This webapp is the newly improved testbed for the Trust Teaming experiment conducted at the SHINE Lab.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Commercial usage is restricted, remixes allowed with proper attribution and same license!
Please attribute code to Michal Bodzianowski, as well as citing the following paper in research publications using this code:
Philip Bobko, Leanne Hirshfield, Lucca Eloy, Cara Spencer, Emily Doherty, Jack Driscoll & Hannah Obolsky (2023) Human-agent teaming and trust calibration: a theoretical framework, configurable testbed, empirical illustration, and implications for the development of adaptive systems, Theoretical Issues in Ergonomics Science, 24:3, 310-334, DOI: 10.1080/1463922X.2022.2086644
For any concerns, please reach out to the SHINE Laboratory via their website
- Have a Redis instance running locally reachable on 127.0.0.1:6379. If first time running make sure db 1 is empty.
- This project was built using
python3.10and the library versions infrozen_req.txt. Try switching to these if future versions break (or better yet, create a PR if you can migrate/upgrade!) - (For Developers) Set
FLASK_ENVenvironment var todevelopment
- Download or clone repo
cdintoTrustTeaming- Run
pip3 install -r requirements.txt - Run
npm install - To start server-
npm run start - Run
npm run buildto build the web scripts.- (For Developers) Use
npm run dev-watchinstead to rebuild webpack items (.js, .jsx, .css)
- (For Developers) Use
- Visit http://127.0.0.1:8080 on participant computers. Admin login is done by
entering
SHINEas the code.
Important - Make sure eventlet and gevent are not installed in your
environment for now.
Warning- Do not trust sensitive data to this program (read- passwords). This is out of scope for this testbed's responsibilities.
Note- If using npm on Windows, please run
npm config set script-shell bash so that the start script runs correctly
Note- Sample config CSVs available under /samples
- Please maintain styling using Prettier
- Currently no CI/CD setup, but coming soon?
- Also needs Dockerization
- Otherwise, modular setup, and adding new features appreciated
- See
requirements.txtandpackage.json
Initial versions of this projects relied on a large database of Denver crime
data in a crime.csv file. This is available upon request, but we now use
src/data/updatedcrimedata, a compressed and Pandas optimized version of the
data. See src/data/datagenerator.ipynb for details.
This was also submitted as the final project for Michal Bodzianowski's CSCI5548 class at the University of Colorado Boulder.