Mooers's law¶
The retrieval-system regularity that a system tends not to be used whenever having the information is more troublesome than not having it — adoption governed by the per-query cost-benefit ratio against a substitute, not the content's absolute value.
Core Idea¶
Mooers's law (Calvin Mooers, 1959) states that an information retrieval system will tend not to be used whenever it is more painful and troublesome for a user to have the information than not to have it. Use is governed not by the content's absolute value but by the per-query cost-benefit ratio: when the cost to retrieve and process information exceeds the cost of doing without, non-use is rational. The system sits idle not because its content is poor but because its use is too expensive relative to the substitute.
Scope of Application¶
Mooers's law lives wherever a user weighs a per-query retrieval cost against an easier substitute — across information retrieval and its neighbours.
- Information retrieval and library science — the home: why good intranets and knowledge bases go unused.
- Knowledge management — why enterprise KM initiatives decay under contribution and retrieval cost.
- Software tooling / developer experience — git grep persisting over heavyweight code search.
- Help-desk and documentation — users asking a colleague rather than searching docs.
- EHR and clinical-decision-support, and disclosure regimes — alert fatigue; bypassed filings.
Clarity¶
The law redirects the designer's first question when a system goes unused: away from "the content is poor" or "users lack discipline" (both content-side and character-focused) and toward the use side — each query costs more than the user will pay relative to the substitute. It separates unused-because-bad-content from unused-because-too-expensive, and makes checkable the counter-intuitive prediction that enriching content can lower use.
Manages Complexity¶
Low-uptake systems present as a heterogeneous catalogue, each inviting a bespoke diagnosis. Mooers's law compresses the catalogue to one inequality per query: does the cost of use exceed the cost of doing without, against the cheapest substitute? The analyst tracks two quantities — per-query retrieval cost and per-query value — relative to a named comparator, and the intervention space sorts into exactly three levers read off the ratio.
Abstract Reasoning¶
The law licenses diagnostic relocation (start the search on the use side, not the content side), a predictive move yielding the counter-intuitive backfire as a sign change (enriching content can lower use), boundary-drawing (phrase adoption as a marginal comparison against a named substitute, not total corpus value), and an interventionist move reasoning to three exhaustive levers — lower retrieval cost, raise per-query value, or shift the substitute's cost (the last usually counter-productive).
Knowledge Transfer¶
Within information retrieval and its neighbours the law transfers as mechanism literally across every retrieval substrate — the per-query inequality, use-side diagnosis, backfire prediction, and three levers carry without translation, because all share a user with an information need. Beyond IR the named law does not transfer non-metaphorically (gym memberships borrow the shape). The general shape — marginal cost of action exceeding inaction against a substitute — is a genuine shared mechanism, but it is carried by the parents friction, cost_benefit_analysis, and satisficing; the per-query information-need machinery stays home.
Relationships to Other Abstractions¶
Current abstraction Mooers's law Domain-specific
Parents (2) — more general patterns this builds on
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Mooers's law is part of Cost–Benefit Analysis Prime
Mooers's Law contains Cost–Benefit Analysis because use is governed by a per-query comparison between the marginal trouble of retrieving and acting on information and the marginal trouble of using a substitute or doing without.
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Mooers's law is part of Satisficing Prime
Mooers's Law contains Satisficing because the user selects the least troublesome option that meets the immediate need rather than maximizing the total informational value available.
Hierarchy paths (18) — routes to 9 parentless roots
- Mooers's law → Cost–Benefit Analysis → Commensurability
- Mooers's law → Cost–Benefit Analysis → Value Commensuration → Commensurability
- Mooers's law → Cost–Benefit Analysis → Decision → Constraint
- Mooers's law → Satisficing → Bounded Rationality → Constraint
- Mooers's law → Cost–Benefit Analysis → Decision → Reversibility and Irreversibility
- Mooers's law → Satisficing → Bounded Rationality → Decision → Constraint
- Mooers's law → Satisficing → Heuristic → Trade-offs → Constraint
- Mooers's law → Satisficing → Bounded Rationality → Decision → Reversibility and Irreversibility
- Mooers's law → Cost–Benefit Analysis → Value Commensuration → Comparison → Self Checking
- Mooers's law → Cost–Benefit Analysis → Value Commensuration → Translation and Conceptual Bridging → Representation → Abstraction
- Mooers's law → Satisficing → Heuristic → Approximation → Representation → Abstraction
- Mooers's law → Cost–Benefit Analysis → Decision → Stage Gate Process → Sequencing → Dependency
- Mooers's law → Cost–Benefit Analysis → Value Commensuration → Translation and Conceptual Bridging → Transformation → Function (Mapping)
- Mooers's law → Cost–Benefit Analysis → Decision → Stage Gate Process → Sequencing → Optimization
- Mooers's law → Cost–Benefit Analysis → Decision → Stage Gate Process → Sequencing → Time
- Mooers's law → Satisficing → Bounded Rationality → Decision → Stage Gate Process → Sequencing → Dependency
- Mooers's law → Satisficing → Bounded Rationality → Decision → Stage Gate Process → Sequencing → Optimization
- Mooers's law → Satisficing → Bounded Rationality → Decision → Stage Gate Process → Sequencing → Time
Neighborhood in Abstraction Space¶
Mooers's law sits in a sparse region of the domain-specific corpus (69th percentile for distinctiveness): few abstractions share its structure, so a faithful description tends to retrieve it precisely.
Family — Surface Form & Underlying Structure (23 abstractions)
Nearest neighbors
- Near-equivalence Mapping — 0.84
- Google Effect — 0.83
- Information Avoidance — 0.83
- Privacy Paradox — 0.83
- Common-Pool Resource — 0.83
Computed from structural-signature embeddings · 2026-07-12