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Individual-based model and statistical code used a paper "Social learning strategies regulate the wisdom and madness of interactive crowds"
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data
parameterRecovery
rds
IBM_simulation.nb
README.md
ToyokawaWhalenLaland2018_figuresScript_revisioin_v2.R
alpha.stan
average_copy_rate.stan
average_invTemp.stan
conformity_exponent.stan
data_all.csv
data_exp012_201601112104.csv
data_exp013_2016011132057.csv
isPositiveCopier.stan
model_AL6_annealing.stan
model_UNC6_annealing.stan
model_UNC6_sReduc_annealing.stan
model_changingBST.stan
model_changingBT.stan
model_fitting_for_exp.R
model_fitting_fullmodel_for_supp.R
posthoc_simulation.R
soc_change.stan
soc_change_full.stan

README.md

ToyokawaWhalenLaland2018

Individual-based model and statistical code used in an article entitled "Social learning strategies regulate the wisdom and madness of interactive crowds"

All the data used are also included in this repository. You can draw all figures shown in the main text of the manuscript using ToyokawaWhalenLaland2018_figuresScript_revision.R, by running the script from the top to the end.

Agent-based model simulation

  • The simulation was written in a Mathematica notebook (IBM_simulation.nb)

Computational models

  • All code used to fit the main model can be found in model_fitting_for_exp.R
  • For the alternative model (where both social learning parameters are time dependent) can be found in model_fitting_fullmodel_for_supp.R
  • Full details of the model are written in stan files (e.g. model_UNC6_sReduc_annealing.stan).

Parameter recovery test

  • Synthetic data are generated in parameterRecovery/parameterRecoverySimulation.nb
  • Then, you can fit the computational model using parameterRecovery/model_UNC6_sReduc_annealing_fitting_for_sim.R.
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