name: Demand Curve Calibration and Response Design

batch: Batch 11

canonical_family: Scarcity, Tradeoffs & Allocation

aliases:
Demand Schedule Calibration
Generalized-Cost Demand Modeling
Demand Curve Design
Quantity-Cost Response Mapping
Demand Model Calibration
Demand Response Curve Calibration
Demand Schedule Mapping
Demand Curve Governance

slug: demand

slug: elasticity

slug: preference

slug: scarcity

slug: allocation

slug: equilibrium

slug: feedback

slug: uncertainty

components:
Demand Object Boundary
Quantity Sought Measure
Generalized Cost Vector
Choice Set and Substitution Map
Segment and Context Partition
Observed Response Evidence Base
Latent and Suppressed Demand Probe
Demand Schedule Model
Elasticity and Threshold Profile
Uncertainty and Extrapolation Band
Decision Integration Rule
Welfare and Access Guardrail
Post-Intervention Recalibration Loop
Cross-Elasticity Matrix
Consumer Surplus or Value Proxy
Strategic Response Screen
Demand Shift Classifier

mechanisms:
Demand Curve Estimation Workbook
Price Sensitivity Experiment
Revealed Preference Choice Log
Waitlist and Stockout Analysis
Conjoint or Discrete Choice Model
Cross-Elasticity Matrix
Demand Segmentation Dashboard
Shadow Price Probe
Scenario Demand Stress Test
Equity Access Impact Review

essence_one_line: Model how much of something is sought at different generalized costs, then use the calibrated response curve to guide allocation, pricing, capacity, and access decisions.

compression_statement: Demand Curve Calibration and Response Design applies when a system must reason about quantity sought as a function of generalized cost: not only money, but time, effort, risk, delay, inconvenience, social cost, uncertainty, or policy friction. The intervention defines the relevant choice set, cost vector, quantity unit, segment boundaries, observed and latent demand signals, elasticity estimates, substitution paths, confidence limits, and update loop. It turns raw asks, sales, visits, queue entries, sign-ups, searches, applications, or usage traces into a governed demand schedule that can be used without mistaking current volume for true need, price for the only cost, or average response for every segment.

core_intuition: Observed quantity is not demand by itself. Quantity changes when generalized cost changes, when substitutes become easier, when access barriers move, when expectations shift, or when segments face different constraints. A useful demand model keeps those relationships explicit so decisions do not overbuild for noisy interest, under-serve suppressed demand, impose prices that miss substitution, or treat every response as if it had the same elasticity.

canonical_formula: choice_set + generalized_cost_vector + quantity_sought_measure + segment_context + observed_or_revealed_response + substitution_map + elasticity_estimate + validation_loop -> governed_demand_schedule_for_decision

structural_problem: A decision maker sees requests, purchases, usage, applications, waitlists, clicks, traffic, load, or survey interest and needs to decide how much capacity, price, access, stock, staffing, or priority to provide. Without an explicit demand schedule, the system treats one observed quantity as if it were stable demand, misses the generalized cost conditions that produced it, ignores substitution paths, overgeneralizes average elasticity, and confuses suppressed, latent, induced, shifted, or strategic demand with simple volume.

condition: Quantity changes with cost or friction

description: Observed uptake, use, applications, requests, trips, purchases, or attention depends on price, time, delay, effort, risk, policy barriers, convenience, or other generalized costs.

condition: Decisions depend on response to a lever

description: The organization is considering a price, discount, subsidy, capacity change, admission rule, queue rule, package, service level, access friction, or nonmonetary cost change.

condition: Substitutes or complements matter

description: Agents can shift to another product, channel, time, location, provider, behavior, or no-action option when generalized cost changes.

condition: Segments face different constraints

description: Users, customers, patients, applicants, neighborhoods, or units differ in budget, urgency, switching options, access barriers, preference strength, or response speed.

condition: Latent or suppressed demand is plausible

description: Current volume may understate demand because people are priced out, deterred by friction, unaware, discouraged, rationed, or blocked by scarce supply.

condition: Induced or rebound demand is plausible

description: Reducing cost or improving efficiency could increase total quantity sought enough to offset savings, capacity gains, or conservation goals.

condition: A forecast alone is insufficient

description: The question is not merely how much quantity will appear next period, but how quantity changes if generalized costs, substitutes, or constraints change.

symptom: Volume is mistaken for demand

signal: Teams say demand is high or low based on one observed quantity without recording price, wait time, barriers, awareness, capacity, or substitutes.

symptom: Suppressed demand surprise

signal: When access is made easier, usage or applications jump far beyond forecasts built from previous observed volume.

symptom: Price or friction changes miss their target

signal: A price, discount, subsidy, penalty, requirement, or queue rule changes behavior much more or much less than expected.

symptom: Average elasticity hides segment harm

signal: Overall uptake looks stable while vulnerable or high-friction segments drop out, switch, or delay.

symptom: Substitution leakage

signal: A policy reduces demand in one channel while pushing it to a substitute, gray market, adjacent time slot, competing service, or informal workaround.

symptom: Capacity chasing noisy peaks

signal: Capacity is expanded for transient or induced demand rather than for durable willingness, need, or value under specified cost conditions.

symptom: Welfare and revenue stories conflict

signal: Pricing, access, and capacity decisions optimize a local metric while ignoring unmet need, consumer surplus, equity, or system-wide substitution.

root_tension: Demand is a relational schedule, but organizations often observe only a point on that schedule. Decisions need a tractable model, yet the relevant cost is multidimensional, response varies by segment, substitutes reshape the curve, and interventions can move along the curve or shift it. The tension is between simple actionable demand signals and the richer behavioral structure needed to make those signals safe and transferable.

anti_signatures: Use price_signal_design when the main task is to create or tune a price-like signal to coordinate behavior, not to calibrate the demand schedule itself.

anti_signatures: Use elasticity_based_leverage when an existing response curve is already good enough and the task is to find high-leverage intervention points.

anti_signatures: Use anticipatory_forecasting when the main task is predicting future volume under a stable policy environment.

anti_signatures: Use load_leveling_or_demand_smoothing when the main issue is temporal concentration of arrivals or workload rather than estimating quantity-cost response.

anti_signatures: Use elastic_capacity_scaling when the primary intervention is adding or releasing capacity in response to measured demand.

anti_signatures: Use service_rate_matching when the central question is matching staffing or service rate to a known demand arrival process.

anti_signatures: Do not use this archetype when quantity sought is fixed by mandate, biology, physics, or contract and does not respond meaningfully to generalized cost or substitution.

structural_intervention: Build a governed demand-schedule model by defining the choice set, quantity unit, generalized cost vector, segment/context partitions, observed and revealed response data, substitution paths, elasticity and confidence estimates, validation tests, and decision rules that translate the calibrated schedule into pricing, allocation, capacity, access, or policy choices.

action_logic: Define what counts as the item, service, behavior, application, visit, request, use episode, unit, or bundle whose quantity sought is being modeled.

action_logic: State the relevant choice set, including substitutes, complements, no-action alternatives, informal workarounds, and rationed or inaccessible options.

action_logic: Decompose generalized cost into monetary price, time, delay, effort, search, compliance burden, risk, uncertainty, stigma, cognitive load, coordination cost, and other domain-specific frictions.

action_logic: Separate observed quantity from latent, suppressed, induced, strategic, or displaced demand.

action_logic: Partition contexts and segments where response is likely to differ, while preserving enough pooling to avoid overfitting.

action_logic: Estimate the demand schedule or response surface across the cost range that matters for decisions, including slope, elasticity, thresholds, saturation, and discontinuities.

action_logic: Map substitution and cross-elasticity paths so reduced demand in one place is not mistaken for reduced demand overall.

action_logic: Validate the schedule against revealed behavior, experiments, natural experiments, historical changes, surveys, waitlists, stockouts, or external benchmarks.

action_logic: Attach uncertainty bands and decision-use boundaries, especially outside observed cost ranges.

action_logic: Feed the model into a specific decision rule for pricing, capacity, allocation, subsidy, access, queueing, packaging, or policy design.

action_logic: Monitor post-decision response and update the schedule when preferences, income, substitutes, capacity, awareness, or constraints shift.

rule: Generalized-cost completeness rule

description: Do not treat money price as the demand driver until time, delay, effort, risk, information, policy friction, and substitute availability have been checked.

rule: Observed-point humility rule

description: A single observed volume is only one point under one cost/context bundle; it cannot by itself justify a whole demand curve.

rule: Segment heterogeneity rule

description: Estimate separate response where groups face materially different constraints, access, urgency, income, alternatives, or switching costs.

rule: Substitution accounting rule

description: Any intervention expected to reduce, expand, or redirect demand must specify where displaced or induced quantity may go.

rule: Range-of-validity rule

description: Do not use elasticity estimates outside the observed or defensibly modeled cost range without marking extrapolation risk.

rule: Latent-demand rule

description: When barriers suppress observed quantity, use waitlists, denials, stockouts, search traces, unmet-need indicators, or access probes before concluding demand is low.

rule: Decision-purpose rule

description: Choose model resolution according to the decision being made: pricing, capacity, equity, conservation, triage, or package design require different tolerances.

rule: Equity guardrail rule

description: For essential goods or public services, a demand schedule must not be used as if ability to pay or tolerate friction equals legitimate need.

target_invariants: Quantity sought is always indexed to a specified generalized-cost bundle and choice context.

target_invariants: The model distinguishes movement along a demand schedule from shifts caused by preference, income, awareness, substitutes, policy, or capacity changes.

target_invariants: Substitution paths remain visible before interpreting local demand reduction as system-level demand reduction.

target_invariants: Elasticity estimates carry segment, range, and uncertainty annotations.

target_invariants: Latent and suppressed demand are not erased by lack of observed transactions.

target_invariants: Decision use is explicit: forecast, pricing, capacity, allocation, access, subsidy, conservation, or triage.

target_invariants: Ethical and access constraints remain separate from purely revenue-maximizing or load-minimizing objectives.

expected_outcomes: Better pricing, capacity, allocation, and access decisions because quantity response is modeled rather than assumed.

expected_outcomes: Reduced surprise from suppressed demand, induced demand, rebound effects, and substitution leakage.

expected_outcomes: Clearer distinction between demand forecasting, demand shaping, price signaling, and demand-schedule calibration.

expected_outcomes: More transparent uncertainty around elasticity, thresholds, and extrapolation.

expected_outcomes: Improved fairness review where demand signals are confounded by ability to pay, time burden, search cost, or exclusion.

expected_outcomes: More stable learning loops as post-intervention behavior recalibrates the demand schedule.

prose_essence:
Demand Curve Calibration and Response Design treats demand as a relationship, not as a raw count. The practical object is a schedule: how much of a bounded item, service, behavior, access right, or bundle is sought at different generalized costs. Generalized cost includes money, but also time, delay, effort, risk, uncertainty, stigma, search, coordination burden, and policy friction. The design problem is to make that schedule explicit enough to guide pricing, capacity, allocation, access, or conservation decisions without confusing one observed quantity for stable demand.

The archetype is useful whenever a decision asks, "What happens to quantity if the cost, access rule, substitute set, package, or service level changes?" It turns demand from an anecdote into a governed model: object boundary, quantity measure, cost vector, choice set, segments, evidence, elasticity, substitution map, uncertainty band, validation loop, and decision rule.

prose_tradeoffs: Demand calibration costs time, data, and interpretive effort. Segment-specific modeling can protect vulnerable groups but may overfit or create discriminatory targeting. Experiments produce stronger causal evidence but can impose real burdens. Transparent models improve governance but may invite strategic manipulation. These tradeoffs are manageable only if the demand model is treated as a decision aid, not as a morally complete account of value or need.

Generalized-Cost Demand Modeling

short_definition: A demand schedule calibrated over money and nonmoney cost dimensions rather than price alone.

distinctive_feature: Expands the cost axis beyond monetary price.

distinct_from_parent: The parent includes all demand-schedule calibration; this variant emphasizes multidimensional cost decomposition.

still_same_parent_because: It retains the same quantity-cost response modeling architecture.

use_when: Time, friction, risk, delay, stigma, or effort materially shape uptake.

use_when: A nonmonetary policy lever changes demand without changing price.

example_domains:
public services
platform design
healthcare access

domain: transportation

example: Model ridership over fare, travel time, transfers, reliability, and parking cost.

failure_modes: Treating all nonmoney cost as a single friction scalar.

Segmented Demand Calibration

short_definition: A demand model that separately estimates response for groups with different constraints, preferences, or substitutes.

distinctive_feature: The segment partition is central to safe use of the curve.

distinct_from_parent: The parent can use one curve; this variant requires segment-specific curves or response surfaces.

still_same_parent_because: It remains demand-schedule calibration rather than a separate intervention lever.

source_primes: demand preference price_elasticity

use_when: Average elasticity hides important group differences.

use_when: Access, income, urgency, switching cost, or context vary materially across groups.

example_domains:
healthcare
software pricing
public benefits

domain: healthcare

example: Separate urgent, discretionary, low-income, and travel-limited appointment demand before changing copays.

failure_modes: Overfitting tiny segments or using protected-class segmentation without a legitimate purpose.

name: Cross-Elasticity and Substitution Mapping

short_definition: Demand calibration focused on where quantity goes when generalized cost changes for substitutes or complements.

distinctive_feature: Substitution structure is the primary object of modeling.

distinct_from_parent: The parent models own-demand and generalized response; this variant foregrounds cross-demand relations.

still_same_parent_because: It uses the same schedule-calibration logic with a richer choice set.

use_when: An intervention can displace demand to adjacent goods, times, channels, or informal workarounds.

use_when: Demand reduction in one place may not reduce system-wide demand.

example_domains:
energy
transportation
consumer products
public policy

domain: energy

example: Check whether higher peak electricity prices move use to off-peak periods or to less efficient alternatives.

failure_modes: Counting local demand reduction as social benefit when demand has merely shifted.

near_names:
Demand Curve Modeling
Demand Schedule Design
Quantity-Cost Response Mapping
Demand Model Calibration


Demand-Triggered Deferred Evaluation

type: archetype

source: generated

batch: Batch 05

aliases:
Lazy Evaluation
Deferred Evaluation
Demand-Driven Evaluation
Need-Driven Computation
On-Demand Evaluation
Deferred Computation

slug: postponement

slug: dependency

slug: callback

slug: caching

slug: scheduling

slug: latency

slug: observability

slug: idempotence

slug: concurrency

slug: algorithm

slug: pipeline

slug: transaction

components: Single-Flight and Duplicate-Suppression Rule

mechanisms:
Thunk or Lazy Promise
Call-by-Need Evaluator
Short-Circuit Evaluation
Lazy Sequence Generator
Lazy-Initialization Proxy
Late-Materialization Query Plan
Predicate Pushdown
Demand-Driven Build Graph
Demand Paging
Copy-on-Write
Single-Flight Request Coalescing
Memoization
Conditional Workflow Gate
On-Demand Report Generation
Just-in-Time Compilation
Deferred Database Query Object

essence_one_line: Represent optional or path-dependent work as a suspended unit, realize only the dependency closure demanded now, and make result sharing, side effects, failure timing, cancellation, lifetime, and first-use latency explicit.

compression_statement: Identify work whose result may never be consumed; specify a demand boundary and need predicate; capture only valid inputs and dependencies; keep the work suspended; on valid demand, coordinate one bounded realization of the minimum required closure; publish, share, memoize, or discard the result according to policy; isolate side effects; expose deferred failures; support cancellation and invalidation; measure avoided work against first-use latency, memory retention, coordination cost, and observability loss; and force selected work eagerly when safety, deadlines, deterministic failure timing, or resource release requires it.

core_intuition: Eager systems pay the cost of every possible branch before knowing which branch matters. Demand-triggered deferral turns “might be needed” into a suspended option and pays only when actual use makes the value relevant. The savings are real only if the system can recognize demand correctly, avoid duplicate realization, preserve valid inputs, control side effects, surface late failures, and keep first-use delay within an acceptable budget.

canonical_formula: Realize W only if Need(W, demand, state)=true; execute Closure(W) once per sharing identity when permitted; prefer laziness when P(not demanded)·Cost(W) > AddedLatency(W) + RetentionCost(W) + CoordinationCost(W) + DeferredFailureRisk(W), subject to side-effect, authorization, validity, and service-level invariants.

structural_problem: A system eagerly computes, loads, initializes, copies, traverses, or generates outputs before it knows whether any downstream path will use them. Optional branches, large sequences, expensive dependencies, remote data, and rarely used features consume time, memory, bandwidth, energy, and failure surface even when abandoned. Ad hoc laziness can reverse the waste but hide execution timing, move failures far from construction, retain stale inputs, duplicate concurrent work, trigger side effects late, and create first-use latency cliffs. The problem is therefore not merely “do less work”; it is to make nonexecution, later realization, and result publication part of an explicit contract.

trigger_conditions: A material fraction of prepared work is never consumed because branches, fields, features, targets, or records are optional.

trigger_conditions: The work can be represented before execution without losing the information needed to realize it safely later.

trigger_conditions: A reliable event can distinguish actual demand from prediction, inspection, logging, or mere object existence.

trigger_conditions: The dependency closure for one requested result is substantially smaller than the full possible work graph.

trigger_conditions: First-use latency can be bounded, hidden, scheduled, or accepted in exchange for avoided work.

trigger_conditions: The work is pure, idempotent, isolated, compensatable, or otherwise safe to delay or potentially repeat.

trigger_conditions: Concurrent demand may be coalesced under a stable identity without crossing security or tenancy boundaries.

trigger_conditions: Inputs, permissions, versions, and resources can remain valid until realization or be reacquired safely.

trigger_conditions: The system can observe which demand forced which work and can compare lazy behavior with eager alternatives.

symptoms: Startup, construction, or request setup is slow even though most initialized features are never used.

symptoms: Queries fetch columns, rows, relationships, or remote objects that no consumer reads.

symptoms: A pipeline materializes a whole dataset although callers inspect only a prefix or a filtered subset.

symptoms: Builds, workflows, or simulations run branches unrelated to the requested output.

symptoms: Memory is occupied by speculative objects or copies that never receive a read or write.

symptoms: Teams add flags to skip work manually because the execution model cannot express demand dependencies.

symptoms: Introducing lazy loading causes N+1 requests, first-click stalls, or failures in distant code paths.

symptoms: Concurrent callers launch the same absent computation and overload the dependency.

symptoms: Deferred closures retain large object graphs, expired sessions, closed handles, or stale credentials.

symptoms: Errors appear only at first use and lack enough provenance to identify the original construction context.

symptoms: Operational traces cannot explain why a supposedly unused computation ran or why a needed one did not.

root_tension: Eager realization offers predictable timing, early failure, and simple lifetime semantics but wastes work on unused possibilities; lazy realization avoids that waste and enables large or infinite structures but transfers cost and uncertainty into first-demand latency, hidden timing, retained state, concurrency, failure attribution, and side-effect governance.

anti_signatures: Nearly every unit is demanded and first-use latency is more expensive than the small amount of avoided work.

anti_signatures: The work must occur by a deadline even if no one explicitly requests its result, such as backups, safety inspections, maintenance, or statutory reporting.

anti_signatures: The central intervention is to perform likely future work early; use Precomputation / Prefetching.

anti_signatures: The result already exists and the primary problem is storing it near repeated demand; use Strategic Caching.

anti_signatures: The central problem is repeated overlapping subproblems rather than whether work runs at all; use Dynamic Subproblem Reuse.

anti_signatures: The system is waiting for a monitored value to cross a threshold rather than for a result to be requested; use Threshold-Based Activation.

anti_signatures: The design delays an irreversible decision to preserve options rather than deferring computation or realization; use Option Preservation.

anti_signatures: Side effects must occur in construction order and cannot be isolated, made idempotent, compensated, or committed explicitly.

anti_signatures: Inputs or authorization cannot remain valid or be revalidated at first use.

anti_signatures: The work is so cheap that suspension, capture, coordination, and tracing cost more than eager execution.

structural_intervention: Replace eager execution with an explicit suspended-work representation. Define the legitimate demand boundary, map dependencies, test semantic need, capture valid inputs, and activate only the minimum required closure when demand arrives. Coordinate duplicate demand, isolate effects, publish results under a sharing policy, expose failures and provenance, support cancellation and invalidation, and continuously tune the latency-waste tradeoff with an eager escape hatch.

action_logic: Inventory eager work and measure how often each result is actually consumed.

action_logic: Select units whose avoided-work value plausibly exceeds added latency, retention, and coordination cost.

action_logic: Define the deferred unit, its inputs, dependencies, output contract, side-effect profile, and owner.

action_logic: Specify the demand boundary and need predicate that distinguish actual use from prediction or incidental inspection.

action_logic: Represent the unit in an explicit suspended state with valid capture and lifecycle semantics.

action_logic: On demand, determine the smallest dependency closure that must be realized for this request.

action_logic: Check authorization, versions, resource availability, cancellation state, and safety constraints before activation.

action_logic: Coalesce equivalent concurrent demand or deliberately allow independent realization according to policy.

action_logic: Execute the realization path, isolate or explicitly commit side effects, validate the output, and record provenance.

action_logic: Publish, memoize, share, stream, or discard the result according to the result-sharing and lifetime policy.

action_logic: Propagate failures, retries, cancellation, invalidation, and cleanup with explicit semantics.

action_logic: Trace demand-to-realization behavior and compare avoided work, first-use latency, retention, duplicate execution, and error timing with the eager baseline.

action_logic: Force or prefetch selected units eagerly when evidence shows that strict laziness violates service, safety, deadline, or resource-lifetime invariants.

decision_rules: Use laziness only when there is a credible probability that the result will not be demanded or only a bounded prefix will be consumed.

decision_rules: Treat actual value use, not mere object creation or forecasted interest, as the default demand trigger.

decision_rules: Do not defer irreversible or externally visible effects unless their timing and multiplicity are explicitly governed.

decision_rules: Coalesce concurrent demand only when identity, authorization, version, and failure semantics are equivalent.

decision_rules: Memoize only when the result remains valid under an explicit key and invalidation rule.

decision_rules: Revalidate permissions and expiring context at realization when the demander may differ from the creator.

decision_rules: Prefer eager evaluation when first-demand latency, deterministic failure timing, or prompt resource release dominates avoided work.

decision_rules: Bound retained captures and lazy chains; release them after completion, cancellation, invalidation, or expiry.

decision_rules: Reject hidden N+1 access patterns: each lazy boundary must expose expected request multiplicity and batching options.

decision_rules: Do not call neglected maintenance “lazy”; work with a must-run deadline belongs in scheduling or backlog governance.

decision_rules: Require observability sufficient to reconstruct why work ran, did not run, failed, or was shared.

target_invariants: A result is never presented as realized before its realization path completes and validation succeeds.

target_invariants: Only a legitimate demand or governed eager exception may activate a deferred unit.

target_invariants: The realized dependency closure is no broader than necessary for the requested result, except where a documented batching tradeoff applies.

target_invariants: Concurrent duplicate demand does not cause unbounded duplicate side effects or resource consumption.

target_invariants: Captured inputs, permissions, and versions are valid at realization or are explicitly reacquired and revalidated.

target_invariants: Result sharing never crosses scope, tenant, authorization, or version boundaries accidentally.

target_invariants: Deferred failure retains enough provenance to identify the suspended unit and original construction context.

target_invariants: Cancellation and invalidation release resources and do not leave unresolved shared state indefinitely.

target_invariants: Safety-critical, deadline-bound, and must-run work cannot disappear merely because no consumer requests a value.

target_invariants: The lazy path remains measurable against an eager or prefetched baseline.

expected_outcomes: Lower compute, I/O, bandwidth, memory, energy, and initialization cost for unused work.

expected_outcomes: Faster startup and setup paths when optional features or branches are common.

expected_outcomes: Ability to represent large or potentially infinite sequences while consuming only finite demanded prefixes.

expected_outcomes: Smaller execution closures for target-specific builds, queries, workflows, and reports.

expected_outcomes: Fewer thundering-herd duplicates when concurrent demand is safely coalesced.

expected_outcomes: Clearer timing, failure, sharing, cancellation, and lifetime contracts than ad hoc lazy loading.

expected_outcomes: Better evidence for when to stay lazy, memoize, prefetch, batch, or force eagerly.

expected_outcomes: Reduced waste without silently transferring unacceptable delay or risk to first users.

prose_tradeoffs:
- Avoided unused work versus higher latency on first demand.
- Lower startup cost versus deferred failure and validation timing.
- Ability to represent large or infinite structures versus retained state and open-resource lifetime.
- Result sharing versus storage, invalidation, authorization, and stale-result risk.
- Concurrent request coalescing versus contention, shared failure fan-out, and head-of-line blocking.
- Composability of deferred values versus harder reasoning about when code actually executes.
- Reduced I/O and bandwidth versus N+1 request storms when demand granularity is too fine.
- Minimal dependency closure versus overhead of maintaining accurate dependency and staleness metadata.
- Cancellation savings versus complex cleanup and shared-demand semantics.
- Late binding to current state versus reduced reproducibility when inputs are not versioned.
- Memory saved by not materializing outputs versus memory retained by captured closures and dependency chains.
- Energy and compute savings versus latency and burst load concentrated at user-visible moments.

Memoized Call-by-Need

variant_type: subtype

short_definition: Realize a deferred expression at most once per valid identity and share the memoized result across all later references.

distinctive_feature: First demand changes the unit from suspended to a shared completed state rather than merely invoking the computation again.

distinct_from_parent: The parent permits recomputation or one-shot results; this subtype requires result sharing and therefore imports storage, identity, freshness, and invalidation obligations.

still_same_parent_because: The defining move remains demand-gated realization of work that otherwise may never execute.

source_primes: lazy_evaluation caching dependency idempotence

use_when: The same deferred value may be demanded repeatedly.

use_when: The result is stable for the chosen identity or version.

use_when: Duplicate evaluation is costly or unsafe.

example: A deferred function argument is computed on first reference and every later reference returns the same stored value.

domain: services

example: The first request for a costly derived profile launches one computation; concurrent and later requests reuse its result until invalidation.

failure_modes: Failure is memoized longer than intended

Repeat-on-Demand Call-by-Name

short_definition: Reevaluate a deferred expression each time its value is demanded instead of sharing a prior realization.

distinctive_feature: Demand gates timing but does not create a persistent completed state.

distinct_from_parent: The parent leaves sharing open; this subtype explicitly chooses repeat evaluation and therefore requires strong side-effect and cost controls.

still_same_parent_because: Work remains suspended until demanded and may still be avoided entirely.

use_when: The expression is cheap, state-sensitive, or intentionally reevaluated.

use_when: Retaining results would be more costly or misleading than recomputation.

use_when: The caller needs current state on every force.

example: A named argument expression is substituted and reevaluated on each reference.

domain: dashboards

example: A live status field is fetched only when opened and is refetched on each open rather than cached.

failure_modes: Different values across references surprise callers

Lazy-Stream Incremental Realization

short_definition: Realize only the next element or bounded chunk of a sequence as downstream consumption advances.

distinctive_feature: Demand is incremental and repeated, producing a finite prefix rather than one all-at-once value.

distinct_from_parent: The parent also covers one-shot deferred units; this subtype centers productive prefixes, backpressure, and resource lifetime across an open sequence.

still_same_parent_because: Each next element remains unrealized until demanded by the consumer.

source_primes: lazy_evaluation pipeline flow recursion

use_when: The full sequence is large, unbounded, or often only partially consumed.

use_when: Producer and consumer can coordinate through pull or bounded buffering.

use_when: Per-element cleanup and failure semantics are defined.

example: A pipeline reads and transforms rows only as the consumer requests the next row.

domain: search

example: Results are ranked and fetched in pages only as the user advances.

failure_modes: Resource remains open after partial consumption

failure_modes: Consumer stalls and retains upstream state

name: Deferred Initialization and Lazy Loading

short_definition: Construct or load an expensive object, module, relationship, or resource only when a qualifying access first requires it.

distinctive_feature: The deferred unit is an object or resource lifecycle transition rather than a pure expression.

distinct_from_parent: This subtype emphasizes initialization state, resource ownership, reentrancy, and use-after-close hazards.

still_same_parent_because: The resource remains unrealized until actual access crosses the demand boundary.

use_when: Many created objects never use the expensive resource.

use_when: Initialization can be separated from object construction without violating invariants.

use_when: First-use delay and failure are acceptable or can be hidden safely.

domain: applications

example: A rarely used analytics engine is initialized only when a user opens the analytics feature.

domain: databases

example: A related collection is queried only when code actually enumerates it.

failure_modes: Captured session is closed before access

Demand-Paged Resource Realization

short_definition: Realize fixed-size pages or chunks only when access reaches them, while retaining a bounded working set and an eviction path.

distinctive_feature: Demand is expressed as access to an unrealized address range and realization is coupled to bounded working-set management.

distinct_from_parent: The parent does not require fixed-size pages, eviction, or locality; this subtype does.

still_same_parent_because: Unneeded chunks remain unrealized and therefore consume no active loading or transformation work.

use_when: The addressable resource is larger than active capacity.

use_when: Access locality makes a small realized working set effective.

use_when: Page-fault latency and eviction are governable.

example: A page is loaded from storage only after a process accesses its virtual address.

domain: mapping

example: Map tiles are decoded only for the viewport and zoom level the user reaches.

failure_modes:
Fault storm
Working-set thrashing
Eviction removes a still-needed chunk
Access pattern defeats locality

Dependency-Pruned Demand Execution

short_definition: Start from a requested target and execute only the stale or missing dependency closure required to produce that target.

distinctive_feature: The key optimization is graph reachability from the demanded target rather than a single suspended expression.

distinct_from_parent: The parent covers any demand-gated work; this subtype requires explicit dependency traversal, staleness checks, and target-oriented scheduling.

still_same_parent_because: Nodes outside the demanded dependency closure remain unrealized.

source_primes: lazy_evaluation dependency algorithm scheduling

use_when: Work units form an explicit dependency graph.

use_when: Different targets require materially different subsets of the graph.

use_when: Staleness or validity can be determined per node.

example: A build tool compiles only the requested target and stale transitive prerequisites.

domain: analytics

example: A workflow computes only the metrics needed by the opened dashboard panel.

failure_modes: Missing dependency causes incorrect result

failure_modes: Overbroad invalidation recomputes the whole graph

failure_modes: Nondeclarative side effects escape the graph

Copy-on-Write Realization

short_definition: Share an existing state representation until a mutation demand forces a private copy or delta to be realized.

distinctive_feature: The deferred work is state duplication and demand is a write rather than a read.

distinct_from_parent: The parent mostly describes value demand; this subtype applies the same timing logic to mutation-triggered state separation.

still_same_parent_because: The expensive copy remains unrealized unless a write actually requires it.

use_when: Read sharing is common and writes are comparatively rare.

use_when: Mutation isolation can be defined precisely.

use_when: Copy cost is high enough that avoiding unused copies matters.

example: Forked processes share memory pages until one process writes to a page.

domain: documents

example: A template-backed document stores only edits until a full independent representation is required.

failure_modes: Copy occurs under lock and creates latency spike

near_names:
Lazy Evaluation
Call-by-Need
Deferred Evaluation
Demand-Driven Evaluation
Need-Driven Computation
On-Demand Evaluation
Lazy Loading
Deferred Computation


Dense-Subset Coverage Design

batch: Batch 09

aliases:
dense subset coverage
dense reference set design
proximity cover design
epsilon-net design
mesh coverage design
anchor set coverage
tolerance-neighborhood coverage

related_primes: measure connectedness convexity topology coverage_reachability surjectivity boundedness sampling_representativeness representation engineering_tolerances observability discreteness continuity

slug: metric

slug: approximation

slug: measure

slug: connectedness

slug: convexity

slug: topology

slug: surjectivity

slug: boundedness

slug: representation

slug: discreteness

slug: continuity

components:
Target Space Boundary
Distance or Neighborhood Relation
Coverage Tolerance
Dense Reference Subset
Gap or Covering-Radius Assessment
Boundary and Transition Anchor Set
Anchor-to-Target Mapping Rule
Update and Drift Monitor
Redundancy Pruning Rule
Local Weighting Model
Multi-Resolution Anchor Layers
Exclusion Register

mechanisms:
Epsilon-Net or Covering Grid
Space-Filling Design
Boundary-Value Test Suite
Coverage Heatmap
Nearest-Neighbor Assignment Rule
Adaptive Refinement Loop
Sensor or Service Radius Map
Anchor Case Library

essence_one_line: Use a smaller, explicitly spaced reference set so every relevant point in a larger domain has a nearby stand-in within an acceptable tolerance.

compression_statement: When exhaustive treatment of a large or continuous target space is impossible, define the relevant closeness relation, build a dense subset of anchors or probes, measure uncovered gaps, and refine until every decision-relevant target is near an anchor or is visibly outside the cover.

core_intuition: A subset can safely stand in for a larger space only when its coverage claim is local and checkable: for any case you may face, there is a known nearby case you have already measured, tested, served, or reasoned through.

canonical_formula: For target space X with distance or neighborhood relation d and tolerance epsilon, choose A ⊂ X such that for every x in X there exists a in A with d(x, a) <= epsilon. The mathematical dense-set ideal repeats this for arbitrarily small epsilon; practical design names the tolerance the decision can absorb.

structural_problem: A system must reason about, test, serve, observe, or represent a large target space, but exhaustive coverage is impossible and ordinary samples leave unmeasured gaps whose relation to the rest of the domain is unclear.

trigger_conditions: The target domain is continuous, very large, high-dimensional, geographically extended, semantically varied, or otherwise too large to enumerate case by case.

trigger_conditions: There is a meaningful distance, similarity, neighborhood, reachability, or tolerance relation between target cases.

trigger_conditions: A nearby anchor can legitimately stand in for an unhandled point for the purpose at hand.

trigger_conditions: The cost of coverage depends on how many anchors, probes, examples, tests, sensors, or service points are maintained.

trigger_conditions: Uncovered interstitial cases, edge cases, or sparse regions would create failures that ordinary category coverage might miss.

symptoms: Stakeholders claim coverage because many examples exist, but cannot say how far any new case is from the nearest tested or represented case.

symptoms: A test suite covers nominal categories while bugs appear between categories, near boundaries, or at unusual combinations of variables.

symptoms: A service, sensor, clinic, inspection routine, or support network has many nodes but leaves geographic or functional gaps.

symptoms: A benchmark, persona set, scenario library, or prototype collection feels representative but has no gap metric or out-of-cover rule.

symptoms: Continuous variables are discretized by convenience rather than by a declared spacing, error, or behavioral-preservation rule.

root_tension: The organization needs a finite handle on an effectively infinite or too-large space, but a finite subset is only safe when its relation to unsampled points is explicit, local, and maintained.

anti_signatures: There is no meaningful closeness relation: nearby cases do not preserve the behavior, risk, meaning, or service need being managed.

anti_signatures: Exact treatment of every point is required and approximation by a nearby anchor would be invalid or unsafe.

anti_signatures: The key question is population proportion, sampling bias, or statistical inference rather than local coverage of all target regions.

anti_signatures: The target domain changes faster than anchors can be updated or monitored.

anti_signatures: A small number of named cases are needed for pedagogy or illustration only, with no claim of covering the larger space.

structural_intervention: Convert an implicit coverage claim into an explicit dense-cover design: define the target space, choose the closeness relation, set a coverage tolerance, place anchors, measure gaps, densify weak regions, and maintain the cover as the domain changes.

action_logic: Name the target space that must be covered and the decision, service, observation, or reasoning task the cover supports.

action_logic: Define what “close enough” means: metric distance, semantic similarity, travel time, signal reach, model error, behavioral equivalence, or another neighborhood relation.

action_logic: Set a tolerance from consequence severity, not from convenient grid spacing or available sample count.

action_logic: Seed the dense reference subset with anchors for known clusters, extremes, boundaries, transition zones, high-value regions, and ordinary interior cases.

action_logic: Assess the current covering radius or gap structure and identify regions that are far from every anchor.

action_logic: Add, move, split, or remove anchors until the largest relevant gaps are below tolerance and redundant anchors are justified by risk or uncertainty.

action_logic: Define how new target cases map to anchors and when a case must be flagged as out-of-cover rather than forced into the nearest existing bucket.

action_logic: Monitor new observations, failures, domain expansion, and drift so the reference subset remains dense enough over time.

decision_rules: If closeness does not preserve the behavior that matters, reject dense-subset coverage and use exact handling, a different representation, or a different model.

decision_rules: If worst-case miss cost is high, optimize maximum gap or boundary coverage rather than average spacing.

decision_rules: If the domain is anisotropic, weighted, obstructed, or discontinuous, use a local or domain-specific distance relation instead of uniform Euclidean spacing.

decision_rules: If boundaries, thresholds, convex-hull edges, or regime shifts are fragile, densify there before adding more ordinary interior anchors.

decision_rules: If the goal is estimating population proportions, use representative sampling design rather than proximity coverage.

decision_rules: If dimensionality makes uniform density infeasible, reduce dimensions, layer resolutions, or explicitly narrow the coverage claim.

target_invariants: The target space boundary is explicit and does not silently expand beyond the declared cover.

target_invariants: The distance or neighborhood relation is tied to the outcome being preserved.

target_invariants: Coverage is stated as a tolerance-specific proximity claim, not a vague impression of many examples.

target_invariants: Boundary, transition, sparse, and high-consequence regions receive explicit attention.

target_invariants: New cases can be flagged as outside the valid cover.

target_invariants: The anchor set has a maintenance trigger when domain shape, use patterns, or consequences change.

expected_outcomes: A large or continuous domain becomes tractable without pretending the subset is exhaustive.

expected_outcomes: Coverage gaps become visible as measurable distances, uncovered neighborhoods, or out-of-cover cases.

expected_outcomes: Tests, examples, sensors, service points, prototypes, or benchmarks have a declared coverage radius or scope.

expected_outcomes: Resources can be spent where they reduce the most consequential gaps rather than merely increasing sample count.

expected_outcomes: Stakeholders can distinguish local proximity coverage from statistical representativeness or generic approximation.

prose_tradeoffs: The main tradeoff is cost versus gap size. More anchors improve coverage but increase maintenance burden. Uniform coverage is easy to explain but may underprotect boundaries. A domain-specific metric improves fidelity but can become opaque. High-dimensional spaces can make density infeasible unless dimensions are reduced, layers are used, or the coverage claim is narrowed.

Epsilon-Net Coverage Design

short_definition: Use a declared radius to construct anchors so every point in a metric domain has a nearby representative.

distinctive_feature: The coverage claim is radius-bound: every target must fall within epsilon of an anchor.

distinct_from_parent: It is the formal metric version of the parent archetype rather than the broader cross-domain pattern.

still_same_parent_because: It uses the same core logic of dense reference coverage and gap control.

source_primes: dense_set metric boundedness

use_when: The domain has a meaningful distance function.

use_when: The decision can tolerate a known maximum local error.

use_when: Worst-case gaps matter more than average sampling density.

example: A numeric-input test suite sets grid spacing so every admissible value is near a tested anchor under the chosen error model.

failure_modes: A convenient radius is chosen without proving that nearby cases preserve the relevant behavior.

Boundary-Dense Testing

short_definition: Densify anchors around boundaries, thresholds, and transitions where small differences can change outcomes.

distinctive_feature: Anchor density is intentionally non-uniform and increases around fragile boundaries.

distinct_from_parent: The parent can cover the whole domain uniformly; this variant focuses density where local behavior is discontinuous or high-consequence.

still_same_parent_because: It still evaluates and repairs proximity gaps in a larger target space.

use_when: Most failures occur near edge cases, thresholds, limits, or regime changes.

use_when: Uniform spacing wastes anchors in stable interior regions.

use_when: A boundary crossing changes classification, safety, legality, validity, or control behavior.

example: A validation suite puts extra cases just below, at, and above eligibility thresholds.

failure_modes: Interior regions become undercovered because every anchor is spent on visible thresholds.

Spatial Radius Coverage Design

short_definition: Place physical anchors so every location in a service, sensing, or access region is within a declared radius or travel-time band.

distinctive_feature: Coverage is expressed in spatial radius, travel time, signal strength, or catchment geometry.

distinct_from_parent: It adds geographic and infrastructure constraints to the general dense-reference pattern.

still_same_parent_because: The core work remains placing anchors and measuring uncovered regions.

use_when: The target domain is geographic or spatially embedded.

use_when: Distance, travel time, signal reach, or service radius is the relevant neighborhood relation.

use_when: Equity or safety depends on no region being too far from an anchor.

example: Clinic locations are evaluated by whether every neighborhood is within an acceptable travel-time radius.

failure_modes: Straight-line distance is used when actual travel time, accessibility, or signal obstruction is the governing distance.

Adaptive Gap Refinement

short_definition: Continuously add or move anchors when observations reveal sparse regions, drift, or new boundary cases.

distinctive_feature: Density is maintained as a feedback process rather than set once.

distinct_from_parent: The parent can be a one-time design; this variant emphasizes drift monitoring and iterative repair.

still_same_parent_because: It preserves the same invariant: every relevant point should have a nearby anchor or be flagged as out of cover.

source_primes: dense_set feedback iteration

use_when: New use cases or failure reports reveal uncovered neighborhoods.

use_when: A static dense subset would become stale quickly.

example: A benchmark adds new evaluation slices when production data reveals a sparsely covered regime.

failure_modes: The system chases recent failures and loses global coverage discipline.

near_names:
Dense Set
Dense Reference Set Design
Proximity Cover Design
Epsilon-Net Design
Mesh Coverage Design
Anchor Set Coverage


Dependency-Aware Change Notification

batch: Batch 01

canonical_family: Anticipation & Forward Models

aliases:
Advance Change Notice
Lead-Time Change Notification
Dependency Change Notice
Directed Change Warning
Preparedness-Oriented Change Notice
Change Notice Governance
Migration Notice Protocol

slug: alertness

slug: interoperability

slug: metaplasticity

slug: postponement

slug: versioning

slug: transparency

slug: fairness

slug: feedback

slug: feedforward

slug: accountability

slug: legitimacy

slug: uncertainty

slug: scheduling

slug: signaling

essence_one_line: Warn the parties who actually depend on a changing system early enough, and specifically enough, that they can prepare before the change binds them.

compression_statement: Dependency-Aware Change Notification is the coordination pattern of identifying who depends on an upcoming system change, specifying what will change and when, calculating the lead time those dependents need, routing an actionable notice through reachable channels, confirming receipt or readiness where needed, and escalating or delaying when notice failure would turn a managed change into a surprise burden.

core_intuition: A change is not coordinated merely because it was announced somewhere. It becomes a real notification only when the right dependents receive a clear enough warning early enough to act. The archetype treats notice as preparation infrastructure rather than as a public-relations afterthought.

canonical_formula: effective_notice = affected_dependents × actionable_content × sufficient_lead_time × reachable_channel × readiness_feedback

structural_problem: A system owner decides or schedules a change, but affected dependents do not receive targeted, actionable, timely warning; they discover the change too late, through the wrong channel, or without enough detail to prepare, causing breakage, mistrust, unsafe adaptation, or coordination failure.

trigger_conditions: A planned change will alter behavior, access, compatibility, obligations, timing, meaning, location, pricing, capacity, or risk for parties outside the change-making team.

trigger_conditions: Affected parties need time to migrate, reconfigure, train, comment, hedge, evacuate, update documentation, adjust schedules, or verify compatibility.

trigger_conditions: The change-maker has privileged knowledge of the change before affected dependents do.

trigger_conditions: Failure to warn creates avoidable harm, service interruption, wasted work, compliance risk, trust loss, or unsafe last-minute adaptation.

trigger_conditions: The affected population is heterogeneous, with different dependency depths, channels, languages, accessibility needs, or preparation windows.

trigger_conditions: A prior change produced surprise, missed deadlines, brittle migration, silent breakage, or claims that parties were informed when they were not realistically prepared.

symptoms: People say they were notified, but recipients say they could not act on the information.

symptoms: The notice reached general audiences but missed the specific parties whose workflows, interfaces, or obligations depended on the old state.

symptoms: Release notes, policy updates, or announcements arrive after implementation or with no meaningful lead time.

symptoms: Recipients receive vague warnings that omit the effective date, action required, compatibility consequences, or fallback path.

symptoms: Critical dependents learn about the change only through failures, rumors, support tickets, monitoring alerts, or downstream complaints.

symptoms: Teams repeatedly compress migration work into an emergency window because notice timing was not matched to preparation work.

symptoms: Low-capacity or peripheral stakeholders are disproportionately harmed because sophisticated insiders prepared earlier.

root_tension: The change-maker wants to move the system forward, but dependents need a protected preparation interval before the new state becomes binding.

anti_signatures: The message is a post-hoc report about a change that already occurred; use documentation, incident review, or version history instead.

anti_signatures: The issue is deciding whether a transition is safe to begin; use transition_readiness_assessment or controlled_phase_transition.

anti_signatures: The issue is persuading or involving stakeholders in the decision itself; use stakeholder_mapping_and_engagement or change_resistance_diagnosis_and_support.

anti_signatures: The issue is only maintaining an auditable sequence of versions; use versioned_evolution.

anti_signatures: The problem is strategic neutralization caused by agents reacting to advance notice; use anticipatory_offset_governance as the parent, with notice as a constraint or mechanism.

anti_signatures: No affected party can take preparatory action, and the announcement does not reduce harm or coordination cost.

structural_intervention: Convert change awareness into dependency-aware preparation: map affected dependents, classify the change and its impact, compute required lead time, write actionable notice content, route it through reliable audience-specific channels, confirm reachability or readiness where stakes require it, and use escalation, support, or delay when notice is inadequate.

action_logic: Define the change boundary: what is changing, what remains stable, when the change starts, and when the old state stops being supported.

action_logic: Map direct and indirect dependents, including interfaces, users, downstream teams, regulators, partners, communities, and automated consumers.

action_logic: Classify the dependency impact by severity, reversibility, preparation effort, safety risk, compliance risk, and compatibility breakage.

action_logic: Determine minimum lead time for each affected class rather than using one universal announcement date.

action_logic: Translate the change into recipient-specific action requirements: what to do, by when, what happens if they do nothing, and where to get help.

action_logic: Choose channels that reach the actual dependents, not just a convenient broadcast audience.

action_logic: Provide migration aids, compatibility bridges, comment windows, practice windows, or contingency paths when preparation is nontrivial.

action_logic: Confirm receipt, reachability, readiness, or migration progress for high-impact dependents.

action_logic: Escalate, repeat, delay, or add support when critical recipients have not had meaningful notice.

action_logic: Evaluate after the change whether notification timing, content, routing, and preparation support were sufficient.

decision_rules: If the change creates breaking dependency effects, use targeted notice and a protected preparation window rather than a generic announcement.

decision_rules: If dependents require different preparation times, set notice by the longest credible readiness path or segment the rollout.

decision_rules: If a recipient cannot act on the notice, revise the content before counting the notice as complete.

decision_rules: If critical dependents cannot be reached, use escalation paths or delay the binding change when feasible.

decision_rules: If advance notice creates strategic neutralization risk, preserve legitimate notice while coordinating with anticipatory_offset_governance for timing, incentives, or monitoring.

decision_rules: If the change is urgent and lead time is impossible, label it as emergency change, provide compensating support, and run a post-change remediation path rather than pretending normal notice occurred.

decision_rules: If notice obligations are legal, ethical, contractual, or safety-critical, treat evidence of notice as an auditable requirement.

target_invariants: Affected dependents are explicitly identified before the change binds them.

target_invariants: Notice includes the change, effective date, affected boundary, required action, support path, and consequence of inaction.

target_invariants: Lead time is matched to preparation effort and impact severity.

target_invariants: Channels are appropriate to the affected audience and are not assumed to reach everyone by default.

target_invariants: High-stakes changes have receipt, readiness, or migration evidence.

target_invariants: Notification does not become a substitute for consent, participation, or fairness when those are required separately.

expected_outcomes: Fewer avoidable breakages, missed deadlines, emergency migrations, and support escalations after changes.

expected_outcomes: Dependents have enough time and information to prepare rather than react under surprise.

expected_outcomes: Change owners can distinguish true resistance from under-notification or unusable notice content.

expected_outcomes: Compatibility and migration risks become visible before the effective date.

expected_outcomes: Trust improves because affected parties experience the change as coordinated rather than imposed without warning.


Dependency-Capture Exit Design

batch: Batch 03

canonical_family: Context Binding & Cue Capture

aliases:
Role-Capture Dependency Disruption
Manufactured Dependency Exit
Problem-Sustaining Role Capture Control

slug: habit

slug: incentive

slug: dependency

slug: observability

essence_one_line: Break role-capture incentives by independently verifying the underlying need, measuring durable resolution, transferring capability, and making exit possible without recreating dependency.

compression_statement: When an agent gains status, income, authority, identity, or access from being indispensable to a recurring problem and can covertly preserve, obscure, or reproduce that problem so the valued role remains necessary., separate need verification from the role holder, expose incentives and dependency pathways, define durable resolution metrics, transfer knowledge and capability, create substitutes, protect challenge, and bind handoff or sunset to independent evidence.

core_intuition: The intervention works by making the hidden structure explicit, binding action to reviewable evidence, and preserving a controlled response when assumptions fail.

canonical_formula: dependency_capture_exit_design = scope + evidence + governed transformation + validation + feedback + recovery

structural_problem: An agent gains status, income, authority, identity, or access from being indispensable to a recurring problem and can covertly preserve, obscure, or reproduce that problem so the valued role remains necessary.

trigger_conditions: The target condition recurs across cases or domains rather than appearing as a one-off exception.

trigger_conditions: Local workarounds do not expose the underlying structural relation.

trigger_conditions: Consequential decisions depend on an assumption that is not yet explicit or validated.

trigger_conditions: The system needs a reusable intervention with monitoring, exception handling, and revision.

symptoms: Results depend strongly on hidden configuration or context.

symptoms: Teams use the same label for materially different structures.

symptoms: Failures are discovered only after downstream use.

symptoms: Evidence, ownership, or update rules are fragmented.

root_tension: The system needs enough structure to act consistently without freezing uncertain assumptions into permanent truth.

anti_signatures: Do not use when a single concrete mechanism fully solves the problem without broader governance.

anti_signatures: Do not use when an accepted neighbor already contains the same source prime, components, and decision logic.

anti_signatures: Do not use as a metaphor when the stated trigger conditions and invariants are absent.

structural_intervention: Separate need verification from the role holder, expose incentives and dependency pathways, define durable resolution metrics, transfer knowledge and capability, create substitutes, protect challenge, and bind handoff or sunset to independent evidence.

action_logic: Define the governed object, scope, and unacceptable outcomes.

action_logic: Make the relevant structure, evidence, and assumptions explicit.

action_logic: Select components and mechanisms appropriate to domain and risk.

action_logic: Apply the transformation in bounded, observable form.

action_logic: Validate outcomes, inspect exceptions, and revise or reverse when evidence fails.

decision_rules: Use stronger validation and independent review as consequence severity increases.

decision_rules: Do not promote an implementation artifact to the archetype level.

decision_rules: Stop or redesign when target invariants are violated.

decision_rules: Refresh the intervention when inputs, context, or downstream use changes.

target_invariants:
explicit scope
traceable evidence
bounded authority
visible uncertainty
reviewable exceptions
recovery or revision path

expected_outcomes:
better structural fit
earlier failure detection
less hidden drift
more accountable decisions
safer transfer across domains

Consultant Dependency-Capture Variant

short_definition: A Dependency-Capture Exit Design subtype distinguished by an external adviser preserves proprietary indispensability.

distinct_from_parent: This subtype foregrounds an external adviser preserves proprietary indispensability.

still_same_parent_because: It uses the same structural components, decision rules, and outcome invariants as the parent.

use_when: The case is distinguished by an external adviser preserves proprietary indispensability.

use_when: The parent intervention structure still applies.

example: Apply Consultant Dependency-Capture Variant where an external adviser preserves proprietary indispensability.

failure_modes: Treating the subtype label as sufficient without the parent controls.

Crisis-Maintainer Capture Variant

short_definition: A Dependency-Capture Exit Design subtype distinguished by an emergency role preserves the crisis that justifies authority.

distinct_from_parent: This subtype foregrounds an emergency role preserves the crisis that justifies authority.

use_when: The case is distinguished by an emergency role preserves the crisis that justifies authority.

example: Apply Crisis-Maintainer Capture Variant where an emergency role preserves the crisis that justifies authority.

Caretaker Dependency-Capture Variant

short_definition: A Dependency-Capture Exit Design subtype distinguished by support is structured to prevent beneficiary autonomy.

distinct_from_parent: This subtype foregrounds support is structured to prevent beneficiary autonomy.

use_when: The case is distinguished by support is structured to prevent beneficiary autonomy.

example: Apply Caretaker Dependency-Capture Variant where support is structured to prevent beneficiary autonomy.


Dependency Concentration Control

batch: Batch 10

canonical_family: Overextension & Load Fragility

slug: redundancy

slug: diversification

slug: resilience

slug: robustness

slug: coupling

slug: network

slug: risk

slug: boundedness

components:
Dependency Population Boundary
Weighted Dependency Inventory
Provider Exposure Unit
Criticality Weighting Rule
Concentration Measure
Common-Mode Dependency Profile
Substitution Feasibility Map
Concentration Limit Band
Independence Evidence Check
Load Rebalancing Plan
Residual Concentration Acceptance
Concentration Drift Monitor
Tiered Provider Portfolio
Segmentation or Firebreak Boundary
Portable Interface Requirement
Shadow Capacity Reserve

mechanisms:
Dependency Concentration Heatmap
Weighted Dependency Graph
Top-K Exposure Share
Effective Independent Provider Count
Concentration Cap Policy
Common-Mode Dependency Audit
Multi-Sourcing Rule
Provider Load Split Table
Substitution Drill
Portability Checklist
Dependency Concentration Stress Test
Residual Concentration Risk Register

compression_statement: Dependency Concentration Control applies when a system has multiple apparent dependencies but most effective reliance, switching difficulty, recovery time, criticality, or control leverage is concentrated in a small number of providers, channels, platforms, regions, teams, standards, or support paths. The intervention converts a dependency map into a weighted exposure portfolio, measures concentration and common-mode correlation, sets concentration limits, verifies independence among substitutes, and rebalances demand, contracts, capacity, interfaces, or contingency paths so no small dependency subset can dominate system viability.

core_intuition: A system can look well defended because it has many listed dependencies, but fragility follows the weight and correlation of those dependencies. Ten names do not help if nine route through the same platform, region, provider, protocol, credential, or expert group.

canonical_formula: dependency_fragility = concentration_index(weighted_dependency_exposure × criticality × switching_time × common_mode_correlation) - independent_substitution_capacity - concentration_controls

structural_problem: A system depends on external or internal providers, channels, teams, platforms, resources, regions, standards, or support paths. The dependency set appears plural, but effective reliance is unevenly distributed: one provider carries most demand, a small cluster shares a common upstream bottleneck, or nominal alternatives are correlated through the same infrastructure. Because the concentration is a structural property of the dependency distribution, internal defenses cannot fully compensate for an overweight provider or common-mode cluster.

trigger_conditions: The system relies on multiple providers, channels, platforms, regions, teams, resources, or support paths.

trigger_conditions: Dependency lists exist, but their relative weights, criticality, switching difficulty, or recovery times are not measured together.

trigger_conditions: A small subset of providers carries most demand, authority, information, legitimacy, capacity, or recovery leverage.

trigger_conditions: Nominally separate providers share upstream infrastructure, ownership, geography, standards, labor pools, funding, credentials, or failure modes.

trigger_conditions: Substitution paths exist on paper but cannot absorb the load, quality requirement, or timing need of the concentrated provider.

trigger_conditions: Decision makers treat the count of alternatives as equivalent to actual independent distribution.

trigger_conditions: The system’s own defenses assume that provider failure is local rather than distribution-dominating.

symptoms: A dependency inventory lists many providers, but incident impact concentrates in one or two of them.

symptoms: Backup suppliers, routes, or teams depend on the same upstream bottleneck as the primary path.

symptoms: A provider outage, regulation change, labor dispute, data loss, price shock, or platform policy change affects many downstream functions at once.

symptoms: Contracts, interfaces, or tacit knowledge make switching slower than the operational tolerance window.

symptoms: Load is rebalanced only after a failure, when alternatives are already saturated or unready.

symptoms: Resilience reviews ask whether alternatives exist, but not whether exposure is weighted, correlated, and capped.

symptoms: Single points of failure are recognized only after the hidden concentration has already become operationally dominant.

root_tension: Efficiency and specialization pull dependency weight toward the strongest, cheapest, most convenient, or most familiar provider, while resilience requires distributed, independent, and switchable exposure. The system gains short-term performance by concentrating reliance and loses structural optionality when the concentrated provider fails.

anti_signatures: The main work is discovering previously unknown dependencies rather than measuring and rebalancing known exposure; use Dependency Exposure.

anti_signatures: The main question is whether a pool of risks diversifies loss; use Correlation Structure Analysis for Pooling Effectiveness.

anti_signatures: The main intervention is adding a backup component to preserve one critical function; use Redundant Backup Provisioning.

anti_signatures: The main intervention is providing multiple distinct ways to fulfill a function, without weighted concentration governance; use Diverse Functional Redundancy.

anti_signatures: The main work is continuing operation after a component fault; use Fault-Tolerant Operation.

anti_signatures: The primary issue is total load exceeding sustainable capacity rather than concentration across dependency providers; use Sustainable Load Envelope Governance.

structural_intervention: Create a concentration-aware dependency governance loop: identify dependency providers and channels, assign weighted exposure by criticality and substitutability, measure concentration and common-mode correlation, set thresholds and caps, verify independent substitution capacity, rebalance or segment reliance, and monitor drift so concentration cannot silently exceed the system’s tolerated fragility envelope.

action_logic: Define the dependency population: providers, channels, teams, platforms, regions, resources, standards, credentials, or support paths.

action_logic: Assign exposure weights using demand share, revenue share, mission criticality, recovery time, switching cost, data/control custody, and substitutability.

action_logic: Calculate concentration using maximum-share, top-k share, effective independent provider count, or a domain-appropriate concentration index.

action_logic: Map common-mode links among providers so nominal alternatives are not counted as independent when they share upstream failure modes.

action_logic: Classify dependencies by failure impact, switching feasibility, load absorption capacity, and time-to-substitute.

action_logic: Set concentration thresholds, caps, and exception rules for critical dependencies and common-mode clusters.

action_logic: Design rebalancing paths: multi-sourcing, segmentation, workload split, interface portability, reserve capacity, contract exit rights, or local fallback.

action_logic: Exercise substitution paths under realistic load before treating them as concentration relief.

action_logic: Monitor drift as demand, provider consolidation, technical integration, specialization, or policy changes increase concentration over time.

action_logic: Document residual concentration that is intentionally accepted because diversification costs exceed risk tolerance or feasibility.

decision_rules: Do not count a provider, channel, or backup as independent until common-mode links and switching feasibility have been tested.

decision_rules: Treat exposure share, criticality, and switching time together; a low-volume provider may still be concentration-critical if it is non-substitutable.

decision_rules: Set stricter concentration caps for dependencies whose failure would affect many functions or exceed recovery-time tolerance.

decision_rules: Prefer segmentation when diversification would introduce too much coordination or quality variation.

decision_rules: Require explicit risk acceptance for concentration above threshold, including owner, time limit, and compensating controls.

decision_rules: Trigger review when provider consolidation, platform integration, geography, regulation, staffing, or architecture changes alter the exposure distribution.

target_invariants: Dependency exposure is measured as weighted distribution, not provider count alone.

target_invariants: Nominal alternatives are only credited when independence and capacity are evidenced.

target_invariants: No critical provider or common-mode cluster exceeds agreed concentration limits without explicit acceptance.

target_invariants: Substitution capacity is exercised before it is relied on in resilience plans.

target_invariants: Concentration drift is visible before incident impact reveals it.

target_invariants: Efficiency gains from concentration are compared against loss of optionality and recovery leverage.

expected_outcomes: Reduced fragility from provider, platform, channel, region, or expert-team concentration.

expected_outcomes: More realistic resilience claims because alternatives are weighted, independent, and load-tested.

expected_outcomes: Earlier detection of single-point and common-mode dependency risks.

expected_outcomes: Better prioritization of diversification, segmentation, portability, and backup investments.

expected_outcomes: Clearer residual-risk ownership when concentration is unavoidable or intentionally accepted.

prose_essence:
Prevent dependency fragility by measuring where reliance is concentrated and capping, diversifying, or isolating overweight dependency providers before their failure can dominate the system.

Dependency Concentration Control is for situations where the dependency map is not enough. A system may list many dependencies, providers, suppliers, platforms, teams, routes, regions, or channels, yet most effective reliance can still be carried by one provider or by a correlated cluster. The archetype converts dependency visibility into concentration governance: it assigns weights, measures concentration, tests independence, sets limits, and rebalances exposure before a dominant dependency becomes the hidden failure path.

prose_tradeoffs: Reducing concentration can increase cost, integration effort, quality variation, contract complexity, and coordination overhead. A good design does not maximize provider count. It finds a distribution that lowers dominant-dependency fragility without creating a more complex and fragile provider portfolio.

Single-Provider Concentration Variant

short_definition: A variant where one provider, platform, team, route, or standard carries enough dependency weight to dominate system failure risk.

distinctive_feature: The concentration is extreme enough to look like a single point of failure.

distinct_from_parent: The parent covers any concentrated distribution; this variant covers the one-provider extreme.

still_same_parent_because: It still uses the same weighted-exposure, limit, and rebalancing logic.

use_when: One provider carries most critical load or switching leverage.

use_when: Failure of that provider would disable multiple functions at once.

failure_modes: Treating a backup contract as real independence when it cannot absorb critical load.

Common-Mode Provider Cluster Variant

short_definition: A variant where apparent alternatives share upstream failure modes, making the effective dependency concentration higher than provider count suggests.

distinctive_feature: Correlation, not just share, creates the concentration.

distinct_from_parent: The parent covers weighted concentration generally; this variant emphasizes common-mode correlation among alternatives.

still_same_parent_because: It still reduces weighted dependency concentration by independence testing and redistribution.

use_when: Providers share geography, infrastructure, ownership, standards, credentials, labor pools, or control planes.

use_when: A common shock would affect several dependencies at once.

failure_modes: Counting correlated providers as independent and overestimating resilience.

name: Platform Dependency Concentration Variant

short_definition: A variant where a digital, institutional, or market platform becomes the dominant dependency channel for many functions.

distinctive_feature: The dependency provider also controls rules, interfaces, or access conditions.

distinct_from_parent: The parent can apply to any provider; this variant emphasizes platform control and switching friction.

still_same_parent_because: The intervention still measures concentration and builds portable, segmented, or alternate paths.

use_when: Many workflows route through one platform, standard, market channel, or identity/control layer.

use_when: The platform can change access, rules, pricing, or availability faster than the dependent system can adapt.

failure_modes: Assuming platform ubiquity is a neutral utility rather than concentration risk.

near_names:
Dependency Portfolio Concentration
Provider Concentration Risk
Supplier Concentration Risk Control
Third-Party Concentration Governance