Introduction to Social Macrodynamics: Secular Cycles and Millennial Trends

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Editorial URSS, 2006 - Demographic transition - 175 pages
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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).  

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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). 

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Page 19 - This implication flows naturally from the nonrivalry of technology... The cost of inventing a new technology is independent of the number of people who use it. Thus, holding constant the share of resources devoted to research, an increase in population leads to an increase in technological change
Page 19 - This implies that for any given level of technological development (7) there is "a unique level of population, n" that cannot be exceeded with the given level of technology (Kremer 1993: 685). Note that n can be also interpreted as the Earth carrying capacity, that is, the maximum number of people that the Earth can support with the given level of technology. However, as is well known, the technological level is not a constant, but a variable. And in order to describe its dynamics Kremer employs...
Page 22 - ... to form coalitions until all elements are so strongly linked that the population as a whole can be considered from a game-theoretical point of view as a single person playing a two-person game with nature as its opponent" (von Foerster, Mora, and Amiot 1960: 1292).
Page 14 - It is true that for most part of human history we cannot be at all confident of the exact value within a given order of magnitude. But with respect to any time-point within any period in question, we can be already perfectly confident about the order of magnitude of the world population. Hence, it is clear that whatever discoveries are made in the future, whatever re-evaluations are performed, the probability that they will show that the overall world...
Page 18 - This statement looks quite convincing. Indeed, throughout most of human history the world population was limited by the technologically determined ceiling of the carrying capacity of land. As was mentioned above, with foraging subsistence technologies the Earth could not support more than 10 million people, because the amount of naturally available useful biomass on this planet is limited, and the world population could only grow over this limit when...
Page 22 - Indeed, at present the mathematical models of world population growth as hyperbolic have not been accepted by the academic social science community [The title of the most recent article by a social scientist discussing Kapitza's model, "Demographic Adventures of a Physicist" (Shishkov 2005), is rather telling in this respect]. We believe that there are substantial reasons for such a position, and that the authors of the respective models are as much to blame for this rejection as are social scientists.
Page 13 - As we see the resulting pattern of world population dynamics has an unmistakably hyperbolic shape. Now you can experiment and move any points within the estimate ranges as much as you like. You will see that the overall hyperbolic shape of the long-term world population dynamics will remain intact. What is more, you can fill the space between the points with any estimates you find.
Page 11 - ВСЕ the estimate range becomes even more dramatic: 1-10 million (Thomlinson 1975). Indeed, it seems evident that with such uncertain empirical data, we are simply unable to identify the long-term trend of world population macrodynamics. However, notwithstanding the apparent persuasiveness of this objection, we cannot accept it. Let us demonstrate why. Let us start with 10000 CE.
Page 19 - population increases above some steady state equilibrium level of per capita income, m, and decreases below it" (Kremer 1993: 685). Hence, with the decline of per capita income, the population growth will slow down and will become close to zero when the per capita income approaches m. Note that such a...
Page 12 - Chinese counts tended to underestimate the population, since before this they were not real census, but rather registrations for taxation purposes; in any country a large number of people would do their best to escape such a registration in order to avoid paying taxes, and it is quite clear that some part of the Chinese population normally succeeded in this (see, eg, Durand 1960).

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