ergodicity-breaking-choice-experiment

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ergodicity-breaking-choice-experiment

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This is a repository for all code and data to reproduce an experiment exploring the effects of ergodicity breaking on economic choice behavior

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# Ergodicity Breaking Choice Experiment
This is a repository for all code and data to reproduce an experiment exploring the effects of ergodicity breaking on economic choice behavior.

# Introduction

Everything contained in this folder is necessary for reproducing/replicating the
analyses of the experiment described in the paper:

Ergodicity-breaking reveals time optimal economic behavior in humans
by David Meder, Finn Rabe, Tobias Morville, Kristoffer H. Madsen, Magnus T. Koudahl, 
Ray J. Dolan, Hartwig R. Siebner, and Oliver J. Hulme.

Preprint available [here](https://arxiv.org/abs/1906.04652).

This folder contains the data as well as code
The readMe file has been structured loosely according to [IEEE suggestions](https://journals.ieeeauthorcenter.ieee.org/create-your-ieee-article/prepare-supplementary-materials/creating-a-readme-file-for-datasets/)

# Data

You will download this repository and save it locally.

Before running the pipeline, you need to download and unzip this auxillary folder from OSF [this file](https://osf.io/fhqnz/).

This will unzip to two folders: `/matjags` and `/samples&stats`. Copy paste the content from each folder into their respective folders (ie '/matjags' and '/samples_stats') in the repository you just downloaded from github.

# Platform/Environment
The original raw data files are in .txt format. A python script (Python 2.7) 
reads in these individual data files (separate files per subject and 
session), computes a number of basic variables and saves them in one
combined .mat file.

Most subsequent analyses have been performed with, or called from Matlab.
The Hierarchical Bayesian modelling has been run with JAGS (via matjags, which allows
running JAGS via matlab code.

Separate statistical analyses were run on the statistical software JASP,
https://jasp-stats.org/ in some cases this is takes the same raw data as input, in 
other cases statistics are run on point estimates from the hiearchical bayesian modelling.
The data that JASP runs on is in the .csv in the JASP folder.

# Folders summary 
"data" contains all data files. For all details on files and the 
variables they contain, please refer to _codebook.txt

"figs" receives figures that are automatically generated by scripts.

"fractals" contains the fractal images that participants were presented
with. These images are a subset of the fractals available at
https://github.com/smathot/materials_for_P0010.5/tree/master/stimuli/exp2/images

"functions misc" contains all miscellaneous functions or toolboxes that
are relied upon, e.g. the Variational Bayes toolbox.

"JAGS" contains different versions of the "JAGS_..." scripts using
JAGS code that are called by the matjags script in the folder "matjags"
(see below)

"JASP" contains the jasp analysis scripts and .csv data files on which they run. 
you need to tell the jasp analysis scripts where the .csv is, and sync so that 
the analysis scripts run.

"jobs" contains the shell script jobs that run different versions of the
script runHLM#.m which sets up different models.

"matjags" contains matjags scripts that are the means by which matlab runs 
JAGS. There are multiple copies of this script, each calling a different
copy of JAGS, allowing many jobs to be run at the same time. matjags1.m
calls the first version of JAGS in "jagstmp1" and so forth.

"samples_stats" contain the outputs of JAGS, including all relevant
statistics and samples for further analysis by plotHLM.m

The remaining files in the base directory are the main matlab scripts and JAGS models

# Major Component Description of Data Set

The original .txt files contain a trial-wise (rows) list of basic
variables such as time points of when different events occurred, which
buttons were pressed at which time and what kind of stimuli were
presented.

The python script "readingData.py" reads these .txt, computes additional
variables and saves them in one .mat file containing all data for all
subjects. For further details on the data, please refer to "Codebook.m"

# Detailed Setup Instructions - setting up and running JAGS

Runnning a model starts with setting up the script runHLM#.m where #
indicates the version number. There are multiple versions according to
the aims of the model, which could vary by type of model, or by the type
of data, varying between real data or synthetic data. To call a given
runHLM# you need to run the job runHLM_job#.sh in the "jobs" directory.

Each version of runHLM contains different specifications for running a 
JAGS model, pertaining to the following key variables:
   which model - runModelNum
   which data to model - synthMode
   which independent copy of JAGS to run - whichJAGS
   which quality levels of the model to run in which order - whichQuals
   whether to generate plots - runPlots
For further details, please refer to one of the different runHLM scripts
(e.g.runHLM1.m) in the folder "jobs" and read the commenting

runHLM#
runHLM#.m (in folder 'jobs') calls setHLM.m (in main folder) which 
contains preset information about what the different quality levels mean 
in terms of burn-in, number of samples etc.

To monitor the progress of the job, open the associated slurm file that
was generated in the jobs directory. old slurms can be archived in
"old_slurms" (in "jobs") to keep in order.

setHLM
setHLM then calls computeHLM which is the main script for computing the
hieararchical bayesian models. This script processes the data, can plot
key information, and principally calls matjags to run the model. This is
done by calling the different versions of the "JAGS_..." scripts using
JAGS code, to be found in the folder "JAGS"

computeHLM
computeHLM is a general script for running several types of hierarchical
bayesian model via JAGS. It can run hiearchical latent mixture models in
which different utility models can be compared via inference on the model
indicator variables, and it can run without latent mixtures of models for
instance in order to estimate parameters of a given utility model.

computeEtaBeta2TimeAv
computeEtaBeta2TimeAv computes the time average growth over the gambles
given an eta and beta value. This script is called by computeHLM

computeCheckJAGSscripts
computeCheckJAGSscripts is used for debugging JAGS scripts. JAGS scripts
cannot be debugged in a sequential manner as in matlab, thus this script 
does sanity check computations whether variables computed by the JAGS 
script are meaningful

plotHLM
plotHLM plots and post-processes HLM results, generating histograms, 
computes MAPs, and generally visualises data

# Scripts summary - generating synthetic data for parameter and model recovery

The two main scripts are computeSyntheticData4ModelRecovery and 
computeSyntheticData4ParameterRecovery.

computeSyntheticData4ModelRecovery
"presents" synthetic agents with the gambles presented to the subjects. 
The agents operate with different utility functions with different 
parameter values. The script generates choice probabilities based on 
these functions, and then probabilistically realises left-right choices.

computeSyntheticData4ParameterRecovery 
does the same as above, but only with the isoelastic utility function 
with different parameter values. This script also calls:

computeEtaDistribution
which draws values from normal distribution with given mean and SD, 
restricting them to be within 3 standard deviations for sampling eta
values around a certain mean

Both computeSyntheticData4ModelRecovery and 
computeSyntheticData4ParameterRecovery are used for setting up variables 
which are then passed to:

computeChoicesModelRecovery / computeChoicesParameterRecovery 
which calculate the utility differences of the gambles and choice 
probabilities given specific parameter values of the utility function.
They call upon:

computeExpectedUtility
which calculates utilities based on prospect theory given the input exponents,
loss aversion parameters (lambda), and probability values (probVals), via
a weighted sum, as per cumulative prospect theory.

computeIsoelasticUtility
which computes isoelastic utility according given an eta value

computeChoiceRealisation
which probabilisitically realises gamble choices based on choice
probabilities.

# Scripts summary - other scripts

computeSyntheticWealthTrajectories
This script computes wealth trajectories for synthetic agents playing the
ergodicity game repeatedly over several timescales, hours to weeks to
years.

computeFactor2Increment
computeFactor2Increment computes linear changes in wealth as a function 
of current wealth and growth rate. This script is called by 
computeSyntheticWealthTrajectories.

computeIncrement2Factor
computeIncrement2Factor computes growth factors from additive growth 
increments and current wealth. This script is called by 
computeSyntheticWealthTrajectories.

# Prefixes
script prefixes denote the scripts primary function. 
runX is a script whose main function is to initiate the running of a model
setX is a script that simply helps set up a run
computeX is a script or function that computes 
JAGS_X is the text file that specifies the JAGS model

# Contact information
In case of questions, please contact Oliver Hulme oliverh@drcmr.dk or
David Meder davidm@drcmr.dk

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