Value-of-Information Methods for Prioritising Photopolymer Research: Uncertainty, Decision Impact and Experimental Choice

Photopolymer research portfolios often contain many uncertain questions but limited experimental capacity. Value-of-information methods provide a decision-theoretic way to rank information by the improvement it could produce in a downstream decision. This review translates expected value of information, expected value of perfect information and expected value of sample information into photopolymer engineering terms. It defines the required decision model, distinguishes reducible from irreducible uncertainty, and shows how experiment cost, feasibility and time modify priority. The method is proposed as a research-allocation tool, not as a universal economic monetisation of scientific knowledge. ER-399.