Civilizations as Dynamic Networks:
Monetization and Organizational Change from Medieval to Modern
Douglas R. White and Peter Spufford
(these are very preliminary notes for a book ms in preparation)
Filesource: FTP/Spufford/citiesNetwork2.doc
See: http://eclectic.ss.uci.edu/~drwhite/Civ/ for additional slides
Table of Contents
Introduction
Abstract of the Argument
1. Banks, Money and Trade Imbalances
2. Commodity Chains: Raw Materials, Finished Goods, and Consumption
3. Roads, Ports, Navigable Rivers, Ships, Vehicles, and Flows
4. Capital Cities, Wealth and Investment of Rulers and Merchants, Town Size and Inflation
5. A Network Perspective on Shifts in Economic Hegemony (Flow Centrality)
6. Volume of Trade and Commercial Transformation (Institutional Change)
7. Landed and Commercial or Capitalist Hegemony (Demographic and Network effects)
8. Transformations in Agent Space – (Paris, Champagne Fairs)
9. Polities, Sociopolitical Violence, and Wars
10. Climate, Event and Agent Data
11. Modeling
12. Conclusion
13. Bibliographic Postscript: Toward Dataset Expansion for the Eurasian World System
14. Acknowledgements
15. References
Introduction
Peter Spufford’s (2002) history of late medieval Europe in Power and Profit: the Merchant in
Medieval Europe provides a new synthesis of the transition from feudalism – based on rights and
services tied to land – to a monetized economy. The history is told in terms of networks – among
cities, merchants, peasants, elites, states and empires, ecclesiastical and other organizations – and a
rising velocity of trade that at successive thresholds transforms sites and organizations. The focus of
this study on network representation allows a reformulation of explanatory concepts that can be
measured and tested as to network effects on social transformation. The goal is to expand and formalize the explanations offered by Spufford for the many organizational and technological transformations that he recounts. Monetization, velocity of trade and thresholds beyond which organizations cannot perform without reorganization are crucial to Spufford’s explanations of the transformation that took place in this historical period, and are equally important in other contexts as well
(Chandler 1977, 1990, Iberall and Soodak 1978, Soodak and Iberall 1978). Qualitative coding of
variables also allows identification of new patterns and generalizations as well as statistical tests of
hypotheses.
This paper adopts the view that pressures towards monetization in this period were not endogenous but largely driven by the dynamics of demographic and sociopolitical instability cycles, as
described by Nefedov (2003), Turchin (2004), and Koratayev, White and Khalturina (2004). The
demographic cycle of the ‘long 13th century’ began with population expansion in 1150 and reached
a peak that provoked crisis at the end of the 13th century, as did the population peak in Braudel’s
‘long 16th century.’ Early in the 1300s famines were rife and extended (Spufford 2002:14).1 The
crisis was exacerbated by the onslaught of Black Death in successive waves, but the Late Medieval
‘economic depression of the Renaissance’ (Spufford 2002:12) did not end until 1480. Nefedov,
Turchin, Goldstone (1991) and others identified a dynamic in which – in the three great 150-300
year swings of European population between 1150 and 1920 as well as other agrarian regimes – the
peaking of population up against a Malthusian carrying capacity that initiates a rise in sociopolitical
violence. Such violence tends to abates only long after population has fallen in the period of demographic crisis. The Nefedov-inspired and Turchin-informed model at the end of this study reviews
2
Dynamics of Civilizational Networks
how the stagflation (slowing growth and rising prices), crisis, and economic depression segments of
the 13th century population cycle spurred the transition from feudalism to a highly monetized
economy, and views the resultant monetization as a key factor in the rising velocity of trade that
spurred organizational transformations leading from feudal to the modern monetized economy.
Network visualizations of historical trends and changes described by Spufford (2002) and the
preliminary hypotheses that might explain them are crucial to this effort at modeling. Six initial caveats must be stated with regard to the visualizations and hypotheses offered here. (1) The network
data presented here are not models but provide visualizations prior to modeling trade-system dynamics. The advantage of the visualizations is that they allow us to inspect the kinds of variables
that might be coded in such models, and how these variables appear, at the time-scales for which
they are coded, in time series representations. (2) The hypotheses are not models, but first-order
attempts to account for patterns in the observed data. (3) Given the kinds of data available, the variables that are coded are qualitative. The codings are largely of nodes representing towns and cities
and edges representing trade between cities. It is useful to be able to visualize this as a time-series
network. (4) The time-series are being rescaled to generations (twenty five years each) but links at
this point are coded only for changes from the 12th to 15th centuries. (5) This is not a representation of a world-system, but only of limited aspects of one region within a larger world-system dealing with cities, politics, and religion; and ignoring for the time being other types of organizations.
The larger world-system stretches through the Middle East to India and is interlinked through bulk
and luxury trade to the contemporaneous Indonesian, Far Eastern and West African regions (Wilkinson 2004). (6) These representations, which proceed in stages, are not as complex as might be
called for in models intended for statistical analyses of quantitative variables, which in any case are
rarely available. Rather, they are simplified in order to render visually – and render judgment of
adequacy – some of the main patterns of Spufford’s synthesis and analysis of medieval trade. Simplification allows lower-dimensional network analyses to be employed, e.g., assuming a few simple
types of weighted edges between comparable nodes.
These caveats stated, a considerable range of patterns of attribute data and network relationships are simultaneously and successively visualized in a network time-series. Attributes are
3
Dynamics of Civilizational Networks
mapped onto the nodes in a series of static or temporal comparisons, using size of nodes and color
(type) or scalar shading (or color intensity) of nodes, shape of nodes, and position of nodes to capture attribute variation. Thus, up to four aspects of attribute variation of nodes may be simultaneously represented. Edge relationships are mapped using width of edges, color (type) or scalar shading (weighting) of edges, solidity (versus various kinds of dotted or broken lines) and direction of
edges. Again, up to four aspects of edge variation may be simultaneously represented. Further, because edges (or arcs as directed edges) can be specified of different types, each type can have its
own weightings. Even these simple representations deal with multidimensional attribute and relational complexes. Table 1 shows some of features of network representation for the cities network.
Table 1A: Information Conveyed in Graphing the Network
NODES
Size of
nodes
Population
Centralities,
Wealth
LINES
Position of
nodes
Geographic
Color or
Shading
Polity
Capitals
Equivalence Cohesion
Scaling
Shape
of Nodes
as
needed
As
needed
Width of
lines
Importance
Solid /
Broken
Trade
Color or Direction
Shading of lines
as needed Export
Flow
War
as needed Import
Table 1B: Information Conveyed in GIS (Geographic Information Systems) Formats
POLYGONS
Size
Shape
Boundaries
Territory Polities
Religion
PIXELS
NODES
PATHS
Color
Radius
Length
Terrain
Water
Cities
Attribute
Distance
Sea
Route
Color or
Shading
The problem is not to overburden our visual representations so that they are too complex to
comprehend and thus carry the ‘story’ in Spufford’s book. The requisite networks are built out of
the maps and descriptions of changing relationships and attributes of the medieval players (cities,
polities, religious and commercial organizations, families). By analyzing the city network, for example, some dimensions are simplified into spatial positions, like that of geographic location. 2 Similarly, relative distances in a more abstract representational space can be computed in terms relative
equivalence of positions within the network (White and Reitz 1983). Such representations supple-
4
Dynamics of Civilizational Networks
ment the historical text in important ways: The network allows measurements made according to
the configuration of relationships in trading networks. Examples are centrality of nodes (Freeman
1977, Freeman, Borgatti and White 1991) and nested levels of structural cohesion of nodes (Moody
and White 2001). The results of these computations may be shown on the same spatial network by
the scalar shading of nodes. In some cases, the results of a network analysis are easier to visualize
when the spatial configuration of nodes reflects an x-y (or 3d) grid of distances determined directly
by the analysis. Such is the case, for example, with structural positions calculated in a multidimensional similarity space (Smith and White 1992). If needed, we can represent 3d outcomes of positional analysis. Hence, visualization allows some flexibility in the ways that we view the ensemble
of attributes and relationships that constitute the network and we have similar flexibility in how to
construct the network for purposes of visualization.
Choice of network visualizations and preparation of data for network measurement evolved
out of the way that Spufford (2002:12-14) organized and began his book with transformations of
trade: the increase in population and money supply; and the capital cities and commercial centers
that were most transformed by the expansion and then the contraction of trade. A strategy for mathematical modeling will be suggested for one aspect of change, namely, the effects of monetization
and network flows on episodic change in organizational complexity.
Spufford’s (2002:26) first map presents a weighting of edges in terms of the flows of credit
among cities as banking places and the concomitant courier routes involved in credit transfers. Coding began with that network, noting the different types of cities and flows, and added to it the maritime trading links which are so important to the physical transport of bulk commodities in addition
to the flow network of credits. The maritime network was not presented by Spufford (p. 398) until
the concluding chapter – exemplified only for the Venetian trade –and required that we supplement
from outside sources the maritime trade routes of the Venetians with those of the Genoese. These
initial data, after Wehbe’s compilation of the cities and populations in the figure on page 26, required only a few days of work to assemble into a network of 91 cities, displayed on the web
(http://eclectic.ss.uci.edu/~drwhite/Civ/Spufford.htm) in a series of introductory slides.
Results of this first stage of network construction led to initial network analyses that can be
Dynamics of Civilizational Networks
5
briefly summarized. Computation of betweenness centralities of the nodes in this network, ignoring
the weights of edges, showed Genoa to be the most central city, commensurate with its role in the
trade network by the early 13th C. Cohesion analysis showed a biconnected core, that is, a set of
cities in which each pair are connected by two or more independent trade routes.3 Positional equivalence analysis showed that this core could be visualized as an overall trading “circle” reminiscent of
the anthropological ‘kula ring’ rather than the core-periphery structure of the modern international
world-system (Smith and White 1992). These, however, were only suggestive and not definitive
results, i.e., provisional rather than final conclusions about network structure.
The harder work then began to decide how to treat the actual flow of commodities, including
credit and the metals used for coinage. The areas of wool production and the towns or cities that
had or imported wool and had a woollens industry are well described by Spufford (see pp. 230-233
and 328-329 for raw wool production) as were the transport routes and intermediary agents of trade.
It is not difficult to envision coding, if only in cursory form, the flows of raw wool and then of finished woollens in the network, and the flows of revenues produced; and similarly for other major
commodities and commodity flows. Revenue flows for commodities, however, were facilitated by
coinage and the scarcity of coins was offset by the flows of precious metals out of mining areas and
towns that served as mining centers. Constructing this part of the network was the next step.
Changes in agent space are exemplified by such processes as the takeover in the 1320s of
London exports by Florentines from the earlier network of Lowland European agents resident in
London (2002:238). The shift of agents-of-trade, while of utmost importance, is difficult to represent. A more complex representation would have networks of agents moving among the nodes of
the network of cities. Also coded into the network for the wool trade were the English ports of export, Lowland countries’ ports of import and changes in major trade routes.
Abstract of the Argument
Spufford’s book is not a dry descriptive historical account of a historical period: It abounds in theoretical summaries of transformations that are conversant with theories of complexity. Spufford’s
argument follows a complex interactional-systems logic. The empirical base for my development of
this argument is Spufford (2002), supplemented by Arrighi (1994), Fischer (1996), Nefedov (2003)
6
Dynamics of Civilizational Networks
and Turchin (2004).
1. Networks lack causality per se; it is strategies and activities in networks that may be causal.
2. Activities transform sites (France’s wealthiest court in Champagne  moves to Paris; Aalst,
whose nobility marries a Burgundian ruler and who shift political membership). Decisions to move
(e.g., by the Champagne nobility) might be highly influenced by changes in agent space (reconcentration of agents in Paris (2002:75).
3. Intensity of activities transforms organizations (intensity  use of agents (division of labor) 
bypassing of markets, contracts made at arm’s length in Italian examples and Champagne fairs)
4. Changes in agent space change the logic of systems.
a. In the period of Genoese economic hegemony, the agents are family oversea diasporas.
b. The new type of agent is a commercial agent that allows the merchant to stay in one place.
This might begin in one place (e.g., Florence) but ends by diffusion to other place (Examples
are the Antwerp Bourse (p.50) and later rise of Dutch firms, transformed by the new possibilities of overseas organization while staying in place).
5. System logics are reversible, although their infrastructural changes are cumulative. For example,
following Dutch economic hegemony, the new types of British Imperial agent include a diasporic
colonist, not so much on a family model as a class model. And prior to the Genoese ‘family capitalist’ diaspora there was a period of Venetian economic hegemony in which the agents were those of
the ship crews who joined Venetian corporations as a form of investment but did not form diasporas
but returned home to their trades.
6. Competition determines which actors are replaced with others. (Lowlander  Florentine agents
in England)
7. Warfare outcomes tend to be influenced by success in other forms of economic competition (e.g.,
wealth  more potential for successful armament) which further eliminates resources of defeated
competitors (e.g., destruction of a large part of Genoese fleet)
8. Population swings (e.g., Black Death) might not be the determinants of system decline. Indeed,
after the Black Death of the 1340s there was a brief period of economic boom before the European
economy went to ground. Climate shift in the 13th century also had a major negative effect.
Dynamics of Civilizational Networks
7
9. The general model of change is one where, while Malthusian constraints are not the determinants
of system decline, since major climate fluctuation and epidemic disease may act as exogenous
shocks, population cycles operate here, as elsewhere in agrarian societies (Turchin 2004), as the
major dynamic driving a host of other factors, including those that tend to affect monetization. Effects of and on monetization are extensively discussed by Spufford as a main focus of the book,
summarized as follows:
The thirteenth-century increase in the demand for luxury goods was backed up by newly-liberated quantities of
ready cash, arising from a revolution in rents. By the end of the century landlords essentially collected their rents in
money in place of a mixture of goods, services and coin, amongst which coin had been the least important. It is no
wonder that this demand for distant luxuries brought about an enormous quantitative change in the volume of international trade. Moreover, as business became focused on a limited number of particular places, or rather along a limited number of routes between those places, a critical mass was reached, so that qualitative as well as merely quantitative changes in the nature of commerce began to take place. This vital transformation could only happen when the
concentrated supply of money, and consequently of trade, rose beyond a certain critical point.
Up to that point, on any particular route, all that occurred was an increase in the volume of trade within the traditional framework. Italian merchants, for example, merely added extra mules loaded with goods to the mule-trains
that accompanied them when they ventured northwards across the Alps. However, once the critical point was
reached, the scale of enterprises allowed for a division of labour. (2002:29)
The proposal adopted here in viewing monetization not as an endogenous driver of the dynamics of
change but as a secondary effect of population dynamics results from Turchin’s (2004) arguments
and empirical evidence for the long-term cycles of population growth, stagflation, crisis and sociopolitical violence, depopulation and economic depression, and eventual recovery and regrowth.
This evidence will not be reviewed here.
Monetization, however, can be seen as the principal driver of reorganization and of increases in the complexity of organizations when velocities of money and commodity exchanges surpass
the capacity thresholds of existing organizations. This is an episodic process and operates through a
variety of channels that will be reviewed at the end of this study. The primary focus here will be on
network predictions that help to predict where, given overall patterns in the velocities of trade, specific reorganizations will tend to occur.
Nine topics of network coding guided construction of a network dataset. These involved
rules for coding new cities, towns, or in some cases, junctions, and the difference in importance of
links between them, for example, as well as changes over time.
8
Dynamics of Civilizational Networks
Generational intervals of 25 years were used to code historical changes, beginning with 1175
and ending with 1500, and thus yielding 14 time series. When a town or city had a given attribute or
industry at one of those dates (plus or minus a few years) that feature was coded as present. Links
were coded by type so that higher numbers would have higher flow or importance. Each node and
link was coded for its presence or importance and duration in time, e.g., [1175-1500]. How these
codings were done is discussed under the nine topics.
1. Banks, Money and Trade Imbalances
The commercial changes of thirteenth-century Europe were largely made possible by very substantial increases in population and an enormous growth in the money supply, to such an extent that early fourteenth-century Europe was both overpopulated in relation to agricultural productivity and also nearly everywhere accustomed to the general use of coined money, supplemented by credit. In most parts of Europe the early fourteenth-century population levels were not equaled again until the seventeenth century, or surpassed until the eighteenth. The money supply was also at a high point, not to be reached again for several
centuries. In England, for instance, over 800 tons of silver was circulating as coined motley by 1319 under Edward II, at least a
twenty-fourfold increase since the mid-twelfth century. However, at the end of Queen Elizabeth I's reign, some three centuries
later, only the equivalent of 500 tons of silver was circulating as coined money, gold and silver together. (2002:12)
The increase in the supply of money may not have been directly a cause of the late medieval commercial revolution, but it
was a necessary pre-condition for it. Without an adequate money supply available in the countryside, even if only seasonally,
the landlords could not have taken advantage of the pressure that growing population was enabling them to put on their tenants
and bring about the revolution in rents that they desired. Without such a revolution in rents the landlords could not have
achieved an enhanced standard of living and obtained a variety of choice of purchases that had not been available before. The
demand generated in the mining areas might have initiated commercial and industrial expansion, but it was the strong and sustained demand from the rulers and landowners that acted as the stimulus to continuous commercial and industrial growth almost
uninterruptedly for a century and a half. Without the concentrated force of that demand in capital cities, rather than scattered
between rural castles throughout western Europe, merchants could not have operated on an adequate scale for the revolutionary
division of labour in commerce to take place, with all its ancillary changes. (2002:59; italics mine)
Network coding began with the banking and courier routes that formed the spine of the medieval trade system. Most of these places and routes were time-coded [12-15], but Spufford will
have to detail which came later, as in the case of Buda and the bank courier route from Buda to
Venice. A careful textual transcription of the data in the entire book might catch 80% of the time
codings, but it is more reliable to get these judgments directly from Spufford.
Monetary flows come in two varieties: credit and money. Because precision in amounts or
even categorical ranking is impossible with extensive missing data, the coding strategy was rather
to estimate trade imbalances – offset by monetary or credit flow imbalances – and construct com-
Dynamics of Civilizational Networks
9
modity flows qualitatively. When the project advances to semifinal stages, it will be useful to have
Spufford check and refine the codings and results. The coding in some way reflects Spufford’s
strategy in organizing the book, where consideration of trade imbalances comes last. Trade imbalances, of course, not only occur within Medieval Europe but along the routes leading through the
Middle East and Central Asia to India and China, and along routes to North Africa and the Gold
Coast. The “spice trade” to and through India and the caravan trade were so broad in the commodities that were transported, for example, as to go well beyond mere trade in luxury goods. India may
be considered as part of the same bulk-goods world-system, recalling as well that bulk goods were
easily transported by boat and camel. Trade with China, and import of silks, for example, would be
considered to fall in the luxury-goods category of trade. While these goods were important, China
in this period would be considered a separate world-system linked by trade in luxury goods. The
world-system that includes Spufford’s Medieval Europe would include India, North Africa, the
Middle East and other regions (the Hanseatic trading network is not covered by Spufford and
should be added to the Medieval European database through auxiliary sources). The reason for considering world-system bulk-goods boundaries here is because of the heavy trade imbalance and
consequent monetary flow from Europe to India to compensate not only for luxury goods but also
countless items that were in common use in Europe’s courts and urban populations.
Hence to represent the dynamics of trade and credit or monetary flows, it was important to
include in the network the mining towns in which gold and silver were mined within Europe (especially silver, because gold was scarce in European mines and more commonly imported from Africa) and to construct the links from the mines through mining towns to urban centers and polities
where coins were minted and monetary flows originated.
From rural rent to courtly living, from banking and international trade to public revenue and military service, the long thirteenth century of the commercial revolution witnessed a series of fundamental transformations, each associated with a complete
change in the scale of, and attitudes to, the use of money. The whole period from this commercial revolution to the industrial
revolution of the eighteenth and nineteenth centuries possessed an economic unity, the basis of which was established by these
radical transformations arising from the new uses of money. These fundamental changes lay behind and underpinned the whole
fabric of medieval European trade to which this book is devoted. (2002:59)
2. Commodity Chains: Raw Materials, Finished Goods, and Consumption.
10
Dynamics of Civilizational Networks
The main economic concept for coding region, town, and city networks was that of commodity chains, starting from raw materials extracted from certain sites or regions. These move to production sites that are often towns or cities in the trade network. They may be transformed and
passed along through trade networks and middlemen until they reach consumers. As noted, this was
not possible in quantitative but only in qualitative terms. After beginning with Spufford’s mapping
of finished goods industry in cities and towns, each commodity was treated as a binary attribute of
the nodes in the network, simply coding presence/absence of each type of industry in each generation. These data were stored in an excel spreadsheet and transferred to binary partitions labeled by
industry and time period. These partitions could then be called up to color the nodes in the network
for a time period as to the distribution of each industry. The next stage (not yet complete) would
then look at the distribution of raw materials and raw material production by region for each industry, and to create nodes or regions outside of cities which would represent centers of accumulation
from which raw materials were exported to other sites or cities for industrial processes such as wool
into woollens.
The network coding of commodities reflects an approach that emphasizes dynamics over
statics. The goal was to construct commodity chains that would reflect Spufford’s description of
historical processes:
The southern Netherlands was in some sense the industrial heartland of medieval Europe. An extraordinary range of goods
was made in a broad cigar-shaped pattern of cities and towns stretching from Calais and Sluys [outport of Bruges] on the coast
to Cologne on the Rhine. Until the fifteenth century the area was intensely politically divided, so that industry was to be found
in the counties of Flanders, Hainault, and Namur, the duchy of Brabant, the prince bishoprics of Cambrai, Liege and Cologne,
the cities of Tournai and Aachen, and to a certain extent a little to the south in the county of Artois and to the north in the county of Holland. Here the finest woollens in Europe were manufactured, and also of a great deal of cheap woollen cloth and linens. The finest tapestries in Europe were made here, as well as poorer ones, and carpets. The greatest centre for brass-working
was here. So also was some iron-working, including armor and the finest swords. Pewter too was made here, and the only coalfield to be exploited on a significant scale in the Middle Ages was here as well. Here was one of the great focal points for artists
in Europe. Some of the greatest of medieval painters and miniaturists worked here, but in the context of a large production of
cheaper art and book production. This continued and expanded when printing from moveable type was invented.
Italy, north of a line from Siena to Perugia to Ancona, formed a second industrial area. An equally extraordinary and overlapping range of goods was made here. This area was split into two by the Appenines. It was also politically fragmented, although by the fifteenth century the three states of Milan, Venice and Florence controlled much of it and had absorbed a large
number of hitherto independent city states. Here there was to be found the manufacture of luxury woollens, only surpassed by
Dynamics of Civilizational Networks
11
the best from Flanders and Brabant, and also a great deal of cheap woollen cloth, of cottons and linens and fustians and the
most astonishing range of superlative silk fabrics, besides leather-working. Working in iron in northern Italy included the principal armor manufacture in Europe. The most important glass manufacture, the finest pottery, and an extensive manufacture of
soap and paper were also to be found. Here, too, was the other great focal point for artists. Most of the other great medieval
painters worked here, once again in the context of large production of cheaper art, much of which was exported, and of books.
Here too, book production continued and expanded when printing from moveable type was invented.
In the third area between the Alps and the Main, industry came relatively late on the scene, but shared many of the products
of the other two areas. Once again there was an important production of textiles, for the most extensive production of linens and
fustians grew up here. At the end of the Middle Ages this area was coming to be the most important centre of metal-working in
Europe as well. There was a growing and significant brass industry, whilst the iron industry of the Upper Palatinate made it a
late medieval Ruhr. Both fine and mass-produced armor were made. Here, too, a manufacture of paper began. The art of printing with moveable type was invented and had its first diffusion. Woodworking was important and a whole host of lesser industries, leather, glass, pottery, contributed something to trade.
The three areas were joined together by the Rhineland, by the Rhône valley and by the ever growing and improving network of Alpine passes. Together they formed a broad band from the North Sea to the Mediterranean, stretching from Umbria to
south Holland, which to a large extent coincides with Jan de Vries' early modern urban belt, or the 'blue banana' of the urban
fabric of contemporary geographers (see pp. 389-90). Outside this broad band, much industry was to be found in capital cities,
like London which still lay outside the band to the north, Paris to the west, and Rome, Naples and Palermo to the south. All
capital cities, whether inside this band or not, were to some extent industrial cities, since the court market stimulated luxury industries, either on the spot or in the immediate vicinity. The importance of such domestic court markets made for the growth of
some luxury industries which later became export industries. The broderers of Opus Anglicanum in London, the ivory-carvers
of Paris, the armour-makers of Milan, the tapestry-weavers of Brussels all catered in the first instance for a domestic, court,
market, but then exported all over Europe. Other aspects of court demand also stimulated local industry. Every capital had its
own goldsmiths and silversmiths. Other aspects of these capital cities also stimulated specialist demand, like the parchmentmakers who were so numerous in the university quarter of Paris. (2002:228-230)
The first stage of analysis was to complete and analysis the distributions of commodity production or industries for nodes in the network at each generational time period.4 The nineteen industries that were amenable to coding are shown in Table 2, together with the frequencies of presence of each industry across our sample of 250 towns and cities over the 14 generational time periods.
The industrial constant across these time period is the linens industry, with Milan and Brescia in Lombardy and Rheims in Champagne as the quality centers and lower quality production in
Northern Italy (7 towns), Southern Germany (7), and Flanders (4). Medieval noble households were
known for their colossal numbers of bed sheets. The only temporal variation in linens is Orleans
(1300-1325), intellectual capital of France in the 13th century. Classification of periods into the
12
Dynamics of Civilizational Networks
four eras shown at the bottom of Table 2 is based on clustering in similarity coefficients for the
commodity production distributions between different generational periods.
The constancy of linen and wollens production as contrasted with the relatively short period
of production of cotton fabrics has two sources. Linens are made from flax which could be grown
locally and flax as well as sheep production have a sustainable agrarian and pastoral base. Cotton
production is environmentally degrading and was practiced in the Middle East and spread in Europe
only to Southern Italy. High quality cotton was imported from Syria. Cotton could be cross-woven
with other fibers, however, which gave rise to the fustian mix of cotton-linen at lower cost than
weaves of pure cotton.
Table 2: Commodity Production City Frequency by Generational Time Periods
1
1
7
5
1
2
0
0
1
2
2
5
1
2
5
0
1
2
7
5
1
3
0
0
1
3
2
5
1
3
5
0
1
3
7
5
1
4
0
0
1
4
2
5
1
4
5
0
1
4
7
5
1
5
0
0
*
0
22
0
1
0
0
2
0
0
0
3
0
0
2
1
0
0
0
*
0
22
0
2
0
0
2
0
2
0
3
0
1
2
1
0
0
0
*
0
22
13
2
0
0
2
0
2
0
3
0
3
2
1
0
0
0
*
0
22
13
3
0
0
2
0
2
0
3
0
4
2
2
0
0
0
*
0
22
13
3
0
0
3
0
2
1
5
0
4
2
2
0
0
1
*
0
23
13
4
0
0
3
0
4
2
5
0
4
2
6
1
0
1
*
10
23
13
5
0
1
3
0
4
3
5
0
4
2
6
1
0
1
*
10
22
0
5
0
2
3
0
4
3
6
0
5
2
7
2
7
1
*
10
22
0
4
0
2
5
0
4
3
7
0
6
2
7
2
7
1
*
15
22
0
4
0
3
7
0
4
5
8
1
6
2
7
2
7
1
*
15
22
0
4
0
3
8
0
4
6
8
1
9
2
7
2
7
1
*
15
22
0
5
0
4
13
1
5
9
8
1
11
2
7
3
7
1
*
15
22
0
7
0
4
13
14
0
8
7
1
10
2
6
4
7
1
*
15
22
0
8
0
4
14
16
0
8
5
0
10
2
7
5
7
1
Era 1
Era 2 (13th C)
Era 3 (14th C)
* Data incomplete
Era 4 (15th C)
Industrial
Commodity
Code
Num.
Woollens
Fustians
Linens
Cottons
Silks
Carpets
Tapestries
Paper
Printing
Manuscripts
Artworks
Brass
Brass canon
Armor arms
Glass
Pottery
Sugar refined
Soapmaking
Coalmining
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
Dynamics of Civilizational Networks
13
Relational distributions discovered by Statistical Entailment Analysis (SEA).5 Because
the commodities data are coded in binary format for each of 14 generations, an analysis of distributional entailments among these binary variables can be made for each period. This involves crosstabulation of all the pairs of binary categories of present/absence of the nineteen industries listed in
Table 2. In addition, population sizes for 1500 for most towns and cities on the European and the
Mediterranean trade routes are coded in six discrete categories (Stier and Kirsten 1965:124-125;
Spufford 2002:94): P0 < 10,000, P1 > 10,000, and P2 > 20,000 for towns; and P3 > 50,000, P4 >
100,000, and P5 > 200,000 for larger cities. Although such estimates for comparably large samples
are not available for earlier periods, it is useful to include in this analysis the entailments between
presence of commodities in the 14 different generational periods (1175-1500) and the population
benchmark data of 1500 for binary categories P1 to P5. The cross-tabulation of all the pairs of binary present/absence variables for the industries in Table 2 is thus extended to include the five population levels P1-P5 and a coding of port cities, or 25 binary variables in toto. These cross-tabs are
done by the SEA software for each of the 14 successive historical periods to determine feature hierarchies. An identified feature sequence X → Y is one where the presence of X entails the presence
of Y, and in longer sequences such as X → Y → Z the entailments are transitive so that if X entails
Y and Y entails Z, then X also entails Z. Because these are statistical tendencies in which exceptions are allowed, whether potential entailment chains are transitive is checked empirically and rejected when the Pearson partial correlation is negative, φxz.y < 0. In the present case, only one exception (2% of the cases coded or a given pair of variables) is allowed.6 The classification of eras based
on similarities in commodity production (Table 2, last row) is validated by correlations between
entailment patterns as expressed in similarities among the entailment matrices that express the second-order or contingent patterns of industry found with statistical entailment analysis.
Six kinds of comparisons of these entailments are considered significant in these analyses.
Comparing results of successive periods for commodities only, (#1) a new entailment between a
pair of industries may appear, or (#2) a previous one may disappear. Comparing results for a commodity Ci as against a population benchmark Pj, the potential entailments Ci → Pj or Pj → Ci may
be considered in one of four ways. A Ci → Pj entailment may be considered as (#3) diffusing
14
Dynamics of Civilizational Networks
down the city rank hierarchy if in the previous period Ci → Pj+k where k > 0. In 1175, for example,
glassworks (15) is found in cities over 100,000 (P4) but diffuses down by 1200 to those with
50,000 (P3) or more. Conversely, a Ci → Pj entailment may be considered as (#4) moving up the
city rank hierarchy if in the previous period Ci → Pj-k where j > j-k > 0. This includes the null case
where Ci → P0, that is, there is no such previous entailment, but more significantly includes all cases where the previous entailment was for a lower population size. During 1300-1325, for example,
artwork (11) moves up from P3 to P4, which means that it was previously found in small cities but
now is found exclusively in the larger ones.
Note in all such entailments with population variables, population size is coded for 1500
while the commodity variables are typically for earlier periods. The entailments thus involve an
eventual size to which the town or city grows. In addition, the population variables are defined such
that P5 → P4 → P3 → P2 → P1 → P0, the last being the inclusion class of all populations. The
two final patterns involving population variables are rare inversions of the previous two: a Pj → Ci
entailment that may be considered as (#5) diffusing down the city rank hierarchy if in the previous
period Pj+k → Ci where k > 0. In 1200 and 1250, for example, P5 → manuscripts (10) because all
cities over 200,000 (in this case only Paris) have manuscripts, but in 1325, P4 → manuscripts because both Arras (P4) and Paris (P5) are now the producers of manuscripts (and by definition, cities
of rank size P4 include those of P5). Conversely, a Pj → Ci entailment would be considered as (#6)
moving up the city rank hierarchy if in the previous period Pj-k → Ci where j > j-k > 0, including
the null case where P0 → Ci, that is, there is no such previous entailment. The first appearance of
P5 → manuscripts (for Paris) in 1200, for example, is such an entailment.
Interpreted accordingly, the entailment analysis of different periods, and comparison of periods, reveal the changes in developmental sequences of successive eras. To exemplify these changes during the first era, 1175-1225, entailments are stated numerically, with arrows from one element
to another for entailments where the first implies a second when the first is present. The patterns
are then explicated verbally. A percentage following an entailment gives the level of exceptions;
those with no percentage have zero exceptions. The variable numbers for commodities are those in
Table 2. New or emergent patterns are underlined. The period that runs from 1175 to 1225 shows
Dynamics of Civilizational Networks
15
the following statistical entailments:
1175: 8 ↔ 16;
5 → P2; 15 → P4; 3 → no port, P1 2%
1200: 8 ↔ 16;
5 → P1; 15 → P3; 3 → no port; 10 → P3; P5 → 10 2%
1225: 8 ↔ 16 2%; 5 → P1; 15 → P3,4 (2%); 3 → no port; 10 → P3; 14 → 3,4 2%
Throughout this period, paper (8) and pottery (16) are found together, linens (3) are not made in
ports (25), and glassworks (15) are found initially in cities over 100,000 (P4) but diffuse to those
with 50,000 (P3) or more. The last of these patterns is of type #3 and the first appearance of glassworks in the urban hierarchy is of type #4. Similarly, silks (5) are found initially in towns over
20,000 (P2) but by 1200 diffuse to still smaller towns (P1). Starting in 1200, manuscripts (10) are
found in towns over 20,000 (P2) and for 1200 only in most cities over 200,000 (P5 → 10), an instance of pattern type #6.
In 1225 the cottons industry (4) is born and a new era begins. Cottons manufacture depended
initially on high quality raw cotton imported through Venice and Genoa from the Levant and especially Syria where the best cotton was grown. Cultivation was soon transported to Sicily and Calabria but did not grow in the north. The 1225-1300 period is the era of cotton fabric production in
Lombardy (Spufford 2002:253-255). Armor and arms (14) tend to be made in centers producing
cottons and linens (3) – and thus not in ports. Centers of glassworks (15 → P4) retain their city
rank placement but now as a subset of burgeoning cotton towns.
Eras 2 (1225-1300), 3 (1325-1400) and 4 (1425-1500) show increasingly complex secondorder patterns of entailment as the number of towns and cities with commodity production increases. Entailments for the 1225-1300 era show incremental changes up to 1275 but by 1300 the cumulative changes are massive, amounting to a developmental transformation of the division of industrial labor. Because of the complexities of the entailment structures in this and subsequent eras, the
numerical listing of entailment patterns is given in the Appendix, along with network graphics (also
viewable at http://eclectic.ss.uci.edu/~drwhite/Civ/svg-ent/) that show the transitive entailment
structure. Two graphics, for 1250-1275, are exemplified in Figure 1.
16
Dynamics of Civilizational Networks
Figure 1: Entailments for Commodity and City Size Distributions, 1250-1275
The horizontal placement of nodes in these and comparable graphs are a function of the
overall frequencies of variables labeled but do not reflect the frequencies in a particular time period.
Thus, when variables 8 and 16 have symmetric entailments, for example they will have equivalent
frequencies for that time period. Nodes are placed on the vertical so that the population size entailments are along the bottom (P5 → P4 → P3 → P2 → P1 but no P0) and other nodes placed further
up as they ascend with evolution of the urban hierarchy, so that many of the lines will slope from
the upper left to the lower right. Thus linens and woollens, for example, tend to be produced in rural
areas with small towns as production centers; and printing is closer to the urban axis than paper,
which tends to be associated with small town manufacture.
To conceptualize the magnitude of the changes in the industrial division of labor in the thirteenth century, sugar refining (17), while only in Venice, emerges in 1300 at the top of an urban
hierarchy in terms of its entailments. Its presence in Venice alone among the sample towns and cities) entails glassworks (15), which moved to P3 city rank by 1225 and to P4 rank by 1250. P4 entails manuscript (10) production, which moved to P2 city rank by 1200 and P3 rank by 1300.
Glassworks entails silk fabric (5) production, which lost P2 city rank in 1200, dropping to P1 although not then entailed by glassmaking; but silk regained P2 rank by 1275. Silk manufacture (17)
in 1300 also entails cotton fabrics (4), which gained city rank at P2 in 1250 but fell to P1 rank in
1300. In 1225 and through 1275, cottons (4) were entailed by armor and arms manufacture (14)
and glassworks (15), but only the latter had high city rank. Their position is high relative to silks (5)
Dynamics of Civilizational Networks
17
in 1275-1300, when silks entail cottons, but by 1300 cottons and silks drop in city rank to P1, i.e.,
outsourced from larger towns and cities. By 1300, glassworks (15) no longer entail cotton fabrics
but are found in silkmaking centers (5) which do not entail cotton. Cotton has by then lost its cache
in the fabrics market.
Armor and arms manufacture (14) becomes a subset of brassworks (12) in 1275 and acquires city rank P2 in 1300, while keeping its entailment of linens (3) from long before.
The early medieval codistribution of paper (8) and pottery (16) is broken in 1300, when
paper gains city rank P1, and artworks (11) emerge as a distributional subset of potterymaking
(16) as both migrate upward to P2 city rank in 1275. In 1300 it is only artworks and not pottery that
gain an entailment to manuscripts (10) while moving up to P3 rank along with manuscripts. This is
the change in the urban hierarchy that facilitated Gutenberg’s invention in 1440-1450. 1300, then,
would seem to provide an analogic precursor to the modern urban hierarchy and represents a peak
in development out of feudalism and into modernism. In 1300, for example, sugar refining in cities
is fueled by elite consumption and diffusing as a produce to smaller towns, while sugar came in the
modern industrial economy to be an urban good fueling urban as well are rural agrarian workers
(Mintz 1988).
This was, however, a tenuous and fragile system. The populations of all the 250 towns and
cities combined might not have exceeded two-three million. Mongols of the Goldon Horde, who
had demolished the city of Bagdad and executed its citizens, had the superior military force capable
of wiping out any city in Europe and now dominated the politics and trade routes of the Middle
East, even while they held open the trade corridors of the silk routes to China. It was through these
routes that the Black Plague was to enter Europe in the 1340s, reducing the urban populations by
2/3rds in many cases. The European peninsula in 1300, however, had made a transition from a periphery of the larger world system whose centers were in China and India to a semiperiphery in
which quality textile production and exports provided the basis for a new division of labor in the
urban hierarchy. The new urban hierarchy, moreover, is most developed in Venice in 1300, but in
1299 the Venetian fleet is defeated by Genoa, so that the economic base of the urban hierarchy even
there, as elsewhere, is vulnerable to conflict.
18
Dynamics of Civilizational Networks
The cottons era ended by 1325 with the development of fustians (2), the linen-cotton mix, in
Lombardy. The innovation here was recombinatory experimentation with cross-fiber fabrics, others
being silk-cotton, wool-cotton, hemp-cotton, and linen-wool. By 1375, however, Swabians were
importing raw Syrian cotton in Milan and Venice and manufacturing their own fustian cloth and
clothing.
Entailments in the period 1325-1400 are initially little changed. In 1325, silks (5) move up in
city rank from P2 to P3, and artwork (11) loses its entailment to potterymaking (16) and moves up
from P3 to P4, which now entails manuscripts (10) which continue at rank P3. Armor and arms
(14) continue their previous entailments (3,12), including city rank P2 and gain one to fustians (2)
which gains one in turn to linens (3), previously entailed by armor. Fustians manufacture is thus
developed now entirely within the linen manufacturing zones. Tapestries (7 → P5) emerge with
the highest city rank, P5, in Paris.
- - - - - - - - - - - - entailments to be completed from here
Trade Dynamics. A moving picture of changes in the commodity entailment network from
1175 to 1300 is enormously dynamic. This is a second-order story of changing division of labor in
the evolution of urban hierarchies. Completion of this picture, however, requires an equally dynamic image of raw material production responding to changing demand by the producers who transform raw goods into end-consumer commodities, and the flows of these raw, intermediate and finished commodities over the network in exchange for currency, credit, or other goods. The data on
these flows is intermittent. Raw materials also need to be mapped not from cities or towns but from
regions such the areas of sheep and wool production (Spufford 2002:328-329), as represented by
shape files in GIS format (Table 1B). Commodity flows for raw materials can be estimated by demand from the numbers and populations of towns and cities in the regions closest to or having the
cheapest shipping routes from the suppliers in relation to the consumption of raw commodities by
of capitals, courts, religious bodies and intermediate producers or merchants. For processed or finished goods the same kind of calculations can be done – given estimates of final consumer demand
-- for directed flows that utilize the bidirected links that represent roads, shipping lanes, and
transport costs. There could then be as many directed flows as needed to represent trade along a
Dynamics of Civilizational Networks
19
transport system.
Simulation. The calculation of economic flows, however, is best done not from manual estimates but from simulation of raw material and finished commodity flows as an optimization problem under the constraints of raw material availability regions, known commodity production locations and final urban demands and cost minimization for transport, tariffs and taxes. Results of programming models under linear constraints – or agent-based modeling under known constraints and
resources – can then be checked against the fragmentary flow data that are available from Spufford’s 1986, 1988, 2002) archives on medieval exchange.
3. Roads, Ports, Navigable Rivers, Ships, Vehicles, and Flows
Thus, as part of modeling the flow of raw materials to producers and of finished goods to final consumers is mapped to the network, directed links can be coded to represent how finished
products move through trade routes to markets. The mapping may be done by software that starts
from itemized lists of sources and sinks, and moves finished commodities from courses and sinks
through the shortest available links. Directed edges to indicate movement of commodities over a
road system would then be checked with Spufford as to reasonable additions to the network.
Figure 2 shows how such a coding might work, classifying cities by the combinatorics of
producing and shipping raw wool and finished woollens. Wool such as produced in northern England must reach sites that make finished woollens. Port transfers are preferred for long distances
since raw wool is a bulk good. An optimal assignment algorithm would minimize distant land
transport over sites, for example, from Hull and Boston as ports and export sites for wool, for example, to fulfill industrial demands in lowland cities across the channel and in northern Italy.
20
Dynamics of Civilizational Networks
Figure 2: Bicomponent Trade positions in the woollens trade
Wool only
Woollens
Woollens
and Woll
Wool Port
Raw wool
and Port
Woollens &
Port
A second set of refinements in coding aims at establishing when new roads or bridges were
built and, on some qualitative scale, how this affected the capacity for transport. Spufford gives detailed data indicative of road- and bridge-building projects, which were the most commonly used
roads, and when shifts took place between alternate routes.
A third refinement involves closer attention to intermediate nodes. After initial assignment of
major routes between banking sites, such as that between Paris and Troyes, that were given edge
values of 3 (2002:26, map), intermediate nodes (such as Provins) were added and for sake of continuity also given a value of three. Provins was not a banking center like Troyes but this is coded by
node attributes. The coded Paris-Troyes link in this period was then downgraded to value 0 so as to
nullify the counting of the link in network calculations such as degree, and to nullify visualization
of the link, keeping track of links at different levels. In the 15th C Provins is largely bypassed in the
Dynamics of Civilizational Networks
21
Paris-Troyes route, which is handled by time coding. (Another route change is the bypass of Aalst
in the route between Brussels and Ghent due to a royal marriage and transfer of sovereignty in the
15th C to Burgundy.)
Spufford provides a map of routes for Venetian maritime trade, and a comparable map was
located for Genoese maritime trade (Abu Lughod 1973). Spufford also describes the maritime exports and imports through ports in north-western Europe and the agents involved.
4. Capital Cities, Wealth and Investment of Rulers and Merchants, Town Size and Inflation.
All capital cities, whether inside this band or not, were to some extent industrial cities, since the court market stimulated
luxury industries, either on the spot or in the immediate vicinity. (Spufford 2002:231)
Capital cities are the centers of consumption and wealth. Spufford (2002) gives comparisons
of available indices of wealth, and elsewhere (1986: flyleaf foldout) calculates rates of inflation. In
the network dataset, cities are mapped into polities according to how political boundaries change
over time.
Population seems to be affected by two factors: political capitals from the demand side
(which furnishes occupations and is an attractor to city immigration) and commodity production on
the producer side, which may take place in rural as well as urban areas. Figure 3 shows a plot of the
population log for 1500 on the x axis and log frequency of these sizes on the y axis.7 The distribution follows a power-law, which is typical for a healthy size rank distribution. The aggregate population in this distribution, which excludes rural areas, is about 3 million people.
Figure 3: Log-Log Population Distribution (N=148)
100
10
y = 152.34e-0.714x
R2 = 0.9882
1
1
2
3
4
5
6
7
22
Dynamics of Civilizational Networks
Correlational analysis among these variables is shown in Table 3. The strongest correlation is between consumption and capitals, with banking and mercantile importance correlated with both. The
fact that inflation (N=12) correlates negatively with populating size, consumption, capitals and
ports entails, conversely, that inflation is greatest in the rural areas and small towns, like Castile
(Toledo size < 20,000) and Cologne (size <20,000 in the 14th C, went to <50,000 by 1500). The odd
case is Paris, capital of capitals, with a much higher rate of inflation between 1300 and 1360 than
expected from its size. This is also a period in which there are five spiking crises of inflation lasting
10-20 years each with fiscal reforms and revaluations that bring down the spikes. London, on the
other hand has no such spikes, is a second rank city, and has the lowest inflation rate. Why the difference between London and Paris, and what is the relation of this difference to the start of the
Hundred Year’s War in 1336, smack in the middle of the wave of Parisian crises? Earlier political
conflicts akin to those of 1336 are linked to the French courts’ inflationary spirals and reforms.8
Table 3a: City Attributes: Capitals and Consumption; Commodities and Population;
Banking and Inflation Correlates
Number of
Commodities
Produced
.56
Inflation
calculate and enter degree, btwn and flow centralities
Population
Consumption
Capitals
Ports
.53
.55
.83
.64
.24
-.55
Banking
-.34
-.49
-.50
-.46
Political conflicts, then, are part of the story of inflationary cycles, with the centralist French state
provoking inflation by loans from merchants and heavy taxation. Apart from this, however, the inflationary spirals of the peripheries pushed peasants from their lands and contributed to the process
of monetization, as we shall see.
Table 3b: Capital Cities tabulated by degree
Degree
0
1
2
3
4
5
6
7
8
Capitals
2
1
11
8
3
2
1
0
0
Other
76
14
52
18
10
6
4
1
1
Ports
Other
Dynamics of Civilizational Networks
23
Figure 4 shows capital cities (in black) for the aggregate network, and a cross-tabulation by
degree shows that capitals are likely to be on trade routes (degree 2 or 3) but not the hubs of networks, there were disproportionately port cities, as might be expected. Table 3 shows the tabulation
of capital and port cities by degree (number of links per node).
Figure 4: Trade Network Capital and Port Cities
Same,
showing
port cities
(a) capitals
(b) port cities
5. A Network Perspective on Shifts in Economic hegemony.
A network is more than a re-presentation of data because it allows measure of structural parameters that may affect strategy and activity. Flow betweenness is a network measure designed to
detect centrality of nodes in an economic system (White 1988) in which mobility is based on profit
from flows of goods sold dear (retail) and bought cheap (wholesale).9 Flow betweenness is a perfect
predictor for testing, in this Medieval era, the applicability of Arrighi’s (1994) hypothesis concerning changes in the dominant economic hegemon in Europe, that of a pendulum swing back and
forth in the type and form in capital accumulation of the hegemon. In his view, the shift in the second millennium CE is from intensive capital accumulation (Venetian trading corporations) to extensive (Genoese diaspora in the city state period) to intensive (the Dutch in the nation-state period
developing international corporate organization like the Dutch East Indies Trading company) back
24
Dynamics of Civilizational Networks
to extensive (British overseas colonization, another diaspora) and finally to intensive (the American
international corporate model, which has its origins in the 1890s, as studied by Chandler 1977,
1990). These alternations between Venetian - Genoese - Amsterdam - London - New York hegemonic forms of capital accumulation from the 12th to the 20th century, in Arrighi’s view, also represent alternations between states (city, state, and continental state, respectively) operating through
legal forms of political hegemony and looser networks (family, class) that operate through movement of administrative personnel and goods and accumulation of capital goods. I will refer to this as
Arrighi’s alternation hypothesis in the binary forms of economic capital.
Computation of flow and betweenness centralities provides a test of Arrighi’s alternation hypothesis. Although a network is not causal, position in a network structure may be predictive of
network dynamics. Taking the banking network of the 12th century as an object, as shown on the
left side of Table 4, Genoa is the central player in terms of betweenness centrality, followed by Tunis with about 2/3rd of Genoa’s centrality, and Cologne, Venice and Bruges having less than 1/4th
that of the betweenness of Genoa. North-western European cities (Bruges, Cologne and Southampton) are italicized. Among the Northern cities Cologne and Bruges were key players. Figure 4 for
the 12th C shows the network on which the analyses in Table 4 were based.
Table 4: Two forms of Network Centrality in the 12th C Banking network (N=81 cities)
Rank
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Betweenness Centrality
8
62
79
73
69
12
23
47
43
29
48
49
22
41
39
60
50
5
2
28
32
Genoa
Tunis
Trapani
Palma
Syracuse
Milan
Alessandria
Corfu
Marseilles
Malaga
Modon
Coron
Venice
Verona
Padua
Tripoli
Negroponte
Bruges
Avignon
Lisbon
Troyes
1337.896
913.624
821.124
744.848
689.757
618.423
552.342
546.667
519.219
447.912
440.000
418.000
337.167
322.006
315.090
314.333
304.000
295.699
277.310
262.346
259.423
Flow Centrality
49
62
55
50
29
5
51
26
12
2
3
14
22
34
47
56
60
23
63
64
65
Coron
Tunis
Candia
Negroponte
Malaga
Bruges
Constantinople
Cologne
Milan
Avignon
Barcelona
Naples
Venice
Ancona
Corfu
Rhodes
Tripoli
Alessandria
Bone
Bougie
Algiers
939.497
855.154
651.497
608.000
602.718
581.012
465.000
451.210
408.622
383.115
345.545
339.872
327.465
318.743
313.282
312.000
307.564
302.453
253.989
253.989
253.989
25
Dynamics of Civilizational Networks
22
23
24
25
26
27
14
55
59
74
75
26
Naples
Candia
Alexandria
Cadiz
Southampton
Cologne
253.575
250.000
250.000
247.973
242.140
238.764
66
67
39
6
32
75
Oran
Melilla
Padua
Florence
Troyes
Southampton
253.989
253.989
227.546
226.396
225.044
221.828
Betweenness is strategic centrality between trading pairs, while flow centrality is inverse to
the reduction in trade that would result if a node were removed from the network. In measuring
flow centrality as shown on the right of Table 4, however, Genoa is below the median (rank 44 of
81), Tunis and Venice are in the same ranks, but Cologne and Bruges, two of the three northern cities in Table 4 move from ranks 18 and 27 in betweenness, respectively, to ranks 6 and 8 in flow
centrality. The latter have over half the flow centrality of the leader. Note in both columns of Table
4 that 89% of the most central cities are from the south, reflecting the predominance of trade in the
Mediterranean by either criteria of centrality, although two key northern cities are positioned much
higher according to the flow measure. Note also how two of the three northern cities move up in
this comparison.
If Spufford is correct about transformations due to the volume of trade, we might hypothesize that Genoa gained its centrality by its strategic placement in the trade network, but that as the
velocity or volume of trade increases and flow centrality – which translates into potential financial
profits – becomes the criteria of success, with north-western Europe’s advantages already immanent
even in the 12th century banking network. This is consistent with Arrighi’s (Arrighi and Silver
2001) view that the economic hegemony of Genoa was based on successful competition in the trade
of commodities (dependent on strategic capacity to move goods and strike contracts) while the
emergence of economic hegemony of the Lowland countries (with Amsterdam emerging as economic hegemon in the 17th C) was based on profit maximization.
Arrighi’s thesis is that successful implementation of each of the two alternating modes of
amassing capital stimulates imitative competition which leads to failure of the current economic
hegemon, but the successful replacement eventually succeeds by substituting or falling back on the
antithetical form of capital accumulation.
6. Volume of Trade and Commercial Transformation (Institutional Change)
Nearly twenty years ago, in an essay for John Day's collection of studies, and as I repeated at the beginning of this book, I
26
Dynamics of Civilizational Networks
pointed out, for the Mediterranean, that as the amount of business focused on a limited number of particular places, or rather
along a limited number of routes between these places, passed a critical mass, qualitative changes in the nature of commerce
began to take place as well as merely qualitative ones. Until the critical scale of operations was reached, on any particular route,
all that occurred was an increase in the volume of trade within the traditional framework. However, once the critical volume
was reached, the scale of enterprises which became possible allowed for a division of labour.10 When that point was reached,
businesses became large enough and continuous enough to maintain three separate parties, sedentary merchants at home, specialist carriers, and fulltime agents. Only now can I see that the difference in scale between Baltic trade, even that of Lübeck,
and Mediterranean trade, explains why the division of labour so noticeable in the south hardly occurred in the north. With this
sort of difference in the scale of trade it is not surprising that the number of inhabitants who could be supported by it was so different. Historical demographers have estimated that in Lübeck in the late fourteenth century the population was barely more
than 25,000, which in itself made it much the largest city on the Baltic, whilst the population of Genoa is supposed to have been
four times as great. A century later the population of Lübeck apparently had not changed, whilst that of Venice was well over
four times as great."
In the last generation historical demographers have been providing more and more sophisticated estimates of population. I
hope that, in the next generation, economic historians will be able to produce some clearer estimates of the scale of trade. We
already have a great many pieces of this jigsaw puzzle, although many have been irrecoverably lost. Yet it still remains to try to
make an overall picture from them. (2002:388-9)
From a strategic point of view within Europe, the high betweenness of Genoa and Venice in
mediating trade between other cities gives them one type of centrality, but centrality based on flow
(measured by how much total network flow is reduced with removal of a node) reflects the potential for profit making on trade flows, and also reflects the flow velocities that Spufford argues are
central to the transformations undergone by different cities and trading organization. Because of the
dispersion of trade along multiple sea and land routs, higher flow velocities and flow centrality
should be distributed away from the strategic betweenness centers of Genoa and Venice. Figure 5
shows the distribution of flow centrality for the 12th C. Genoa and Venice are low in the flow centrality hierarchy (levels 3 and 4, shown by smaller than average silver spheres). At the high end are
Tunis and Malaga (the largest node and next largest), involved in the Hafsid Dynasty’s political
transformation as successors to the Almohids, with establishment of the European merchant funduks enclaves in their Ifriqiya or North African cities (p20), Bruges (third largest), undergoing
commercial transformation, and Provins (fourth largest) of the Champagne Fairs, engaged in a
transformation of agent space involving money and trade representatives that leads to the migration
of the Provins nobility, the wealthiest in France, to the new political capital at Paris at century's
end.
Dynamics of Civilizational Networks
27
The rising levels of flow centrality in the Lowland Countries of north-western Europe, even in
the 12th C, presages the type of capital accumulation -- profit making rather than commodity trade
betweenness -- that Giovanni Arrighi sees as involved in the swing from Genoese commodity trade
hegemony in the early 13th century to the eventual rise of Dutch financial capital hegemony in the
17th C., with the financial center shifting to Amsterdam.
Bruges was one of several cities that underwent transformations involving arcaded galleries
where financial functions were concentrated (the Loggia dei Mercanti on the Piazza of the Rialto in
Venice, 1322, the Lottja or Lonja in Barcelona, 1392). Prior to the development of such galleries,
brokers had come to concentrate in particular squares that gradually came to be set aside for their
use. In Bruges the van der Beurse square came to serve this function, organized by a wealthy family of innkeeper-brokers, giving rise to the origin of the name ‘Bourse.’ Similarly, the Piazza dei
Banchi in Genoa and Plaça dels Canvis de la Mar in Barcelona, or the Place du Change in Lyons,
all places with substantial flow centrality.
Antwerp underwent a further step in financial reorganization in the 15th century:
“[It] evolved from a place notable for its regular, but temporary, fairs into a city in which business was continuously being
transacted. In the 1480s, a period of civil war in the Netherlands, Antwerp replaced Bruges as the principal permanent international marketplace for north-western Europe. The ‘nations’ of foreign merchants, Venetian, Genoese, Florentine, Lucchese, Milanese, Spanish, Portuguese, Breton, English and Hanseatic, moved there.
The focus of business was the exchange, at which specialized brokers of various sorts were to be found, at fixed hours
of the day, ready to introduce to each other buyers and sellers of particular commodities, borrowers and lenders, shippers
and underwriters and to put deals together between them. (2002:50)
28
Dynamics of Civilizational Networks
Figure 5: Flow centralities of cities in the twelfth century.
7. Landed and Commercial or Capitalist Hegemony. While Arrighi identified two distinct
forms of commercial hegemony based on material flows and wealth versus monetary flows and
profits, the other major form of hegemony in the 13th century is that of territorially-based political
power. Neither Venice nor Genoa nor the financial hegemony of Amsterdam in the later period had
much of a large landed political base. All three had strong merchant navies. France, in the 13th century, was the uncontested political power emulated by other monarchies. It was also the most powerful center of elite consumption driving the European economy. Compared to the political hegemony of France, that of Genoa or Amsterdam was short lived. In relation to demographic cycles, the
commercial hegemons seem to emerge when populations throughout the region are close to carrying capacity and inflation is at its peak (‘stagflation’). Regional crisis and new economic hegemons
Dynamics of Civilizational Networks
29
of this type seem to occur together, and the duration of their hegemony seems to extend during economic depression (often with stable or falling prices and declining real wages), when the landed
powers or hegemon(s) are at their lowest in terms of wealth or energy with which to undertake new
conquests.11
While the conditions for emergence of commercial hegemons might be predictable from the
phasing of regional demographic cycles, which of the competitors for commercial hegemony is
most likely to emerge ought to be predictable from network variables concerning position or flow,
as in section 4.
8. Transformations in Agent Space.
Transformations due to growing scope and scale, and tipping points in reorganization, are one of
the dominant findings across a host of topics in Spufford’s book. The transformations of the Champagne fairs and its nobility (Spufford, 143-150, see maps on 144 and 149),12 the wealthiest in
France, are but one example. The Counts of Brie had made their capital at Provins (143), county of
Champagne. They resided in Provins in 12thC but in the 13th were more frequently at Troyes. The
fairs, concentrated in Troyes and Provins, grew in importance between 1135 and 1285. By 1170
the ‘Troy’ had become the major international currency. Two factors contributed to the decline of
the Champagne fairs. One was increased volume of trade both by sea and land that effected a division of labor (148), as in the Levant, whereby agents or partners attended the fairs while merchants
remained in Siena, Lucca, Florence, and other cities. Agents created contracts, for example, between Flanders and Italy, without having to use Champagne as an intermediary market. Perhaps
more important was the opening of a sea route to Bruges in the 1270s (147) made possible by the
Castilian conquest of Seville and Cadiz. This route was first navigated by Genoa, and then in the
early 1300s by Venice and Barcelona as well. Both factors contributed to replacement of agents
residing in Champagne with those residing in Paris or, starting in 1250, in London. The effects on
the fairs were ruinous (134-136). The fairs remained a money market for a time, but by 1400
Troyes had become a route to be bypassed on the road to Paris. After a marriage with Royalty, in
1284 the court of Champagne moved to Paris, and the fairs died. Paris absorbed the attributes of
Troyes and Provins (147).
30
Dynamics of Civilizational Networks
Figure 6: Princes and their Paris palaces c. 1400 (2002:75)
Princes and their Paris palaces c. 1400. On the left the hôtels of some of the great princes are shown within the walls of Paris. The
royal palaces are marked in bold in the key. On the right are the areas ruled by these princes. Their presence in Paris made the city
a centre of consumption that drew on princely revenues from immense areas of territory, including principalities that were not at
that time within the Kingdom of France: the Kingdom of Navarre, which was entirely independent; and the counties of Provence,
Burgundy and Hainault, and part of Flanders which were all then quite clearly within the Empire, not France. Taxpayers in Navarre or Hainault had the mortification of seeing their taxes spent in Paris, not in Pamplona or Valenciennes! (DRW note: the line
of Counts of Brie and Champagne had ceased in the 13th C as their nobility had become Royals and moved to Paris with the marriage of the heiress of Champagne and Navarre to Phillip IV in 1284).
Capital cities first sprang into prominence in the 13th C (Spufford, 60) with the remarkable
growth in urbanization, monetization and government, and as the leading landed polity, Paris was
the leader in population, attraction and influence. Figure 6 shows for the end of the 13th C how six-
Dynamics of Civilizational Networks
31
teen of the great princes in the French sphere of influence, from Flanders to Navarre, had established their hôtels within the walls of Paris.
9. Polities, Sociopolitical Violence, and Wars.
To map political systems and their changes, a list of polities was compiled from Nüsli
(2002), Periodical Historical Atlas of Europe and from McEvedy (1961), The Penguin Atlas of
Medieval History, checking them with Spufford’s text [revision is needed from the Westerman atlas
pertinent to the Germanic alliance groups]. All polities in the region with trading locations were
listed, for all dates for which McEvedy provided data (1173, 1212, 1230, 1278, 1360, 1401, 1430,
and 1478). To characterize the polities for the 12th through 15th centuries, the dates of 1173, 1230,
1360 and 1430 were chosen for vectors of polity identifiers for each location. These are maintained
in an excel file of cities and towns classified by polity membership, so that changes through time
and attributions pertinent to characterizing centuries can be examined in detail. Figure 7 shows
nodes colored by polity memberships over four centuries for those nodes for which we have coded
trade routes.
[[note: need better political atlases recommended by Spufford, especially for the political alliance groups within the German Empire]]
In the networks of Figure 7, most of the new routes appear for the 15th century, which is a
temporary artifact of not having fine-tuned as yet the addition of new routes for centuries 12-14.
What is evident, however, is that Venice (in red) and the Papal States (white) expand their territories, the major Muslim polities (purple) are either taken by Castile or other caliphates, the Byzantine Empire (orange) falls in the 13th C, and the routes from Italy to north-western Europe change
both through France (from Champagne fairs to the east to Tour to the east) and through the Lowlands (from south to north) as France expands as a kingdom in the 15th C(?).
32
Dynamics of Civilizational Networks
Figure 7: Trade and Political Units over 12-15th C (needs operation “generate in time”)
12th Century
13th Century
14th Century
15th Century
Key: node size=# of links; color=polity; width of lines=importance of the banking route
In the networks of Figure 7, most of the new routes appear for the 15th century, which is a
temporary artifact of not having fine-tuned as yet the addition of new routes for centuries 12-14.
What is evident, however, is that Venice (in red) and the Papal States (white) expand their territories, the major Muslim polities (purple) are either taken by Castile or other caliphates, the Byzantine Empire (orange) falls in the 13th C, and the routes from Italy to north-western Europe change
Dynamics of Civilizational Networks
33
both through France (from Champagne fairs to the east to Tour to the east) and through the Lowlands (from south to north) as France expands as a kingdom in the 15th C(?).
Genoa established its independence as a sea power by the decisive defeat of Pisa in 1284. In
1294 and 1298 it handed Venice decisive defeat as well. Its political hegemony began in the 14th C.
It seized Cyprus from Venice in 1373. In the War of Chioggia the Genoese were defeated at Anzio
(1378), but were victorious at Pola (1379) and then blockaded Venice. They were obliged to surrender when the blockade was broken. While this great maritime rivalry was at a standstill in the
late 14th C, by the 15th C (1431) Genoa’s fleet was defeated by Florence and Venice. Venice had
taken Verona, Vicenza into its expanded Republic, defeated Milan, and taken Brescia and Bergamo,
giving it some control over Alessandria and the route from Genoa to the north (Qn: Was Alessandria actually in the Venetian Republic in the 14th C as shown?).
10. Event and Agent Data
The coding of agency requires a separate and later stage of coding. This requires going
through Spufford’s book not only to annotate all the details of the first nine areas of coding, plus
major historical events such as warfare or annexation of territory, but to identify agents of trade: the
Monarchies, royalties and aristocratic elites that play a role in events, and similarly for churches,
monasteries, merchant families, and guilds. Conflicts and wars need also to be inventoried.
At the current time the network representation has 205 cities (222 nodes in file 222.net, seven of which are used in the legend), and extensive time-coded attribute data in excel files. Attributes save in Pajek format are collected with the time-series networks in a *.paj formatted summary
file.
Outside the scope of Spufford’s focus on trade is the possibility of overlapping the relatively
complete data on European Royalty for which genealogical links are already coded in network format. Codes might include succession to office, marriage ties, and successive places of rule and residence.
11. Modeling
Figure 8 represents the process of transformation by which higher velocities of trade affect
the reorganization of social, political, ecclesiastic, or commercial entities and their agents. As popu-
34
Dynamics of Civilizational Networks
lation grows relative to carrying capacity, possible effects are shown in each of the four columns.
Figure 8: Cascade of Population Cycle Processes driving Monetization beyond
Reorganization Thresholds
Population Growth
(Relative to Environmental Limits)
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)
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
Quantitative analysis of time series across different populations show correlations or timelags among these variables (generally, effects run from upper left to lower right). Their net effect is
to increase monetization, through a variety of routes. Population pressure, for example, often causes
inflation (price rises) and diminished real wages (with increased poverty creating incentives for
melting silver or gold objects which can be turned into currency; holding this tendency constant the
price of silver and gold would tend to rise). For landowners, whose welfare is cushioned relative to
peasants, the inventive is to reduce payment in kind (given the inflated value of land and reduced
Dynamics of Civilizational Networks
35
real wages) and substitute salaries for laborers, while demanding cash for rents. With greater cash
income, elites are more free to move to cities, and curtail loses of obligated land while increasing
land rents, diminishing labors costs, and appointing agents to run their estates. Once aggregated into
cities, rules and elites will tend to spend more on prestige goods, increasing imports. For polities to
pay for conspicuous consumption to increase status in this context there will be strong demand for
more bullion and coinage and demands for example for silver mining.
The net effect of all these processes is increased monetization in terms of the circulation velocity of money in exchange for prestige and other commodities. For any given organization – a
family enterprise, or commercial business, for example, after the threshold surpassed whereby the
higher velocity cannot be processes without reorganization, e.g., through division of labor and
adoption of new techniques.
The model of monetization and reorganization in Figure 8 differs modestly from the discussion of these issues by Spufford. He tends to refer to the twelve elements between population and
monetization as driven by modernization. He discusses the interactions among these elements in
detail. Following recent findings of Turchin and others, however, I tend to regard monetization as
an aggregate outcome of these twelve and other related factors, and to see the driver behind these
changes as differing phases of population phases. The quantitative analysis for this model will look
for correlated factors that can be aggregated into main effects, and then examines time lags. The
underlying dynamic is assumed to be population cycles as altered or affected by exogenous shocks
(e.g., Black Death, climate change). Reorganization, however, alters carrying capacity in support of
the upward millennial trend.
12. Conclusion
Organizational transformations due to critical mass in growing scope and scale (or tipping
points in the velocity of trade) are found in scores of processes described by Spufford. With rising
velocities of trade there are pressures and incentives for these transformations. Would closer investigation dependent on a better population database show that these transformations are more likely
in periods of demographic upswing, or that they lead to population growth? Many of these transformations remain in place, however, after a downswing. Indeed, after the Black Death of the 1340s
36
Dynamics of Civilizational Networks
there was a brief period of economic boom before the European economy went to ground. The
causes of this larger cycle of growth and decline, however, do not appear to be linked to demographic cycles or carrying capacity, nor to changes in Arrighian system logic. In the larger worldsystem context, the closure of corridors to trade through the Mongol Empire’s Khanate of the Golden Horde (1242-1357 NEED SOURCES) and various routes in the Middle East certainly plays a
role in the larger collapse. So does the fact that the defeat of the Genoese as the economic hegemon
trading state crippled one of the powerful merchant Navies at a time when maritime trade was accelerating and being reorganized through specialized agents. These conclusions, however, are only
tentative. A review of causal or feedback principles is given in the abstract of the argument, derived
from the complex interacting systems principles evident or expressed in Spufford’s book.
Much more can and remains to be done in terms of network analysis. Only one set of hypotheses has been presented and tested: that Genoa’s network hegemony rested on betweenness
centrality, while the rising economic power of certain of the north-western European cities, even in
the 12th century, was based on very different principles, those of flow centrality and, presumably,
profit-taking. The extended implication is that the eventual shift to Dutch economic hegemony,
which took centuries, was based on this shift. Aspects of this set of hypotheses could be tested for
the 13th through 15th centuries with the network data.13 If the analysis were to move into agent
space, the motivations for conflicts and wars might be useful to model in terms of network-based
hypotheses.
Expansion of the project could go in several directions: one, to expand the linkage data to
the Hanseatic League, Mongols and Middle East, India, Southeast Asia, Africa and China. Second,
the temporal coding at generational intervals rather than centuries seems to have greater payoff in
terms of providing a statistical basis for testing dynamical hypotheses and eventually doing more
generalized dynamical modeling. Third, the exploration of agent spaces can be done with considerable effort, both at the level of adding additional layers to the database, and in terms of simulations.
What has been gratifying in this project, still far from complete even for this first stage, is
that network measures and principles appear to play a useful role in understanding macrosocial dynamics. The Arrighian hypotheses as to competition among and alternations of forms in economic
Dynamics of Civilizational Networks
37
hegemony can be given a foundation in network measurements and testable hypotheses. They play
into hypotheses and economic data provided by Fischer (1996) and seem to provide a basis for seeing some of the regularities that come out of Spufford’s exquisite descriptions of the many critical
mass phenomena in organizational transformations due to growing scope and scale, many of which
a cumulative and lay the foundation for the urban, transport, economic, infrastructural and operating principles of the subsequent macrosystems.
13. Bibliographic Postscript: Toward Dataset Expansion for the Eurasian World System
Chaudhuri, K. N. 1990. Asia before Europe – Economics and Civilization of the Indian Ocean from
Rise of Islam to 1750. New York: Cambridge University Press.
1978. Trade and Civilisation in the Indian Ocean: An Economic History from the Rise of
Islam to 1750. (2nd Ed. Cambridge, 1985.).
Ptak, Roderich. 2004. China, the Portuguese, and the Nanyang: oceans and routes, regions and
trades (c. 1000-1600). Burlington, VT: Ashgate/Variorum.
China's seaborne trade with South and Southeast Asia, 1200-1750. Aldershot; Brookfield, Vt.: Ashgate, 1999. UCD UCSB UCSD UCB UCLA UCI
China and the Asian seas: trade, travel, and visions of the others (1400-1750). Aldershot, Hampshire [England]; Brookfield, Vt.: Ashgate, 1998. UCD UCSB UCR UCB UCLA UCI
edited by Roderich Ptak. Portuguese Asia: aspects in history and economic history, sixteenth and
seventeenth centuries. Wiesbaden: Steiner, 1987. UCSD UCB UCLA
edited by Karl Anton Sprengard and Roderich Ptak. Maritime Asia: profit maximisation, ethics and
trade structure c. 1300-1800. Wiesbaden: Harrassowitz, 1994. UCLA
ed. by Roderich Ptak and Dietmar Rothermund. Emporia, commodities, and entrepreneurs in Asian
maritime trade, C. 1400-1750. Stuttgart: Steiner Verlag, 1991. UCSD UCB UCLA UCI
edited by Sabine Dabringhaus and Roderich Ptak; with the assistance of Richard Teschke. China
and her neighbours: borders, visions of the other, foreign policy 10th to 19th century. UCSD
UCLA
Fedorov-Davydov, G. A. 2001. The Silk Road and the cities of the Golden Horde English editor,
Jeannine Davis-Kimball; English translator, Aleksandr Naymark. Berkeley, Calif.: Zinat Press.
UCD
38
Dynamics of Civilizational Networks
Lloyd, T. H. 1991 .England and the German Hanse, 1157-1611: a study of their trade and
commercial diplomacy. Cambridge: Cambridge University Press.
Wilkinson, T.
14. Acknowledgments
This work, with help from Joseph Wehbe, was supported by ISCOM, a European Union project on
Information Society as a Complex System, with co-PIs David Lane, Sander van der Leeuw and
Geoffrey West. Funding for participation of Joseph Wehbe in the coding of city data was also supported by ISCOM. An early draft of the paper was presented at the Working Group on Macrosocial
Systems, April 28-May 2, 2004, at the Santa Fe Institute. The paper benefited from comments by
Working Group participants, including those of Peter Spufford. Errors of fact or interpretation,
however, are mine along. The slides at http://eclectic.ss.uci.edu/~drwhite/Civ that accompany this
paper were given at the Workshop on Dynamics of groups and institutions: Their emergence, coevolution and environment. Santa Fe Institute and the Research Centre of the Slovenian Academy
of Sciences, from June 7 to June 11, 2004. I am grateful in both presentations for funding from the
Santa Fe Institute.
Initially drafted by White with agreement by Spufford to initiate a possible collaboration
with workshop support through the Santa Fe Institute, Spufford joined the collaborative publication
endeavor in July, 2004, following several days of joint work at Cambridge reviewing sources and
dating commodity production by 25 year generational periods. Acknowledgements for his work,
which involved him and family members in extensive fieldwork in historical sites as well as archives, are found in his Preface (Spufford 2002).
15. References
Abu-Lughod, J. 1993. The World System in the Thirteenth Century: Dead-End or Precursor?
Washington, D.C.: American Historical Association.
Arrighi, G. 1994. The Long Twentieth Century. London: Verso
Arrighi, G. and B. J. Silver. 2001. 'Capitalism and World (Dis)order.' Review of International
Studies 27:257-279.
Chandler, A. D. 1977. The Visible Hand: The Managerial Revolution in American Business.
Cambridge: Harvard University Press.
--------. 1990. Scale and Scope: The Dynamics of Industrial Capitalism Corporation. Cambridge, MA: Harvard University Press.
Chandler, T. 1987. Four Thousand Years of Urban Growth. Lewiston: St. Gavid’s.
Dynamics of Civilizational Networks
39
Modelski, G. 2003. World Cities -3000 to 2000. Washington, D.C.: Faros 2000.
-----------------------------------Hindle, Brian Paul. 'Roads and Tracks.' The English Medieval Landscape. Leonard Cantor, ed.
London: Croom Helm, 1982
UCSD
Storey, R. L. 1994. Chronology of the medieval world, 800 to 1491. New York: Simon & Schuster.
Nicholas, David. 1992. The evolution of the medieval world: society, government and thought
in Europe, 312-1500. New York: Longman.
Linehan. Peter and Janet L. Nelson, eds. 2001. The Medieval World. London; New York:
Routledge.
Abulafia, David. 1997. The western Mediterranean kingdoms, 1200-1500: the struggle for dominion. London; New York: Longman, 1997.
Kleinschmidt, Harald. 2000. Understanding the middle ages: the transformation of ideas and
attitudes in the Medieval world. Woodbridge, Suffolk; Rochester, NY: Boydell Press.
Howe, Helen, Robert T. Howe. 1988. The medieval world. White Plains, NY: Longman,
Schulman, Jana K., ed. 2002. The rise of the medieval world, 500-1300: a biographical dictionary. Westport, Conn.: Greenwood Press
UCB only
Lowick, Nicholas M. 1990. Islamic coins and trade in the medieval world; edited by Joe Cribb.
Aldershot: Variorum collected studies series; 318
UCB UCD
Haywood, John, Andrew Jotischky, Sean McGlynn. 1998. The Cassell Atlas of the Medieval
world: AD 600-1492 [cartographer, Nathalie Johns]. London: Cassell.
UCLA GTU
Williams, Neville. 1994. Chronology of The Expanding World, 1492 to 1762. New York: Simon
& Schuster.
The Expanding world of man. Editor, Neville Williams.
London, Weidenfeld and Nicolson [1970] GTU
UCD UCSD UCB UCLA
40
Dynamics of Civilizational Networks
Bairoch, Paul, Jean Batou, and Pierre Chèvre. 1988. La Population des Villes Européenes de 800
à 1850. Genève: Droz. HB2251 .B35 1988
AVAILABLE
Cities and economic development: from the dawn of history to the present / Paul Bairoch; translated by Christopher Braider.
Chicago: University of Chicago Press, 1988.
UCR UCB UCD UCSB UCR UCSC UCLA
Grove, Jean M. 1990. The Little Ice Age. London; New York: Routledge,
Not in ucsd
Andalusī, Ṣāʻid ibn Aḥmad, 1029-1070.
Uniform Title [ Ṭabaqāt al-umam. English]
Science in the medieval world: book of the Categories of nations / by Ṣāʻid al-Andalusī; translated and edited by Semaʻan I. Salem and Alok Kumar.
Austin: University of Texas Press, 1991 (1996 printing)
Capelli, A. 1969. Cronología, Cronografía e Calendario Perpetuo.
Mellersh, H E et al. 1995. Chronology of World History. Gale Group ISBN or UPC: 0-13326430-0(Active Record) Price: $ 350.00 Market: USA
Konstam, Angus.
Atlas of medieval Europe / Angus Konstam; [maps, Roger Kean].
New York: Checkmark Books, c2000.
Format Map
UCD UCSB UCI UCSC UCSD UCR
Matthew, Donald, 1930Atlas of medieval Europe / by Donald Matthew.
New York, N.Y.: Facts on File, Inc., 1992
UCB UCSC UCSD UCB UCLA GTU CSL
Not as good as georg westerman Atlas of medieval Europe / edited by Angus Mackay with David
Ditchburn. London; New York: Routlege, 1997. UCD UCSB UCI UCR UCSC UCSD UCB
UCLA
Shepherd, William R. (William Robert), 1871-1934 Atlas of medieval and modern history,
New York, Henry Holt and company, 1932 UCD UCSF SRLF CSL NRLF
McEvedy, Colin The new Penguin atlas of medieval history / Colin McEvedy
London, England; New York, USA: Penguin Books, 1992 UCSB UCI UCSC SRLF
Dynamics of Civilizational Networks
41
Platt, Colin. The atlas of medieval man. London, Macmillan, 1979. UCSB UCI UCR UCSC
UCSD UCB UCLA GTU
Shepherd, William R. (William Robert), 1871-1934. Atlas of medieval and modern history, by William R. Shepherd. New York, Henry Holt and Company, 1932. UCSB CSL NRLF
Matthew, Donald. Atlas of medieval Europe / by Donald Matthew. Oxford: Phaidon, 1983.
UCD UCSB UCI UCR
-----------------------------------Fischer, D. H. 1996. The Great Wave: Price Revolutions and the Rhythm of History. Oxford:
Oxford University Press.
Freeman, L. C. 1979. ‘Centrality in social networks: Conceptual clarication.’ Social Networks
1:215-239.
Freeman, L. C., S. Borgatti, D. R. White. 1991. ‘Centrality in valued graphs: A measure of betweenness based on network flow.’ Social Networks 13:141-154.
Goldstone, J. A. 1991. Revolution and Rebellion in the Early Modern World. University of California Press. eScholarship edition http://ark.cdlib.org/ark:/13030/ft9k4009kq/
Iberall, A.S., and H. Soodak. Physical basis for complex systems--Some propositions relating levels of organization. 1978, Collective Phenomena ,3:9-24.
Koratayev, White and Khalturina (2004)
McEvedy. C. 1961. The Penguin Atlas of Medieval History. Middlesex: Penguin Books.
Mintz, Sidney W. 1986. Sweetness and Power: The Place of Sugar in Modern History. Penguin
Books, New York.
Moody, J., D. R. White. ‘Structural Cohesion and Embeddedness: A Hierarchical Conception of
Social Groups.’ American Sociological Review 68(1):1-25.
.
Nefedov, S. 2003. “The Theory of Demographic Cycles and the Social Evolution of Ancient and
Medieval Oriental Societies,” (English translation) Vostok-Oriens 3:5-22.
Nüsli, 2002. Periodical Historical Atlas of Europe. http://www.euratlas.com/time2.htm.
Pomeranz. K. L. 2000. The Great Divergence: China, Europe, and the Making of the Modern
World Economy. Princeton: Princeton University Press.
42
Dynamics of Civilizational Networks
Smith, D. and D. R. White. 1992. ‘Structure and Dynamics of the Global Economy: Network Analysis of International Trade 1965-1980.’ Social Forces 70:857-894.
Soodak, H. and A.S. Iberall. 1978.Homeokinetics: A physical science for complex systems. Science, 201: 579-582.
Spufford, P. 2002. Power and Profit: The Merchant in Medieval Europe. London: Thames &
Hudson.
UCSD
--------. 1986. Handbook of Medieval Exchange. London: Royal Historical Society, Guides and
Handbooks.
UCSD
--------. 1988. Money and its Use in Medieval Europe. Cambridge: Cambridge University Press.
--------. 2001. Trade in Fourteenth-Century Europe. New Cambridge Medieval History 6. 155208. Cambridge.
Stier, Hans-Erich, and Ernst Kirsten. 1965. Grosser Atlas zur Weltgeschichte. Georg Westerman
Verlag.
Turchin, P. 2004. Dynamical Analysis of Socio-Economic Oscillations: England, 1100-1900. Unpublished.
White, D. R. 1988. ‘Predictors of Mobility in the World-Trade Hierarchy.’ 12th Annual Sunbelt
Social Networks Conference. San Diego.
White, D. R.,, Michael L. Burton, and Lilyan A. Brudner, 1977. Entailment Theory and Method: A
Cross-Cultural Analysis of the Sexual Division of Labor. Behavior Science Research 12:1-249.
http://eclectic.ss.uci.edu/~drwhite/entail/sea.html
White, D. R., and H. G. McCann. 1988. Cites and fights: Material Entailment Analysis of the
Eighteenth-Century Chemical Revolution. Social Structures: A Network Approach, 380-400.
Barry Wellman and S.D. Berkowitz, eds. New York: Cambridge University Press.
http://eclectic.ss.uci.edu/~drwhite/pub/Chemical.pdf
White, D. R., and K. P. Reitz. 1983 ‘Graph and Semigroup Homomorphisms.’ Social Networks
5:193-234.
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Dynamics of Civilizational Networks
White, D. R., and D. A. Smith. 1988 Large-Scale Network of World Economy: Social scientists use
the CRAY. Interview: Douglas R. White, David A. Smith. Science at the San Diego Supercomputer Center 1987: 27-28.
Wilkinson, D. 2004. ‘The Globalization of the World Systems, with Sequences of their Power
Structures.’ Specialist Workshop on Globalization in the World-System: Mapping Change over
Time. Institute for Research on World-Systems. University of California, Riverside. Feb., 2004.
Wilkinson, T. J. 2003. Archaeological Landscapes of the Near East. Phoenix: University of Arizona Press.
(saved from appendixes: some preliminary Entailment Analysis of Commodities
First: Presence/Absense over all time periods
Each of the urban scale variables run separately
(20-24), 25=port
Next: 14 Analyses, one for each period
Next: Correspondence among the 14
Next: Collapse into major periods, comparison with first
Figure 3nd text: City feature entailments for Spufford's Medieval Power and Profit network, 12th-15th century Europe. Items of lesser frequency (shown by node size and number) entail those of higher frequency when indicated by an arc. Click right for stronger inferences. All relationships statistically significant (88% not expected by
chance), <2% exceptions, 262 cities. Lowest five nodes are city population features. A
town hierarchy dimension might be constructed with vertical reordering. 14 such
entailgrams are constructed for generational intervals of 25 years from 1175-1500.
1175
1225
1200
1250
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Dynamics of Civilizational Networks
45
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1275
1300
46
Dynamics of Civilizational Networks
1325
1350
1375
1400
47
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1425
1450
1475
1500
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Dynamics of Civilizational Networks
Grove (1990) … cites on climate
Latitude and longitude were codes for each city so that GIS mappings could be made for cities and attributes as
shown at http://eclectic.ss.uci.edu/~drwhite/Civ/Spufford.htm. Eventually the network links will be available in GIS as
well.
3
Two routes from A to B are (node) independent if they have no intermediate points in common.
4
The coding was a collaborative effort carried out by Spufford and White, with assistance from Wehbe, during several
days of July, 2004. Coding of woollens was so complex that we completed the other 18 commodities and left the woollens coding for Spufford.
5
Distributional entailment methods are described in White and McCann (1988) and White, Burton and Brudner (1977).
6
A judgment of the statistical validity of allowing exceptions is made by SEA on the basis of signal detection theory.
7
These figures use the Stier and Kirsten (1965: 124-125) map of the European Economy in 1500, corrected with figures supplied in Spufford (2002:94).
8
France was split between the Counts of Flanders who because of their trade alliances with England in the woolens
trade favored English king Edward III who in 1337 had a good claim to through his French mother, Eleanor, to inherit
the French throne when the the last Capetian king Charles IV died in 1328 with no children. The faction of the Count
1
2
Dynamics of Civilizational Networks
49
of Valois, the closest French male cousin to Charles IV (third son of Phillipe IV), rushed to crown him king although
his claim. was not as good as Edward's. They attacked Edward's lands in Aquitaine and in 1337, Edward III declared
war. Three major bouts of inflation followed on the French side, finally put to rest with the treaties of Calais and of
Bretigny, but two had preceded 1328 and were provoked by the difficulties of Valois’ father Phillipe IV with the Pope,
heavy taxations, and battles with the English both in Flanders and in the south.
Timeline: Philippe IV Le Bel’s rule was marked by almost continious termoil. He had problems with the Popes,
scandles within his family (labeled 'Tour de Nesle'), resentment of his increased taxes, and confrontations with
English in Flanders, in La Manche [the Channel], and on the frontiers in southwestern France. The English defeated the French and Castilian fleet at Battle of Winchelsea (1293). The French king 'confiscated' French territories
under the English duke-king Edward I. Edward I of England invaded northern France (1294, 1296, and 1297), in
an alliance with the count of Flanders, who was attempting to win independence from France. The conflict ended
in a truce. The French defeated a Flemish uprising at Furnes (1297). First well-authenticated convocation of the
Estates-General (1302). Flemish town-militia defeated French knights at the Battle of Courtrai ['Battle of the
Spurs' (1302)]. Gascony was 'returned' to Edward I in 1303. French crush Flemish militia at Battle of Mons-enPevele (1304).
9
White (1988) designed the flow-centrality algorithm and asked Steve Borgatti to implement it in the UCINET software package. White used flow centralit to test the hypotheses about data on the world economy, 1965-1980 (White
and Smith 1988, Smith and White 1992). Freeman came upon this algorithm in UCInet in 1989 drafted the first article
on the algoritm itself, Freeman, Borgatti and White (1991).
10
A stronger statement of this principle is made by Iberall and Soodak (1984). If we look at the capacity of a given
organization for recurrent movement of goods, a critical capacity is reached where the volume exceeds this capacity
and reorganization is not only possible but necessary. A city like Genoa, with thousamds of extended family cells and
ships for moving goods might, however, be able to tolerate expansion of trade without reorganization. Florence, on the
contrary, lacking ships and large family warehouses, might have a lower threshold before reorganization occurs.
11
This discussion is based on discussions with Peter Turchin who has shared unpublished work bearing on the timing
of European population cycles and their correlates. These observations may be expanded once these papers by Turchin
and others are published.
12
See references in Spufford to Troyes 27,140,143-5-,160,201,257-8, Barsur-Aube 146,150-1, Lagny 146-50, Provins
143-50, 60, 63-4; Châlons-sur-Marne 238-40, Rheims 251,253,266
13
The question raided by Pomeranz ( ) is whether profit-taking could have boosted the European economy once the
mines of Eastern Europe were exhausted, i.e., whether Europe’s rebound was due to the opening of the New World.