Social Networks:Agent-based Modelling and

Social Network Analysis with PAJEK

ESRC Research Methods Festival, Oxford, 17th-20th July 2006, &

Oxford Spring School, Dept. of Politics and International Relations

Richard Taylor¹ and Gindo Tampubolon²¹Centre for Policy Modelling, Manchester Metropolitan University

²Centre for Research on Innovation and Competition, University of Manchester

ABMs and Social Network Studies: What and Why?

As will be seen in the next few slides (What is ABM?), there are several similarities of focus:

• Dense interaction among the components of a social system (social embeddedness)

• The behaviour of the whole as well as the parts (limited functional decomposability)

• Formalisation of social organisation

This suggests that techniques developed in one field may be applied to the other to bring insight in some studies

Properties of Agents

•Autonomy

•Adaptation

•Interactive

•Heterogeneous

Properties of Agent Systems

•Flexible

•Scalable

•Distributed

•RobustMany of these properties are shared with social systems argument for the usability of the approach

Background in Distributed Artificial Intelligence (DAI)

Background on Agent-based Modelling

• Agents represent the actors in the system, i.e. firms, institutions

• We define agent characteristics as well as their behaviour

• These are implemented as rules in the computer program

• An agent is like an object in OOP ….

• … but normally it has some goals, some means of perceiving its environment, and some kind of reasoning mechanism

• Agents should be embedded within social context

Basic principles

• Behavioural norms such as fashion trends or religion

• Group behaviour such as in crowds, traffic or urban spaces

• Environmental models of land use change or water resources

• Consumer behaviour in retail markets

• Auctions and supply-chain models

Examples

Note that we have seen a quick overview of ABMs: more practical information on methodology of ABM will follow in the afternoon session

Software• Java Development Kit (JDK) version 1.5.0 –

object orientated, platform independent, widely used. Arranged into packages.

• RePast 3.1 – Set of Java packages for ABM. GUI for visualisation. Bytecode in repast.jar

• RealJ IDE – Simple environment for editing java files, compiling and running programs

• PAJEK – network analysis software

JDK consists of the bytecodes for the whole Java core, as well as the tools for compiling (javac.exe) and running (java.exe) your own Java programs

RealJ is a text editor for working on Java projects which has some built-in functions for linking with the Java toolsA.K.A. Integrated Development Environment (IDE)

RealJ splits the workspace into three components: text editor, project window, console panel

Introducing the JDK and RealJ

Introducing RePast

RePast can be used to implement dynamic agent-based models that describe state changes in simulated time

RePast is a set of Java packages, which incorporates a Graphical User Interface (GUI) for visualisation.

It has packages for importing and exporting network data

Bytecode (Java class files) are contained in repast.jar

RePast basicsRePast divides model implementation into

separate parts:

Setup sets (or resets) any initial parameters to their defaults and sets any objects to ‘null’

BuildModel creates the representational parts of the simulation, i.e., the agents and their environment

BuildDisplay builds those parts of the simulation needed for graphically displaying the simulation

BuildSchedule schedules ‘actions’ that change the simulation’s state i.e., that describe dynamic simulation of social processes

1. Launch RePast (Repast.exe), Add and Load the model, and input your parameters in the RePast toolbar

2. To generate .net files you will need to fill in the following fields:

pajekInterval – the number of ‘ticks’ between each recording

filenamePath – the location for saving net files

(user directory) + mydirectory/myrun + (tick number)

3. Press (Set up) and then (Run). (Pause) or (Stop) the simulation and investigate via the RePast display

4. Locate (in user directory/data) the output files for your network

Running the RePast Demos

Jin, Girvan, and Newman working paper: “The Structure of Growing

Social Networks”

(1)meetings take place between pairs of individuals at a rate which is high if a pair has one or more mutual friends and low otherwise;

(2)acquaintances between pairs of individuals who rarely meet decay over time;

(3)there is an upper limit on the number of friendships an individual can maintain

Demo Models - JinGirNewNet

RED (Random) and GREEN (Neighbour) links

Characterisation of outcomes:

(1)Initially the network rapidly increases in density due to the addition of random links

(2)Eventually the network becomes more cliqueish or clustered due to the formation of neighbour links

Demo Models - JinGirNewNet

• Aims at innovating, either individually or in partnership with other firms

• Endowed with a ‘skill profile’ (SP) of possessed skills• Involved in an ‘individual learning’ process to acquire

new skills in the universe of firms’ skills

located upon 2d grid, and connected to neighbours in cardinal directions (N,S,E,W) within visible range

From Epstein and Axtell, Growing Artificial Societies

The agent is a Firm,

Demo Models (2): Innovation Networks

Agent develops SP through depth-first search

Advanced skills depend upon prior acquisition of more basic skills

Specialisation and differentiation of each agent

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Demo Models (2): Innovation Networks

• Innovations are specified as a set of skills which can be combined to develop a new product or production process

The simulation cycle: 1. Firm’s individual learning step2. Individual innovation step3. Joint innovation step

RePast displays initial neighbour (RED) and current partnership (WHITE) relations as well as the partnership history (BLUE)

Assumption that innovating firms gain visibility:Their neighbourhood (which defines possible partners) increases in size