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.
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.
Displaying 5 of 5 results for 'Benjamin Davies'
Zooarchaeological evidences indicate that rabbit hunting became prevalent during the Upper Palaeolithic in the Iberian Peninsula.
The purpose of the ABM is to test if warren hunting using nets as a collective strategy can explain the introduction of rabbits in the human diet in the Iberian Peninsula during this period. It is analyzed whether this hunting strategy has an impact on human diet breadth by affecting the relative abundance of other main taxa in the dietary spectrum.
Model validity is measured by comparing simulated diet breadth to the observed diet breadth in the zooarchaeological record.
The agent-based model is explicitly grounded on the Diet Breadth Model (DBM), from the Optimal Foraging Theory (OFT).
This model is used to simulate the influence of spatially and temporally variable sedimentary processes on the distribution of dated archaeological features in a surface context.
The model explores how two types of information - social (in the form of pheromone trails) and private (in the form of route memories) affect ant colony level foraging in a variable enviroment.
This multi-model (i.e. a model composed of interacting submodels) is a multi-level representation of a collective motion phenomenon. It was designed to study the impact of the mutual influences between individuals and groups in collective motion.
This model is intended to explore the effectiveness of different courses of interventions on an abstract population of infections. Illustrative findings highlight the importance of the mechanisms for variability and mutation on the effectiveness of different interventions.