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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The aim of this model is to explore and understand the factors driving adoption of treatment strategies for ecological disturbances, considering payoff signals, learning strategies and social-ecological network structure
The model studies the dynamics of risk-sharing cooperatives among heterogeneous farmers. Based on their knowledge on their risk exposure and the performance of the cooperative farmers choose whether or not to remain in the risk-sharing agreement.
This model builds on another model in this library (“diffusion of culture”).
The simulation model LAMDA investigates the influences of varying cognitive abilities of the decision maker on the truth-inducing effect of the Groves mechanism. Bounded rationality concepts are represented by information states and learning models.
Agent-based version of the simple search and barter economy conceived by Peter Diamond in 1982. The model is also known as Coconut Model.
This spatially explicit agent-based model addresses how effective foraging radius (r_e) affects the effective size–and thus the equilibrium cultural diversity–of a structured population composed of central-place foraging groups.
This model illustrates how the effective population size and the rate of change in mean skill level of a cultural trait are affected by the presence of natural selection and/or the cultural transmission mechanism by which it is passed.
The code contains four experiments for well-being based IMRL reward features.
We developed an agent-based model to explore underlying mechanisms of behavioral clustering that we observed in human online shopping experiments.
Agents can influence each other if they are close enough in knowledge. The probability to convince with good knowledge and number of agents have an impact on the dissemination of knowledge.
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