MMP.16:11 - SoTA-Echoing

Design according to its goal. Huan, Jagalur and Marzouk, Optimal experimental design: Formulations and computations (Acta Numerica, 2024), §2.2 and the goal-oriented and model-discrimination formulations, distinguish information targets and decision utilities. The adopted contribution is the explicit selection of what an observation should improve in :4.4. A design maximizing information about every parameter is a serious default when that is the actual aim; it is replaced when the receiving target differs. Numerical optimization methods are delegated to the applicable computational construction. Source.

Information that can change a decision. Heath and colleagues, Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial (2022), “Background and Notation,” gives the pre-observation comparison of decisions after possible records. Its loss-form equivalent is used in :4.4 and the original recovery example in :5.2. The model’s costs and consequences must describe the receiving work; the source’s health-economic conventions are not generalized into mandatory monetary valuation. Source.

An informative design can misdirect learning. Tang, Sloman and Kaski, Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification (arXiv v2, 2026), §§2–4, studies prediction under a fixed, potentially inadequate model family. Its analysis separates approximation error, estimation error and their interaction under the intended input distribution. This motivates the explicit receiving conditions in :4.6. Their proposed acquisition rule is not a universal remedy: its assumptions and quantities need their own justification before use. The adopted general move is to compare consequential model inadequacy and observation placement, rather than assuming that higher information gain implies better prediction. Read version.

These sources develop statistical branches. The bounded-error construction in :5.1 is an elementary set-valued derivation; it needs no prior distribution. Reopen the selected design when the target, record law, feasible access, receiving population or substantive model alternatives change. A newer optimization technique matters when it improves that same construction at worthwhile effort.