Nested Feedback Alignment

batch: First Iteration/Batch 35/Wave 2

Cross-Scale Feedback Alignment

notes: Useful descriptive name, but the canonical draft emphasizes nested loops and not merely cross-scale comparison.

Multi-Level Feedback Alignment

notes: Common phrasing for the same pattern; should point here when scale-specific feedback loops interact.

Nested Feedback Calibration

notes: Emphasizes the tuning phase after loops are mapped and compared.

source_primes: feedback scale circular_causality

related_primes: oscillation homeostasis goal_congruence coupling amplification damping observability hierarchy equilibrium nonlinearity controllability requisite_variety adaptation interoperability constraint

slug: feedback

slug: scale

slug: oscillation

slug: homeostasis

slug: amplification

slug: damping

slug: hierarchy

slug: equilibrium

slug: nonlinearity

slug: controllability

slug: adaptation

slug: constraint

components:
Feedback Loop by Scale
Nested Level Boundary
Target Alignment Check
Signal Translation Rule
Timing Alignment
Gain Adjustment
Escalation and De-escalation Rule
Cross-Scale Effect Monitor
Conflict-Resolution Priority Rule
Learning Recalibration Path
Noise Filtering Rule
Autonomy Band
Aggregation Window
Override Audit Trail
Feedback Owner by Scale

mechanisms:
Multi-Level KPI Review
Nested Control-System Tuning
Governance Escalation Protocol
Local/System Feedback Cadence
Balanced Scorecard Cascade
Ecological Adaptive-Management Cycle
Bullwhip Effect Review
Incident-Command Feedback Rhythm
Cross-Scale Retrospective
Aggregation/Disaggregation Dashboard

essence_one_line: Align feedback loops across nested levels so local correction does not create system-level instability.

compression_statement: When local, subsystem, and system-wide feedback loops interact, align their targets, timing, signal translation, gain, and escalation rules so corrections reinforce rather than fight each other.

core_intuition: A nested system can fail even when every level is responding rationally to its own feedback. The problem is not the existence of feedback, but that each loop may be reading a different signal, optimizing a different target, updating at a different speed, or correcting with a different strength. Alignment makes the loops legible to one another and tunes them so local responsiveness and system stability can coexist.

canonical_formula: nested_feedback_alignment = map(feedback_loops_by_scale) + compare(targets, signals, timing, gain) + tune(escalation, translation, damping) + monitor(cross_scale_effects)

structural_problem: Feedback loops at different scales send conflicting signals, optimize incompatible targets, or operate at mismatched speeds, causing local correction to produce larger-scale instability or system-level correction to suppress useful local adaptation.

trigger_conditions: Local teams, subsystems, regions, or agents appear to be making sensible corrections, yet aggregate performance oscillates or deteriorates.

trigger_conditions: Higher-level governance responds to aggregate signals in ways that arrive too late, override local knowledge, or create policy whiplash.

trigger_conditions: Metrics, alarms, reviews, or incentives are defined separately at each scale without checking how one level reacts to another level’s correction.

trigger_conditions: A system repeatedly alternates between local autonomy and central intervention without a stable rule for escalation, de-escalation, or authority return.

trigger_conditions: A technical, organizational, ecological, or public system shows delayed overcorrection, bullwhip effects, counterproductive KPI gaming, or cross-level blame loops.

symptoms: Local optimization improves unit metrics while system-level outcomes worsen.

symptoms: Aggregate dashboards look stable while local units experience stress, overload, or hidden deterioration.

symptoms: A higher-level correction cancels or reverses local learning before its effects can be observed.

symptoms: Feedback arrives at one cadence but decisions are made at another, creating stale mandates or repeated policy reversals.

symptoms: Escalation thresholds are unclear, so routine issues climb too high while systemic risks remain trapped locally.

symptoms: Small corrections at one level amplify into oscillation, queue swings, stockouts, capacity surges, or public-trust cycles at another level.

root_tension: Nested systems need local loops that are fast enough to adapt and higher-level loops that are broad enough to coordinate, but speed, scope, authority, and target differences can make those loops interfere with one another.

anti_signatures: There is only one feedback loop and the issue is simply choosing a better signal or response rule.

anti_signatures: The loops operate at the same scale and do not produce cross-scale effects.

anti_signatures: The main issue is missing observability rather than misaligned correction; use observability instrumentation or multi-scale signal monitoring first.

anti_signatures: The problem is goal conflict without dynamic feedback behavior; goal congruence alignment may be more appropriate.

anti_signatures: The system is in immediate crisis and needs stabilization before feedback alignment can be diagnosed safely.

structural_intervention: Map feedback loops by scale, compare their targets, signals, timing, gain, and authority rules, then tune translation, escalation, damping, and learning paths so corrections at one level do not destabilize another.

action: Map feedback loops by scale.

purpose: Make visible which local, intermediate, and system-level loops sense conditions, interpret signals, decide, act, and update.

action: Compare loop targets and preserved invariants.

purpose: Identify where one level’s success metric or stability target undermines another level’s function.

action: Trace signal translation between levels.

purpose: Protect meaning as signals are aggregated upward or disaggregated downward, and identify lost context or misleading normalization.

action: Tune timing and gain.

purpose: Adjust cadence, delay, sensitivity, thresholds, and response strength to reduce lag-driven oscillation and overcorrection.

action: Define escalation, de-escalation, and priority rules.

purpose: Clarify when local correction is enough, when higher-level intervention is warranted, and how control returns after stabilization.

action: Monitor cross-scale effects and recalibrate.

purpose: Detect whether the alignment intervention is preserving local responsiveness and system stability, then update loop settings when mismatches appear.

rule: If a symptom appears at one level but correction repeatedly creates consequences at another, treat the issue as nested feedback misalignment rather than local incompetence.

rule: If local and aggregate metrics disagree, inspect translation and aggregation before imposing stronger control.

rule: If oscillation is present, compare cadence, delay, gain, and escalation thresholds before adding more feedback signals.

rule: If higher-level intervention suppresses local adaptation, define an autonomy band and de-escalation rule rather than leaving central override open-ended.

rule: If a loop is safety-critical, preserve the safety invariant even while adjusting target alignment or response speed.

invariant: Local responsiveness

description: Lower-level units retain enough discretion and speed to respond to conditions they can sense directly.

invariant: System-level stability

description: Local corrections do not create aggregate oscillation, hidden overload, systemic fragility, or risk shifting.

invariant: Signal meaning across scales

description: Signals remain interpretable as they are aggregated, disaggregated, normalized, or translated between levels.

invariant: Proportional correction

description: Response strength remains appropriate to the scale, confidence, reversibility, and potential harm of the signal.

invariant: Legitimate escalation

description: Authority movement across levels follows explicit thresholds, accountability, and return conditions rather than ad hoc power grabs.

invariant: Learning capacity

description: The system can revise feedback settings when evidence shows that the current alignment is producing unintended effects.

outcome: Reduced cross-scale oscillation

description: Corrections at one level stop amplifying delays, surges, reversals, or instability at another level.

outcome: Better local-global coherence

description: Local units can act on local evidence while staying compatible with larger system goals and constraints.

outcome: Clearer escalation behavior

description: People know when to resolve locally, when to escalate, when central action is legitimate, and when to return authority.

outcome: Lower metric gaming and risk shifting

description: Feedback loops are less likely to reward local improvements that merely displace costs or failures elsewhere.

outcome: More trustworthy governance and operations

description: Participants see why signals matter, how they are interpreted, and how their corrections affect the whole system.

prose_essence:
Nested Feedback Alignment is the practice of making feedback loops at different scales work with one another instead of against one another. A local loop may be fast, concrete, and context-sensitive. A system-level loop may be slower, broader, and more focused on stability or fairness across units. Both may be reasonable on their own, yet produce oscillation when their signals, targets, timing, or response strength are misaligned.

The core move is not merely to add more feedback. It is to ask: **which loops are sensing what, at which level, on what cadence, with what target, and how strongly do they correct?** Once those loops are visible, the intervention tunes translation, timing, gain, escalation, and learning rules so local adaptation and system coherence can coexist.

prose_when_to_use:
Use this archetype when a system has nested levels—individual/team/organization, device/service/platform, local/regional/national, site/ecosystem/region—and each level has a feedback loop that can influence the others. It is especially useful when local units appear to be doing the right thing locally but the larger system becomes unstable, or when central correction appears rational at the aggregate level but suppresses useful local adaptation.

It is also useful when signals change meaning as they move between levels. A local anomaly might be an early warning, ordinary noise, or the visible edge of a system-level shift. An aggregate trend might justify support, but it might also hide the fact that different local units need opposite actions.

Do not use this archetype when the problem is only missing measurement, a single badly calibrated loop, or ordinary disagreement about goals. In those cases, observability instrumentation, feedback loop redirection, homeostatic regulation, or goal congruence alignment may be better starting points.

prose_structural_problem:
Nested systems often contain multiple feedback loops operating at different scales. A frontline team senses local demand and adjusts staffing. A regional manager sees aggregate utilization and changes resource allocation. A central policy unit sees budget drift and imposes limits. Each loop may be sensible when viewed alone, but their interaction can create delay, contradiction, amplification, or suppressed learning.

The structural problem appears when feedback loops have incompatible targets, incompatible signal meanings, mismatched update rhythms, or different correction strengths. One loop may optimize throughput while another optimizes reliability. One loop may update daily while another updates quarterly. One loop may treat a local signal as noise while another treats the aggregate trend as proof of failure. The result is not simply “bad feedback.” It is feedback whose nesting has not been aligned.

Common signatures include local metric success with system-level decline, policy whiplash, repeated escalation confusion, delayed overcorrection, bullwhip effects, and a recurring blame pattern between local and central actors.

prose_intervention_logic:
The intervention begins by mapping each feedback loop by scale. For each loop, identify the signal, interpreter, decision rule, corrective action, delay, response strength, and target. Then compare the loops: are they correcting toward compatible states, or is one level’s success another level’s failure?

Next, examine how signals move between levels. Aggregation can erase local context; disaggregation can create misleading comparisons; normalization can hide real stress. The intervention defines translation rules so a signal remains meaningful as it moves upward or downward.

Then tune timing and gain. A fast local loop can overreact to noise; a slow central loop can respond to stale conditions. High gain can create oscillation; low gain can let risk accumulate. Alignment often means changing cadence, thresholds, response strength, aggregation windows, or escalation triggers.

Finally, define escalation and de-escalation rules. Local loops should know when they can adapt independently, when they must escalate, when a higher-level loop may override, and how control returns after stabilization. A cross-scale effect monitor then checks whether the new alignment is actually reducing oscillation, hidden risk transfer, and local-global conflict.

prose_key_components:
### Feedback Loop by Scale

A feedback loop by scale record names each loop that matters: what it senses, who interprets it, what action it triggers, how long it takes to act, and what target it is trying to preserve. This prevents the draft from becoming generic alignment language. The archetype only applies when multiple scale-specific loops interact.

### Nested Level Boundary

Nested level boundaries identify where one loop ends and another begins. A boundary might sit between teams and departments, services and platforms, clinics and public-health agencies, local ecosystems and regional policy, or tactical and strategic incident response. Boundaries show where signals are translated, delayed, filtered, escalated, or overridden.

### Target Alignment Check

The target alignment check compares what each loop is trying to optimize or protect. Local cost control may conflict with system reliability. Team throughput may conflict with customer outcome quality. Central fairness rules may conflict with local feasibility. The check does not force every level to share one metric; it makes target conflict explicit.

### Signal Translation Rule

Signal translation rules preserve meaning across levels. A local spike may be noise, early warning, or the beginning of a system-wide transition. An aggregate average may hide extremes. Translation rules govern aggregation, disaggregation, normalization, and contextual explanation so feedback does not become misleading as it travels.

### Timing Alignment

Timing alignment compares cadence, latency, horizon, and review rhythm. Fast loops can chase noise; slow loops can arrive too late. A quarterly planning loop may invalidate what a daily operational loop has already learned. Timing alignment tunes update intervals and decision windows to fit the dynamics being governed.

### Gain Adjustment

Gain adjustment tunes how strongly each loop responds. If several loops respond aggressively to the same signal, they can amplify one another. If every loop responds weakly, the system may drift into failure. Gain adjustment asks which level should respond first, how strongly, and under what confidence or risk conditions.

### Escalation and De-escalation Rule

Escalation rules define when a condition moves upward; de-escalation rules define when authority returns downward. Without both, a system may abandon local units until it is too late, or centralize control permanently after a temporary disruption.

### Cross-Scale Effect Monitor

The monitor tracks whether corrections at one level create hidden costs or instability at another. It watches for oscillation, risk shifting, local metric gaming, aggregate smoothing that hides harm, central overcorrection, and suppressed local adaptation. This component turns the intervention into a learning loop.

### Conflict-Resolution Priority Rule

Nested loops sometimes disagree. A conflict-resolution priority rule states which invariant or authority has priority and why. The rule should not simply privilege the highest level. It should account for safety, legitimacy, evidence quality, reversibility, local knowledge, and system-wide consequence.

### Learning Recalibration Path

Feedback alignment needs maintenance. The learning recalibration path updates targets, thresholds, timing, gain, and escalation when evidence shows that the nested feedback system is still misaligned. Without this component, the design becomes a static chart that decays as conditions change.

prose_common_mechanisms:
### Multi-Level KPI Review

A multi-level KPI review is a mechanism for comparing local, intermediate, and system-level indicators before action is taken. It implements the archetype when it changes correction behavior, not merely when it displays several metrics on one screen.

### Nested Control-System Tuning

Nested control-system tuning implements the archetype in technical or operational settings. It adjusts thresholds, delays, setpoints, and gains so one controller or operational routine does not destabilize another. The mechanism is not the archetype itself; the archetype is the cross-scale alignment logic.

### Governance Escalation Protocol

A governance escalation protocol implements the escalation component. It states when local governance should act, when regional or central governance should intervene, and how authority returns. It is an implementation mechanism, not a standalone archetype unless it develops separate governance logic.

### Local/System Feedback Cadence

A local/system feedback cadence synchronizes review rhythms. It may align daily operations reviews with weekly regional reviews and quarterly strategy updates. Its purpose is to reduce stale correction and cadence mismatch.

### Balanced Scorecard Cascade

A balanced scorecard cascade can instantiate target alignment by connecting strategic goals to local measures while preserving counterbalancing metrics. It fails as an implementation when it becomes a compliance cascade without timing, gain, and cross-scale effect review.

### Ecological Adaptive-Management Cycle

In ecological management, nested monitoring and action cycles can connect local observations, habitat interventions, regional ecosystem indicators, and policy revision. The mechanism fits when local and regional loops learn together.

### Bullwhip Effect Review

A bullwhip effect review is a supply-chain mechanism for detecting whether ordering and forecasting loops amplify variability as signals move between tiers. It is a domain-specific implementation of nested feedback alignment.

### Incident-Command Feedback Rhythm

Incident response often needs tactical, operational, and strategic loops to run simultaneously. A feedback rhythm coordinates reports, decisions, and after-action learning so one level does not overload or starve another.

### Cross-Scale Retrospective

A cross-scale retrospective brings participants from several levels together after a cycle or incident. It looks for mismatched signals, timing, gain, thresholds, and escalation behavior. It is especially useful for updating the learning recalibration path.

### Aggregation/Disaggregation Dashboard

This dashboard lets users inspect aggregate patterns and drill down to local variation. It is only a mechanism: it supports signal translation and monitoring, but it does not align loops unless paired with decision rules and response tuning.

prose_failure_modes:
The most common failure mode is alignment by centralization: leaders interpret alignment as making every level obey one central signal. This destroys the local responsiveness the archetype is meant to preserve. A second failure mode is dashboard theater, where metrics are displayed across levels but no one changes decision rules, gain, timing, or escalation.

Other failure modes include local metric gaming, lag-driven overcorrection, gain amplification, suppressed weak signals, and permanent escalation. Each failure mode results from confusing visibility or authority with actual feedback alignment. The mitigation is to keep the full loop structure in view: signal, target, timing, gain, correction, and cross-scale effect.

prose_neighbor_distinctions:
Nested Feedback Alignment is distinct from Feedback Loop Redirection because it is not about changing one loop. It is about coordinating multiple loops across levels. It is distinct from Homeostatic Regulation because it does not merely stabilize one variable around a setpoint; it aligns several nested setpoints and correction routines.

It is distinct from Goal Congruence Alignment because goals can be aligned while feedback timing or gain remains destructive. It is distinct from Whole System Alignment because it focuses narrowly on feedback loops rather than all elements of a system. It is distinct from Multi-Scale Signal Monitoring because monitoring makes signals visible, while this archetype changes how those signals drive correction. It is distinct from Cross-Scale Causal Mapping because the central object is a recurrent sensing-correction loop, not a one-directional causal path.

prose_cross_domain_examples:
In supply chains, local reorder loops, distributor forecasts, and manufacturing plans can amplify variability. Nested Feedback Alignment compares the tiers’ signals, aggregation windows, thresholds, and response gains to dampen bullwhip effects without making every local unit passive.

In organizations, teams may optimize sprint velocity or call-handling time while the larger system cares about quality, retention, or customer trust. Alignment compares team, department, and executive loops so local metrics do not become a machine for system-level harm.

In software reliability, service-level autoscaling, platform capacity planning, release governance, and incident response can interact. Alignment tunes alert thresholds, capacity signals, escalation, and review cadences so one service’s rational correction does not exhaust the shared platform.

In public health, clinic-level sentinel signals, municipal response rules, state guidance, and national reporting can move at different speeds. Alignment protects early local warning while avoiding stale or overbroad central mandates.

In ecological management, local observations, habitat interventions, regional indicators, and policy cycles may operate on different temporal and spatial scales. Alignment helps local adaptation support long-term ecosystem stability rather than undermine it.

prose_non_examples:
A single team retrospective is not Nested Feedback Alignment unless it interacts with feedback loops at other levels. A dashboard with many metrics is not the archetype unless it changes cross-scale correction behavior. A central compliance mandate is not alignment merely because it applies to every unit. A single controller tuned to reduce overshoot is ordinary feedback calibration unless nested loops are involved.

A hierarchy is also not enough. The archetype requires nested feedback loops, not simply nested boxes on an org chart.

prose_tradeoffs:
Nested Feedback Alignment trades some local freedom for system coherence, but it should not eliminate local adaptation. It trades fast response against noise resistance. It trades transparent escalation against flexibility. It trades comparable metrics against local context. It trades stability against learning.

The most delicate tradeoff is between damping and weak-signal sensitivity. If the system damps every local variation, it may miss real early warnings. If it escalates every local variation, it may create noise-driven overcorrection. The right balance depends on consequence severity, reversibility, signal reliability, and the speed of system dynamics.

prose_invariants_to_preserve:
The first invariant is local responsiveness. Lower-level units must retain enough ability to act on conditions they can see directly. The second is system-level stability: local corrections should not create aggregate oscillation, risk shifting, or hidden overload. The third is signal meaning across scales: a signal should not become misleading merely because it has been aggregated, normalized, or translated.

Other invariants include proportional correction, legitimate escalation, safety protection, and learning capacity. A well-aligned nested feedback system is not perfectly smooth. It remains responsive, interpretable, bounded, and capable of revising itself.

prose_target_outcomes:
The target outcomes are reduced oscillation, clearer local-global coherence, more legitimate escalation, lower metric gaming, and better trust in feedback-driven governance. A good implementation should make it easier to explain why a signal led to action at one level rather than another.

The archetype also aims to reduce blame loops. When feedback is misaligned, local actors accuse central actors of overreach, while central actors accuse local actors of irresponsibility. Alignment replaces some of that blame with a shared map of signals, targets, timing, and correction rules.

prose_parameter_dimensions:
The main tuning dimensions are scale granularity, target specificity, signal translation, feedback cadence, aggregation window, response gain, escalation threshold, autonomy band, noise filter, override authority, and recalibration cadence.

Scale granularity determines how many levels are recognized. Too few levels hide important loop interactions; too many levels make the map unusable. Target specificity determines whether each loop knows what it is trying to preserve. Signal translation determines how meaning survives aggregation or disaggregation. Cadence and aggregation windows determine how quickly loops update and whether they chase noise or hide emerging risk. Gain determines correction strength. Escalation thresholds and autonomy bands determine when authority moves across levels. Recalibration cadence determines how often the feedback design itself is reviewed.

prose_variants_and_near_names:
Important variants include Local/System KPI Alignment, Temporal Feedback Alignment, Escalation Loop Alignment, and Nested Control Tuning. Local/System KPI Alignment applies when the main medium is performance measurement. Temporal Feedback Alignment applies when cadence, latency, and planning horizon are the dominant problem. Escalation Loop Alignment applies when authority movement across levels is the recurring failure. Nested Control Tuning applies when the system can be treated as interacting technical or operational control loops.

Near names include Cross-Scale Feedback Alignment, Multi-Level Feedback Alignment, Feedback Loop Alignment, Organizational Feedback Cascade, and Nested Feedback Calibration. These should point to the parent when nested scale-specific feedback loops are explicit. Dashboards, scorecards, incident command protocols, retrospectives, and control-loop diagrams should remain mechanisms unless they develop independent cross-domain intervention logic.

Local/System KPI Alignment

short_definition: A nested feedback alignment variant focused on ensuring local performance metrics and system-level outcome metrics do not drive conflicting correction.

distinctive_feature: The main feedback signals are metrics or scorecards, and the central risk is local optimization against system goals.

distinct_from_parent: The parent covers any nested feedback loops; this variant focuses specifically on measurement, incentives, and performance review loops.

still_same_parent_because: The intervention still requires mapping loops by scale, aligning targets, translating signals, tuning cadence and gain, and monitoring cross-scale effects.

source_primes: feedback scale goal_congruence

use_when: Local units appear to be meeting their metrics while system-level outcomes degrade.

use_when: Aggregate targets are being pushed downward without preserving local context or feasible control.

use_when: Performance dashboards trigger corrective action at several levels and those actions interfere with each other.

Target Alignment Check

role: Compares what each loop is trying to protect, optimize, stabilize, or learn so local correction does not undermine higher-level function or vice versa.

Signal Translation Rule

role: Specifies how feedback signals move between levels through aggregation, disaggregation, normalization, interpretation, filtering, and context preservation.

Gain Adjustment

role: Tunes the strength, sensitivity, or proportionality of corrective action at each scale so feedback responses stabilize rather than amplify oscillation.

Cross-Scale Effect Monitor

role: Tracks whether corrections at one level are creating instability, hidden costs, or delayed effects at another level.

Multi-Level KPI Review

role: Reviews local, intermediate, and system-level indicators together so a correction that improves one level is checked for consequences at the others.

Balanced Scorecard Cascade

mechanism_type: method

role: Translates strategic goals into nested local indicators while preserving counterbalancing metrics so units do not optimize one target at the expense of another.

Aggregation/Disaggregation Dashboard

role: Lets users inspect aggregate patterns while drilling down to local variation so feedback decisions do not hide heterogeneity.

domain: operations

example: A company reviews team-level throughput alongside customer outcome and defect signals before changing quotas.

example: A district compares classroom formative feedback with school-level assessment trends before revising curriculum pacing.

failure_modes: Metrics are aligned in language but still trigger conflicting actions.

failure_modes: Aggregate targets erase local constraints and produce performative compliance.

failure_modes: Local units hide bad news because the higher-level loop responds punitively.

Temporal Feedback Alignment

short_definition: A variant focused on aligning feedback cadence, sensing delay, response delay, and planning horizon across nested levels.

distinctive_feature: Timing, latency, cadence, and horizon mismatch are the dominant failure source.

distinct_from_parent: The parent also aligns targets, gain, signals, and escalation; this variant foregrounds the time structure of feedback.

still_same_parent_because: Temporal tuning still functions by aligning nested feedback loops rather than introducing a separate intervention family.

source_primes: feedback scale oscillation

use_when: Fast local loops and slow system loops create whiplash, stale mandates, or repeated overcorrection.

use_when: Different levels interpret the same condition using incompatible time windows.

use_when: Delayed aggregate response arrives after local conditions have already shifted.

Timing Alignment

role: Coordinates feedback cadence, sensing delay, decision delay, response horizon, and review rhythm across nested levels.

Aggregation Window

role: Sets the time interval or population scope over which local signals are combined before they are interpreted at a higher level.

Noise Filtering Rule

role: Prevents local variation, measurement noise, or transient anomalies from triggering excessive higher-level response.

Local/System Feedback Cadence

role: Synchronizes the rhythm of local reviews, aggregate reviews, retrospectives, budget cycles, incident reviews, or policy updates.

Cross-Scale Retrospective

mechanism_type: ritual

role: Brings participants from multiple levels together after a cycle, disruption, or intervention to identify mismatched signals, timing, gain, and escalation rules.

Incident-Command Feedback Rhythm

role: Coordinates tactical reports, operational decisions, strategic priorities, and after-action updates during incident response.

example: Retail replenishment signals are reviewed against manufacturing planning windows before reorder thresholds are changed.

example: Autoscaling thresholds, deployment review, and capacity planning cycles are tuned so they do not chase different time horizons.

failure_modes: Cadences are synchronized administratively but not dynamically.

failure_modes: Slow loops suppress legitimate fast local adaptation.

failure_modes: Fast loops amplify noise before slower loops can interpret system effects.

Escalation Loop Alignment

short_definition: A governance-oriented variant focused on when local correction should escalate upward, when central control should stand down, and how authority returns to lower levels.

distinctive_feature: The primary alignment problem is not the signal itself but authority movement across nested levels.

distinct_from_parent: The parent includes technical, metric, ecological, and organizational loops; this variant focuses on governance handoff and authority return.

still_same_parent_because: Escalation remains a nested feedback problem: scale-specific signals trigger scale-specific corrective authority.

source_primes: feedback hierarchy scale

use_when: Local units either escalate too late or escalate routine issues that they could resolve.

use_when: Central intervention continues after the triggering condition has stabilized.

use_when: The system lacks an explicit rule for returning authority after emergency or exception handling.

Escalation and De-escalation Rule

role: Defines when a local loop should hand a condition upward, when higher-level loops should intervene, and when control should return to the lower level.

Autonomy Band

role: Defines the range within which local loops may self-correct without higher-level intervention.

Conflict-Resolution Priority Rule

role: Specifies how to decide between nested loops when their targets, signals, or corrective actions conflict.

Override Audit Trail

role: Records why a higher-level loop overrode local correction or why a local unit resisted system-level guidance.

Governance Escalation Protocol

mechanism_type: protocol

role: Specifies when local governance handles a signal, when regional or central governance intervenes, and how authority returns after the condition stabilizes.

example: A response structure escalates from local triage to regional coordination only when capacity or risk thresholds are crossed.

example: A moderation policy defines when local community rules are overridden by platform-wide safety rules and when control returns.

failure_modes: Local abandonment disguised as autonomy.

failure_modes: Ambiguous thresholds that invite political override rather than feedback-based escalation.

Nested Control Tuning

short_definition: A technical or operations-heavy variant focused on tuning interacting control loops across nested subsystems.

distinctive_feature: The variant emphasizes explicit tuning of thresholds, gain, delay, and setpoints in nested control arrangements.

distinct_from_parent: The parent is a broader socio-technical archetype; this variant is most useful when the system can be treated as nested control loops.

still_same_parent_because: It still uses the same structure of mapping loops by scale and aligning their signals, targets, timing, and effects.

source_primes: feedback controllability scale

use_when: Multiple controllers, teams, or routines respond to related variables at different scales.

use_when: Local correction creates oscillation, saturation, or load shifting elsewhere in the system.

use_when: The problem can be represented as interacting setpoints, thresholds, delays, and response gains.

Feedback Loop by Scale

role: Identifies each relevant local, intermediate, and system-level feedback loop, including its signal, interpreter, corrective action, delay, and intended target.

Nested Control-System Tuning

role: Tunes controller thresholds, gains, delays, and override rules when technical or operational control loops interact across nested subsystems.

Bullwhip Effect Review

role: Checks whether ordering, forecasting, or inventory feedback at one tier is amplifying variability at another tier of a supply chain.

example: Local grid stabilization settings are tuned against regional balancing constraints.

example: Service-level autoscaling rules are tuned against platform-wide capacity and incident response loops.

failure_modes: Technical tuning ignores human decision loops.

failure_modes: Optimization at one subsystem shifts instability to another.

failure_modes: Model assumptions hide nonlinear or delayed cross-scale effects.

near_names:
Cross-Scale Feedback Alignment
Multi-Level Feedback Alignment
Feedback Loop Alignment
Organizational Feedback Cascade
Nested Feedback Calibration


Net-Additive Contribution Intake

type: archetype

source: generated

batch: Batch 15

canonical_family: Contribution Intake, Absorptive Capacity, and Core-Work Protection

aliases: Contribution Fit and Capacity Governance

alias_records: Contribution Fit and Capacity Governance

slug: queueing

slug: consent

slug: screening

slug: gatekeeping

slug: attention

slug: prioritization

slug: boundary

slug: trust

slug: reputation

slug: legitimacy

slug: classification

slug: observability

slug: standardization

slug: satisficing

slug: scarcity

essence_one_line: Accept, reshape, redirect, defer, or decline well-intended contributions according to their full net value, available sponsorship, and effect on protected primary work.

compression_statement: When unsolicited prosocial offers arrive, their visible resource value can conceal evaluation, translation, supervision, integration, compliance, communication, maintenance, and exit costs borne by a coordinator gate. Social norms may make refusal feel illegitimate, so offers bypass prioritization and displace the work they intend to help. The intervention publishes current contribution demand; channels offers through one intake boundary; records the offer and its restrictions; computes full lifecycle handling and displacement cost; tests net additivity; requires a named sponsor and coordination budget; chooses accept, reshape, redirect, defer, or decline; trials uncertain offers in a bounded environment; assigns integration, maintenance, and exit ownership; protects contributor dignity; monitors side-channel bypass and core-work displacement; and updates the intake design from realized outcomes.

core_intuition: Help is not free merely because it is donated or well intended. A receiving system creates value only when it has enough fit, attention, sponsorship, interface capacity, and legitimate refusal authority to convert the offer without sacrificing higher-priority work.

canonical_formula: For offer o, let V(o) be expected mission value; C_eval, C_coord, C_integrate, C_comply, C_maintain, C_communicate, and C_exit be full lifecycle handling costs; D_core(o) be the value of protected work displaced; R(o) be risk-adjusted residual and uncertainty; and B_s be available sponsor and coordination budget. Admit o only if a legitimate sponsor exists, required invariants hold, B_s covers the bounded handling load, and N(o)=V(o)-[C_eval+C_coord+C_integrate+C_comply+C_maintain+C_communicate+C_exit+D_core+R] exceeds the domain threshold. Otherwise reshape, redirect, defer, or decline through a dignified path.

structural_problem: A system receives unsolicited, well-intended offers of labor, goods, code, data, ideas, access, or support. The offer looks additive at the boundary, but a scarce coordinator must evaluate, translate, supervise, integrate, communicate, maintain, and eventually retire it. Because refusal carries relational or reputational cost, the system admits, delays, or informally handles offers beyond its absorptive capacity, causing primary-work displacement, queue growth, hidden obligations, and resentment.

trigger_conditions: Offers are initiated by contributors rather than pulled from a current, bounded demand signal.

trigger_conditions: The receiving system must spend scarce expert attention or coordination capacity before value can be realized.

trigger_conditions: Offer fit, quality, timing, restrictions, provenance, maintenance, or exit obligations are uncertain.

trigger_conditions: The organization lacks a normalized, respectful way to redirect, defer, or decline prosocial offers.

trigger_conditions: Side channels, personal relationships, publicity, executive escalation, or direct delivery can bypass ordinary intake.

trigger_conditions: Primary work is delay-sensitive, interruption-sensitive, safety-critical, or already near capacity.

trigger_conditions: A contribution may be valuable in principle but unsuitable for the current recipient, time window, format, or support budget.

symptoms: Coordinators spend more time explaining, sorting, fixing, supervising, or thanking than the contribution saves.

symptoms: Staff privately avoid volunteers or contributors because accepting help creates another project.

symptoms: Unrequested goods, features, data, or ideas accumulate in storage, review queues, inboxes, or backlogs.

symptoms: Offers are accepted to preserve goodwill even when no owner, integration path, maintenance plan, or use window exists.

symptoms: Contributors receive ambiguous encouragement, long silence, or repeated handoffs because no one can cleanly decline.

symptoms: Operational staff become the default gatekeepers and lose focus through interruptions and context switching.

symptoms: High-status or persistent contributors bypass the intake route, producing inequitable access and priority inversion.

symptoms: The organization celebrates gross contribution volume while service latency, rework, supervision load, or disposal cost worsens.

root_tension: Prosocial intent and apparent resource gain create a moral and reputational pressure to accept, while realized value depends on scarce receiving capacity. A system must preserve openness, dignity, trust, and future cooperation without treating every offer as an obligation or externalizing its hidden coordination cost onto primary workers.

anti_signatures: The incoming item is an ordinary service request or demand unit that already qualifies for admission; use Intake Queue Staging or Priority-Based Admission.

anti_signatures: The system has already accepted the work and only needs to cap concurrency; use Work-in-Progress Limiting.

anti_signatures: The main problem is overload from required demand and selective dropping during degradation; use Load Shedding.

anti_signatures: The exchange is clearly beneficial and the main barrier is search, negotiation, or verification friction; use Transaction Cost Reduction.

anti_signatures: The only need is tactful wording for a refusal or criticism; use Face-Saving Directness Calibration as a communication archetype or mechanism.

anti_signatures: The organization is actively recruiting contributions against well-defined roles with adequate supervision and no material displacement risk; ordinary onboarding may suffice.

anti_signatures: The proposed control suppresses participation for convenience, status protection, discrimination, or avoidance of legitimate accountability rather than protecting real capacity and invariants.

structural_intervention: Create a contribution intake system that first makes useful demand legible, then holds all offers outside primary work until fit, full lifecycle cost, sponsor capacity, and net additivity are established. Give the gate several legitimate dispositions—not only accept or reject—and connect acceptance to bounded trial, ownership, maintenance, exit, contributor protections, and realized-value feedback.

action_logic: Define the protected primary work, its service or mission priorities, displacement signals, and maximum handling budget.

action_logic: Publish a current contribution demand signal that states useful roles, goods, formats, quantities, timing, quality, restrictions, and exclusions.

action_logic: Route unsolicited offers through a common intake boundary and prevent direct delivery to operational teams except through declared emergency exceptions.

action_logic: Create a standardized offer record covering intent, capability or material, provenance, condition, dependencies, restrictions, timing, support needs, access, and requested recognition.

action_logic: Estimate expected mission value and the full cost of evaluation, translation, coordination, supervision, integration, compliance, communication, maintenance, storage, disposal, and exit.

action_logic: Measure probable core-work displacement and uncertainty rather than assuming donated inputs have zero acquisition cost.

action_logic: Apply a net-additivity and invariant test; reject offers whose expected total burden exceeds value or violates rights, safety, fairness, privacy, labor, or professional boundaries.

action_logic: Require a named contribution sponsor and explicit coordination budget before acceptance; absence of a sponsor is a valid reason to defer or decline.

action_logic: Choose among accept, reshape, redirect, defer, decline, or self-service rather than forcing a binary decision.

action_logic: Use a bounded trial for uncertain, reversible offers with scope, access, success, stop, and handoff criteria.

action_logic: For accepted offers, document onboarding, interfaces, standards, credit, communication, maintenance ownership, dependency removal, and exit obligations.

action_logic: Make refusal and redirection socially legitimate through clear policy, respectful language, prompt closure, and alternate paths when available.

action_logic: Monitor side-channel bypass, queue age, sponsor load, displacement, accepted-offer realization, hidden maintenance, and contributor experience.

action_logic: Periodically update the demand signal, thresholds, self-service paths, capacity limits, and redirection network from observed outcomes.

decision_rules: Do not accept an offer merely because its acquisition price is zero, its intent is prosocial, or refusal feels uncomfortable.

decision_rules: No sponsor, no acceptance: an offer without a named lifecycle owner is redirected, deferred, or declined.

decision_rules: Use the full receiving-system cost, including opportunity cost and displacement, not only contributor effort or material value.

decision_rules: Protect non-negotiable safety, consent, labor, privacy, accessibility, legal, and professional-scope invariants before net-value comparison.

decision_rules: Prefer requested, standardized, self-service, reversible, low-coupling offers when value is otherwise similar.

decision_rules: Use bounded trials when benefit is plausible but integration or maintenance uncertainty is material.

decision_rules: Pause intake when sponsor capacity, protected-work indicators, storage, supervision, review, or maintenance budgets cross limits.

decision_rules: A respectful decline is better than ambiguous encouragement that transfers uncertainty and waiting cost to the contributor.

decision_rules: Do not use the intake gate to conceal favoritism; criteria, exceptions, and escalation paths must be reviewable.

decision_rules: Re-evaluate accepted contribution types when realized net value or contributor experience diverges from the intake estimate.

target_invariants: Primary mission work retains a declared minimum capacity and service level.

target_invariants: Consent, dignity, safety, privacy, accessibility, labor rights, and professional boundaries are preserved.

target_invariants: Acceptance does not create unowned maintenance, disposal, security, or dependency obligations.

target_invariants: Criteria apply consistently enough to prevent status, persistence, or side-channel access from silently dominating priority.

target_invariants: Contributors receive truthful expectations; an intake acknowledgment is not represented as acceptance.

target_invariants: Emergency exceptions are bounded, logged, reviewed, and do not become a permanent bypass.

target_invariants: The receiving system may say no without retaliation, humiliation, or fabricated justification.

expected_outcomes: More accepted offers produce measurable mission value after full handling cost.

expected_outcomes: Primary staff experience fewer interruptions, context switches, and hidden coordination obligations.

expected_outcomes: Contributors receive faster, clearer, and more respectful dispositions.

expected_outcomes: Accepted contributions have explicit sponsorship, integration, maintenance, recognition, and exit ownership.

expected_outcomes: Requested and self-service contribution channels become more useful because demand and specifications are clearer.

expected_outcomes: Storage, review, supervision, backlog, and disposal burdens remain within capacity.

expected_outcomes: Side-channel advantage and acceptance-by-social-pressure decline.

expected_outcomes: The organization preserves future goodwill by matching openness to genuine absorptive capacity rather than making unreliable promises.

Volunteer-Surge Intake

short_definition: Govern a burst of well-intended volunteer offers so supervision and coordination do not displace the mission work the volunteers intend to support.

distinctive_feature: The offered resource is human effort whose value depends on role fit, supervision, consent, safeguarding, and the receiving organization’s absorptive capacity.

distinct_from_parent: This variant adds volunteer-specific role, safeguarding, workload, access, recognition, and duty-of-care controls.

still_same_parent_because: The decision remains whether an unsolicited prosocial inflow is net additive after its full handling and lifecycle cost.

use_when: A crisis, campaign, event, or public appeal produces more volunteer interest than the operating team can safely absorb.

use_when: Volunteer roles require screening, orientation, supervision, access, safeguarding, or scheduling.

use_when: Some offers can be standardized or self-served while others need a named sponsor.

domain: disaster response

example: Publish bounded roles and batch onboarding rather than assigning every walk-in volunteer directly to incident staff.

failure_modes: Volunteers consume scarce expert supervision without adding usable capacity.

failure_modes: Refusal is delayed until volunteers have already traveled, gained access, or formed expectations.

Unsolicited Digital-Contribution Intake

short_definition: Screen unsolicited code, data, content, automation, and design contributions for integration and maintenance burden before admitting them to a production workflow.

distinctive_feature: The offered artifact may be easy to create but expensive to review, secure, integrate, observe, maintain, migrate, or remove.

distinct_from_parent: This variant emphasizes technical interfaces, provenance, licensing, security, test evidence, maintenance ownership, and hidden integration debt.

still_same_parent_because: The central control is still a full-cost net-additivity test with sponsor capacity and a legitimate decline path.

use_when: External or cross-team contributors can submit code, data, models, content, automations, or designs.

use_when: Review, security, testing, documentation, licensing, deployment, or maintenance may cost more than creation.

use_when: The receiving team must preserve roadmap and production reliability.

domain: open source software

example: Require issue alignment, tests, licensing, and a maintainer sponsor before accepting a large unsolicited feature.

failure_modes: A seemingly free contribution becomes unowned production debt.

failure_modes: Review and integration queues crowd out planned reliability work.

Material-Donation Fit Screening

short_definition: Admit donated goods only when condition, quantity, timing, storage, handling, compliance, distribution, and disposal make the donation net useful.

distinctive_feature: The inflow occupies physical capacity and can create contamination, safety, storage, logistics, and disposal liabilities even when acquisition price is zero.

distinct_from_parent: This variant adds condition inspection, quantity and timing windows, storage and transport constraints, chain of custody, and end-of-life handling.

still_same_parent_because: The governing question remains whether the offered inflow adds usable value after full handling cost and protected-work displacement.

use_when: People or organizations offer physical goods in response to a need or public appeal.

use_when: Storage, sorting, transport, inspection, sanitation, liability, distribution, or disposal costs are material.

use_when: The receiver can publish accepted specifications and redirect unsuitable goods.

domain: humanitarian logistics

example: Accept only requested, packaged, date-compliant goods within current warehouse and distribution capacity.

failure_modes: Unsorted donations block docks and warehouses needed for priority supplies.

failure_modes: Social pressure prevents refusal of unsafe, unusable, or mistimed goods.

near_names:
Helpful-Inflow Displacement
Helpful Inflow Governance
Contribution Absorptive Capacity
Prosocial Intake Control
Net-Positive Help Intake