Sadie is an agent-based modelling environment for stochastic agents in discrete time in Euclidean space.
Sadie: Stochastic Agents in DIscrete time and Euclidean space
Sadie is an agent-based modelling environment for stochastic agents in discrete time in Euclidean space. It is intended to serve as a simple, convenient replacement for more complex agent-based modelling frameworks such as Repast and Mesa where customisation and adaptation to specific use cases is required. In particular, Sadie is designed for simulating random walks in various complex interactions, including Lévy walks, avoidant walks, homesick and other stochastic mobility models in Euclidean space.
Free software: MIT license
Spatial agents, including various random walk agents
Targetable objects, with target-following ability
Model objects with extensive ability to report and collate data
Easily extensible over a wide range of use cases in mobility, analytics, foraging, spatial statistics, epidemiology and many other areas
This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.
Sadie is named after Dickin medal winner Sadie, an explosives detection dog who saved hundreds of lives when detecting a secondary explosive charge after a bombing outside UN Headquarters, Kabul, Afghanistan, in November 2005.
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