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Now the environment does not deal with history directly, it delegates it to a specific class. The analysis also uses history instances instead of either using the database directly or creating a proxy environment. This should make it easier to change the implementation in the future. In fact, the change was motivated by the large size of the csv files in previous versions. This new implementation only stores results in deltas, and it fills any necessary values when needed. |
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examples | ||
soil | ||
tests | ||
.gitignore | ||
debug.py | ||
docker-compose.yml | ||
Dockerfile | ||
LICENSE | ||
logo_gsi.png | ||
logo_gsi.svg | ||
MANIFEST.in | ||
README.md | ||
requirements.txt | ||
setup.py | ||
test-requirements.txt |
SOIL
Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks. Learn how to run your own simulations with our documentation.
Follow our tutorial to develop your own agent models.
If you use Soil in your research, don't forget to cite this paper:
@inbook{soil-gsi-conference-2017,
author = "S{\'a}nchez, Jes{\'u}s M. and Iglesias, Carlos A. and S{\'a}nchez-Rada, J. Fernando",
booktitle = "Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection",
doi = "10.1007/978-3-319-59930-4_19",
editor = "Demazeau Y., Davidsson P., Bajo J., Vale Z.",
isbn = "978-3-319-59929-8",
keywords = "soil;social networks;agent based social simulation;python",
month = "June",
organization = "PAAMS 2017",
pages = "234-245",
publisher = "Springer Verlag",
series = "LNAI",
title = "{S}oil: {A}n {A}gent-{B}ased {S}ocial {S}imulator in {P}ython for {M}odelling and {S}imulation of {S}ocial {N}etworks",
url = "https://link.springer.com/chapter/10.1007/978-3-319-59930-4_19",
volume = "10349",
year = "2017",
}
@Copyright GSI - Universidad Politécnica de Madrid 2017