Simulation is a common technique in the natural and social sciences. It is less common (though not unheard of) in the humanities. We have developed and are exploiting the PolyGraphs simulation framework to conduct philosophical research surrounding the nature of rational opinion formation (in individuals and groups), the effectiveness of various information processing strategies under adverse conditions, and the truth-conduciveness (and other pro-democratic characteristics) of various social network structures. Our Python code is scalable, allowing us to explore large datasets on realistic networks (e.g. on the National Internet Observatory, or through access enabled under the EU’s Digital Services Act); and it is built to enable machine learning, on the deep graph learning library.
News
A video of the CPL/FDM event, ‘Fake News, Real Risks: AI, Deepfakes, and the Battle for Truth’ is available: here.
‘Compositional Understanding in Signalling Games’ has been published (open access) in Synthese: here.
The Women Writers Project has published visualizations from a PolyGraphs-based simulational study of Margaret Cavendish (1623-1673) and her interactions with the founding members of the Royal Society: here. The study has also received some coverage in Northeastern Global News: here. And here is a video of the project symposium on women in (the history of) philosophy and science.