Old world city systems and economic networks 950-1950 how the growth and decline of cities and the rise and fall

of city-size hierarchies is related to the network structure of intercity connections

Doug White UC Irvine

November 20 2009 talk

Social Dynamics and Complexity

ASU

economic networks and city systems: using physics models & measures with large samples, time series, & inferential statistics

Physical measures examples

1 Entropy – minimum energy configuration given constraints/processes

2 LDC: Long-distance correlations3 q-scale entropy is (1) with (2) and power

law tails when q > 14 q-scale network – degree distribution

parameter for a network where size is a LDC q-scale attractor to & between hubs

5 Time-lag cross-correlations for city size distribution q-scale parameters.

1.Species, species populations, & energy in habitat areas (J Harte)

2.Cities depend on trading partners 3.City size distributions, power law tails 1 <

q < 24.q-scale social circles simulation model of

complex networks (White,Tsallis, Kejzar,Farmer,White 2006)

5.q-scales of cities in city-system regions show temporal time lags from 0 (synchrony) to hundreds of years

P(X ≥ x) ~ (1-(1-q)x/κ)1/(q-1) (1 < q ≤ 2)

As x max and P 0 the tail of this distribution converges to a log-log power-law slope -ß 1/(1-q), so P ~ (1- ßx/κ)-ß

As q → 1, q-scale entropy converges to Boltzmann-Gibbs entropy

Probability distribution q-fits for a person being in a city in the region with at least population x (fitted by MLE)

Smooth lines are fitted curves in successive time periods, jagged lines bootstrap point distributions used to estimate error boundsEach distribution is for all the cities of a region, e.g., China, in one of the 8 time periods, at 50 year intervals from Chandler 1987

city systems in the last millennium Shalizi (2007) right graphs=variant q-fits

The mle Pareto Type II q-scale. Measures the shape of the body of the curve, while beta10 measure fits the log-log slope of the tails, which vary independently of q.

Goodness of fit for q and beta10 are found by bootstrap probability simulation, with iterations added around each of four of the 8 periods

x = City size log of 10 thousand 1 million City size log of 10 thousand 1 million

Cum prob P(X ≥ x) on a log scale

1.0

.1

.01

.001

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.1

.01

.001

0001

Cum prob P(X ≥ x) on a log scale

(2)

(1)

Are there inter-region synchronies? Time-lag cross-correlations give

lag 0 = perfect synchrony

lag 1 = state of region A predicts that of B 50 years later

lag 2 = state of region A predicts that of B 100 years later

lag 3 = state of region A predicts that of B 150 years later, etc.

The relation of q-scales in region “MiddleEast&Afghan&India” to Chinese cities is (1) synchronously inverse but with (2) 100-150 year lags affects them positively

Whole period 900 – 1950 Credits: White, Tambayong, Kejzar 2008

76543210-1-2-3-4-5-6-7

Lag Number

0.9

0.6

0.3

0.0

-0.3

-0.6

-0.9

CC

F

mle_MidAsia with mle_China

Lower Confidence Limit

Upper Confidence Limit

Coefficient

Moving to inter-Asian regions on the Silk Road, excluding India:Time-lagged cross-correlation effects of Mid-Asian q-scale on China q-scale

(1=50 year lagged effect, 2=1 year lag, etc.)

MiddleEast&Afghan Robust Cities affect Robust Chinese Cities with 50 year lag

These and the other cross-correlations hold on average for the 1000 year time period.

76543210-1-2-3-4-5-6-7

Lag Number

0.9

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-0.3

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CC

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mle_China with mle_Europe

Lower Confidence Limit

Upper Confidence Limit

Coefficient

Chinese cities q-scale affect European cities q-scale with 100 and 300 year lags

For endpoints further away on the Silk Roads:Time-lagged cross-correlation effects of China q-scale on Europe q-scale

(100 year lagged effect)

76543210-1-2-3-4-5-6-7

Lag Number

0.9

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CC

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logSilkRoad with EurBeta10

Lower Confidence Limit

Upper Confidence Limit

Coefficient

Time-lagged cross-correlation effects of the Silk Road trade on Europe’s beta(beta is the slope of the power-law tail of the urban distribution)

(50 year lagged effect)

76543210-1-2-3-4-5-6-7

Lag Number

0.9

0.6

0.3

0.0

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-0.9C

CF

mle_MidAsia with mle_Europe

Lower Confidence Limit

Upper Confidence Limit

Coefficient

Chinese Silk road trade affects Elite tails of European Cities with a 50 year lag

Mideast cities q have a small effect on European cities q with 150 year lag

76543210-1-2-3-4-5-6-7

Lag Number

0.9

0.6

0.3

0.0

-0.3

-0.6

-0.9

CC

Fmle_Europe with ParisPercent

Lower Confidence Limit

Upper Confidence Limit

Coefficient

European cities q-scale synchrony with % of France population living in Paris, with 100 year decay

Variations in q and the power-law slope β for 900-1970 in 50 year intervals

city systems in the last millennium

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MinQ_BetaBeta10MLEqExtrap

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Credits: White, Tambayong, Kejzar, Tsallis, 2006, 2008

Are these random walks or historical Periods? Runs Test Results

city systems in the last millennium

Runs Tests at medians across all three regions

MLE-q Beta10 Min(q/1.5,

Beta/2) Test Value(a) 1.51 1.79 .88 Cases < Test Value 35 36 35 Cases >= Test Value 36 37 38 Total Cases 71 73 73 Number of Runs 20 22 22 Z -3.944 -3.653 -3.645 Asymp. Sig. (2-tailed) .0001 .0003 .0003

Runs Test for temporal variations of q in the three regions mle_Europe mle_MidAsia mle_China Test Value(a) 1.43 1.45 1.59 Cases < Test Value 9 11 10 Cases >= Test Value 9 11 12 Total Cases 18 22 22 Number of Runs 4 7 7 Z -2.673 -1.966 -1.943 Asymp. Sig. (2-tailed) .008 .049 .052

a Median

Is there Eurasian synchrony or are there time-lagged effects?

Some synchrony when dependent variable closely related in same region but conservative (Euro beta, Paris %)

Mostly time-lagged effects of leading regions, i.e., directional not symmetric, with lagging regions, consistent with Modelski & Thompson 1996 w Devezas 2008, i.e., globalizing econ./pol. leaders

Next: we test whether, if economic competition is increased by multiconnectivity (structural cohesion):

The dynamics of trade is influenced by the trading network: whether monopolized by chokepoints or competitive.

Leaders-to-lagger economic effects follow the dynamics of trade.

0900 AD

low q with thin power law tails of global hubs CORRELATES with global network links

From first stirrings of globalization to the 21st Century Credits: White, Tambayong, SFI

Europe

Central Asia

Medit. China

Near East

India

In these slides I will connect the city network & city size distributions and power-law tails connected to q-exponential scaling of city sizesQ

(sc

alin

g si

zes)

ChanganChanganChangan

Bagdad & Changan (Xi’an)

Silk routes

1000 AD

960: Song capital at Kaifeng, invention of national markets, credit mechanisms diffuse

Global network links characterize low q (power law tail for city sizes)

Silk routes

N~3

1100 AD

Global network links characterize low q (more exponential body with power law tail for city sizes)

Silk Routes

1150 AD

Global network links characterize low q (more exponential body with power law tail for city sizes)

1127: No. Song capital of Kaifeng conquered, Song move to south, capital at Hangchow

Silk Routes diminish

1200 AD

Song capital at Hangchow

Golden Horde silk routes

Global network links characterize low q

Silk Routes diminish

1250 AD

Broken network links lead change to high q – led by China, 50 years

cutnodes edgecut

1300 AD

Broken network links characterize high q (here: tenuous interregional connectors)

1279: Mongols conquer Song

Kublai Khan Mongol trade

1350 AD

Broken network links characterize high q (here: tenuous interregional connectors)

Mongols refocus on Yuan administration of China

Silk routes unimportant

1400 AD

Renewed network links characterize low q (power law tail)

1368 Ming retake China

Silk routes unimportant

1450 AD

Renewed network links characterize low q (power law) – high q led by China, 100 years

1421 Ming move capital to Peking

Silk routes unimportant

World population growth turns super-exponential

1500 AD

Renewed network links characterize low q (power law tail) – but China high q leads change

1550 AD

Broken network links characterize high q

1600 AD

Renewed network links will lead change to low q (here: tenuous interregional connectors)

Erikson, Emily and Peter S. Bearman. 2006 Malfeasance and the Foundations for Global Trade: The Structure of English Trade in the East Indies, 1601–1833 American Journal of Sociology (2006) 112(1):195-230. Fig. 3

British/East India: circumferences of the trading circles are small, sufficient by 1720 and 1760 to induce fully competitive market pricing Network cohesion plus close regional distances

Britain

India

COMPANY ROUTES in 1620 evolve thru malfeasance by ship captains to independt market price capitalism from 1720

1650 AD

Renewed network links characterize low q (power law tail) – China crash synchronized

1650 AD

Renewed network links characterize low q (power law tail) – China crash synchronized

1700 AD

Broken network links return to high q – esp. for China leading

1750 AD

Broken network links typify high q – China leading – bifurcated world

1800 AD

Broken network links typify high q – bifurcated world

Circum-European cities start to overtake China in number

1825 AD

Broken network links typify high q – trifurcated world – best example of high local navigability

European cities overtake China in number and size

Industrial revolution

British opium trade from India

1850 AD

Broken network links typify high q – trifurcated world – but China developing power-law tail

(here: tenuous interregional connectors)

British benefit from peace treaty

1875 AD

Broken network links typify high q – bifurcated - China power-law tail thinning toward low-q

(here: tenuous interregional connectors)

British benefit as opium legalized

1900 AD

Broken network links typify high q – trifurcated Eurodominant - China leads shift to low-q 50 yrs

British benefit opium legal

1925 AD

Broken network links typify high q – trifurcated - rise of Japan - China returns to high q

British trade but opium banned

Britain lease on Hong Kong from 1898

Start of a low q Zipfian tail for world city distribution – trifurcated – but linked by airlines

1950 AD

N-cohesion (2=competitive 1=monopoly land trade) leads q-scale, dichotomized (city rise/fall),

in moving averages for 150 year periods:World land C ↑F ↑CF ↑F ↑C ↑F routes integrating

N q

N leads q to 1500, competitive trade cities

Inverse of N leads q to 1750, Portuguese & British Indian markets create choke-point trade city q-scale

q leads inverse of N (more choke-points) 1750-1900 (industrial revolution; maritime displaces land trade)

N q

N q

q N

N.Sung S.Sung Genoa Portugal Dutch Engl.British USA--- Decol. __________________ /Mongols /Venice

World land routes disintegrating

Transaction costs, hegemony and inflation as q-correlated temporal variables

Conflict on Land Sea trade routes safer than land, 1318-1453/4+ (Spufford:407)

Inflation Lo/hi

Landed Armies: safe land routes 1500-1650 Maritime Conflicts (Jan Glete)

Landed Trade Secure

Dominant Routes

Sea routes safe French Sov.

Peace of Westphalia

Baltic conflicts: connection to Novgorod and Russia (lost)

Swedish hegemony

European access

Struggle for Empire: Sea Battles to 1815

Global Maritime

Economy Industrial Rev. from 1760

Political Revolutions to 1814

Trade net

(low cost)

versus

(high cost)

Maritime (low cost)

versus

Land routes trade

(pop. growth)

Financial capital

CommercialC C C C C C C C C C I ? ? ? ? I I I I I I I I I I I ? ? I I I I I I 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 ? ? ? E E E E E E E ? ? E E E E E E E ? E E E 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

Europe and Mediterranean

Euro-Hegemon examples

(Arrighi 1994)

Commercial

Financial

Constantinople

Venice

Genoa

Amsterdam

London

New York

The Medieval pause and Conclusions• WHY DOES THE MEDIEVAL EUROPEAN RENAISSANCE ECONOMY FALTER CIRCA 1300? • Major problem in Population Growth/Resource base• Partly conflicts on land, internecine struggles • Credit crisis between North and Southern Europe• China invaded, change in Silk routes• Trade dominance in the long terms begins to shift from betweenness centrality (Genoa;

commercial capital) to global Flow Centrality (Bruges; financial capital), with later oscillations.• Major collapse, long recovery "Long 13th century” reaches to today

• Conclusions: city systems in the last millennium

• City systems unstable; have historical periods of rise and fall over hundreds of years; exhibit collapse.

• City system growth periods in one region, which are periods of innovation, have time-lagged effects on less developed regions if there are active trade routes between them. 

• NETWORKS AFFECT DEVELOPMENT. 

Parts of the story in a nutshell

Pax Mongolica: The routes are subject to policies of polities and empires, part of periods of pol./econ. dominance (Modelski et al. p.78,217)___Regional

N. Sung 930-

S. Sung 1060-

Genoa/Venice 1190-___to Global____

Mongols 1250-90-1360 trans Eurasian

Portugal 1430- Global system mapping

Holland 1540- Global capital

England 1640- Global industrial exports

Britain 1740- Global organization

United States 1850-Global information- Global market

United States 1950- Decolonization

-1990 Depolarization - Global hyperspace

Globalization• The Mongol administrative improvements of postal routes and support for

merchants on the Silk roads were key to the rise of the Mongol Empire (on the scale of the later British Empire) and a first planned policy attempt at creating new routes for global trade and political globalization, i.e., going beyond earlier Roman and Greek (Alexandrian) attempts, for example. Modelski refers to the Mongols as a failed empire because they retreated to the east to dominate China until 1912. Their success and the benefits of East-West trade, however, were the spur to Portuguese and subsequent attempts at policy engineering towards globalizing trade and the periods of attempted West-East domination.

• A next study of planned globalization will start in 1290 and review globalization policies and pitfalls.

– Globalization as a learning process– Globalization policies at attempts as dominance– The cycles of leading polities

• And the two shorter economic cycles within them– The costs of losing dominance– The effects of wars over dominance– Paths to mutual regional support and peaceful resolution of competition

end

(figures courtesy of Andrew Sherratt, ArchAtlas)

Cohesive extension of trade routes leads to a host of other developments…

Multiconnected regions => structural cohesion variables

(but the circumferences of these trading circles are large, not sufficient to induce fully competitive market pricing)

Multiconnected regions => structural cohesion variables

Multiconnected regions => structural cohesion variables

Some changes in the medieval network from 1000 CE

Multiconnected regions => structural cohesion variables

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