Broadcasting model
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README.md
heterog_learners(45,100,1)(5,1,1).mov
ind_learners(50,100,1).mov
sm_1_main_datasheet.xlsx
sm_2_eProg_Star.pdf
sm_3_adding_ruggedness.pdf
sm_4_adding_individuals.pdf
soc_learners(50,1,1).mov

README.md

Broadcasting model

Supplementary materials

  • Document 1: sm_1_main_datasheet.xlsx
  • Document 2: sm_2_eProg_Star.pdf
  • Document 3: sm_3_adding_ruggedness.pdf
  • Document 4: sm_4_adding_individuals.pdf

IPython notebook code files

  • EL_replication.ipynb (EL model)
  • broadcasting_may2016.ipynb (main simulation file)
  • broadcasting_plots.ipynb

Sample animation files

  • 50 agents; 1000 rounds; lambda = 0.01; beta = 200
  • ind_learners(50,100,1).mov: homogeneous individual learners: alpha = 100 (sd=1)
  • soc_learners(50,1,1).mov: homogeneous social learners: alpha = 1 (sd=1)
  • heterog_learners(45,100,1)(5,1,1).mov: heterogeneous population, 45 social & 5 individual
  • Not particularly pretty at this point. The used animation library didn't support exporting, so had to do this with screen capture. Notice that (a) due to the rendering, you can't really see the gaussian noise (those graytones are too dark) Furthermore, (b) landscape depletion does not show.