UC Four Campus - Human Sciences and Complexity
Videoconference 1.1.1 (year 1 quarter 1 videoconf 1)
1
UC Four Campus - Human Sciences and Complexity
Please send and encourage submissions and commentaries
Our newly inaugurated electronic journal
Videoconference 1.1.1 (year 1 quarter 1 videoconf 1)
2
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)
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)
4
Outline re: civilizations as dynamic networks
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 transitions
2A 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
5
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
6
Co-evolution of Cities and City Networks
City attributes and
distributions
Dynamics from
City Networks
Pop. Size Hierarchy
Structural Cohesion
Routes, Capacities
Urban Industries plus
Unit Formation (e.g. polities)
Velocities and Magnitudes
of trade
Commerce, Finance
Hinterland Productivity
STATES
from factions & coalitions
to sovereignty - emergent
Spatiopolitical units
Demography/Resources
Conflicts
Interference and
attempts at regulation
Sources of boundary
conflicts
Organizational transformation
of nodes
MARKETS
from structurally cohesive
k-components - emergent
Network units (overlap)8
Co-evolution of Cities and City Networks
City attributes and
distributions
Dynamics from
City Networks
Pop. Size Hierarchy
Structural Cohesion
Routes, Capacities
Urban Industries plus
Unit Formation (e.g. polities)
Velocities and Magnitudes
of trade
Commerce, Finance
Hinterland Productivity
Demography/Resources
Conflicts
Organizational transformation
of nodes
9
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.
Lübeck
(early slide, merely
illustrative, not to scale,
network incomplete)
banking network
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.
10
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.
Red 3-components
Middle East and its
3-component also
In Northern Europe the main Hanse League port of Lubeck had about 1/6th the trade of Genoa, 1/5th that of
11
Venice.
Bipartite network cohesion in Hanse saintly brotherhood trade organization
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.
Core towns
Northeast
Linking
kaufmannskirchen
(by Saint name)
Southwest
Distant towns
Additional linking
kaufmannskirchen
Given its 13th C betweenness centrality, Genoa generated the most wealth
Betweenness 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
13
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.
14
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?
15
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
16
During classical antiquity
trade routes become much
more structurally cohesive
from China to France
17
Cohesive extension of trade routes leads to a host of other developments…
(figures courtesy of Andrew Sherratt, ArchAtlas)
18
Multiconnected regions => structural cohesion variables
19
Multiconnected regions => structural cohesion variables
20
Multiconnected regions => structural cohesion variables
21
Multiconnected regions => structural cohesion variables
Some
changes in
the medieval
network from
1000 CE
22
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 25year 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)
23
Co-evolution of Cities and City Networks
City attributes and
distributions
Dynamics from
City Networks
Pop. Size Hierarchy
Structural Cohesion
Routes, Capacities
Urban Industries plus
Unit Formation (e.g. polities)
Velocities and Magnitudes
of trade
Commerce, Finance
Hinterland Productivity
Demography/Resources
Conflicts
Organizational transformation
of nodes
24
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)
25
1A World city sizes scaling: inadequacy of power law slope α, scale K variables
1000
Illustrative "power-law" y(x) ≈ K x-α with a superlinear
slope coefficient α
1000
100
10
100
1
Not a good fit to
overall city size
distributions
10
1
10
100
But does fit the upper bins
frequencies for city sizes
1
1
10
100
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"
26
1A World city sizes scaling: empirically fitted slope q, scale κ (kappa) variables
8
The governing
equation fitted here
is q-exponential
q (NLS)
κ detrended
6
4
2
0
-250
0
250
500
750
1000
1250
1500
1750
2000
The observed
relation of q and
detrended κ is not
one of definition.
-2
-4
At time t, population y(x) ≈ y(0) [1 + (1–q) x/κ)]1/(1–q), as function of q, κ, binned size x
q ~ 1.8 ± .3, y(0) follows from q and k
A lower q is a more egalitarian city
size distribution with few big hubs. A
lowering begins after 1000 CE and
ends between 1250 and 1290.
Inegalitarian q to 1550. Lowering
with New World colonization ends
btwn 1750 and 1800. Inegalitarian
to 1925. Currently low (few big).
100.00
y = 0.000052x + 1.700698
R2 = 0.016064
10.00
1.00
0
5
10
15
0.10
20
25
Hundreds
-1.5684
y = 12298x
2
R = 0.8263
0.01
27
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
= 1/(1-q) is the slope of the upper bins in the curve fitting.
α
At time t, population y(x) = y(0) [1 + (1–q) x/κ)]1/(1–q), as function of q, κ, binned size x
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.
28
1B Contiguous time periods (verified by runs test), discrete (1-7) variable
1985
15.00
1980
kDetrended
10.00
1965
1975 1960
1970
1950 1955
5.00
1700
1650
1600
1250
1100
0.00
1200 1150
1.20
1.40
361
-200
622
800 100
1850
1750
1000 1400
1450
1925
1800 1825
1.60
qNLS
Egalitarian hierarchy (few hubs)
1.80
2.00
1900
2.20
Inegalitarian hierarchy
29
1B, Periodization: Value clusters for historical q-period hierarchy variable q
Inegalitarian
Egalitarian q (high)
Intermediate
κ below trend line
Inegalitarian
Egalitarian
(few hubs) q (low)
κ above trend line
(1) 200 BCE–900
(2) 1000
Conflict
(3) 1100–1250
Medieval .
(4) 1300–1550
Conflict (p = .05; p = .01)* (5) 1600–1750 European Expansion
Period
Renaissance
(6) 1800–1925
Industrial (p = .01; p = .0001)* (7) 1950–1995
Consumer
.
Period
EExpansion
* Significance tests for left versus previous and current right entries. Only NLS values are used.
Revol.
Revolution
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.
30
power law coef. α = – 2 = = -1/(q-1)
thus q = 1.5 at the asymptote (few hubs, more equality)
(for 2005)
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 = 5.5
q =2.08 κ = 2.4
q =2.01 κ =2.2
q =1.84 κ = 1.7
q =2.1 κ =0.68
q =1.9 κ = 0.59
more inequality
at the asymptote:
(for 1800)
α = –1
q=2
The entire city-size distributions for these 18 time
periods are fitted by q and κ, not just the upper size bins
This is what the actual
scaling looks like.
In this series the upper bin
slope is going from q ~ 2 in
1800 (inegalitarian, α= - 1)
to q ~ 1.5 (egalitarian) 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 sizebins, 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.
31
Co-evolution of Cities and City Networks
City attributes and
distributions
Dynamics from
City Networks
Pop. Size Hierarchy
Structural Cohesion
Routes, Capacities
Urban Industries plus
Unit Formation (e.g. polities)
Velocities and Magnitudes
of trade
Commerce, Finance
Hinterland Productivity
Demography/Resources
Conflicts
Organizational transformation
of nodes
Scarcity;
Periods of: Inflation;
Competition;
Sociopolitical
violence;
32
2A Cities and hinterlands context variable
World population 'response'
1250
Kremer data; Fitted Coefficients of Equation 1, Nt = CN / e(t0 – t)
k
CN
Up to (following period)
Period
Length
-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
?
c.100?
2.0
Start Year
Post-1962 (ditto)
Log of Length
.
(linearly decreasing)
33
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.
34
Hegemony-type and inflation as q-correlated temporal variables
C
CCC
CCCC CC
I ???
?I I I I I I I I I I I ?
?I I I I I I
q Hi
? ? ? ? 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 Inflation Lo/hi
P
P
?
? pP
? ? ? EEEEEEE?
? EEEEEEE?
EEE q Lo
FFFFFFF
FFFFF
FFF
1
1
1
1
1
1
1
1
1
1
2
0
1
2
3
4
5
6
7
8
9
0
02570257025702570257025702570257025702570
05050505050505050505050505050505050505050
L/h lo/hi inflation figures (L=depression) are for that year forward
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 => I (inegalitarian city hierarchy)
initial F => E (egalitarian city hierarchy)
L gives way to h (high inflation) within E, I
35
Summary of historical correlates of hierarchy variable 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.
Inegalitarian q (high)
Egalitarian q (low)
(hubs)
κ below trend line
κ above trend line
(growth)
Periods of Low Inflation
Periods of High Inflation
(#s are q - κ periods)
High
2-3
-1320
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
EuroHegemonic
hubs
(1340-85)
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
36
Euro-Hegemon
examples
(Arrighi 1994)
Commercial
Financial
Constantinople
Venice
Genoa
Amsterdam
London
New York
37
Stylized facts:
Economic macro variables
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.
Renaissance
Equilibrium
(begins with
economic
depression)
1900
38
Dynamic interaction and organizational outcome variables
• 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.
39
Chinese phase diagram
40
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
41
Turchin tests statistically the interactive prediction versus the inertial prediction for
England, Han China (200 BCE -300 CE), Tang China (600 CE - 1000)
42
Co-evolution of Cities and City Networks
City attributes and
distributions
Dynamics from
City Networks
Pop. Size Hierarchy
Structural Cohesion
Routes, Capacities
Urban Industries plus
Unit Formation (e.g. polities)
Velocities and Magnitudes
of trade
Commerce, Finance
Hinterland Productivity
Demography/Resources
Conflicts
Organizational transformation
of nodes
43
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.
(Relative to Carrying Capacity)
Prices
Real wages
(low)
Inflation
In kind payment of serfs,
retainers salaried laborers
Demand for
prestige goods
Poverty forces more
meltdown of silver
Demand for
money rents
Peasants
to cities
Elites to cities
Conspicuous
consumption
Demand for
silver mining
Coinage
Monetization
(Velocity of Money in Exchange)
Effects of Inflation of Land
Thresholds
(Variables affecting transition)
on Monetization
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
44
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 macroorganizational 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.
45
Co-evolution of Cities and City Networks
City attributes and
distributions
Dynamics from
City Networks
Pop. Size Hierarchy
Structural Cohesion
Routes, Capacities
Urban Industries plus
Unit Formation (e.g. polities)
Velocities and Magnitudes
of trade
Commerce, Finance
Hinterland Productivity
STATES
from factions & coalitions
to sovereignty - emergent
Spatiopolitical units
Demography/Resources
Conflicts
Interference and
attempts at regulation
Sources of boundary
conflicts
Organizational transformation
of nodes
MARKETS
from structurally cohesive
k-components - emergent
46
Network units (overlap)
References
– 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.
47
The current updates of these slides can be found at
http://eclectic.ss.uci.edu/~drwhite/Conferences.html
Some slides were
removed for purposes
of simplification
48
No good measures yet worldwide for everything in this set of variables
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.
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.
49
Email comments from collaborators regarding variables kappa and q
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 lowerlevel trade networks to show
cities varying in size through time
the industries that have located or relocated in the cities, and
the industrial processing chains that run through the cities --drw
50
year
urban
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
51
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 history
A 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, ecologies
A economic growth
B organizational change at macro level and micro level
3 Intercity networks
A Cohesion: Core versus Peripheries
• Contribution to Market equilibrium vs. Structural Inflation
B Centrality: Betweenness versus Flow
• Contribution to Commercial vs. Financial capital and organization
52
Hegemony-type and inflation as q-correlated temporal variables
C
CCC
CCCCCC
E? ? ?
? EEEEEEEEEEE?
? EEEEEE
q Hi
? ? ? ? L ? ? ? h h h h L L L L L L L L h h h h h h L L L L h HL L L L L h h h L Inflation Lo/hi
P
P
?
? pP
???I I I I I I I ?
?I I I I I I I ?
I I I q Lo
FFFFFFF
FFFFF
FFF
1
1
1
1
1
1
1
1
1
1
2
0
1
2
3
4
5
6
7
8
9
0
02570257025702570257025702570257025702570
05050505050505050505050505050505050505050
L/h lo/hi inflation figures (L=depression) are for that year forward
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 => I (inegalitarian city hierarchy)
initial F => E (egalitarian city hierarchy)
L gives way to h (high inflation) within E, I
53