Interpreting Dynamical Systems as Bayesian Reasoners
Abstract
A central concept in active inference is that the internal states of a physical system parametrise probability measures over states of the external world. These can be seen as an agent's beliefs, expressed as a Bayesian prior or posterior. Here we begin the development of a general theory that would tell us when it is appropriate to interpret states as representing beliefs in this way. We focus on the case in which a system can be interpreted as performing either Bayesian filtering or Bayesian inference. We provide formal definitions of what it means for such an interpretation to exist, using techniques from category theory.
Cite
@article{arxiv.2112.13523,
title = {Interpreting Dynamical Systems as Bayesian Reasoners},
author = {Nathaniel Virgo and Martin Biehl and Simon McGregor},
journal= {arXiv preprint arXiv:2112.13523},
year = {2021}
}
Comments
11 pages + 26 pages appendix, to be published in the proceedings of the 2nd International Workshop on Active Inference 2021