Title: Introduction to social macrodynamics
Description: From the review by Robert Bates Graber (Professor Emeritus of Anthropology, Division of Social Science, Truman State University) of "Introduction to Social Macrodynamics" (Three Volumes. Moscow: URSS, 2006) (published in "Social Evolution & History". Vol. 7/2 (2008)): This interesting work is an English translation, in three brief volumes, of an amended and expanded version of the Russian work published in 2005. In terms coined recently by Peter Turchin, the first volume focuses on “millennial trends,” the latter two on “secular cycles” a century or two in duration. The second volume is subtitled "Secular Cycles and Millennial Trends". Chapter 1 stresses that demographic cycles are not, as often has been thought, unique to China and Europe, but are associated with complex agrarian systems in general; and it reviews previous approaches to modeling such cycles. Due to data considerations, the lengthy chapter 2 focuses on China. In the course of assessing previous work, the authors, though writing of agrarian societies in particular, characterize nicely what is, in larger view, the essential dilemma reached by every growing human population: "In agrarian society within fifty years such population growth [0.6 percent per year] leads to diminishing of per capita resources, after which population growth slows down; then either solutions to resource problems (through some innovations) are found and population growth rate increases, or (more frequently) such solutions are not found (or are not adequate), and population growth further declines (sometimes below zero)" (p. 61–62). (Indeed, for humans, technological solutions that raise carrying capacity are always a presumptive alternative to demographic collapse; therefore, asserting—or even proving—that a particular population “exceeded its carrying capacity” is not sufficient to account logically for the collapse of either a political system or an entire civilizations.) Interestingly, the authors find evidence that China’s demographic cycles, instead of simply repeating themselves, tended to increase both in duration and in maximum pre-collapse population. In a brief chapter 3 the authors present a detailed mathematical model which, while not simulating these trends, does simulate (1) the S-shaped logistic growth of population (with the effects of fluctuating annual harvests smoothed by the state’s functioning as a tax collector and famine-relief agency); (2) demographic collapse due to increase in banditry and internal warfare; and (3) an “intercycle” due to lingering effects of internal warfare. Chapter 4 offers a most creative rebuttal of recent arguments against population pressure’s role in generating pre-industrial warfare, arguing that a slight negative correlation, in synchronic cross-cultural data, is precisely what such a causal role would be expected to produce (due to time lags) when warfare frequency and population density are modeled as predator and prey, respectively, using the classic Lotka-Volterra equations. Chapter 4 also offers the authors’ ambitious attempt to directly articulate secular cycles and millennial trends. Ultimately they produce a model that, unlike the basic one in chapter 3, simulates key trends observed in the Chinese data in chapter 2: "the later cycles are characterized by a higher technology, and, thus, higher carrying capacity and population, which, according to Kremer’s technological development equation embedded into our model, produces higher rates of technological (and, thus, carrying capacity) growth. Thus, with every new cycle it takes the population more and more time to approach the carrying capacity ceiling to a critical extent; finally it “fails” to do so, the technological growth rates begin to exceed systematically the population growth rates, and population escapes from the “Malthusian trap” " (p. 130).
Subjects: Mathematical models, Historical sociology, Demography, Demographic transition, History, History -- Mathematical models, Demography -- History -- Mathematical models, Demographic transition -- Mathematical models