2014/12/17 by Ariel Caticha, Caticha, Ariel
Arts and Humanities · Computer Science · Physics and Astronomy · #Bayesian Modeling and Causal Inference #FOS: Physical sciences #History and Philosophy of Physics (physics.hist-ph) #Philosophy and History of Science #Statistical Mechanics and Entropy
paper · pdf · doi:10.48550/arxiv.1412.5644
openalex publication_date 2014/12/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
I discuss the design of the method of entropic inference as a general framework for reasoning under conditions of uncertainty. The main contribution of this discussion is to emphasize the pragmatic elements in the derivation. More specifically: (1) Probability theory is designed as the uniquely natural tool for representing states of incomplete information. (2) An epistemic notion of information is defined in terms of its relation to the Bayesian beliefs of ideally rational agents. (3) The method of updating from a prior to a posterior probability distribution is designed through an eliminative induction process that singles out the logarithmic relative entropy as the unique tool for inference. The resulting framework includes as special cases both MaxEnt and Bayes' rule. It therefore unifies entropic and Bayesian methods into a single general inference scheme. I find that similar pragmatic elements are an integral part of Putnam's internal realism, of Floridi's informational structural realism, and also of van Fraasen's empiricist structuralism. I conclude with the conjecture that their valuable insights can be incorporated into a single coherent doctrine - an informational pragmatic realism.