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.
We also maintain a curated database of over 7500 publications of agent-based and individual based models with detailed metadata on availability of code and bibliometric information on the landscape of ABM/IBM publications that we welcome you to explore.
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To investigate the potential of using Social Psychology Theory in ABMs of natural resource use and show proof of concept, we present an exemplary agent-based modelling framework that explicitly represents multiple and hierarchical agent self-concepts
An ABM, derived from a case study and a series of surveys with greenhouse growers in the Westland, Netherlands. Experiments using this model showshow that the greenhouse horticulture industry displays diversity, adaptive complexity and an uneven distribution, which all suggest that the industry is an evolving system.
Several taxonomies for empirical validation have been published. Our model integrates different methods to calibrate an innovation diffusion model, ranging from simple randomized input validation to complex calibration with the use of microdata.
How do households alter their spending patterns when they experience changes in income? This model answers this question using a random assignment scheme where spending patterns are copied from a household in the new income bracket.
Must tax-benefit policy making be limited to the ‘experts’?
This model explores the effects of agent interaction, information feedback, and adaptive learning in repeated auctions for farmland. It gathers information for three types of sealed-bid auctions, and one English auction and compares the auctions on the basis of several measures, including efficiency, price information revelation, and ability to handle repeated bidding and agent learning.
Displaying 6 of 56 results income clear search