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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This model includes an innovation search environment. Agents search and can share their findings. It’s implemented in Netlogo-Hubnet & a parallel Netlogo model. This allows for validation of search strategies against experimental findings.
An ABM to simulate the behaviour of households within a village and observe the emerging properties of the system in terms of food security. The model quantifies food availability, access, utilisation and stability.
Implemented as a virtual laboratory, this model explores transitions in land-use and livelihood decisions that emerge from changing local and global conditions.
The Agent-Based Wildfire Simulation Environment (ABWiSE) translates the concept of a moving fire front as a set of mobile fire agents that respond to, and interact with, vegetation, wind, and terrain. Presently, the purpose of ABWiSE is to explore how ABM, using simple interactions between agents and a simple atmospheric feedback model, can simulate emergent fire spread patterns.
This model simulates the emergence of a dual market structure from firm-level interaction. Firms are profit-seeking, and demand is represented by a unimodal distribution of consumers along a set of taste positions.
Complete Library for object oriented development of Classifier Systems. See for the concept behind.
NeoCOOP is an iteration-based ABM that uses Reinforcement Learning and Artificial Evolution as adaptive-mechanisms to simulate the emergence of resource trading beliefs among Neolithic-inspired households.
IMine is a flexible framework which can be adopt multiple criteria for convergence to solve Influence Minig problems. It can use any diffusion model, as well as resilience to compute the influence of a set of nodes base on the use case.
The code is written and tested on ‘R’ v3.5
Simulation-Framework to study the governance of complex, network-like sociotechnical systems by means of ABM. Agents’ behaviour is based on a sociological model of action. A set of basic governance mechanisms helps to conduct first experiments.
ABM focused on examining the dissemination of opinions through a notional terrorist network to generate terrorist attacks caused by drone strikes.
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