Presentation by: Petros Kavassalis ATLANTIS Group, University of Crete & ICS_FORTH, Greece

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Explain emergence of structure in the World Wide Web Aggregation and competition under informational increasing returns. Presentation by: Petros Kavassalis ATLANTIS Group, University of Crete & ICS_FORTH, Greece Contact at: petros@itc.mit.edu. FET. together with:. - PowerPoint PPT Presentation

Text of Presentation by: Petros Kavassalis ATLANTIS Group, University of Crete & ICS_FORTH, Greece

  • Explain emergence of structure in the World Wide Web

    Aggregation and competition under informational increasing returns

    Presentation by:Petros KavassalisATLANTIS Group, University of Crete &ICS_FORTH, GreeceContact at:petros@itc.mit.edu

  • together with:

    Stelios LELIS, ATLANTIS Group, Univ. of Crete, GreeceCharis LINA, ATLANTIS Group, Univ. of Crete, GreeceManolis PETRAKIS, Dpt of Economics & ATLANTIS Group, Univ. of Crete, GreeceJakka SAIRAMESH, IBM IAC, USA

    Presentation at BT meeting: M. Vavalis, iCities Project Manager

  • agendaA Web Simulated Economy (WSE)

    To explain agglomeration and fast growth in the Web

    Network approach to Webs Hidden Order

    Urban explanations of the web sites fast growth and differentiated competition

  • iCities project funded by FET Economic frameworks Bounded rationality User heterogeneous preferences Sites with differentiated offerings Info propagation networks Sites linked hierarchically Network externalitiesWSE Design of iCities ?BehaviorLanguage Modeling experience Analysis of existing information cities Speed Data-strucuture design Parallel/distributed execution Scalability Configurability (programability) Multiple models Component-based Data structures/interfaces Conceptual framework Behavioral rulesInternetBehavioral ModelsiCitiesprojectSimulationFrameworkEconomic Geography& Case studies

  • A Web Simulated Economy (iCities WSE)On top of Mozart/Oz (SICS): rigorous simulation environmentCapturing essential characteristics of the real web economy: agglomeration & scale-free state in distribution of population across web sitesCapable to provide insight on empirical regularities: result of the joint action of superposed networksAble to explain web organization and progressive, fast, web formation: reveal patterns of Internet population clustering into web locations

    Reference: New Economic GeographyAgglomeration in the real worldIncreasing returnsP. Krugman, B. Arthur

  • What the EconGeo has to say to the Web? P. Krugman, The Self-organizing Economy The geographical space reveals different forms of concentration of population and economic activity. These are not only the result of inherent differences between locations but also of some set of cumulative processes, necessarily involving some forms of increasing returns, whereby concentration can be self-reinforcing.

    B. Arthur, Increasing Returns and Path Dependence in the Economy Increasing returns are the tendency for that which is ahead to get further ahead, for that which loses advantage to further lose advantage. They are mechanisms of increasing returns that operate to reinforce that which gains success or aggravate that which suffers loss.

  • Towards an economic geography of the Web

    H1: Heterogeneous populations of agents

    H2: Network structures matter

    H3: There are Informational Increasing Returns

  • H1: An economy with two populations...Internet Users with partial information

    Web Sites with performance varying over the course

  • H2: Decision embedded in nets of interactionWord-of-mouth network or network externalitiesUnderlying networkPortfolio of sites

  • Social networks

    Linkages (includingnavigation hierarchies)

    Units of action

    preferences

    increasing returns

    increasing returns

  • H3: Informational Increasing ReturnsNetworks carry increasing returns

    Word-of-mouth information propagation network (social network with local ties and long distance relationships)

    Underlying network linking sites (navigation is hierarchical, produces linkages)

    Amazon.com-like network externalities (agglomeration benefit)

  • The issue: explain power law regularityA Web Simulated Economy (WSE)

    To explain agglomeration and fast growth in the Web

    Network approach to Webs Hidden Order

    Urban explanations of the web sites fast growth and differentiated competition

  • Hubermans diagnostic: Web Hidden Order!

    The distribution of Internet users per web site follows a universal power law

    A power law distribution is a straight line on a log-log scale

    Xerox Internet Ecologies ProjectAOL Data,

  • We have reproduced it!

  • Why is this important?We provide a network-base explanation for the power law regularity!

    Internet consumers:Surf the webLearn about web sites by asking other people (word-of-mouth) or by surfing from one site to another along hyperlinksVisit these sites, evaluate and include them in a portfolio of FVS (U = performance + e)Have loyal behavior

    Web sites

  • j

    web topology

    social network

    portfolio of web infohabitant i

    inhabitants of the web location j

    i

  • What does this imply?A network approach to the power law issue:

    Previous attempts: random growth models (from Simon to Huberman)Question: Where does such a growth come from?Direction: Krugman sees in percolation models, one possible way around the problems with random growth modelsWe took that way: online concentration should be the result of a process involving random transport networksWord-of-mouth information diffuses over a social network structure linking Internet usersSites link network transport users from one site to another (navigational hierarchies)

  • In a nutshellNetworks carry increasing returnsINFORMATIONAL INCREASING RETURNSWord-of-mouth networkSites link networkSmall world assumptionWatt-Storgatz (WS) beta model with new nodes entering the gameShort path lengthLarge clustering coefficient1. Small world (WS model)2. Scale free network (Barabasi)Directed linksNew nodes enter the gameRewiring of existing linksPreferential attachment

  • Small world-Small World: findings (I)Scatter plot: Size versus AgeScatter plot: Size versus Performance

  • Small world-Small World: findings (II)Evolution of growth rate for site ranked at position 125Evolution of growth rate for site ranked at position 1

  • Small world-Small World: findings (III)Sites succeeding to be ranked at the higher positions belong to neighborhoods of highly visited sites

  • Small world-Small World: findings (IV)Word of mouth (Centripetal)Exploration (Centrifugal)Users loyalty (Centrifugal)Clustering coefficient (Centrifugal) : power law exponent : proportion of sites that are visited at least by one user at final timestep

  • Small world-Scale free: findings (I)Most findings are confirmed (slope: 1.4)

  • Small world-Scale free: findings (II)Scatter plot: Size versus Performance and In-degree

  • Small world-Scale free-InvestmentsSites performance varies over timeSites decide to make investments in predefined time intervals, to improve their performance (affront clutter costs)Accumulated investments depreciate over timeInvestments are made on the basis ofGrowth rateMarket share (for established sites)Investments produce a performance increment with a certain probability (there are attention costs)Entry strategies suppose an investment to obtain a good performance and a number of in-links

    Out- links are also growing over timeAlgorithm for out-links growth

  • Small world-Scale free-Investments: findings (I)A power law distribution in sites sizes is again obtained (in general and within categories)

  • Small world-Scale free-Investments: findings (II)Sites growth rates fluctuate between time intervals in an uncorrelated fashion but about a positive mean value

    This is evident in Huberman-Adamics data and they use it as an assumption to build their model

    Right picture: Fractional fluctuations in the number of users of site ranked at position 60.

  • Small world-Scale free-Investments: findings (III)Web sites age and popularity are slightly correlated

    This is evident in Huberman-Adamics data.

    Right picture: Scatter plot of the number of unique visitors versus age.

  • Small world-Scale free-Investments: findings (IV)In- and out-degree distribution of sites follow power-laws.

    In-degree distribution

    Out-degree distribution

  • Small world-Scale free-Investments: findings (V)Slight correlation between the age of sites and their number of in-coming links.

    This is evident in Huberman-Adamics data.

    Right picture: Scatter plot of the number of incoming links versus age.

  • Small world-Scale free-Investments: findings (VI)Again:Relative performance is awarded more than absolute performanceA number of late entrants may survive and prosper (our model spans over Huberman and Barabasis models)

    But:As economic variables enter directly the model, they are able to break down the power law stabilityOr, a power law distribution survives as long as new sites enter regularly the game (our assumption: exponential entry rate)Then? Instability? What kind of instability?

  • The issue: provide directly economic explanationsA Web Simulated Economy (WSE)

    To explain agglomeration and fast growth in the Web

    Network approach to Webs Hidden Order

    Urban explanations of the web sites fast growth and differentiated competition

  • An info-economy for experience goods

  • Internet usersHave preferences over content/service categories (e.g. Books, Internet communication) and versions (generic/scientific, e-mail/instant messaging/chat rooms etc)Have a portfolio of frequently visited sitesFind new sites to visit through:Search Engine. Users periodically submit queries related to their preferences to a search engineExploration. Users surf from one site to another following the links of sites networkEvaluate new sites and include in their portfolio the sites with