2. Social Networks: Agent-based Modelling and Social Network Analysis with PAJEK ESRC Research Methods Festival, Oxford, 17 th-20th July 2006, & Oxford Spring School, Dept. of Politics and International Relations Richard Taylor¹ and Gindo Tampubolon² ¹Centre for Policy Modelling, Manchester Metropolitan University ²Centre for Research on Innovation and Competition, University of Manchester

3. 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

4. Properties of Agents Autonomy Adaptation Interactive Heterogeneous Properties of Agent Systems Flexible Scalable Distributed Robust Many 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

5. 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

6. 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

7. 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

8. 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 tools A.K.A. Integrated Development Environment (IDE) RealJ splits the workspace into three components: text editor, project window, console panel Introducing the JDK and RealJ

9. 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

10. RePast basics RePast 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

11. 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

12. 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

13. 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

14. 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

15. Agent develops SP through depth-first search Advanced skills depend upon prior acquisition of more basic skills Specialisation and differentiation of each agent 1 43 2

16. 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 step 2.Individual innovation step 3.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