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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A Complex Model of Voter Turnout
Bruce Edmonds Laurence Lessard-Phillips Ed Fieldhouse | Published Monday, October 13, 2014 | Last modified Tuesday, August 18, 2015This is a complex “Data Integration Model”, following a “KIDS” rather than a “KISS” methodology - guided by the available evidence. It looks at the complex mix of social processes that may determine why people vote or not.
Land Use in the Chitwan Valley
Alex Zvoleff | Published Monday, June 02, 2014chitwanabm is a spatially explicit agent-based model of population and land use in the Chitwan Valley, Nepal, designed to explore feedbacks between population and environment, with a heavy focus on community context and individual-level variation.
“Food for all” (FFD)
Andreas Angourakis José Manuel Galán Andrea L Balbo José Santos | Published Friday, April 25, 2014 | Last modified Monday, April 08, 2019“Food for all” (FFD) is an agent-based model designed to study the evolution of cooperation for food storage. Households face the social dilemma of whether to store food in a corporate stock or to keep it in a private stock.
ForagerNet3_Demography_V2
Andrew White | Published Thursday, February 13, 2014ForagerNet3_Demography_V2 is a non-spatial ABM for exploring hunter-gatherer demography. This version (developed from FN3D_V1) contains code for calculating the ratio of old to young adults (the “OY ratio”) in the living and dead populations.
Mobility USA (MUSA)
Giangiacomo Bravo Davide Natalini | Published Sunday, December 08, 2013 | Last modified Monday, December 30, 2013MUSA is an ABM that simulates the commuting sector in USA. A multilevel validation was implemented. Social network with a social-circle structure included. Two types of policies have been tested: market-based and preference-change.
ForagerNet3_Demography: A Non-Spatial Model of Hunter-Gatherer Demography
Andrew White | Published Thursday, October 17, 2013 | Last modified Thursday, October 17, 2013ForagerNet3_Demography is a non-spatial ABM for exploring hunter-gatherer demography. Key methods represent birth, death, and marriage. The dependency ratio is an imporant variable in many economic decisions embedded in the methods.
Smallholder Behavioural Decisions During Times of Drought Stress
Samantha Dobbie | Published Sunday, September 15, 2013 | Last modified Saturday, September 27, 2014An empirical ABM of smallholder decisions in times of drought stress.
Income Model
Tony Lawson | Published Monday, August 26, 2013This is the code for the model described in an article in the International Journal of Microsimulation. Lawson (2013) ‘Modelling Household Spending Using a Random Assignment Scheme’, International Journal of Microsimulation, 6(2) Autumn 2013, 56-75.
We propose here a computational model of school segregation that is aligned with a corresponding Schelling-type model of residential segregation. To adapt the model for application to school segregation, we move beyond previous work by combining two preference arguments in modeling parents’ school choice, preferences for the ethnic composition of a school and preferences for minimizing the travelling distance to the school.
Peer reviewed Swidden Farming Version 2.0
C Michael Barton | Published Wednesday, June 12, 2013 | Last modified Wednesday, September 03, 2014Model of shifting cultivation. All parameters can be controlled by the user or the model can be run in adaptive mode, in which agents innovate and select parameters.
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