powerpoint: Rethinking World Historical Systems from Network Theory Perspectives
advertisement
ISA Conference, Historical Long Run Session Rethinking World Historical Systems from Network Theory Perspectives: Medieval Historical Dynamics 1175-1500 Douglas R. White UC Irvine Powerpoint at http://eclectic.ss.uci.edu/~drwhite/Conferences.html Paper at http://convention2.allacademic.com/index.php?cmd=isa06 • For the medieval period the data from Spufford (2003) includes cities and industries coded in 25 year periods, 1175-1500, and trade routes For Eurasia generally, the data from Chandler are city size distributions, and the data from Turchin relate to secular cycles of population growth/decline, social conflict, and other variables The goal here is to include city network variables in the study of world historical system dynamics. Operation of a Double Negative as the key to 2:1 Turchin’s Secular Cycles / Trade Cycles Social Conflict Population Disintegrative Decline ?≡ phase Population growth lag lag Social Conflict lag Population decline lag Disintegrative phase ?≡ Population growth lag lag Trade expansion Trade contraction Double negative Secular Cycle ca. 220 years: nadirpop growthmaxdeclinenadir Full Trade Network expansion/contraction ca. 440 years 2:1 Cycles: Secular follows – Network Size leads • Network Size Cycle 350-27BCE Global 27BCE-285 Regional Time lag • E. Roman Empire • Carolingian Cycle Time lag • 2nd Roman Cycle • 3rd (Principate) Time lag • (earlier cycle) • 1st Roman Cycle • Network Size Cycle Global 600-350BCE Regional • Network Size Cycle 285-600 Global 600-900 Regional Figure 1: Operation of the Double Negative as the key to 2:1 City Size/Secular Phasing 600BCE-900CE lag Trade expansion Integrative phase Integrative phase Disintegrative phase lag Disintegrative SECULAR CYCLE phase SECULAR CYCLE Double negative Secular Cycle ca. 220 years: nadirpop growthmaxdeclinenadir Full Trade Network expansion/contraction ca. 440 years Trade contraction • After 900 CE, the shapes of Chandler’s city size distributions begin to differentiate by historical period • These differentations by historical period relate to trading network expansion/contraction Figure 2: City Size Scaling: Zipfian in the upper city size bins Extreme global hubs, lesser hubs in the neighborhood of most cities are absent. q-scaling is streched exponential scaling that models the entire size distribribution, including the power-law slope of the upper sized bins. (cumulative binned, CDF) In these periods, highest profits are acrued by cities with the highest glpbal flow centralities Flow centrality (how much total network flow is reduced with removal of a node) predicts the potential for profit-making on trade flows, emergence of financial centers, and (reflecting flow velocities, as Spufford argues) organizational transformations in different cities. Here, Bruges is a predicted profit center, prior to succession by Amsterdam. This type of centrality is conceptually very different. It maps out very differently than strategic betweenness centers like Genoa, which are relatively low in flow centrality. Figure 3: City Size Scaling: sub-Zipfian in the upper city size bins Global hubs less differentiated, but lesser hubs in the neighborhood of most cities are present. In these periods, highest wealth is accrued by cities with the highest betweenness centralities Betweenness centrality in the trade network predicts accumulation of mercantile wealth and emergence of commercial hegemons. e.g., in the 13th century, Genoa has greatest betweeness, greatest wealth, as predicted. Later developments in the north shift the network betweeness center to England. Episodically, in 1298, Genoa defeated the Venetians at sea. Repeating the pattern, England later defeats the Dutch at sea Size of nodes adjusted to indicate differences in betweenness centrality of trading cities in the banking network Betweenness Centralities in the banking network Given its 13th C betweenness centrality, Genoa generated the most wealth Figure 4: Operation of the Double Negative as the key to 2:1 City Size/Secular Phasing lag Trade & city sizes expansion Integrative phase Integrative phase Disintegrative phase lag Disintegrative SECULAR CYCLE phase SECULAR CYCLE Double negative Secular Cycle ca. 220 years: nadirpop growthmaxdeclinenadir Full Trade Network and City Size expansion/contraction ca. 440 years Trade & city sizes contraction 2:1 Cycles: 900-2000 Secular follows – Network Size leads along with city size distributions • German Emp. Cycle 900-1150 Regional • Network/City Size Cycle • Medieval Cycle 1150-1450 Global • 1st Modern Cycle 1450-1660 Regional • Network/City Size Cycle • 2nd Modern Cycle 1660-1850 Global • 3rd Modern Cycle 1850-2006+ Regional • Network/City Size Cycle Figure 5: Operation of the Double Negative as the key to 2:1 Secular / City Size Phasing Social Conflict Population Disintegrative Decline ? phase Population growth lag lag Social Conflict lag Double negative Population decline lag Disintegrative phase ?≡ Population growth lag lag Trade expansion Trade contraction Figure 6: Lag times in 2:1 Phasing for Modelski Polity Leadership Cycles (ca. 220/110 years) Population 2 growth Polity 2 expansion Half cycle lag Polity 2 challenge ? Social Conflict lag Population growth lag lag Polity 1 expansion Population Disintegrative phase 1 Decline ? At the foundation of a rethinking in a network perspective of long and convoluted world historical systems change is a compelling need to study dynamics in terms of specific interactions, the ebbs and flows of differential network histories that nonetheless can be seen to operate in large part under some set of generalizable processes. Network theory can clarify basic concepts that can be used to test specific interactional hypotheses derived from principles that are both very general, but also specifically tailored to the phenomena at hand. If the reasoning of this paper is correct, then a speculative hypothesis is that a network approach to world historical systems, coupled with other theoretical frameworks, offers a series of supplemental hypotheses and potential explanations for historical change that are very specific regarding the channeling of change and that take different network contexts into account. The speculation is that with additional understanding of network predictions and explanations of network and historical dynamics, we should find that what have been taken as idiosyncratic “path dependencies” will to a large extent turn out to be network independencies, subject partly to the role of agency, but much more predictable than previously thought possible. Vertical text Time lag
