in warfare

(14)    Amount of land under cultivation

(15)    Irrigation

(16)    Episodes of epidemic disease

III.   Climate data for world regions (as close to the geographical center as possible)

(1)      Year, period, region

(2)      Temperature (cold-normal-hot)

(3)      Precipitation (rain/snowfall)

(4)      Timing of precipitation (season)

(5)      Incidence of violent storms

(6)      River or lake levels

IV.  Epidemic disease data and population well-being

(1)      Year, period, region

(2)      Settlement name

(3)      Longitude of center

(4)      Latitude of center

(5)      Heights (indicator of well-being)

(6)      Real wages (indicator of well-being)

(7)      Grain prices

(8)      Disease severity (mortality rate)

a.                    Number of deaths per capita

b.                    Epidemic reported in nearby cities

Data Sources

Most of the data on city sizes has already been coded in connection with other projects. It comes primarily from Chandler (1987) and Modelski (2003) supplemented from other regional sources. The estimates of the territorial sizes of states and empires come from Taagepera (1978a,1978b, 1979,1997) also supplemented by newer regional atlases.  

PROJECT MANAGEMENT other advisors who play a part in the project. Students will gain interdisciplinary data analysis and modeling skills and will be encouraged to collaborate in the writing and presentation of scholarly research papers for professional meetings and the development of web-accessible materials stemming from this project.

Coordination and Management Plan

Assemblage of an integrated geo-chronological database will involve a great deal of coordinated labor. The Internet Collaboratory website will enable senior advisors and student researchers to consolidate their efforts and to share access to all materials, data, and analyses of the research project.  Statistical, and mathematical analyses to test causal hypotheses and the plausibility of models by comparing model results, simulation output and real world data will be achieved only after a considerable amount of prior work, and is ultimately dependent on the development of an interoperable knowledge system of spatially and chronologically tagged information.

The co-Principal Investigators are Christopher Chase-Dunn (Sociology, UCR), Joel Sachs (Biology, University of California-Riverside), Peter Turchin (Ecology and Evolutionary Biology, University of Connecticut) and Robert A. Hanneman, (Sociology, UCR). Chase-Dunn will carry out the overall administration of the project (with grants management help from the Sociology Department at University of California-Riverside). He will oversee the project’s Biomes and Anthromes in World Regions dataset and the modeling of the causes of sociocultural evolution. Chase-Dunn will supervise a post-doctoral fellow and a graduate student research assistant. Sachs will supervise a post-doctoral fellow and undergraduate Research Assistants. He will coordinate the development of the microbial evolution model and will contribute to the overall linkage model.  Hanneman will coordinate the development of the epidemic and multipolity models, especially the network aspects of these, and will supervise a Graduate Research Assistant and some undergraduate research assistants. Turchin will build the single polity model, help with the integration of the single-polity and multipolity sociocultural models and will coordinate the integration of the three models. The project post-docs will help to build the epidemiological and multipolity models and with the building of the data set and with the testing and write-ups. The project will have 18 members of the Senior Advisory Committee (see below) who will attend a January conference in the first year of the project to provide guidance on model and research design and data issues. These are specialists who will be charged with helping improve the project. They will also provide advice and criticism of the projects products in the fourth year. The evaluations of the project activities will be done by Senior Advisors and other project participants and other colleagues who see the presentations of results at professional conferences

The postdoctoral mentorship plan:
Recruitment: Advertisements will be placed in the communications hubs of professional organizations such as the Evolution Directory (EvolDir, McMaster University), EcoLog, the American Sociological Association, the Social Science History Association, the International Studies Association and others. Particular effort will be made to advertise positions in a way that will attract underrepresented minorities and women to apply. Applicants will be identified by areas of doctoral research and stated interest and application material will be reviewed by the Senior Personnel. Mentoring: Postdoctoral fellows will be assigned faculty mentors whose research is in an area closely related to their areas of interest and expertise.  Primary mentors will be involved in close collaboration with the post-doctoral fellows in pursuing the assigned aspects of the research program. They will encourage them to present their own research in seminars and research conferences and to write their own research papers.  Mentors will provide opportunities to fellows to assist in preparing research proposals, or assist them in pursuing their own funding. Post-docs will participate in research, teaching, discuss time management issues. They will also to help with mentoring graduate and undergraduate research assistants.
Teaching: Postdoctoral fellows will have a wide variety of teaching opportunities in connection with this project. They will participate in and sometimes lead graduate seminars, advanced undergraduate classes and basic undergraduate courses. Presentations: Post-doctoral fellows will make presentations at group project meetings and will be encouraged to present their own work at professional meetings. Publications: Post-doctoral fellows will be credited with their work on the research project by being listed as co-authors on appropriate publications and they will be encouraged and assisted in preparing their own articles for publication. Post-project placement.  Mentors will make the long term success and career of the post-doctoral fellows a priority by writing recommendation letters and notifying post-docs of opportunities that become available.   

Schedule of Work

1st year (July 1, 2011-June 30, 2012)

Organize and implement coordination and communication among principal investigators and advisors. Begin weekly Project Meetings at UC, Riverside. Set up the web site for the research project. Hire and train undergraduate and graduate research assistants.

January Working Conference with the Advisory Committee at University of California-Riverside

Data: Establish standards: spatial notations, time, location, and resolution.  Develop coding protocols for demographic, city, empire, core/periphery status, climate change and epidemiological data. Develop separate bibliographies for city sizes, empire sizes climate change and epidemiological information. Systematically search libraries at the University of California and the University of Connecticut, and Interlibrary Loan Collections. Begin search and acquisition of climate change databases. Develop specialized search engine software for digital data acquisition from JSTOR and other digitized databases. Develop separate initial databases using easily obtained information for city sizes, empire sizes, climate change, warfare, trade, and migrations. Merge the already-coded settlement, polity and climate change data in a prototype of the integrated geo-chronological database (BAWR). Fine-tune design of the database.  Locate significant gaps in the data. Make a plan for efficiently filling them given resource constraints.

Software: Acquire and customize state-of-the-art software for simulation modeling, GIS and formal network analysis.

Model Development: Fine-tune the overall modeling plan with feedback from all participants.  Develop modeling standards. Acquire existing models. Formulate base models. Create simple regional models (limited time-frames, limited numbers of variables). Arrange all components in a format where they can communicate with one another. Redo existing models for our purposes. Work on parameters, initial values. Get them running separately. Couple them. Work on nesting, linking problems and further develop connections with GIS. Report on revision of prototypes to incorporate emerging models and interoperability considerations. Develop prototype complex causal models of macrosocial processes in interaction with the environment.

Education: Develop interdisciplinary courses on Coupled Human and Natural Systems. Send graduate research assistants from our project to participate in summer GIS internships at Environmental Studies Research Institute (ESRI) in Redlands, California.

2nd year (July 1, 2012-June 30, 2013)

Data: Develop the mechanism for updating and cross-linking databases. Work on filling in gaps, splicing series, generating statistics that can be input into statistical analyses. Compare different approaches to interpolation and uncertainty management.  Produce the next version of BAWR.

Analyses: Examine the interactions within regions among urbanization, empire formation, epidemics and climate change. Examine the hypothesis of cross regional synchronization of city and empire growth/decline phases with the BAWR.

Education: Give interdisciplinary courses. Establish educational web site on “Globalization, Coevolution and the Environment.” Involve students in research, teaching, publications and conferences

3rd year (July 1, 2013-June 30, 2014)

Fine-tune latest versions of models;  Produce penultimate version of BAWR; Produce the results of statistical analysis of the integrated database. Model Development: Re-parameterize complex models with BAWR. Further refine the models. Analyses: Use preliminary data to test causal propositions. Use completed dataset to test the decisions that were made regarding parameters in the simulations models and to test the implications of the simulations.

Education: Continue courses and student involvement in research. Students present early versions of their own research papers at conferences.

4th year (July 1, 2014-June 30, 2015)

Revise or fine-tune the data model, the resulting database and the analyses taking criticisms and suggestions from advisors into account. Revise models taking criticisms and suggestions into account. Produce final models.

Education: Continue courses and student involvement in research. Publish final versions of our models. Students present their own research papers at conferences. Put the BAWR into most recent formats compatible with Alexandria Digital Library and the Electronic Cultural Atlas Initiative. Present papers at conferences. Write books and articles for publication.

Prepare final project report for NSF. Publish final versions of project models. Present final research papers at the World Congress of Sociology in Yokohama and at other conferences. Write books and articles for publication.



E.N. Anderson (Anthropology, University of California-Riverside) Food, domestication

Karl Butzer (Geography, University of Texas-Austin) landscapes and evolution

Robert Carneiro (American Museum of Natural History)  state formation

Claudio Cioffi-Revilla (Computational Social Science, George Mason University, warfare, computational social science

Alan G. Fix, (Anthropology, University of California-Riverside) human genetic change, migration

Roland Fletcher (Anthropology, University of Sydney) settlement sizes, city design

Lee Fung (Harry), (University of Hong Kong)

Jack Goldstone (Sociology, George Mason University, state crises, revolutions

Thomas D. Hall (Depauw University) nomadic empire formation, Central Asian climate change

David Kowalewski  (Political Science, Alfred University, world-systems modeling)

Augustine Kposowa, (Sociology, UCR) epidemiology, demography

Bai-lian Li (Botany and Plant Sciences, UCR) dynamical modeling of ecological systems

Patrick Manning, (University of Pittsburgh) world historical data, migration

William R. Thompson (Political Science, Indiana University) interstate system modeling, migration, incursions)

Douglas White (Anthropology, University of California-Irvine) network analysis, city-size distributions

David Wilkinson (Political Science, UCLA) power configurations of state systems, core/periphery hierarchies

David Zhang, (University of Hong Kong)

Marlene Zuk, Biology, (University of California-Riverside) coevolution, sexual selection


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[1]The issue of individual versus group selection in both biological and sociocultural evolution now seems to have been decided in favor of the notion of multilevel selection applying to both (Borrello 2005).


[2] More recently microbes have intentionally been used in water treatment, energy production and an explosion of uses in science.

[3] The basic reproductive number, R0, is the average number of secondary infections caused by an infected individual over the period of infection in a disease free population.  Usually an R0 value greater than one implies conditions leading to an epidemic but this depends on factors such as the rate of progression of the disease relative to the demographics of the population or permanence of immunity (Hethcote, 2000; Diekmann and Heesterbeek, 2000).


[4] Similar in meaning to esprit de corps at the level of a tribe, nation or civilization, but goes beyond partisanship or nationalism in as much as it forms a social cohesion that leads to a sense of common purpose across a broad range of situations. It is a concept developed by Ibn Khaldun in the 14th century (see Turchin, 2003).

[5] A metaethnic frontier is a region in which peoples with distinct cultures and identities share a border.

[6] When the prices of different goods, or revenues and expenses move in diverging directions.

[7] World-systems are regional interpolity human interaction networks. The size of a world-system is mainly determined by the nature of transportation and communications methods, so earlier world-systems were small regional interaction networks. Only in recent centuries have these merged and been engulfed into the global system in which we now live.

[8] This is the phenomenon of “semiperipheral development.”

[9] https://irows.ucr.edu/research/citemp/bawr/bawrdat.htm

[10] It would be desirable to also include Mesoamerica, the Andes and the U.S. Southwest, but documentary evidence for establishing the territorial sizes of the largest polities are not available for these regions.

[11] A Political-Military Network is a group of polities that are allying or making war with one another, like the modern interstate system. David Wilkinson (1987) traces the growth of the Central System as it eventually incorporates all the regional systems into a global PMN in the nineteenth century (CE).