1 UC Four Campus - Human Sciences and Complexity Videoconference 1.1.1 (year 1 quarter 1 videoconf...

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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)

http://www.santafe.edu/sfi/research/inResidence/june99.html

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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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Pop. Size Hierarchy


Commerce, Finance


City Networks

Routes, Capacities



Dynamics from

Structural Cohesion



Conflicts


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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?

http://eclectic.ss.uci.edu/~drwhite/Civ/silkjadeporcelain.gif



http://eclectic.ss.uci.edu/~drwhite/Civ/spicetr.gif

http://eclectic.ss.uci.edu/~drwhite/Civ/spicetr.gif

http://eclectic.ss.uci.edu/~drwhite/Civ/goldsalt.gif

http://eclectic.ss.uci.edu/~drwhite/Civ/goldsalt.gif

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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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21


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Some changes in the medieval network from 1000 CE


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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)


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Pop. Size Hierarchy


Commerce, Finance


City Networks

Routes, Capacities



Dynamics from

Structural Cohesion



Conflicts


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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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Pop. Size Hierarchy


Commerce, Finance


City Networks

Routes, Capacities



Dynamics from

Structural Cohesion



Conflicts


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

40Chinese phase diagram

http://eclectic.ss.uci.edu/~drwhite/Civ/Spuf/Tur/ChinaPopWarPhaseSpace.jpg

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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)

http://eclectic.ss.uci.edu/~drwhite/Civ/Spuf/Tur/ChinaEnglandPopWarPredictions.jpg

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Pop. Size Hierarchy


Commerce, Finance


City Networks

Routes, Capacities



Dynamics from

Structural Cohesion



Conflicts


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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



STATES MARKETSfrom factions & coalitions from structurally cohesiveto sovereignty - emergent k-components - emergent Spatiopolitical units Network units (overlap)


Pop. Size Hierarchy


Commerce, Finance


Dynamics from

Structural Cohesion



Conflicts


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

48

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

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