7:00 – 8:00, 8:30 – 3:30 Research

• Working title Interpreting ‘The Law of Group Polarization” with flocking behavior
  • Multidimensional exposes information distance and diversity issues (low dimensions = easier flocking, and the converse)
  • Reynolds-style flocking behavior means that agreement is not a static value, but changes. This brings up questions about how to identify GP, particularly in high dimensions
  • Adjusting social horizons results in three states (Phase change)
    • Random
    • Flocking
    • Polarized Group
  • The impact of visible diversity of GP.
  • Machine learning for identification of states/types
• Wired up RunConfig to support border types
• Closing the loop with Tim Champ for server space at UMBC
• Downloaded the format and created a CollectiveIntelligence 2017 folder. Looking back through the 2015 conference, there were visible abstracts. Going to read a few to get a sense.
• Uploaded the executable jar https://philfeldman.com/GroupPolarization/GroupPolarizationModel.jar
• Adding ARFF output – done! First try:

  === Stratified cross-validation ===
  === Summary ===
  
  Correctly Classified Instances          98               98      %
  Incorrectly Classified Instances         2                2      %
  Kappa statistic                          0.898 
  Mean absolute error                      0.02  
  Root mean squared error                  0.1414
  Relative absolute error                 10.6977 %
  Root relative squared error             47.1207 %
  Total Number of Instances              100     
  
  === Detailed Accuracy By Class ===
  
                   TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                   1.000    0.022    0.833      1.000    0.909      0.903    0.989     0.833     EXPLORER
                   0.978    0.000    1.000      0.978    0.989      0.903    0.989     0.998     EXPLOITER
  Weighted Avg.    0.980    0.002    0.983      0.980    0.981      0.903    0.989     0.981     
  
  === Confusion Matrix ===
  
    a  b   
   10  0 |  a = EXPLORER
    2 88 |  b = EXPLOITER

• One run vs another, using average angle difference:

  === Summary ===
  
  Correctly Classified Instances          96               96      %
  Incorrectly Classified Instances         4                4      %
  Kappa statistic                          0.8837
  Mean absolute error                      0.04  
  Root mean squared error                  0.2   
  Relative absolute error                 12.3636 %
  Root relative squared error             49.9946 %
  Total Number of Instances              100     
  
  === Detailed Accuracy By Class ===
  
                   TP Rate  FP Rate  Precision  Recall   F-Measure  MCC      ROC Area  PRC Area  Class
                   1.000    0.050    0.833      1.000    0.909      0.890    0.975     0.833     EXPLORER
                   0.950    0.000    1.000      0.950    0.974      0.890    0.994     0.998     EXPLOITER
  Weighted Avg.    0.960    0.010    0.967      0.960    0.961      0.890    0.990     0.965     
  
  === Confusion Matrix ===
  
    a  b   -- classified as
   20  0 |  a = EXPLORER
    4 76 |  b = EXPLOITER

• Need a checkbox for cross-bias interaction. Done! Now I can train against two populations with and without interactions
• Spreadsheet with new tabs and some nifty charts: meanangletest_01_18_17-14_07_32

3:30 – 4:30

• Walked through scoring issues with Aaron
• Realized that the above work can be used for classifying clusters with ML.