About the CoMSES Model Library more info
Our mission is to help computational modelers at all levels engage in the establishment and adoption of community standards and good practices for developing and sharing computational models. Model authors can freely publish their model source code in the Computational Model Library alongside narrative documentation, open science metadata, and other emerging open science norms that facilitate software citation, reproducibility, interoperability, and reuse. Model authors can also request peer review of their computational models to receive a DOI.
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 contact us if you have any questions or concerns about publishing your model(s) in the Computational Model Library.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with additional detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
Displaying 7 of 17 results for 'Matthew Johnson'
Peer reviewed JuSt-Social COVID-19
Jennifer Badham | Published Thursday, June 18, 2020 | Last modified Monday, March 29, 2021NetLogo model that allows scenarios concerning general social distancing, shielding of high-risk individuals, and informing contacts when symptomatic. Documentation includes a user manual with some simple scenarios, and technical information including descriptions of key procedures and parameter values.
An Individual-Based Mechanistic Model of Mussel Bed Boundary Formation and Intensity
Matthew Schumm | Published Saturday, September 28, 2019Individually parameterized mussels (Mytilus californianus) recruit, grow, move and die in a 3D environment while facing predation (in the form of seastar agents), heat and desiccation with increased tide height, and storms. Parameterized with data collected by Wootton, Paine, Kandur, Donahue, Robles and others. See my 2019 CoMSES video presentation to learn more.
An agent-based artificial stock with a dynamic investor network
Matthew Oldham | Published Tuesday, June 25, 2019The model is an agent-based artificial stock market where investors connect in a dynamic network. The network is dynamic in the sense that the investors, at specified intervals, decide whether to keep their current adviser (those investors they receive trading advise from). The investors also gain information from a private source and share public information about the risky asset. Investors have different tendencies to follow the different information sources, consider differing amounts of history, and have different thresholds for investing.
Sunshine or Shield?: Secret Voting Procedures and Legislative Accountability
Michele Buttò Carlos Pereira Matthew Taylor | Published Tuesday, June 24, 2014 | Last modified Monday, April 08, 2019An agent-based model is used to simulate legislators’ behavior under secret voting rules, as influenced by the power of the accused politician, the composition of the voting body, and the publicity of the accusations.
Drafting agent-based modeling into basketball analytics
Matthew Oldham | Published Tuesday, February 19, 2019An agent-based simulation of a game of basketball. The model implements most components of a standard game of basketball. Additionally, the model allows the user to test for the effect of two separate cognitive biases – the hot-hand effect and a belief in the team’s franchise player.
Peer reviewed DogFoxCDVspillover
Aniruddha Belsare Matthew Gompper | Published Thursday, March 16, 2017 | Last modified Tuesday, April 04, 2017The purpose of this model is to better understand the dynamics of a multihost pathogen in two host system comprising of high densities of domestic hosts and sympatric wildlife hosts susceptible to the pathogen.
Multi Asset Variable Network Stock Market Model
Matthew Oldham | Published Monday, September 12, 2016 | Last modified Tuesday, October 10, 2017An artifcal stock market model that allows users to vary the number of risky assets as well as the network topology that investors forms in an attempt to understand the dynamics of the market.
Displaying 7 of 17 results for 'Matthew Johnson'