Post on 22-Dec-2015
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UC Four Campus - Human Sciences and Complexity
Videoconference 1.1.1 (year 1 quarter 1 videoconf 1)
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UC Four Campus - Human Sciences and Complexity
Videoconference 1.1.1 (year 1 quarter 1 videoconf 1)O
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Civilizations as dynamic networks
Cities, hinterlands, populations, industries,
trade and conflict
Douglas R. White
© 9/30/2005
All rights reserved(45 slides follow - can also be viewed on the
web-my 'conference paper' site)
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acknowledgements
Thanks to the International Program of the Santa Fe Institute for support of the work on urban scaling with Nataša Kejžar and Constantino Tsallis, and thanks to the ISCOM project (Information Society as a Complex System) principal investigators David Lane, Geoff West, Sander van der Leeuw and Denise Pumain for ISCOM support of collaboration with Peter Spufford at Cambridge, and for research assistance support from Joseph Wehbe. Also thanks to David Krakauer at SFI in suggesting how our multilayered models of rise and fall of city networks could be guided by sufficient statistics modeling principles and to Lane and van der Leeuw for suggestions on the slides. This study is complemented by others within the ISCOM project concerned with urban scaling and innovation.
I also want to thank Peter Spufford for his generous support in providing systematic empirical data on intercity networks and industries in the medieval period to complement the data in his book, Dean Anuska Ferligoj, School of Social Sciences, University of Ljubljana, for five weeks of support for work carried out with Kejzar in Ljubljana in summer, 2005, Celine Rozenblat (ISCOM project) for providing the historical urban size data, and Camille Roth (Polytechnic, Paris) for his collaborations on representing medieval evolutions of multiple industries across city networks.
A jointly authored on this project is in draft with Spufford. (possibly others)
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some main approaches and areas of findings
1 Rise and fall of intercity networks (e.g., trade and disruption)1A Key concept: structural cohesion and its effects, such as market zones
and price equilibrium vs. inflation in cohesive cores versus peripheries (White and Harary 2002 SocMeth, Moody and White 2003 ASR)
1B Similarly, effects of network betweenness versus flow centrality on commercial vs. financial capital and institutional organization
2 Urban scaling: distributional scaling and historical transitions2A City growth and inequality parameters: From Zipf's rank size laws to
power laws to a stronger scaling theory of q-exponentials
2B Periodization: Historical q-periods and their correlates• Commercial vs. Financial capital and organization• Market equilibrium vs. Structural Inflation
3 Interactive dynamics: world population, cities and hinterlands, polities 3A economic growth versus sociopolitical conflict
3B organizational change at macro level and micro level
Outline re: civilizations as dynamic networks
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variables
Will look first at networks - as dependent outcome (rise and fall of intercity networks) and as entailing potential variables shaping outcomes such as– Network unit formation (with overlaps): trading zones (structural
cohesion) and market pricing, relation to emergence of political units– city centralities (betweenness and flow) and hegemony– question how and why networks of cities rise and fall
Then attributes of cities and hinterlands– How city distributions scale by size– How the scaling varies with time– What are the oscillations, periods, and critical variables as scaling
parameters change
Finally, the variables that connect them dynamically
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City Networks
Routes, Capacities
Velocities and Magnitudes of trade
Organizational transformationof nodes
STATES MARKETSfrom factions & coalitions from structurally cohesiveto sovereignty - emergent k-components - emergent Spatiopolitical units Network units (overlap)
City attributes and distributions
Pop. Size Hierarchy
Urban Industries plus
Commerce, Finance
Hinterland Productivity
Dynamics from
Structural Cohesion
Unit Formation (e.g. polities)
Demography/Resources
Conflicts
Co-evolution of Cities and City Networks
Interference and attempts at regulation
Sources of boundary conflicts
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City attributes and distributions
Pop. Size Hierarchy
Urban Industries plus
Commerce, Finance
Hinterland Productivity
City Networks
Routes, Capacities
Velocities and Magnitudes of trade
Organizational transformationof nodes
Dynamics from
Structural Cohesion
Unit Formation (e.g. polities)
Demography/Resources
Conflicts
Co-evolution of Cities and City Networks
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For example, among medieval merchants and merchant cities of the 13th century, cohesive trade zones (gold nodes) and their potential for market pricing supported the
creation of wealth, with states benefiting by marketplace taxation and loans.
The Hanse League port of Lübeck at its peak had about 1/6th the trade of Genoa, 1/5th that of Venice; its network (slides ahead) had a well documented colonial and religiously-based organization.
(early slide, merely illustrative, not to scale, network incomplete)
Lübeck
banking network
11In Northern Europe the main Hanse League port of Lubeck had about 1/6th the trade of Genoa, 1/5th that of Venice.
Red 3-components
Middle East and its 3-component also
With expanded coding and further road identification for the medieval network, 2nd- (gold) and 3rd-order cohesiveness (red nodes) reveals multiple cohesive zones such as those of Western Europe or the Russian plains. Again, this cohesion supported the creation of wealth among merchants and merchant cities, with states benefiting by taxation and loans.
Core towns
Linking kaufmannskirchen (by Saint name)
Distant towns
Additional linking kaufmannskirchen
Medieval Hanse trading towns had religious brotherhoods under a Patron Saint for a distant church of the same Saint (kaufmannskirch), which hosted the traders and protected their goods. The more distant the trading locations, into foreign lands, the more frequent the construction of matching kaufmannskirchen.
Northeast
Southwest
Bipartite network cohesion in Hanse saintly brotherhood trade organization
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Given its 13th C betweenness centrality, Genoa generated the most wealthBetweenness centrality in the trade network ought to predict accumulation of mercantile wealth. Genoa has greatest wealth, as predicted. As a distinct episodic event, on September 7th 1298 Genoa defeated the Venetian fleet in battle.
Size of nodes adjusted to indicate differences in betweenness centrality of trading cities in the banking network
Betweenness Centralities in the banking network
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Flow centrality (how much total network flow is reduced with removal of a node) predicts something entirely different: the potential for profit-making on trade flows. It necessarily reflects flow velocities central to the organizational transformations undergone in different cities, as Spufford argues.
This type of centrality is conceptually very different. It maps out very differently than strategic betweenness centers like Venice or Genoa, which are relatively low in flow centrality.
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RISE AND FALL
Silk, Jade and Porcelain from China
- Spice trade from India and SE Asia
- Gold and Salt from Africa
The lead-up to the 13th C world-system and its economy
was a period of population expansion and then crisis as
environmental carrying capacities were reached.
In the 14th C, economic depression set in, inflation abated and
population dropped, with famines beginning well before the Black
Death. After closure of the Golden Horde/Mongol Corridor (1360s), the EurAsian network crashed.
To illustrate the effects of structural cohesion in the trade route network on the development of market pricing versus structural inflation, we could start with the AfroEurasian world-system at the end of the pre-classical period in 500 BCE -
What came before the medieval networks rise and fall?
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These trade routes mostly form a tree, with
a narrow structurally cohesive trading zone (with market potential) from India to Gibraltar
Trade networks before 500 BCE were smaller, even more tree-like, and
lacking cohesion
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During classical antiquity trade routes become much more structurally cohesive
from China to France
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(figures courtesy of Andrew Sherratt, ArchAtlas)
Cohesive extension of trade routes leads to a host of other developments…
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Multiconnected regions => structural cohesion variables
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Multiconnected regions => structural cohesion variables
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Multiconnected regions => structural cohesion variables
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Some changes in the medieval network from 1000 CE
Multiconnected regions => structural cohesion variables
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to 1500 CE
(note changes in biconnected zones of structural cohesion)
Project mapping is proceeding for cities and trade networks for all of AfroEurasia and urban industries for Europe in 25-year intervals, 1150-1500
(our technology for cities / zones / trade networks / distributions of multiple industries across cities for each time period includes dynamic GIS overlays, flyover and zoomable web images)
Multiconnected regions => structural cohesion variables
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City attributes and distributions
Pop. Size Hierarchy
Urban Industries plus
Commerce, Finance
Hinterland Productivity
City Networks
Routes, Capacities
Velocities and Magnitudes of trade
Organizational transformationof nodes
Dynamics from
Structural Cohesion
Unit Formation (e.g. polities)
Demography/Resources
Conflicts
Co-evolution of Cities and City Networks
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1-3 Independent and dependent variables
focus now on 1 and 2, taking 3 as dependent variable
1 Attributes of cities: historical scaling of city sizes (28 periods, data from Tertius Chandler)
– Three bears analogy– Classic Zipf rank size law 1/f except that shape varies by context, rather than constant. Porridge too cold.
Chair too big. Bed too soft. Momma bear.
– Power law α holds for upper bins only. Too hot. Too big. Too hard. (Papa bear). power-law y(x) ≈ K x-α with a superlinear slope coefficient α.
– q-exponential holds for entire distributions and for upper bins q = 1 - 1/α. Oscillations in q break history into pieces (Baby bear's chair). No universal law of cities, to the disappointment of physicists.
2 Hinterland context of cities– Including total population (rural and urban)– Rural industry and agriculture
3 Networks of cities: rise and fall of intercity networks as dependent variable (also mediating network variables)
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1A World city sizes scaling: inadequacy of power law slope α, scale K variables
United Nations data for world cities, 1950
Vertical axis y = cumulative number of cities at this bin or higher
(inset: y = cumulative number of people in these cities)
Horizontal axis x = binned logs (city size)
Dashed line = portion of distribution that is "power-law"
Illustrative "power-law" y(x) ≈ K x-α with a superlinear slope coefficient α
1
10
100
1000
1 10 100
1
10
100
1000
1 10 100Not a good fit to overall city size distributions
But does fit the upper bins frequencies for city sizes
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y = 12298x-1.5684
R2 = 0.8263
y = 0.000052x + 1.700698
R2 = 0.016064
0.01
0.10
1.00
10.00
100.00
0 5 10 15 20 25Hundreds
1A World city sizes scaling: empirically fitted slope q, scale κ (kappa) variables
At time t, population y(x) ≈ y(0) [1 + (1–q) x/κ)]1/(1–q), as function of q, κ, binned size x
-4
-2
0
2
4
6
8
-250 0 250 500 750 1000 1250 1500 1750 2000
q (NLS)
κ detrended
q ~ 1.8 ± .3, y(0) follows from q and k
A lower q is a more inegalitarian city size distribution in the upper size bins.
A lowering begins after 1000 CE and ends between 1250 and 1290. Egalitarian q to 1550. Lowering with New World colonization ends btwn 1750 and 1800. Egalitarian to 1925. Lowering may end by 2005.
The governing equation fitted here is q-exponential
The observed relation of q and detrended κ is not one of definition.
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At time t, population y(x) = y(0) [1 + (1–q) x/κ)]1/(1–q), as function of q, κ, binned size x
1A World city sizes scaling: slope q, scale κ (kappa) variables
Why do I use the two-parameter scaling formula below rather than fitting an urban scaling power-law y(x) = K x-α with a superlinear slope coefficient α?
1. City size power-law scaling only fits a differential slope across upper-sized bins, which is precisely what q measures, recalling that q = 1 -1/α, and that α = 1/(1-q) is the slope of the upper bins in the curve fitting.
2. More generally, superlinear phenomena have some self-amplifying process in which the pull of the more active elements operates against drag in the bulk of the network interactions they are drawing upon. There are over 8,000 recent articles in physics reexpressing the basic laws of Boltzmann-Gibbs in terms of this generalization, for near-equilibrium phenomena that retain structure over time. In contrast, where there are no self-amplifying processes, but only random interactions, the equation asymptotes for structureless processes to q → 1, the standard measure of entropy (see Tsallis 1988)
3. Kappa and q give more accurate assessment of change and epochal shifts.
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1B Contiguous time periods (verified by runs test), discrete (1-7) variable
1.20 1.40 1.60 1.80 2.00 2.20
qNLS
0.00
5.00
10.00
15.00
kDet
ren
ded
-200
100
361622
80010001100
11501200
12501400
1450
160016501700
1750
1800 1825
1850
19001925
1950 1955
19601965
19701975
1980
1985
Inegalitarian hierarchy Egalitarian hierarchy
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1B, Periodization: Value clusters for historical q-period hierarchy variable q
Egalitarian q (high) Intermediate Inegalitarian q (low) κ below trend line κ above trend line
(1) 200 BCE–900 (2) 1000 Conflict
Period
(3) 1100–1250 Medieval .
Renaissance (4) 1300–1550 Conflict
Period
(p = .05; p = .01)* (5) 1600–1750 European Expansion
EExpansion (6) 1800–1925 Industrial
Revol.
(p = .01; p = .0001)* (7) 1950–1995 Consumer .
Revolution * Significance tests for left versus previous and current right entries. Only NLS values are used.
Historical Variations and Correlates of Urban Hierarchy Scalar q
There are Egalitarian (E) and Inegalitarian (I) periods up to 250 years in length, highly nonrandom in runs tests (p <.00000001). Lengths of both types of q-periods grow shorter with time. The shortening is expected with superlinear growth trends which "expire" at a certain date.-----------------------------background------------------------------------------------------------------
For more background on q-scaling for network interactions, see references: White, Kejzar, Tsallis, Farmer and White, 2005, "A generative model for feedback networks" Physica A forthcoming http://arxiv.org/abs/cond-mat/0508028). For the historical city size application see White, Kejzar, Tsallis and Rozenblat Ms. "Generative Historical Model of City Size Hierarchies: 430 BCE – 2005." Santa Fe Institute wp series.
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power law coef. α = – 2 = = -1/(q-1) thus q = 1.5 (more inequality) at the asymptote
q =1.81 κ = 25 q =1.61 κ = 28
q =1.70 κ = 19 α = –2
q =1.57 κ = 19 q = 1.5
q =1.50 κ = 15 q =2.01 κ = 5.5q =2.08 κ = 2.4q =2.01 κ =2.2q =1.84 κ = 1.7 q =2.1 κ =0.68 q =1.9 κ = 0.59
more equality at the asymptote:
α = –1q = 2
This is what the actual scaling looks like.
In this series the upper bin slope is going from q ~ 2 in 1800 (egalitarian, α= - 1) to q ~ 1.5 (inegalitarian) in 2000.
If these distributions were actual power laws, they would be best-fitted by a straight line in this log-log graph.
The x axis has the city size-bins, e.g., 20.0 = 200,000 people or more.
The dotted lines show number of cities in multiples of two: 4, 8,16,32,etc.The entire city-size distributions for these 18 time
periods are fitted by q and κ, not just the upper size bins
(for 1800)
(for 2005)
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City attributes and distributions
Pop. Size Hierarchy
Urban Industries plus
Commerce, Finance
Hinterland Productivity
City Networks
Routes, Capacities
Velocities and Magnitudes of trade
Organizational transformationof nodes
Dynamics from
Structural Cohesion
Unit Formation (e.g. polities)
Demography/Resources
Conflicts
Co-evolution of Cities and City Networks
Scarcity; Inflation; Competition; Sociopolitical violence;
Periods of:
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World population 'response'2A Cities and hinterlands context variable
Kremer data; Fitted Coefficients of Equation 1, Nt = CN /
e(t0 – t)
Start Yeark CN Up to (following period) Period
Length Log of Length . (linearly decreasing)
-5000 or earlier 1.19 560000000 Classical Antiquity n.a. n.a.
-200 (q turns hi?) 0.26 36000 Medieval Renaissance c.7000 3.8
1250 (q turns hi) 0.175 19000 Industrial Revolution c.1450 3.2
1750-1860 (ditto) 0.15 1700 Consumer Economy c.610 2.8
Post-1962 (ditto) ? c.100? 2.0
1250
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q-dependent variables• Total population growth rate slower with flat city growth, but also tends to diminish at the
end of each type of q-period. Possibly a failure of innovation rate because leading cities depend on innovation.– Superlinear growth is unsustainable, generate decreasing lengths of oscillations, also general
inflection points (e.g., flattening, crisis)
• Look to historical correlates (Circum-Med): is there– Alternation in economic market trends (Inflationary vs. near-equilibrium)? (evaluated with 13
datasets: Spufford 1982; Fischer 1996)
– Alternation of forms of organization (Commercial vs. financial capital)? (evaluated with Arrighi data, 1994, 5 periods, 1100-1990)
– Alternation of trade hegemon with every new q-period (evaluated with dates of q- and other periods)
• Near the start of a q-period (following diminishing growth rate at the end of the prior q-period): Does the evidence support the following?– Market stability begins but doesn't last through the q-period.
– Structural inflation takes over, may last into the next q-period.
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Hegemony-type and inflation as q-correlated temporal variables
time-series data coded by 25 year periods, hegemonic economic organization:
C = commercial capital the (e.g., colonizing or diaspora traders)
F = financial capital the hegemonic economic organization (e.g., corporate traders)
supported propositions:
initial C, F => L (low inflation), little or no time lag
initial C => E (egalitarian city hierarchy)
initial F => I (inegalitarian city hierarchy)
L gives way to h (high inflation) within E, I
C C C C C C C C C C E ? ? ? ? E E E E E E E E E E E ? ? E E E E E E q H i ? ? ? ? L ? ? ? h h h h L L L L L L L L h h h h h h L L L L h H L L L L L h h h L P P ? ? p P ? ? ? I I I I I I I ? ? I I I I I I I ? I I I q L o F F F F F F F F F F F F F F F 1 1 1 1 1 1 1 1 1 1 2 0 1 2 3 4 5 6 7 8 9 0 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 2 5 7 0 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 5 0 L/h lo/hi inflation figures (L=depression) are for that year forward
Inflation Lo/hi
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Summary of historical correlates of hierarchy variable q
Egalitarian q (high) Inegalitarian q (low)
κ below trend line κ above trend line
Periods of Low Inflation Periods of High Inflation
(#s are q - κ periods) High 2-3 -1320 (1340-85)
1350-1520 Low 3 High 3-4 1500-1650
1650-1780 Low 4 High 4-5 1750-1810
1830-1910 Low 5 High 5-6 1925-2005
Periods of Commercial Capital
Euro-Hegemonic
hubs
Periods of Financial Capital
c.1000 Constantinople Venice c.1100-1297
1298-1380 Genoa Holland 1610-1730
1797-1917 Britain U.S.A. 1950-?Historical Variations and Correlates of Urban Hierarchy
Scalar q
As a sidebar to the last slide, all major industries and their distributions across cities in the trading city networks are also coded in generational (25 year) intervals, and the capacities of transport routes are similarly coded in 25 year intervals. Coding now complete for the Circum-Mediterranean, awaiting completion for the rest of Eurasia.
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Euro-Hegemon examples
(Arrighi 1994)
Commercial
Financial
Constantinople
Venice
Genoa
Amsterdam
London
New York
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Economic macro variables
1900
Renaissance Equilibrium (begins with
economic depression)
Stylized facts:
1. Gross World Economic Product grows not in proportion to 1/(time to singularity), as does population, but 1/ /(time to singularity)2
2. Inflation, however, is more sensitive to global and local fluctuations of population above and below its superlinear trend-line, which also correlate with q-periods.
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• Peter Turchin - in Structure & Dynamics– (2005) gives the key to dynamic interactions
between governance, conflicts, unraveling, on the one hand, and population oscillations on the other
• Peter Spufford - in Power & Profit (2002)– shows how rises in the velocity of trade in
intercity networks causes transformations in organizations.
Dynamic interaction and organizational outcome variables
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English sociopolitical violence cycles don’t directly correlate but lag population cycles. Detrended English population cycles, 1100-1900, occur every 300-200 years.
Source: Turchin
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Turchin tests statistically the interactive prediction versus the inertial prediction for England, Han China (200 BCE -300 CE), Tang China (600 CE - 1000)
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City attributes and distributions
Pop. Size Hierarchy
Urban Industries plus
Commerce, Finance
Hinterland Productivity
City Networks
Routes, Capacities
Velocities and Magnitudes of trade
Organizational transformationof nodes
Dynamics from
Structural Cohesion
Unit Formation (e.g. polities)
Demography/Resources
Conflicts
Co-evolution of Cities and City Networks
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Effects of Inflation of Land on Monetization
(Relative to Carrying Capacity) Prices Inflation Demand for Peasants money rents to cities Real wages In kind payment of serfs, Elites to cities Conspicuous (low) retainers salaried laborers consumption Demand for Poverty forces more Demand for Coinage prestige goods meltdown of silver silver mining
Monetization (Velocity of Money in Exchange)
Thresholds (Variables affecting transition)
Reorganization (to handle higher velocities)
e.g., Division of labor, new techniques, road building, bridge building, new transport
Merchants/agents Governments/agents Churches/agents Elites/agents
GET TURCHIN VARIABLES
The population and sociopolitical crisis dynamic that drove inflation in the 12th-15th centuries also drove monetization and trade in luxury goods. Inflation of land value created migration of impoverished peasants ejected from the land, demands of money rents for parts of rural estates, and substitution of salaries for payments in land to retainers.
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Modeling combined effects of layered variables
Overall, with rise and fall of networks of cities as a dependent variable, and so many independent variables, a stagewise overlay-modeling strategy becomes the focus:– Model 1: self-amplifying economic growth, with thresholds for organizational
transformation. E.g., in Medieval period studied by Spufford (2002), inflation drives monetization drives trade flows given organizational transformations that drive both elites and commoners to cities.
– Overlay model 2: Structural demography (Turchin 2005) circumscribed state population fluctuations interact dynamically with sociopolitical violence, violence interrupts trade, brings down trading velocities and cuts off links because of interpolity conflicts, flattens city hierarchies.
– Overlay model 3: q-period correlates bring in changes in macro-organizational forms (commercial vs. financial capital forms). Those models were my focus of this presentation.
At each stage, the principle of sufficient statistics is used to purge unnecessary detail.
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City Networks
Routes, Capacities
Velocities and Magnitudes of trade
Organizational transformationof nodes
STATES MARKETSfrom factions & coalitions from structurally cohesiveto sovereignty - emergent k-components - emergent Spatiopolitical units Network units (overlap)
City attributes and distributions
Pop. Size Hierarchy
Urban Industries plus
Commerce, Finance
Hinterland Productivity
Dynamics from
Structural Cohesion
Unit Formation (e.g. polities)
Demography/Resources
Conflicts
Co-evolution of Cities and City Networks
Interference and attempts at regulation
Sources of boundary conflicts
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– Arrighi, Giovanni. 1994. The Long Twentieth Century. London: Verso.
– Fischer, David Hackett. 1996. The Great Wave: Price Revolutions and the Rhythm of History. Oxford University Press
– Sherratt, Andrew. (visited) 2005. ArchAtlas. http://www.arch.ox.ac.uk/ArchAtlas/
– Spufford, Peter. 2002. Power and Profit: The Merchant in Medieval Europe. Cambridge U Press.
– Tsallis, Constantino. 1988. Possible generalization of Boltzmann-Gibbs statistics, J.Stat.Phys. 52, 479.
– Turchin, Peter. 2005. Dynamical Feedbacks between Population Growth and Sociopolitical Instability in Agrarian States. Structure and Dynamics 1(1):Art2. http://repositories.cdlib.org/imbs/socdyn/sdeas/
– West, Geoff, Luis Bettencourt, José Lobo. 2005 ms. The Pace of City Life: Growth, Innovation and Scale.
– Douglas R. White, Natasa Kejzar, Constantino Tsallis, Doyne Farmer, and Scott White. 2005. A generative model for feedback networks. Physica A forthcoming. http://arxiv.org/abs/cond-mat/0508028
– White, Douglas R., Natasa Keyzar, Constantino Tsallis and Celine Rozenblat. 2005. Ms. Generative Historical Model of City Size Hierarchies: 430 BCE – 2005. Santa Fe Institute.
– White, Douglas R., and Peter Spufford. (Book Ms.) 2005. Medieval to Modern: Civilizations as Dynamic Networks.
References
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Some slides were removed for purposes of simplification
The current updates of these slides can be found at http://eclectic.ss.uci.edu/~drwhite/Conferences.html
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Hypotheses moderately supported by data but still to be reexamined:
In the "flatter" city growth periods -- more egalitarian, as in 1800-1925 -- urban functions are also pushed out to hinterlands, with higher rates of general conflict, including the colonialization era of the industrial revolution, but at these time scales these are slow-growth periods for total population.
No good measures yet worldwide for everything in this set of variables
The more inegalitarian city growth periods, such at the Medieval urban revolution, push faster urban growth rate changes (pink line to the right shows the lagged effect of κ) and also total population growth. This may be due to financial and administrative inventiveness at the upper urban size scales that creates mechanisms for support and exploitation of larger populations, e.g., state-building. These systems reach their limits more quickly, however. The figure at right supports a Medieval dynamic in these terms that is obscured in later periods by ease of migration.
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I went through your power point: wow, this is going to be a quite impressive lecture! >Best, >> Constantino
drw reply:> thanks! but have I made any mistakes with the q-exponential and its interpretation? For example I assumed that q and kappa were, in theory, independent parameters... and used the fact they were not to help periodize the time series.
reply> I detected no major flaw in what I read. Constantino--At 11:12 27/9/2005 ---------------------------------- ---------------------------------- ----------------------------------These studies -- from a "micro-grounding" viewpoint -- link to broader complex system issues like your point about why strict power laws are maladapted to city-size hierarchies.
-- email from Camille Roth, 9/27
----------------------------------background---------------------------------------------Camille Roth at the polytechnic and I are working on "micro-grounding" the high-level phenomena (distributions and other stylized facts) using the lower-level trade networks to show
cities varying in size through time the industries that have located or relocated in the cities, andthe industrial processing chains that run through the cities --drw
Email comments from collaborators regarding variables kappa and q
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yearurban
population (%)
1300 10
1800 12
1850 20
1900 38
1950 52
1994 75
Source: Huriot and Thisse (2000, p. ix)
Percentage Urban Population in Europe http://www.few.eur.nl/few/people/vanmarrewijk/geography/zipf/
http://www.few.eur.nl/few/people/vanmarrewijk/geography/stephan/excel_figures_and_data_material.htm
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Outline: recent work on a book in progress with Peter Spufford; discoveries of some main empirical findings about civilizational
dynamics
1 cities themselves: distributional scaling and historyA City growth and inequality parameters: From Zipf's rank size laws to
power laws to a stronger scaling theory of q-exponentials
B Periodization: Historical q-periods and their correlates• Commercial vs. Financial capital and organization• Market equilibrium vs. Structural Inflation
2 Relation to world population, hinterlands, ecologiesA economic growth
B organizational change at macro level and micro level
3 Intercity networksA Cohesion: Core versus Peripheries
• Contribution to Market equilibrium vs. Structural Inflation
B Centrality: Betweenness versus Flow• Contribution to Commercial vs. Financial capital and organization