Our mission is to help computational modelers develop, document, and share their computational models in accordance with community standards and good open science and software engineering practices. Model authors can publish their model source code in the Computational Model Library with narrative documentation as well as metadata that supports open science and emerging norms that facilitate software citation, computational reproducibility / frictionless reuse, and interoperability. Model authors can also request private peer review of their computational models. Models that pass peer review receive a DOI once published.
All users of models published in the library must cite model authors when they use and benefit from their code.
Please check out our model publishing tutorial and feel free to contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
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Peer reviewed Dynamic Equilibria Prediction: Experience-Weighted Attraction (EWA), Python Implementation
Vinicius Ferraz | Published Friday, December 02, 2022This project is based on a Jupyter Notebook that describes the stepwise implementation of the EWA model in bi-matrix ( 2×2 ) strategic-form games for the simulation of economic learning processes. The output is a dataset with the simulated values of Attractions, Experience, selected strategies, and payoffs gained for the desired number of rounds and periods. The notebook also includes exploratory data analysis over the simulated output based on equilibrium, strategy frequencies, and payoffs.
3D Urban Traffic Simulator (ABM) in Unity
Daniel Birks Sedar Olmez Obi Thompson Sargoni Alison Heppenstall Annabel Whipp Ed Manley | Published Friday, January 22, 2021 | Last modified Monday, March 22, 2021The Urban Traffic Simulator is an agent-based model developed in the Unity platform. The model allows the user to simulate several autonomous vehicles (AVs) and tune granular parameters such as vehicle downforce, adherence to speed limits, top speed in mph and mass. The model allows researchers to tune these parameters, run the simulator for a given period and export data from the model for analysis (an example is provided in Jupyter Notebook).
The data the model is currently able to output are the following:
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Urban/Rural Adaptive Culture Model
Nick LaBerge | Published Sunday, July 19, 2020Contains python3 code used to replicate the culture model from the JASSS submission: “Modeling Cultural Dissemination and Divergence between Rural and Urban Regions.”