tomsup#
This website contains the documentation for tomsup. tomsup is a Python package for agent-based simulations. The package provides a computational eco-system for investigating and comparing computational models of hypothesized Theory of mind (ToM) mechanisms and for using them as experimental stimuli. The package notably includes an easy-to-use implementation of the variational Bayesian k-ToM model developed by Devaine, et al. (2017). This model has been shown able to capture individual and group-level differences in social skills, including between clinical populations and across primate species. It has also been deemed among the best computational models of ToM in terms of interaction with others and recursive representation of mental states. We provide a series of tutorials on how to implement the k-ToM model and a score of simpler types of ToM mechanisms in game-theory based simulations and experimental stimuli, including how to specify custom ToM models, and show examples of how resulting data can be analyzed.
📰 News#
Version 1.1.5
New plotting features were added
Speed and memory improvements as well as support for multicore simulations 🏎
Added workflows to ensure dependencies are being kept up to date
Minor bugfixes
Version 1.1.0
A speed comparison between the matlab implementation was introduced, showing the the tomsup implementation to be notably faster.
An extensive testsuite was introduced, for how to run it see the FAQ.
A documentation site was introduced.
Continiuous integration to ensure that the package always works as intended.
A new logo was introduced 🌟
Version 1.0.0
Contents#
The documentation is organized into two parts:
Getting Started contains the installation instructions, guides, and tutorials on how to use tomsup.
Package References contains the documentation of each public class and function. Use this for looking into specific functions.
Getting Started
Package References