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Conceptual Framework for Agent-Based Modeling and Simulation: The Computer Experiment. Yongqin Gao Vincent Freeh Greg Madey CSE Department CS Department CSE Department University of Notre Dame NCSU University of Notre Dame NAACSOS Conference Pittsburgh, PA

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conceptual framework for agent based modeling and simulation the computer experiment

Conceptual Framework for Agent-Based Modeling and Simulation: The Computer Experiment

Yongqin Gao Vincent Freeh Greg Madey

CSE Department CS Department CSE Department

University of Notre Dame NCSU University of Notre Dame

NAACSOS Conference

Pittsburgh, PA

June 25, 2003

Supported in part by the

National Science Foundation - Digital Society & Technology Program

agent based simulation as a component of the scientific method
Agent-Based Simulation as a Component of the Scientific Method

Modeling

(Hypothesis)

Social Network

Model of F/OSS

Agent -Based

Simulation

(Experiment)

Observation

Analysis of

Grow Artificial

SourceForge

SourceForge

Data

outline
Outline
  • Investigation: Free/Open Source Software (F/OSS)
  • Conceptual framework(s)
  • Model description
  • ER model
  • BA model
  • BA model with constant fitness
  • BA model with dynamic fitness
  • Summary
open source software oss
Open Source Software (OSS)

GNU

Linux

  • Free …
    • to view source
    • to modify
    • to share
    • of cost
  • Examples
    • Apache
    • Perl
    • GNU
    • Linux
    • Sendmail
    • Python
    • KDE
    • GNOME
    • Mozilla
    • Thousands more

Savannah

free open source software f oss
Free Open Source Software (F/OSS)
  • Development
    • Mostly volunteer
    • Global teams
    • Virtual teams
    • Self-organized - often peer-based meritocracy
    • Self-managed - but often a “charismatic” leader
    • Often large numbers of developers, testers, support help, end user participation
    • Rapid, frequent releases
    • Mostly unpaid
typical charismatic leaders
TypicalCharismatic Leaders?

Larry Wall

Perl

Linus Tolvalds

Linux

Richard Stallman

GNU Manifesto

Eric Raymond

Cathedral and Bazaar

f oss significance
Contradicts traditional wisdom:

Software engineering

Coordination, large numbers

Motivation of developers

Quality

Security

Business strategy

Almost everything is done electronically and available in digital form

Opportunity for Social Science Research -- large amounts of online data available

Research issues:

Understanding motives

Understanding processes

Intellectual property

Digital divide

Self-organization

Government policy

Impact on innovation

Ethics

Economic models

Cultural issues

International factors

F/OSS: Significance
sourceforge
SourceForge
  • VA Software
  • Part of OSDN
  • Started 12/1999
  • Collaboration tools
  • 58,685 Projects
  • 80,000 Developers
  • 590,00 Registered Users
savannah
Savannah
  • Uses SourceForge Software
  • Free Software Foundation
  • 1,508 Projects
  • 15,265 Registered Users
slide11

F/OSS: Importance

  • Major Component of e-Technology Infrastructure with major presence in
    • e-Commerce
    • e-Science
    • e-Government
    • e-Learning
  • Apache has over 65% market share of Internet Web servers
  • Linux on over 7 million computers
  • Most Internet e-mail runs on Sendmail
  • Tens of thousands of quality products
  • Part of product offerings of companies like IBM, Apple
    • Apache in WebSphere, Linux on mainframe, FreeBSD in OSX
    • Corporate employees participating on OSS projects
free open source software
Free/Open Source Software
  • Seems to challenge traditional economic assumptions
  • Model for software engineering
  • New business strategies
    • Cooperation with competitors
    • Beyond trade associations, shared industry research, and standards processes — shared product development!
  • Virtual, self-organizing and self-managing teams
  • Social issues, e.g., digital divide, international participation
  • Government policy issues, e.g., US software industry, impact on innovation, security, intellectual property
data collection monthly
Data Collection — Monthly
  • Web crawler (scripts)
    • Python
    • Perl
    • AWK
    • Sed
  • Monthly
  • Since Jan 2001
  • ProjectID
  • DeveloperID
  • Almost 2 million records
  • Relational database

PROJ|DEVELOPER

8001|dev378

8001|dev8975

8001|dev9972

8002|dev27650

8005|dev31351

8006|dev12509

8007|dev19395

8007|dev4622

8007|dev35611

8008|dev8975

slide15

dev[72]

dev[67]

dev[52]

dev[65]

dev[70]

dev[57]

7597 dev[46]

6882 dev[47]

dev[64]

dev[45]

dev[99]

7597 dev[46]

7597 dev[46]

dev[52]

dev[72]

dev[67]

7597 dev[46]

6882 dev[47]

dev[47]

dev[55]

dev[55]

dev[55]

7597 dev[46]

7028 dev[46]

dev[70]

7597 dev[46]

7028 dev[46]

dev[57]

dev[45]

dev[99]

dev[51]

7597 dev[46]

7028 dev[46]

6882 dev[47]

6882 dev[58]

dev[61]

dev[51]

dev[79]

dev[47]

7597 dev[46]

dev[58]

dev[58]

dev[46]

9859 dev[46]

dev[54]

15850 dev[46]

dev[58]

9859 dev[46]

dev[79]

dev[58]

9859 dev[46]

dev[49]

dev[53]

9859 dev[46]

15850 dev[46]

dev[59]

dev[56]

15850 dev[46]

dev[83]

15850 dev[46]

dev[48]

dev[53]

dev[56]

dev[83]

dev[48]

F/OSS Developers - Social Network Component

Developers are nodes / Projects are links

24 Developers

5 Projects

Project 7597

2 Linchpin Developers

1 Cluster

dev[64]

Project 6882

Project 7028

dev[61]

dev[54]

dev[49]

dev[59]

Project 9859

Project 15850

models of the f oss social network alternative hypotheses
Models of the F/OSS Social Network(Alternative Hypotheses)
  • General model features
    • Agents are nodes on a graph (developers or projects)
    • Behaviors: Create, join, abandon and idle
    • Edges are relationships (joint project participation)
    • Growth of network: random or types of preferential attachment, formation of clusters
    • Fitness
    • Network attributes: diameter, average degree, power law, clustering coefficient
  • Four specific models
    • ER (random graph)
    • BA (scale free)
    • BA ( + constant fitness)
    • BA ( + dynamic fitness)
er model degree distribution
ER model – degree distribution
  • Degree distribution is binomial distribution while it is power law in empirical data
  • R2 = 0.9712 for developer network
  • R2 = 0.9815 for project network
er model diameter
ER model - diameter
  • Average degree is decreasing while it is increasing in empirical data
  • Diameter is increasing while it is decreasing in empirical data
er model clustering coefficient
ER model – clustering coefficient
  • Clustering coefficient is relatively low around 0.4 while it is around 0.7 in empirical data.
  • Clustering coefficient is decreasing while it is increasing in empirical data
er model cluster distribution
ER model – cluster distribution
  • Cluster distribution in ER model also have power law distribution with R2 as 0.6667 (0.9953 without the major cluster) while R2 in empirical data is 0.7457 (0.9797 without the major cluster)
  • The actual distribution is different from empirical data
  • The later models (BA and further models) have similar behaviors
ba model degree distribution
BA model – degree distribution
  • Power laws in degree distribution, similar to empirical data (+ for simulated data and x for empirical data).
  • For developer distribution: simulated data has R2 as 0.9798 and empirical data has R2 as 0.9712.
  • For project distribution: simulated data has R2 as 0.6650 and empirical data has R2 as 0.9815.
ba model diameter and cc
BA model – diameter and CC
  • Small diameter and high clustering coefficient like empirical data
  • Diameter and clustering coefficient are both decreasing like empirical data
ba model with constant fitness
BA model with constant fitness
  • Power laws in degree distribution, similar to empirical data (+ for simulated data and x for empirical data).
  • For developer distribution: simulated data has R2 as 0.9742 and empirical data has R2 as 0.9712.
  • For project distribution: simulated data has R2 as 0.7253 and empirical data has R2 as 0.9815.
  • Diameter and CC are similar to simple BA model.
ba model with dynamic fitness
BA model with dynamic fitness
  • Power laws in degree distribution, similar to empirical data (+ for simulated data and x for empirical data).
  • For developer distribution: simulated data has R2 as 0.9695 and empirical data has R2 as 0.9712.
  • For project distribution: simulated data has R2 as 0.8051 and empirical data has R2 as 0.9815.
advantage of ba with dynamic fitness
Advantage of BA with dynamic fitness
  • Intuition: Fitness should decreasing with time.
  • Statistics: project has life cycle behavior which can not be replicated by BA model with constant fitness but can be replicated by BA model with dynamic fitness
conceptual framework agent based modeling and simulation as components of the scientific method
Conceptual FrameworkAgent-Based Modeling and Simulation as Components of the Scientific Method

Hypothesis

Observation

Experiment

summary
Summary
  • We use ABM to model and simulate the SourceForge collaboration network.
  • Conceptual framework is proposed for agent-based modeling and simulation.
  • Case study of this framework: SourceForge study through ER, BA, BA with constant fitness and BA with dynamic fitness.