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RePast Tutorial II

RePast Tutorial II. Today’s agenda. IPD: Experimental dimensions EvolIPD model Random numbers How to build a model (2) Scheduling Homework C. Three crucial questions:. 1. Variation : What are the actors’ characteristics? 2. Interaction : Who interacts with whom, when and where?

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RePast Tutorial II

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  1. RePast Tutorial II

  2. Today’s agenda • IPD: Experimental dimensions • EvolIPD model • Random numbers • How to build a model (2) • Scheduling • Homework C

  3. Three crucial questions: 1. Variation: What are the actors’ characteristics? 2. Interaction: Who interacts with whom, when and where? 3. Selection: Which agents or strategies are retained, and which are destroyed? (see Axelrod and Cohen. 1999. Harnessing Complexity)

  4. Experimental dimensions • 2 strategy spaces:B, C • 6 interaction processes:RWR, 2DK, FRN, FRNE, 2DS, Tag • 3 adaptive processes:Imit, BMGA, 1FGA

  5. “Soup-like” topology: RWR In each time period, a player interacts with four other random players. ATFT ALLC ALLD ALLD TFT TFT ALLC

  6. ALLD ALLD ALLD TFT TFT ALLC TFT ALLC ATFT 2D-Grid Topology: 2DK The players are arranged on a fixed torus and interact with four neighbors in the von-Neumann neighborhood.

  7. ATFT TFT ALLC ATFT ALLD ALLD TFT ALLC TFT Fixed Random Network: FRN The players have four random neighbors in a fixed random network. The relations do not have to be symmetric.

  8. Adaptation through imitation Imitation ATFT ALLC ALLD ALLD TFT TFT? ALLC Neighbors at t

  9. Adaptation with BMGAComparison error (prob. 0.1) Genetic adaptation 6.0 Fixed spatial neighborhood 2.8 2.2 9.0 0.8

  10. BMGA continued Copy error (prob. 0.04 per “bit”) Genetic adaptation 6.0 Fixed spatial neighborhood p=0; q=0 => p=1; q=0 2.8 6.0 9.0 0.8

  11. Tutorial Sequence December 7SimpleIPD: strategy space TodayEvolIPD: RWR December 21GraphIPD: charts and GUI GridIPD: 2DK January 11ExperIPD: batch runs and parameter sweeps

  12. EvolIPD: flowchart setup() buildModel() resetPlayers() interactions() adaptation() play() play() remember() remember() addPayoff() addPayoff() reportResults() step()

  13. Markovian vs. asynchronous adaptation Markovian t-1 t asynchronous

  14. privatevoid stepMarkovian() { // We carry out four sub-activities: // Reset the agents' statistics // Loop through the entire agent list for (int i = 0; i < numPlayers; i++) { // Pick the agent final Player aPlayer = (Player) agentList.get(i); resetPlayer(aPlayer); } // Let them interact with their neighbors for (int i = 0; i < numPlayers; i++) { final Player aPlayer = (Player) agentList.get(i); interactions(aPlayer); } // FIRST STAGE OF DOUBLE BUFFERING! // Let all agents calculate their adapted type first for (int i = 0; i < numPlayers; i++) { final Player aPlayer = (Player) agentList.get(i); adaptation(aPlayer); } // SECOND STAGE OF DOUBLE BUFFERING! // Second, once they know their new strategy, // let them update to the new type for (int i = 0; i < numPlayers; i++) { final Player aPlayer = (Player) agentList.get(i); updating(aPlayer); } reportResults(); // Report some statistics } privatevoid stepAsynchronous() { // We carry out four sub-activities: for (int i = 0; i < numPlayers; i++) { // Pick an agent at random final Player aPlayer = (Player) agentList.get(this.getNextIntFromTo(0, numPlayers - 1)); // Reset the agent's statistics resetPlayer(aPlayer); // Let it interact with its neighbors interactions(aPlayer); // Let it adapt adaptation(aPlayer); // Let it update its new type updating(aPlayer); } reportResults(); // Report some statistics } Going sequential

  15. How to work with random numbers • RePast full-fledged random number generator:uchicago.src.sim.util.Random • Encapsulates the Colt library random number distributions:http://hoschek.home.cern.ch/hoschek/colt/ • Each distribution uses the same random number stream, to ease the repeatability of a simulation • Every distribution uses the MersenneTwister pseudo-random number generator

  16. Pseudo-random numbers • Computers normally cannot generate real random numbers • “Random number generators should not be chosen at random” - Knuth (1986) • A simple example (Cliff RNG): X0 = 0.1 Xn+1 = |100 ln(Xn) mod 1| x1 = 0.25850929940455103 x2 = 0.28236111950289455 x3 = 0.4568461655760814 x4 = 0.3408562751932891 x5 = 0.6294370918024157 x6 = 0.29293640856857195 x7 = 0.7799729122847907 x8 = 0.849608774153694 x9 = 0.29793011540822434 x10 = 0.08963320319223556 x11 = 0.2029456303939412 ...

  17. “True” random numbers • New service offered by the University of Geneva and the company id Quantique http://www.randomnumber.info/ • No (yet) integrated into RePast

  18. Simple random numbers distribution • Initialization:Random.setSeed(seed);Random.createUniform();Random.createNormal(0.0, 1.0); • Usage:int i = Random.uniform.nextIntFromTo(0, 10);double v1 = Random.normal.nextDouble();double v2 = Random.normal.nextDouble(0.5, 0.3); Automatically executed by SimpleModel standard deviation mean standard deviation mean standard deviation

  19. Beta Binomial Chi-square Empirical (user-defined probability distribution function) Gamma Hyperbolic Logarithmic Normal (or Gaussian) Pareto Poisson Uniform … Available distributions Normal Beta

  20. Custom random number generation • May be required if two independent random number streams are desirable • Bypass RePast’s Random and use the Colt library directly: import cern.jet.random.*;import cern.jet.random.engine.MersenneTwister;public class TwoStreamsModel extends SimModel { Normal normal; Uniform uniform; publicvoid buildModel() {super.buildModel(); MersenneTwister generator1 = new MersenneTwister(123); MersenneTwister generator2 = new MersenneTwister(321); uniform = new Uniform(generator1); normal = new Normal(0.0, 1.0, generator2); }publicvoid step() {int i = uniform.nextIntFromTo(0, 10);double value = normal.nextDouble(); }} seeds

  21. How to build a model (2) • If more flexibility is desired, one can extend SimModelImpl instead of SimpleModel • Differences to SimpleModel • No buildModel(), step(), ... methods • No agentList, schedule, params, ... fields • Most importantly: no default scheduling • Required methods:public void setup()public String[] getInitParam()publicvoid begin()public Schedule getSchedule()public String getName()

  22. SimModelImpl import uchicago.src.sim.engine.Schedule; import uchicago.src.sim.engine.SimInit; import uchicago.src.sim.engine.SimModelImpl; public class MyModelImpl extends SimModelImpl { public static final int TFT = 1; public static final int ALLD = 3; private int a1Strategy = TFT; private int a2Strategy = ALLD; private Schedule schedule; private ArrayList agentList; public void setup() { a1Strategy = TFT; a2Strategy = ALLD; schedule = new Schedule(); agentList = new ArrayList(); } public String[] getInitParam() { returnnew String[]{"A1Strategy"}; }

  23. SimModelImpl (cont.) public String getName() { return "Example Model"; }publicvoid begin() { Agenta1 = newAgent(a1Strategy); Agenta2 = newAgent(a2Strategy); agentList.add(a1); agentList.add(a2); schedule.scheduleActionBeginning(1, this,"step"); } publicvoid step() { for (Iterator iterator = agentList.iterator(); iterator.hasNext();) { Agentagent = (Agent) iterator.next(); agent.play(); } } introspection

  24. SimModelImpl (cont.) public String[] getInitParam() { returnnew String[]{"A1Strategy"}; } publicint getA1Strategy() { returna1Strategy; } publicvoid setA1Strategy(intstrategy) { this.a1Strategy = strategy; } publicstaticvoid main(String[] args) { SimInit init = new SimInit(); SimModelImpl model = new MyModelImpl(); init.loadModel(model, null, false); }

  25. How to use a schedule • Schedule object is responsible for all the state changes within a Repast simulation schedule.scheduleActionBeginning(1, new DoIt()); schedule.scheduleActionBeginning(1, new DoSomething()); schedule.scheduleActionAtInterval(3, new ReDo()); tick 1: DoIt, DoSomething tick 2: DoSomething, DoIt tick 3: ReDo, DoSomething, DoIt tick 4: DoSomething, DoIt tick 5: DoIt, DoSomething tick 6: DoSomething, ReDo, DoIt

  26. Different types of actions • Inner class class MyAction extends BasicAction {publicvoid execute() {doSomething(); } }schedule.scheduleActionAt(100, new MyAction()); • Anonymous inner classschedule.scheduleActionAt(100, new BasicAction(){ publicvoid execute() {doSomething(); } ); • Introspection schedule.scheduleActionAt(100, this, "doSomething");

  27. Schedule in SimpleModel publicvoid buildSchedule() { if (autoStep) schedule.scheduleActionBeginning(startAt, this,"runAutoStep"); else schedule.scheduleActionBeginning(startAt, this, "run"); schedule.scheduleActionAtEnd(this, "atEnd"); schedule.scheduleActionAtPause(this, "atPause"); schedule.scheduleActionAt(stoppingTime, this, "stop", Schedule.LAST); } public void runAutoStep() {public void run() { preStep();preStep(); autoStep();step(); postStep();postStep(); } } private void autoStep() { if (shuffle) SimUtilities.shuffle(agentList); int size = agentList.size(); for (int i = 0;i < size; i++) { Stepable agent = (Stepable)agentList.get(i); agent.step(); } }

  28. Scheduling actions on lists • An action can be scheduled to be executed on every element of a list: publicclass Agent {publicvoid step() { }}schedule.scheduleActionBeginning(1, agentList, "step"); • is equivalent to: publicvoid step() { for(Iterator it = agentList.iterator(); it.hasNext();) { Agent agent = (Agent) it.next(); agent.step(); } }schedule.scheduleActionBeginning(1, model, "step"); step() inAgent step() in SimpleModel

  29. Different types of scheduling • scheduleActionAt(double at, …)executes at the specified clock tick • scheduleActionBeginning(double begin, …)executes starting at the specified clock tick and every tick thereafter • scheduleActionAtInterval(double in, …)executes at the specified interval • scheduleActionAtEnd(…)executes the end of the simulation run • scheduleActionAtPause(…)executes when a pause in the simulation occurs

  30. Homework C Modify the EvolIPD program by introducing a selection mechanism that eliminates inefficient players. The current adaptation() method should thus be modified such that the user can switch between the old adaptation routine, which relies on strategic learning, and the new “Darwinian” selection mechanism. The selection mechanism should remove the 10% least successful players from the agentList after each round of interaction. To keep the population size constant, the same number of players should be “born” with strategies drawn randomly from the 90% remaining players. Note that because it generates a population-level process, the actual selection mechanism belongs inside the Model class rather than in Player. Does this change make any difference in terms of the output?

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