From its early days, the World Wide Web space has demonstrated strong agglomeration trends with a very small number of web sites capturing the larger part of the Internet population. At a first glance, agglomeration over the virtual space sounds as a paradox. Web sites are numerous and highly diversified and can be easily reached from everywhere and anybody, with no particular transportation or search cost. However, Internet users use only a small number of sites for searching for information and products, interacting with others and socialize, thus producing dense concentrations and locational patterns similar to those observed in the physical space where few cities and industrial clusters host the huge majority of population and the entire industrial activity. Is that depending on the attractiveness of the popular web sites or are there agglomeration economies providing incentives to users to be in a location which have been visited by other users or pointed-in by other sites? This paper provides a sound basis for the dynamics of population concentration in the Web and put forward an explanation to web sites’ growth by developing an agent-based computational model, with behavioural and economic variables, where the aggregate outcome emerges from the interaction of individual decisions. The model reproduces the empirically observed power law distribution of Internet users across web sites, demonstrating that a plausible explanation of web agglomeration phenomena can lie on the assumption of increasing returns and the percolation-like diffusion of the information over the Internet. 1 This paper summarizes some of the findings of iCities project, funded by European Commission (Information Cities Project: IST-1999-11337, FET: Future and Emerging Technologies).
[1]
Jon Kleinberg,et al.
The Structure of the Web
,
2001,
Science.
[2]
J. Tirole.
The Theory of Industrial Organization
,
1988
.
[3]
Glenn Ellison,et al.
Word-of-Mouth Communication and Social Learning
,
1995
.
[4]
Eli Upfal,et al.
The Web as a graph
,
2000,
PODS.
[5]
P. Krugman.
The Self Organizing Economy
,
1996
.
[6]
Lada A. Adamic,et al.
The Nature of Markets in the World Wide Web
,
1999
.
[7]
W. Arthur,et al.
Increasing Returns and Path Dependence in the Economy
,
1996
.
[8]
Seif Haridi,et al.
Regularities in the Formation and Evolution of Information Cities
,
2001,
Digital Cities.
[9]
Lada A. Adamic,et al.
The Web's hidden order
,
2001,
CACM.
[10]
Drew Fudenberg,et al.
Word-of-mouth learning
,
2004,
Games Econ. Behav..
[11]
Andrei Z. Broder,et al.
Graph structure in the Web
,
2000,
Comput. Networks.
[12]
Duncan J. Watts,et al.
Collective dynamics of ‘small-world’ networks
,
1998,
Nature.
[13]
Albert,et al.
Emergence of scaling in random networks
,
1999,
Science.
[14]
Lada A. Adamic,et al.
Power-Law Distribution of the World Wide Web
,
2000,
Science.
[15]
David M. Pennock,et al.
Winners don't take all: Characterizing the competition for links on the web
,
2002,
Proceedings of the National Academy of Sciences of the United States of America.
[16]
A. Venables,et al.
The Spatial Economy: Cities, Regions, and International Trade
,
1999
.