Policy capturing¶
A judgment-analysis method that fits a statistical model to repeated decisions in order to estimate how a person weights available cues.
Core Idea¶
Policy capturing infers an implicit judgment policy by modeling observed ratings or choices as a function of systematically varied informational cues. Repeated profiles expose cue-outcome covariation; estimated coefficients summarize relative influence, interactions, and inconsistency in the judge's expressed policy. The abstraction is therefore identified by a declared carrier, a transformation or constraint over that carrier, and an invariant that tells an analyst whether the named structure is genuinely present.
The load-bearing residual is not the broad topic of judgment analysis. It is Stated importance ratings do not capture revealed weighting, and coefficients need not establish causal beliefs or a normatively correct policy..
Scope of Application¶
Policy capturing belongs to judgment analysis and is useful where the analyst can specify a decision maker, standardized judgment profiles, cue values, observed judgments, regression or related model, cue weights, fit diagnostics, and validation cases, then evaluate the design supplies adequate cue variation and the fitted model predicts the same decision maker's judgments under validated conditions. The scope is broad within that domain but bounded by the need for the design supplies adequate cue variation and the fitted model predicts the same decision maker's judgments under validated conditions. The entry records a descriptive analytical identity; practical use requires the governing domain's evidence, standards, and safety obligations.
Clarity¶
The abstraction clarifies a crowded vocabulary by making the design supplies adequate cue variation and the fitted model predicts the same decision maker's judgments under validated conditions the center of the account. A claim should name the carrier, the governing operation or relation, the applicable assumptions, and the recognition test. A bare label is insufficient because the name Policy capturing can be used for a formal identity, an implementation, or a neighboring result unless carrier and convention are stated.
Manages Complexity¶
Without the abstraction, an analyst must reason directly over many local details: the carrier roles, admissibility assumptions, competing conventions, derived invariants, boundary cases, and proof or validation obligations specific to Policy capturing. Policy capturing compresses them into the roles in the structural signature. That compression permits comparison across instances without erasing the variables that determine validity. It also exposes which details may be varied safely and which are constitutive.
Abstract Reasoning¶
- Identify the carrier. State what the elements, states, objects, or observations are: a decision maker, standardized judgment profiles, cue values, observed judgments, regression or related model, cue weights, fit diagnostics, and validation cases. Reject examples whose alleged carrier belongs to a different problem. 2. Lock the constitutive rule. Express the design supplies adequate cue variation and the fitted model predicts the same decision maker's judgments under validated conditions independently of one notation or implementation.
Knowledge Transfer¶
Knowledge transfers strongly among subfields of judgment analysis because they reuse a decision maker, standardized judgment profiles, cue values, observed judgments, regression or related model, cue weights, fit diagnostics, and validation cases, Repeated profiles expose cue-outcome covariation; estimated coefficients summarize relative influence, interactions, and inconsistency in the judge's expressed policy., and type the carrier, state every parameter and convention in the definition, test that the design supplies adequate cue variation and the fitted model predicts the same decision maker's judgments under validated conditions, compare the nearest accepted identity, and report counterexamples, uncertainty, and limiting cases.
Relationships to Other Abstractions¶
Current abstraction Policy capturing Domain-specific
Parents (1) — more general patterns this builds on
-
Policy capturing is a kind of Revealed Preference Prime
The proposed strict upward parent is
prime:revealed_preference.
Hierarchy path (1) — routes to 1 parentless root
- Policy capturing → Revealed Preference → Preference
Neighborhood in Abstraction Space¶
Policy capturing sits in a moderately populated region (41st percentile for distinctiveness): it has near-neighbors but no dense thicket of look-alikes.
Family — Health Motivation & Outcome Expectations (6 abstractions)
Nearest neighbors
- Considered purchase — 0.90
- Regression analysis — 0.90
- Item analysis — 0.89
- Critique — 0.89
- Outcome primacy — 0.89
Computed from structural-signature embeddings · 2026-09-08