multi-armed bandit simulation models in python
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analysis
bandito
output
.gitignore
PL2012_fig1.py
PL2012_fig3.py
PLfig1robustness.py
PLfig3robustness.py
README.md
clark_experiment_01.py
clark_experiment_02.py
sample_experiment.py

README.md

Stories in Ready

###Bandit Model Research Simulations This work begins by replicating the multi-armed bandit simulation of Posen & Levinthal (2012), "Chasing a Moving Target: Exploitation and Exploration in Dynamic Environments", Management Science 58(3), pp.587-601. It extends this into a general-purpose bandit simulation with additional variables that can be manipulated for further research.

###Contributors Joseph W Clark, joe.clark@maine.edu

###File Structure

  • All files in this directory (except README.md) define experiments.
  • The output subfolder will hold the data and log output from simulation experiments.
  • The analysis subfolder contains some sample scripts for visualizing the data with Python.
  • Files in the bandito subfolder run the actual experiments:
    • /bandito/bandit.py runs one replication of a bandit simulation.
    • /bandito/banditexperiment.py runs an experiment with any specified set of treatments and number of replications.
    • /bandito/banditfunctions.py contains algorithms for turbulence, strategy, beliefs, and payoffs. To implement a new algorithm, place it there.

###How to Use Copy the file sample_experiment.py and modify to set up your experiment. Run it with something like the following:

python sample_experiment.py

Warning: You may want to change the number of replications for a faster test. Posen & Levinthal used 25000 replications for each experimental treatment, and replicating their experiments may take hours or days.

Tested on Mac OS X with Python 3.5.2