Towards an Informational Pragmatic Realism

TL;DR

This paper presents a unified framework for reasoning under uncertainty by combining entropic inference and Bayesian methods, emphasizing pragmatic elements and defining information in relation to rational agents.

Contribution

It introduces an informational pragmatic realism that unifies entropic and Bayesian inference methods through a pragmatic, epistemic perspective on information and reasoning.

Findings

Entropy is identified as the unique inference tool.

The framework includes MaxEnt and Bayes' rule as special cases.

Pragmatic elements are integral to various philosophical realism theories.

Abstract

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.