Is there any framework similar to Bayesian Preposterior Analysis for Value of Information? I am trying to use the Value of Information concept using Bayesian Preposterior Analysis as proposed by Raiffa and Schlaifer 1961. However, due to certain limitations, mainly associated with the decision model, I am looking for alternate conceptions of quantifying the Value of Information.
Is there any alternate way of quantifying the value of information without tying it to a decision model itself?
For example, using Kullback Leibler divergence, the impact of the information on the prior and the posterior can be one such conception of the Value of information.
Any help would be appreciated.

You're probably looking for "Entropy" or "Information Gain," also called mutual information. It's the expected value of the Kullback–Leibler divergence from the conditional distribution.
https://courses.cs.washington.edu/courses/cse455/10au/notes/InfoGain.pdf
You can also look for articles on info-gain ratios and related terms.
