Quantitative history
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Quantitative history is an application of statistical methodology developed in social science into the field of history. This type of history is often ignored by historians who conceptualize history as a record of events and view the theoretical and quantitative analysis of these events with skepticism and sometimes disdain.
Quantitative history uses numerical data analysis as a primary source for the analysis and interpretation of historical events. Historians have made use of numerical data and simple statistics, but quantitative history use more complex statistical method utilising computer-assisted data analysis, facilitating Fourier transforms of data series, quantitative modeling, filtering of stochastic drifts and other advances in the analysis of secular trends.
[edit] Cyclical theories of history
Early theories of historical change were cyclical. This concept can be found among Greek and Roman historians of the classical era, such as Empedocles, Polybius, or Marcus Aurelius. In its classic formulation by Aristotle, the same things have always existed, passing through a cycle of changes. Giambattista Vico (1688-1744) elaborated this notion of history in his Scienza Nuova, (1725) a favorite of Karl Marx and James Joyce, who used the Vico's New Science to structure his Finnegan's Wake. The pattern Vico perceived in history was cyclical, encompassing development and repetition. With respect to the cyclic nature of historical trends Russell (1971) maintains that every community is exposed to two opposite dangers: ossification and dissolution, elaborating on this postulate as follows:
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- Civilizations start with a rigid belief system based on a dogma. If the dogma is relaxed, civilization may reach a point of balance between discipline and freedom, often its period of brilliant genius. This stage typically dissolves into anarchy. The anarchy leads to tyranny, justified by a new dogma.
A single cycle of this pattern was elaborated within Spengler's (1920) theory of historical change, outlined in terms of growth, flourishing, and decline of civilizations. Similar approaches can be observed in the historical theories of Edward Gibbon (1776-1788) and the early writings of Toynbee (1934-1939).
The interest in the cyclical theories of history has resurrected recently with the development of mathematical models of long-term sociodemographic ("secular") cycles (see, for example, Historical Dynamics by Peter Turchin, or Introduction to Social Macrodynamics by Andrey Korotayev).
[edit] The spiral model of history
The prototypes of historical theories are the chaotic, cyclical, and linear theories of historical change. These basic forms exist in many modifications, e.g., Moyal’s (1949) use of Poisson distribution to model variations of the cyclical theory, logistic curve modification of the linear theory by the Club of Rome, or the convoluted spiral model of Marx and Hegel. Marxist theory of societal development spans the history of humanity in a series of five stages:
- primordial communism
- slavery
- feudalism
- capitalism
- postindustrial communism
Since the last stage of this model is seen as a return to the initial stage of primordial communism, albeit on a higher level of social development, this model is sometimes referred to as a spiral model. Accordingly, the communist society should return to the natural state of humankind, not perverted by the greed of capitalist society. Marx patterned this theory after the philosophy of Georg Hegel (1770-1831). In his lectures on the Philosophy of History, Hegel elaborated on the polarized tensions in reality (thesis and antithesis), as antecedents of transcendence, synthesis and emergence of new knowledge. Within the context of history, Hegel outlined the historical stages of humanity as a series of progressions from subjectivity to objectivity, partiality to unity, and bondage to freedom. The hope in this progression of events was shared by Hegel with the philosophers of the Enlightenment. However, this course of events is also subject to reversal, as observed with respect to the subjectivity-objectivity trend by Guy Debord (1995) in his société du spectacle, and in the other respects by Umberto Eco commenting
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- "It almost seems like we are going back to the Middle Ages..."
on the opening years of the 21st century.
[edit] Chiliastic (linear) theory of history
The linear theory of historical change is exemplified in writings of St. Augustine. In his De civitate Dei (413) Augustine introduced his theological perspective on history, resolutely linear, a tendency which can be traced to the Judeo-Christian scriptural tradition describing history as the sequence of events from the time of Creation to the End of the World. The linear movement of human history aims at the eventual separation of the Chosen, a small minority saved by God's unmerited grace, from the rest of the humanity.
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- The resurrected bodies of the Chosen will experience eternal bliss while most of the humanity will encounter the second death and will be subject to eternal torment by flames that will inflict pain without consuming the body. The people not chosen must suffer without end, for to suffer any less would be to undermine our confidence in the eternal blessedness of the Chosen and would also contradict the scriptures. (De civitate Dei, 21, 23).
The time dilation, characteristic of linear theories of societal development, may be also observed in writings of Immanuel Kant. Scanning the story of civilization from ancient Greece, through the Roman Empire and Middle Ages to the end of the First German Empire, he concluded that
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- One will discover a regular progress in the constitution of states on our continent. This [idea of human history] gives us a consoling view of the future, in which there will be exhibited in the distance how the human race finally achieves the condition in which all the seeds planted in it by Nature can fully develop and in which the destiny of the race can be fulfilled here on earth (Kant on History, 1963, pp. 24-25).
[edit] Chaotic theory of history
The chaotic concept of history is characteristic of contemporary historians. This view is exemplified by Geyl's assertion that there is an
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- ingrained habit of the human mind to try to construct a vision of history in which chaos is reduced to order, characteristic of the discredited theories of historical change proposed during the nineteenth century. (Debates with Historians, 1955)
In other words, reality exists independently of ideas concerning it, is basically random, and we construct our subjective realities by imposing meaning on flow of events that, in fact, are no more than stochastic drifts. The primary mover behind this type of reasoning was Karl Popper, a vocal critic of the logical positivism, who in his book (1957) The Poverty of Historicism rejected the concept of history that aims at discovery of historical trends. Popper's (1934) Logik der Forschung (logic of scientific discovery) was criticized as being only a restatement of Kantian synthetic a priori propositions and their quid facti verity and some assumptions in The Poverty of Historicism as simple reversals of Kant's beliefs, similar to those by Marx of Hegel's. In a similar vein, Francis Fukuyama's (1992) The End of History, with the passage of time, seems less and less to be a viable theory of history.
[edit] Methodology
The theory described above on Quincy Wright's (1965) database is simulated in below section, containing frequencies of wars from the 15th to the 20th century, by using the method of moving average (Krus and Blackman, 1980; Krus and Ko, 1983; Southworth, 1960) and Fast Fourier Transforms (Press, Teukolsky, Vetterling & Flannery, 1986). The method of moving average, used for analysis of secular trends, is similar to the low-pass filters used in signal processing. This convolutionary method averages the values adjacent to a central value of an interval, rolled over the entire span of the series. The main parameter of this method is called the order of the moving average, which can vary from 1 to n, where the n is the length of the series. The central value of the moving average can be any of the methods used for estimation of the central tendency of data; commonly used method is the algebraic mean. If the value of the order of the moving average is 1, the moving average does not abstract values of the series from the values adjacent to its central value, but merely reproduces the data series (Makridakis and Wheelwright, 1978). This is analogous to observation and description of historical events with no attempt at their generalization. As the value of the order of the moving average increases, the values adjacent to the central value of the moving average are averaged over larger and larger intervals, simulating more and more general abstractions from the observed events.
[edit] Simulation of the chaotic theory of history
Initially, the order parameter of moving averages to unity is set, so the data were reproduced, but not generalized. This model simulated descriptions of historical events with no attempts at abstraction of historical trends. When visualized, no compelling pattern was discernible, creating impression that observed events are chaotic and unrelated, suggesting that the prevailing trend among contemporary historians to describe historical even without attempts to discern their patterns may be related to a low level of abstraction.
[edit] Simulation of the linear model of historical change
An increase of the order parameter to a value resulting in averaging of adjacent data points over a moving interval of 300 years would run into technical difficulties, so we had to resort to Fast Fourier Transforms for simulating generalizations of this magnitude. Smoothing the obtained monotonically ascending trend resulted in a linear, rapidly increasing trend. This linear component of the time series data for wars in Western civilization captures the progress of war making capabilities, along the timeline, as a linear increase in the ability to project power. With the passage of time, the severity of armed conflicts of Western Civilization steadily increased. Reminiscent of linear models of historical change, telescoping the time interval over which the cognitive generalizations are made to the whole history of mankind is characteristic of these theories, typical, among others, of St. Augustine and Immanuel Kant.
[edit] Simulation of the cyclical trends of historical change
Generalizations over twenty year intervals were simulated by the 20th order of the moving average. Era that started with Columbus sailing to America in 1492 and sometimes called the Century of Spain was punctuated by the Thirty Years' War (1618-1648) when the Protestant States of Europe (England, Scandinavian states, some German principalities and others) were at war with the Empire of Spain.
In the series of wars between the Thirty Years' War and the Wars of the French Revolution and the Napoleonic Wars, Britain's main adversaries were Spain and France.
During the Napoleonic Wars (1789-1815) the principal adversary of Britain was France and it was the British-Russian alliance that defeated Napoleon.
The British-Russian alliance which in its final phases also included the United States as a principal combatant also won the World Wars (1914-1918, 1939-1945). At this level of generalization, the cycles of war show average amplitude of about 50 years.
[edit] Assumptions of quantitative history
Quantitative history was pioneered by Lewis F. Richardson, a student of Karl Pearson, who assimilated his mentor's maxim that beliefs ought to be tested by statistics. Richardson's assumptions pertained to questions debated over centuries in the philosophy of history. His key assumptions were that
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- The past could serve to guide the future, for what has happened often is likely to happen again, as human affairs are partly free, and partly determined. The statistical methods allow one to find patterns, association, and sequences that regularly recur. Mathematical expressions of these regularities help to make the implicit assumptions explicit, their consequences deduced, absurd implications deleted, and disputable statements pinpointed. (Richardson, 1960, Wilkinson, 1980).
[edit] Methods of quantitative history
Methods of quantitative history are those of statistics, reinforced by computer algorithms for data analysis and visualization. A landmark in the study of secular trends was the publication of Makridakis and Wheelwright's (1978) Interactive forecasting: univariate and multivariate methods. In the 1970's, 1980's and 1990's William B. Michael edited a section on innovative computer programs in the Educational and Psychological Measurement. Most of these programs, supplementing the standard statistical packages as SAS or SPSS, were collected in Silver and Hittner's (1998) Guidebook of statistical software for the social and behavioral sciences. Also, during these decades Robert B. Ammons edited a section in the Psychological Reports on psychohistory and quantitative history where he published articles on new innovative methodologies, such as trans-temporal cognitive matching and designs for Auguste Comte's natural experiments. Some of these studies are described in Cruise Scientific (2005) Visual statistics studio.
Origins of quantitative history can be also traced to the work of David McClelland, who summarized findings of his numerous empirical studies about quantitative history in his books The Achieving Society (1961), and Power: the inner experience. (1975). McClelland’s empirical methodology was coined within the theoretical framework of social theories of Talcott Parsons and Karl Marx and theories of history of Pitirim Sorokin and Arnold Toynbee with his empirical studies revolving around the measurement of the need for achievement, need of affiliation, and need for power. Using content analysis of historical documents, McClelland have described the role these theoretical constructs play in the formation, flourishing, and fall of societies and civilizations. Analyses of documents from early stages of a civilization typically show high levels of the need for achievement. With the passage of time, the need for achievement is replaced by the need for affiliation and the need for power. Changes in these motivational patterns are reflected by the increase in violence and the probability of war.
Stories expressing the dominant needs of a society may be found in magazines, books, and children's readers or portrayed in movies and theaters. If a story is popular, it may express not only the motivation of the writer, but can also reflect the needs that the readers, audience, and motivational currents within a society. Achievement motivation also finds its expression in art. Sculptures, paintings, and architecture may reflect these motivational traits. Straight lines are generally indicators of achievement motivation; convoluted lines indicate the lack of it. Thus, for instance, the Doric order marks the rise of classical Greece, the Ionic order it’s flourishing, and the Corinthian order its decline. These architectural changes paralleled changes in the general level of achievement motivation of classical Greece. Shortly after the invention of scuba diving apparatus, treasure hunters began to recover Greek amphorae from the ancient Greek ship sunk in the Mediterranean. Adorned by scenes from Greek mythology, amphorae served as containers for transport of grain. Analyzing paintings on the amphorae recovered from the sea floor, the level of achievement motivation encoded within the painting’s design was related to the distance of the location of their recovery from Athens. The result showed a significant relationship between the prosperity of Greece and the need for Achievement, reflected in paintings on her amphorae resting for centuries on the bottom of the Mediterranean. Another landmark study (cf; Fig. 7), claiming to confirm validity of McClelland's theories, was the study of Bradburn and Berlew (1961) who analyzed achievement motives in British school readers and showed a strong correlation of these themes, a generation later, with the Britain's industrial growth.
[edit] Retrospect and prospectus
Toward the end of his life, David McClelland contemplated the impact of his life-long work that aimed at quantification of history. McClelland writes that he tried to show historians how things could be done, but that in the intervening years, he did not notice a slightest inclination on the part of the historians to follow his example. History in general, and historians in particular, with few notable exceptions, do not describe historical events by using the methodology of quantitative analysis. However, the number of the quantitative history entries on the Internet search engines is rapidly growing, as well as use of quantitative history by sociology, economics and political science. Let us hope that McClelland's observation only affirms that he was well ahead of his time.
[edit] View from the other side
At the beginning of our discussion we have stated that many historians view quantitative history, as conceptualized here, with skepticism and sometimes disdain. To keep the balanced view, let us look at the other side of this controversy.
Traditional historians have made use of numerical data in such field as demography and economic. However, what separate quantitative history from actual academic history is not use of statistics per se but the former's use of mathematical model/theory developed in social science, which is often the extension of their philosophical proposition. Understandably, most academic historians are skeptical of this approach given that all mathematical modeling developed in social science such as in economics perform dismally in predicting future events, suggesting that the models have only "explanatory" capability of various philosophical ideas, which is meaningless for academic historians. Historians do not have objection to the compilation of statistical data in history and this is particularly true in the demographic and economic history. However, they are wary of the methodology of social science in general and mathematical modeling in particular.
[edit] References
- Aristotle (1927). Metaphysics. In W. D. Ross (Ed.), Aristotle. NY: Scribner, pp. 105-118.
- Bradburn, N.M., & Berlew, D.G. (1961). Need for achievement and English economic growth. Economic Development and Cultural Change, 10, 8-20.
- Debord, G. (1995). The society of the Spectacle. Cambridge, MA: The MIT Press (Zone Books).
- Fukuyama (1992) The end of history and the last man. Penguin Group (USA).
- Geyl, P. (1955). Debates with historians. London: Collins.
- Gibbon, E. (1776-1788) The History of the Decline and Fall of the Roman Empire. New York, NY: Random House (Boxed edition, 1993).
- Hegel, G. W. F. (1900). The philosophy of history. New York: Collier.
- Kant, I. (1963). Idea for a universal history from a cosmopolitan point of view. In L. W. Beck (Ed.). Kant on history. New York: Bobbs-Merrill, pp. 11-26.
- Krus, D. J. & Blackman, H. S. (1980) Time scale factor as related to theories of societal change. Psychological Reports, 46, 95-102. (Request reprint).
- Krus, D. J. & Ko, H. O. (1983) Algorithm for autocorrelation of secular trends. Educational and Psychological Measurement, 43, 821-828. (Request reprint).
- Makridakis, S. & Wheelwright, S. C. (1978). Interactive forecasting: univariate and multivariate methods. San Francisco, CA: Holden-Day.
- Marx, K. (1932). Capital and other writings. New York, NY: Modern Library.
- McClelland, D. C. (1975) Power: The inner experience. New York: Halstead.
- McClelland, D. C. (1961) The achieving society. Princeton: Van Nostrand.
- Moyal, J.E. (1949) The distribution of wars in time. Journal of the Royal Statistical Society, 112, 446-458.
- Nash, R. H.(1969). Ideas of history. New York, NY: Dutton.
- Popper, K. (1935). Logik der Forschung. Wien, Ősterreich.
- Popper, K. (1957). The poverty of historicism. London, UK: Routlege.
- Press, W. H., Teukolsky, S. A., Vetterling, W. T. & Flannery, B. P. (1986) Numerical Recipes: The art of scientific computing. Cambridge: Cambridge University Press. online)
- Richardson, L. F. (1960). Statistics of deadly quarrels. Pacific Grove, CA: Boxwood Press.
- Russell, E. W. (1971). Christianity and militarism. Peace Research Review, 4, (3), 1-77.
- Silver, N. C. & Hittner, J. B. (1998). Guidebook of statistical software for the social and behavioral sciences. Boston, MA: Allyn & Bacon.
- Southworth, R. W. Autocorrelation and spectral analysis. In A. Ralston and H. S. Wilf (Eds.) Mathematical methods for digital computers. New York: Wiley, 1960.
- Spengler, O. (1920). Der Untergang des Abendlandes. Můnchen, Deutschland: Beck.
- Toynbee, A. J. (1934-1939). A study of history. Oxford, UK: Oxford University Press.
- Tucker, R. (1990, 2nd ed.). Philosophy and myth in Karl Marx. Cambridge, MA: Cambridge University Press.
- Vico, G. (1968). New science. NY: Cornell University Press.
- Wilkinson, D. (1980). Deadly quarrels: Lewis F. Richardson and the statistical study of war. Berkeley, CA: University of California Press.
- Wright, Q. (1965, 2nd ed.). A study of war. Chicago: University of Chicago Press.
[edit] See also
- Dampening effect
- Psychohistory (fictional)
- Social cycle theory
- Stochastic drift
- Supreme crime
- War cycles
