Federated inference and belief sharing

Friston et al (2024). Federated inference and belief sharing. Neuroscience and Behavioral Reviews (Vol 156, Jan).

I’ve been thinking a lot lately about Beyesian inference, mostly in the context of systems of devotion over long time scales, and how we might disentangle true collective will from agregious manipulation of such will, which is something that requires ethical consideration by all who endeavor to preserve and advance human dignity.

The paper begins with three clearly stated highlights addressing communication as an emergent property of conspecifics, how “evidence-maximizing” processes can explain the emergence of language and belief transmission across generations, and how leveraging Bayesian terms can ground these concepts.

An explanation of “active inference” is given, stating it depends on a generative model of observeable outcomes, which is used to infer “the most likely causes of outcomes in terms of expected states of the world.” The states are hidden and must be inferred from observation. I had never heard the phrase “expected free energy” before, used in relation to prior probability, but it is a supercase of mutual information.