Emergent Network Models Prof. Lars-Erik Cederman Center for - - PowerPoint PPT Presentation
Introduction to Computational Modeling of Social Systems Emergent Network Models Prof. Lars-Erik Cederman Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2, lcederman@ethz.ch Nils Weidmann, CIS Room E.3,
Center for Comparative and International Studies (CIS) Seilergraben 49, Room G.2, lcederman@ethz.ch Nils Weidmann, CIS Room E.3, weidmann@icr.gess.ethz.ch http://www.icr.ethz.ch/teaching/compmodels Lecture, January 18, 2005
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actor actor actor actor actor actor actor actor actor
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constituted as property configruations
segregation model; Carley 1991; Axelrod 1997
references
actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor actor
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– Albert and Barabási’s scale- free networks – Economics and evolutionary game theory: e.g. Skyrms and Pemantle
frequency degree d d-α
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normally assume the actors to be given
– Axelrod’s model of new political actors – Axtell’s firm-size model – Geopolitical models in the Bremer & Mihalka tradition
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Erdös and Rényi 1959
Fraction of nodes in largest component
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Average number
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Sharon, MA
Stanley Milgram
Omaha, NE
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Watts and Strogatz’s Beta Model
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p(k) p(k) k Normal distribution
log p(k) log k log p(k) log k
k Power law
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Node nodeA = new DefaultNode(“A”); Node nodeB = new DefaultNode(“B”);
Edge edge = new DefaultEdge(nodeA, nodeB); nodeA.addOutEdge(edgeAB); nodeB.addInEdge(edgeAB);
Edge edge = EdgeFactory.createEdge(nodeA, nodeB);
A B Edge Node Direction
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Typically distance or similarity To encode the type of relationship , e.g. a marriage
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public void buildModel() { GraphLayout layout = new DefaultGraphLayout(agentList, 500, 500); Network2DDisplay display = new Network2DDisplay(layout); dsurf.addDisplayableProbeable(display, "Display"); addSimEventListener(dsurf); dsurf.display(); } public void step() { dsurf.updateDisplay(); }
Contains the list of nodes
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graphLayout = new FruchGraphLayout(...); Controller c = (Controller)getController(); c.addStopListener(graphLayout); c.addPauseListener(graphLayout); c.addExitListener(graphLayout)
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List list = NetworkFactory.getNetwork( “network.dl”, NetworkFactory.DL, MyNode.class, MyEdge.class, NetworkFactory.BINARY);
NetworkRecroder recorder = new NetworkRecorder( NetworkRecroder.BINARY, “network.dl”, this); recorder.record(nodeList, "tick: " + getTickCount(),NetworkRecorder.BINARY); recorder.write();
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Download and install the NetIPD model from the course website (www.icr.ethz.ch/teaching/compmodels/models/netipd). The model extends the IPD introduced in the RePast tutorial to a network
and FRNE) described in (Cohen et al., 1999). The third topology (PN) is a parametrized network and uses the „Beta“-parameter. D1) Perform simulation runs with the PN topology for different values of beta (at least 10), ranging from 0 to 1. For each value, perform 50 runs of the model with different random seeds. Plot the percentage of runs where there is a TFT majority at the end of the 200th step against the value of beta. D2) Look at the implementation of the parametrized network. What topology described in the (Cohen et al., 1999) paper does it resemble (a) for beta=0 and (b) for beta=1? D3) What is your explanation for the results‘ variation with beta?
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