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.
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Dynamic bipartite network model of agents and games in which agents can participate in multiple public goods games.
The network-based trust game is a hybridization of both the repeated trust games and the network games.
This theoretical model includes forested polygons and three types of agents: forest landowners, foresters, and peer leaders. Agent rules and characteristics were parameterized from existing literature and an empirical survey of forest landowners.
This version of the accumulated copying error (ACE) model is designed to address the following research question: how does finite population size (N) affect the coefficient of variation (CV) of a continuous cultural trait under the assumptions that the only source of copying error is visual perception error and that the continuous trait can take any positive value (i.e., it has no upper bound)? The model allows one to address this question while assuming the continuous trait is transmitted via vertical transmission, unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission. By varying the parameter, p, one can also investigate the effect of population size under a mix of vertical and non-vertical transmission, whereby on average (1-p)N individuals learn via vertical transmission and pN individuals learn via either unbiased transmission, prestige biased transmission, mean conformist transmission, or median conformist transmission.
This model illustrates actor interaction in the construction sector, according to information gathered in NL. It offers a simple frame to represent diverse interests, interdependencies and effects on the number of built sustainable houses.
The model represents empirically observed recycling behaviour of Chinese citizens, based on the theory of reasoned action (TRA), the theory of planned behaviour (TPB) and the theory of planned behaviour extended with situational factors (TPB+).
This model was design to test parameters that affects the number of people shot during mass shooting. This basic formulation places a gunman in a crowd and allows the users to manipulate parameters of the gunman.
This model looks at implications of author/referee interaction for quality and efficiency of peer review. It allows to investigate the importance of various reciprocity motives to ensure cooperation. Peer review is modelled as a process based on knowledge asymmetries and subject to evaluation bias. The model includes various simulation scenarios to test different interaction conditions and author and referee behaviour and various indexes that measure quality and efficiency of evaluation […]
The TERROIR agent-based model was built for the multi-level analysis of biomass and nutrient flows within agro-sylvo-pastoral villages in West Africa. It explicitly takes into account both human organization and spatial extension of such flows.
PSoup is an educational program in which evolution is demonstrated, on the desk-top, as you watch. Blind bugs evolve sophisticated heuristic search algorithms to be the best at finding food fast.
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