Wavefront Propagation Management

batch: First Iteration/Batch 21

Leading Edge Response

notes: Common shorthand for acting at the advancing edge, but narrower than the full archetype.

Spread-Edge Intervention

notes: Plain-language alias for intervening where spread is entering new territory.

Advancing Front Management

notes: Highlights the moving boundary between affected and not-yet-affected zones.

Propagation Front Management

notes: Very close synonym, but propagation is treated as proposed prime-like rather than canonical.

Wavefront Control

notes: Engineering-style shorthand that may be too narrow for social and organizational contexts.

source_primes: wave diffusion network

related_primes: signaling flow boundary amplification damping observability resilience fault_tolerance threshold teleconnection topology scheduling

slug: signaling

slug: threshold

slug: teleconnection

slug: scheduling

components:
Propagation Map
Wavefront Indicator
Leading Edge Intervention
Attenuation or Amplification Rule
Response Timing Window
Front Priority Rule
Behind-Front Stabilization
Front Feedback Loop
Sentinel Node Network
Prepositioned Response Capacity
Frontier Boundary
Secondary Wave Watch

mechanisms:
Firebreak
Vaccination Front
Staged Rollout
Rumor Edge Containment
Flood Wave Preparation
Cascading Failure Containment
Adoption Wave Support Team
Leading Edge Dashboard
Prepositioned Resource Cache

essence_one_line: Manage a spreading disturbance, signal, or adoption wave by acting at the advancing front rather than only at the origin.

compression_statement: When change propagates as a wave through a medium or network, manage the wavefront to accelerate, slow, redirect, or prepare ahead of spread.

core_intuition: A wave is easiest to influence where it is currently advancing or where it is about to arrive. Once the front has passed, options narrow to recovery and stabilization; far behind the origin, local spread can continue even if the source is addressed. Wavefront Propagation Management treats the front as the practical intervention object: map it, track it, decide what should happen to it, act in the timing window, and update as it moves.

canonical_formula: propagating change across medium or network -> map medium + locate advancing front + choose accelerate/attenuate/redirect/prepare objective + act at or ahead of front + stabilize behind front -> controlled spread, reduced downstream surprise, or supported adoption

structural_problem: A disturbance, signal, behavior, adoption pattern, failure, or resource shock spreads through a medium or network over time, but the system responds as if the issue were only at the origin, everywhere at once, or already static. This causes late action, misallocated effort, missed preparation windows, avoidable downstream harm, or stalled beneficial spread.

condition: A phenomenon is spreading rather than staying local.

description: The relevant change moves across space, network ties, dependency chains, populations, teams, markets, or channels.

condition: The moving edge is observable enough to guide action.

description: There are indicators, sentinel nodes, reports, traces, sensors, or leading measures that can estimate where the front is and where it is likely to go.

condition: Timing at the front changes outcomes.

description: Intervening before, during, or immediately after front passage is more effective than waiting for full saturation or only addressing the origin.

condition: Downstream areas differ in vulnerability, readiness, or strategic value.

description: Some nodes, regions, teams, or communities need earlier protection, support, prepositioning, or acceleration than others.

condition: The desired front behavior is explicit.

description: The system can say whether the wave should be slowed, stopped, redirected, amplified, accelerated, absorbed, or prepared for.

condition: The front may change route or intensity after intervention.

description: The design needs feedback because waves can split, rebound, route around barriers, accelerate through high-coupling paths, or fade in unexpected places.

symptom: Response chases yesterday’s front

example: Teams continue acting where the wave was first noticed while the leading edge has already moved to new regions or nodes.

symptom: Downstream surprise repeats

example: Each newly affected site, team, or community treats the arriving disturbance as an unexpected local incident.

symptom: Origin-focused control underperforms

example: Stopping the initial source does not prevent spread that has already become self-propagating through the network.

symptom: Beneficial adoption stalls at cohort boundaries

example: A new tool, practice, norm, or policy spreads among early adopters but loses momentum when it reaches less prepared groups.

symptom: Containment barriers are placed too late or in the wrong channel

example: Firebreaks, quarantines, filters, resource staging, or corrective messages are deployed after the front has passed or after it has found another path.

symptom: Aggregate metrics hide front location

example: Total cases, adoption counts, tickets, or incident volume rise, but the team cannot tell where the next affected zone will be.

symptom: Secondary waves appear after apparent success

example: The first front is managed, but unmonitored areas behind the front re-seed later spread or a side channel creates another front.

root_tension: The system needs to act locally and temporally at a moving edge, while most organizational attention, data systems, and accountability structures prefer static locations, aggregate totals, origin narratives, or after-the-fact recovery. The design tension is to make the front legible and actionable without overclaiming certainty about a dynamic, adaptive path.

case: No propagation path exists.

why_not_this_archetype: A fixed local incident, isolated backlog, or one-time failure may need incident response, buffering, or capacity management rather than wavefront management.

case: The entire system is already saturated.

why_not_this_archetype: When there is no meaningful advancing edge left, the practical problem is recovery, stabilization, or broad containment rather than front management.

case: The desired action is simply to broadcast everywhere.

why_not_this_archetype: A universal communication blast may be appropriate, but the archetype adds value when front timing, location, and path-specific targeting matter.

case: The phenomenon is only cyclic, not spreading.

why_not_this_archetype: Oscillation damping, cadence design, cycle staggering, or resonance tuning may fit better when timing repeats without movement through a medium.

case: The spread objective is general and not front-centered.

why_not_this_archetype: Diffusion acceleration or diffusion containment may be the better parent if the key design question is total spread rate rather than edge intervention.

structural_intervention: Construct a working representation of the propagation medium, identify the advancing front, define the intended front behavior, intervene at or just ahead of the front, stabilize behind it, and update the map as the wave changes.

step: Map the medium of spread.

description: Identify the channels, dependencies, adjacency relationships, social ties, geographic corridors, workflows, or network links through which the wave can move.

step: Locate and characterize the front.

description: Estimate where the wave is newly arriving, how fast it is moving, how intense it is, which branches are emerging, and how uncertain the estimate is.

step: Choose the front objective.

description: Decide whether to slow, block, weaken, redirect, accelerate, seed, support, prepare for, or safely absorb the wave at the edge.

step: Prioritize front segments.

description: Select action points based on exposure, vulnerability, criticality, leverage, equity, reversibility, and available timing window.

step: Place leading-edge intervention.

description: Deploy barriers, support, warnings, resources, correction, enablement, immunization, isolation, or channeling where the front is arriving or expected next.

step: Stabilize behind the front.

description: Prevent re-seeding, consolidate beneficial adoption, clear damage, monitor relapse, and prevent the passed region from becoming an invisible secondary source.

step: Update and adapt.

description: Use feedback to revise the propagation map, detect side channels, recalibrate front speed, and adjust interventions as the wave changes.

rule: Act at the front when marginal influence is highest.

description: Favor locations where the wave is not yet saturated and where intervention can still change downstream outcomes.

rule: Match intervention to front objective.

description: Use attenuation for harmful spread, amplification for beneficial adoption, redirection for unavoidable waves, and preparation when passage is likely.

rule: Treat uncertainty as part of the front estimate.

description: Do not draw a false-precision boundary; use tiers such as confirmed front, probable front, exposed next, and sentinel watch.

rule: Do not neglect behind-front stabilization.

description: A front that has passed can still create backlog, relapse, re-seeding, adoption failure, or secondary waves.

rule: Prioritize exposed critical nodes before visible high-volume nodes when timing is short.

description: A small vulnerable bridge, hospital, dependency service, or trusted community may be more important than a larger already-saturated site.

rule: Prefer reversible and proportional front actions when the wave estimate is uncertain.

description: Avoid heavy containment, coercive communication, or costly staging when the front signal is weak and the harm of false positives is high.

rule: Monitor for rerouting effects.

description: Barriers and channeling can move the front into unintended paths; acceleration can shift burden to unprepared downstream nodes.

invariant: The front remains legible.

description: Decision-makers maintain a current, uncertainty-aware view of where the wave is and where it is likely to go.

invariant: Front action remains tied to propagation outcomes.

description: Each intervention can explain whether it is intended to attenuate, amplify, redirect, prepare, or stabilize.

invariant: Downstream surprise is reduced.

description: Nodes ahead of the front receive warning, resources, configuration, or support before the wave arrives when feasible.

invariant: Intervention does not create hidden sacrifice zones.

description: Containment or redirection must not protect visible areas by quietly imposing disproportionate harm on less visible areas.

invariant: Beneficial spread does not outrun support.

description: Acceleration must preserve absorption capacity, trust, safety, quality, and follow-through.

invariant: Passed regions do not re-seed unmanaged spread.

description: Behind-front stabilization prevents relapse, rebound, backlog, or unresolved damage from becoming a new source.

outcome: Earlier intervention at high-leverage points

description: The system acts where timing still matters rather than waiting for downstream saturation.

outcome: Reduced downstream harm or surprise

description: At-risk nodes are warned, protected, resourced, or decoupled before the wave arrives.

outcome: More controlled beneficial adoption

description: A desired practice, signal, or capability spreads through cohorts with support and stabilization.

outcome: Improved containment of harmful spread

description: Barriers, corrections, immunization, or dependency isolation are placed at the edge where they can still slow propagation.

outcome: Better resource placement

description: People, tools, supplies, authority, communications, or capacity are staged where the front is expected rather than where attention is loudest.

outcome: Adaptive propagation intelligence

description: Front observations improve the propagation map and future response.

prose_essence:
Wavefront Propagation Management is the pattern of treating a spreading change as a moving edge rather than as a static incident. The useful question is not only “where did this begin?” or “how big is it now?” but “where is the front, where will it arrive next, and what can still be changed at that edge?”

This archetype applies to harmful fronts, such as fires, outbreaks, rumor spread, cascading failures, contamination, or panic. It also applies to beneficial fronts, such as adoption of a tool, spread of a practice, rollout of a standard, or movement of a warning through a population. In both cases, the front is the actionable object.

prose_when_to_use:
Use this archetype when something is moving through a medium or network over time. The medium can be physical terrain, a river system, a communication network, a social graph, an organizational dependency map, a market, a supply chain, or a software architecture.

It is especially useful when acting at the leading edge is more effective than acting only at the source or waiting until the whole system is affected. The front may need to be slowed, blocked, amplified, redirected, prepared for, or stabilized after passage.

Do not use it for every spreading phenomenon. If the main problem is simply “make this beneficial thing spread,” use Diffusion Acceleration. If the main problem is “stop harmful spread” and front timing is not the central design issue, use Diffusion Containment. If the problem is a local spike rather than a moving front, use Intermittent Burst Absorption.

prose_structural_problem:
The structural problem is that many systems respond to spreading events with static categories. They focus on the origin, aggregate totals, broad geographic regions, or after-the-fact recovery. Those views can be useful, but they often miss the moving edge where intervention still has leverage.

A front creates timing asymmetry. Ahead of it, preparation and prevention are still possible. At the edge, support, containment, correction, isolation, or seeding can influence what happens next. Behind it, the problem shifts to stabilization, recovery, consolidation, and prevention of secondary waves.

When the front is not explicitly represented, teams repeatedly act late. Downstream sites are surprised. Barriers are placed after the wave has passed. Adoption support arrives after users have already failed. Corrections are sent after a rumor has already saturated a community. Cascading failures cross dependency boundaries before operators realize the edge has moved.

prose_intervention_logic:
The intervention begins by mapping the propagation medium. The map does not have to be perfect, but it must describe how the wave can move: through geography, adjacency, relationships, dependencies, channels, incentives, or trust pathways.

Next, identify the current and likely front. A front estimate should include location, direction, speed, intensity, and uncertainty. A mature design distinguishes confirmed front, probable front, exposed next zone, and watch zone.

Then choose the desired behavior of the front. A harmful wave may need attenuation, containment, or redirection. A beneficial wave may need amplification, support, and consolidation. An unavoidable wave may need preparation ahead of arrival. An ambiguous wave may need reversible low-regret action until the signal improves.

Finally, act in the response timing window. Place the intervention at or ahead of the front, monitor what changes, stabilize the area behind the front, and update the propagation map. The archetype works only when front intelligence and front action stay coupled.

prose_key_components:
### Propagation Map

The propagation map describes the medium through which the wave can travel. In a wildfire, this includes terrain, fuel, wind, and roads. In a rumor, it includes social ties, trusted channels, media habits, and community boundaries. In software, it includes service dependencies, rollout cohorts, traffic paths, and operational ownership.

### Wavefront Indicator

The wavefront indicator marks the current advancing edge. It may be a case map, incident dashboard, adoption metric, sensor reading, field report, social trace, or dependency graph. Its job is to reveal where the wave is newly arriving, not merely where totals are highest.

### Leading Edge Intervention

The leading edge intervention is the action placed at or just ahead of the front. It might be a firebreak, vaccination campaign, pre-bunking message, support team, dependency isolation, resource cache, or staged rollout. This component is central, but it is still a component: the archetype includes the map, timing, priority rule, feedback, and stabilization around it.

### Attenuation or Amplification Rule

This rule states what should happen to the front. Should it slow down, weaken, stop, reroute, speed up, strengthen, or pass with preparation? Without this rule, teams may perform front activity without agreeing on the intended propagation outcome.

### Response Timing Window

The response timing window defines how long the system has before the front arrives or passes. If the wave moves faster than decision-making and mobilization, front management needs automation, preauthorization, or prepositioning. If the window is longer, the system can use staged preparation and review.

### Front Priority Rule

A front often branches. The priority rule decides which segments deserve action first. It should include exposure, vulnerability, criticality, leverage, reversibility, equity, and downstream burden. The loudest or most visible front segment is not always the most important.

### Behind-Front Stabilization

Areas behind the front are not finished simply because the edge has moved on. They may need recovery, adoption consolidation, repair, backlog clearance, relapse monitoring, or prevention of re-seeding. Without stabilization, the system chases the front while leaving secondary waves behind.

### Front Feedback Loop

The feedback loop updates the map and plan as the front moves. It detects side channels, rebound waves, acceleration, attenuation, and false positives. It prevents the system from defending yesterday’s edge.

prose_common_mechanisms:
### Firebreaks

A firebreak is a containment mechanism. It creates a gap or decoupling point ahead of a harmful front. In this archetype, the firebreak is not the archetype itself; it is one way to implement containment-edge management.

### Vaccination Fronts

A vaccination front prepares susceptible nodes before a contagion or harmful influence reaches them. The same logic appears in cybersecurity hardening, public health, training, and risk communication.

### Staged Rollouts

A staged rollout advances a beneficial wave cohort by cohort. It implements the archetype when rollout order is tied to front indicators, readiness gates, support capacity, and behind-front stabilization.

### Rumor Edge Containment

Rumor edge containment targets communities where a narrative is entering. It may use trusted messengers, pre-bunking, local context, or rapid correction. It differs from generic fact-checking because timing and entry path matter.

### Flood Wave Preparation

Flood wave preparation uses upstream measurements and front forecasts to prepare downstream locations before a crest arrives. The mechanism illustrates the broader preparation-ahead-of-front variant.

### Cascading Failure Containment

In infrastructure and software, failure can propagate through dependencies. Cascading failure containment isolates, reinforces, reroutes, or degrades gracefully at the advancing failure edge.

### Adoption Wave Support Teams

Support teams can follow a beneficial adoption front with training, troubleshooting, social proof, and feedback capture. The mechanism keeps acceleration from outrunning absorption.

### Leading Edge Dashboards

A dashboard can show front location, speed, uncertainty, exposure, and intervention effects. It is a mechanism for observability, not a substitute for a front priority rule or intervention design.

prose_failure_modes:
A stale front map causes teams to act on yesterday’s edge. The mitigation is to maintain sentinel nodes, front dashboards, local feedback, and uncertainty-aware updates.

Late action occurs when decision latency exceeds front speed. The mitigation is preauthorization, response tiers, automation where appropriate, and prepositioned capacity.

Wrong-medium modeling happens when the team maps the wrong channel of spread. A geographic map may miss social contagion; an org chart may miss informal communication; a service diagram may miss hidden dependencies. The mitigation is to validate the map against actual transmission.

Barrier bypass occurs when the front routes around a firebreak, filter, quarantine, or dependency isolation point. The mitigation is layered monitoring and adaptive attenuation rather than one brittle barrier.

Beneficial front overrun happens when adoption spreads faster than support. The mitigation is readiness gating, support teams, stabilization after rollout, and feedback from recent adopters.

Harm displacement happens when containment or redirection protects visible areas while shifting risk to less visible areas. The mitigation is explicit downstream burden review.

Intervention amplification occurs when correction, enforcement, or attention at the front increases the very wave being managed. The mitigation is to test communication strategy, use trusted messengers, and monitor salience effects.

prose_neighbor_distinctions:
Wavefront Propagation Management is distinct from Diffusion Acceleration because acceleration is about increasing beneficial spread overall. Wavefront management is about timing and placement at the moving edge, whether the goal is acceleration, containment, redirection, or preparation.

It is distinct from Diffusion Containment because containment is only one front objective. This archetype also covers beneficial adoption waves and unavoidable waves that should be prepared for rather than stopped.

It is distinct from Teleconnection Mapping because mapping distant influence is not enough. This archetype uses the map to act at a moving front.

It is distinct from Flow Diversion or Rerouting because the object is not just flow volume or path; it is a propagating change with an advancing edge. Redirection can be a variant, but not the whole pattern.

It is distinct from Intermittent Burst Absorption because a burst can overload one place without spreading. Wavefront management applies when the key issue is movement through a medium.

It is distinct from Signal Amplification because increasing signal strength may help at the front, but the archetype is organized around propagation position and timing.

prose_cross_domain_examples:
In wildfire response, the burn edge is mapped from terrain, fuel, wind, and reports. Crews cut or defend lines ahead of the front, stage evacuation support, monitor spot fires behind the line, and update the plan as wind changes.

In public health, a vaccination or communication campaign can follow an outbreak edge. Instead of waiting for local case counts to explode, the system prepares exposed communities before the front arrives.

In software operations, a cascading failure can move through services and dependencies. Engineers isolate links, degrade noncritical paths, or reinforce vulnerable services at the advancing failure edge.

In organizational change, a new practice spreads through departments. The change team tracks the adoption front, sends support to the next cohort, and stabilizes the previous cohort before expanding further.

In information integrity, a rumor may enter one community after another. Trusted messengers can pre-bunk or contextualize the claim at the entry edge rather than only correcting the original post.

In supply chains, a port closure creates a disruption wave through warehouses and delivery commitments. The team maps downstream exposure, stages inventory, and communicates with sites likely to be hit next.

prose_non_examples:
A static backlog is not wavefront management. It may need prioritization, queuing, or burst absorption, but there is no moving front.

A generic broadcast announcement is not wavefront management. It becomes relevant only if communication is staged or targeted according to how a signal moves through a network.

A fixed local incident is not wavefront management unless it creates downstream spread. A broken machine, local staffing gap, or isolated outage usually needs local repair or incident response.

A recurring seasonal peak is not wavefront management unless the demand or disruption moves through connected regions or dependencies as a front. Otherwise, use cadence, cycle, or capacity archetypes.

A one-time software rollout with no cohort tracking, support wave, or propagation feedback is not a full use of this archetype. It may be ordinary implementation planning.

prose_tradeoffs:
Wavefront management trades certainty for timing. Waiting for perfect evidence often means the front has moved. Acting early means accepting uncertainty and designing reversible, proportionate responses.

It trades broad fairness for targeted effectiveness. Targeted action can be more useful, but it can also stigmatize or burden particular places, communities, teams, or nodes.

It trades connectivity for containment. Barriers and isolation slow harm, but they can also disrupt useful flow, coordination, trade, trust, or support.

It trades acceleration for absorption. Pushing a beneficial adoption front faster can spread value, but it can overload support channels and reduce quality.

It trades resource readiness for possible waste. Prepositioned resources reduce response latency, but they may sit unused if the front changes path.

prose_invariants_to_preserve:
The front must remain legible. The system should know, with honest uncertainty, where the wave is, where it is likely to go, and how intervention has changed it.

Front action must remain tied to a propagation outcome. Every action should be explainable as attenuation, amplification, redirection, preparation, stabilization, or observation.

The design must avoid hidden sacrifice zones. Containment and redirection can protect one region by shifting harm to another. The priority rule must make that burden visible.

Beneficial spread must not outrun support. A tool, practice, or policy that spreads faster than training, trust, infrastructure, or feedback can create backlash and abandonment.

Behind-front regions must not be abandoned. The front may have passed, but the region may need repair, consolidation, monitoring, and learning capture.

prose_target_outcomes:
The primary target outcome is earlier action where timing matters. The system no longer waits until every node is affected or until the original source is fully understood.

A second outcome is reduced downstream surprise. Areas ahead of the front receive warning, preparation, support, or protective changes before arrival.

A third outcome is controlled propagation. Harmful waves can be slowed or contained; beneficial waves can be paced and supported; unavoidable waves can be prepared for.

A fourth outcome is better resource placement. Scarce people, tools, supplies, compute capacity, communications, or authority go where the front is likely to matter next.

A fifth outcome is improved propagation intelligence. Each front passage teaches the system more about its medium, channels, vulnerabilities, and leverage points.

prose_parameter_dimensions:
The first tuning dimension is medium granularity. A coarse map is faster and easier to maintain, but it may miss side channels. A fine map is more precise, but it can be expensive and falsely authoritative.

The second dimension is front detection latency. A dashboard that updates after the front has passed is not enough. The useful signal must arrive early enough to change downstream outcomes.

The third dimension is front objective. Harmful fronts usually call for attenuation, containment, immunization, or redirection. Beneficial fronts usually call for amplification, support, pacing, and stabilization. Unavoidable fronts call for preparation.

The fourth dimension is response lead time. If resources, authority, or communication need days to mobilize, the wavefront indicator must look farther ahead. If response can be automated, the system can act closer to the edge.

The fifth dimension is coupling strength. Strongly coupled systems carry waves quickly and require rapid or upstream action. Loosely coupled systems allow more localized intervention.

The sixth dimension is acceptable leakage. Few barriers are perfect. A front-management design should specify how much leakage, bypass, false alarm, or delayed arrival is tolerable.

The seventh dimension is behind-front consolidation. Beneficial adoption needs retention and support after the front passes; harmful spread needs cleanup and monitoring so affected zones do not become new sources.

prose_variants_and_near_names:
Containment-Edge Management slows a harmful front. It includes firebreaks, isolation, targeted correction, quarantine, circuit isolation, and similar methods. It should remain a variant unless containment-edge design becomes distinct enough from both Wavefront Propagation Management and Diffusion Containment.

Adoption-Front Acceleration supports beneficial spread at the current adoption edge. It includes staged rollouts, local champions, enablement waves, and follow-the-front support teams. It differs from generic diffusion acceleration by emphasizing the moving cohort boundary.

Preparation Ahead of Front uses forecasts to prepare downstream nodes before arrival. Flood response, public health warnings, supply-chain staging, and rollout support often use this variant.

Front Redirection or Channeling changes the path of an unavoidable wave. It should be reviewed carefully because it can overlap with Flow Diversion or Rerouting and can create burden-shifting risks.

Near names include Leading Edge Response, Spread-Edge Intervention, Advancing Front Management, Propagation Front Management, and Wavefront Control. The roadmap treats Leading Edge Response as mechanism/component-level material, not as a separate archetype.

Wave Attenuation remains a second-wave candidate. It may become a full archetype if attenuation has distinct components and failure modes beyond ordinary front management or diffusion containment.

Containment-Edge Management

short_definition: Slows or stops a harmful front by intervening at the moving edge where spread can still be interrupted.

distinctive_feature: The intervention objective is attenuation, containment, or isolation of a harmful advancing front.

distinct_from_parent: The parent can accelerate, redirect, prepare for, or stabilize waves; this variant focuses on harmful spread and front-edge containment.

still_same_parent_because: It still depends on mapping a moving front and timing action at or ahead of that front.

source_primes: wave diffusion boundary damping

condition: A harmful disturbance is spreading through connected nodes or adjacent zones.

notes: Examples include fire, disease, misinformation, contamination, cascading outages, fraud, or social panic.

condition: The origin is not the only effective intervention point.

notes: The front has already moved, or the source cannot be quickly neutralized.

condition: Barriers, decoupling, immunization, correction, or isolation can reduce downstream spread.

notes: The front must have enough structure for edge action to matter.

Propagation Map

role: Shows likely harmful spread paths.

notes: Containment is only as good as the path model.

Frontier Boundary

role: Separates affected and at-risk zones.

notes: Often used to place isolation, warning, or firebreak measures.

Attenuation or Amplification Rule

role: Chooses slowing or blocking over acceleration.

notes: The rule should specify acceptable leakage and downstream risk.

Firebreak

slug: firebreak

mechanism_type: procedure

role: Breaks continuity ahead of the front.

Cascading Failure Containment

mechanism_type: protocol

role: Isolates dependencies at the failure edge.

Rumor Edge Containment

role: Corrects or pre-bunks where rumor spread is entering.

domain: Wildfire management

example: Crews cut and defend lines ahead of the advancing burn edge rather than only attacking the ignition point.

domain: Grid reliability

example: Operators isolate overloaded links at the edge of a cascading outage before the failure reaches the next region.

failure_mode: Leaky containment

cause: Unmapped side paths let the front route around the barrier.

mitigation: Update the propagation map and add sentinel observations beyond the barrier.

failure_mode: Containment harm displacement

cause: Blocking the front protects one zone by shifting impact into a less protected zone.

mitigation: Include equity, vulnerability, and downstream burden in the priority rule.

Adoption-Front Acceleration

short_definition: Supports a beneficial practice, tool, norm, or capability as it reaches each new cohort so adoption continues instead of stalling.

distinctive_feature: The desired outcome is propagation of a beneficial wave, not suppression of a harmful one.

distinct_from_parent: The parent is neutral about desired front direction; this variant tunes the front for acceleration and adoption support.

still_same_parent_because: It still uses wavefront position, front timing, and leading-edge intervention as its organizing logic.

source_primes: wave diffusion network amplification

condition: A beneficial change spreads cohort by cohort, region by region, or node by node.

notes: The wave may be a product rollout, training adoption, standard adoption, or practice diffusion.

condition: The front stalls when new adopters lack support or social proof.

notes: The leading edge needs help at the moment of exposure.

condition: Acceleration must not outrun absorption capacity.

notes: Front speed should be matched to training, support, infrastructure, and trust.

Wavefront Indicator

role: Shows where adoption is newly entering.

notes: Prevents support teams from concentrating only on early champions or mature adopters.

Leading Edge Intervention

role: Provides timely support to new adopters.

notes: Often includes enablement, coaching, social proof, or local troubleshooting.

Behind-Front Stabilization

role: Keeps recent adopters from reverting.

notes: Adoption waves often require consolidation after initial uptake.

Staged Rollout

mechanism_type: workflow

role: Moves the front through cohorts deliberately.

Adoption Wave Support Team

role: Helps each new cohort through the transition.

Campaign Wave

role: Creates pulses of attention that move through the target network.

domain: Software rollout

example: A support team follows each department as a new tool reaches it, then stabilizes adoption before moving to the next group.

domain: Public programs

example: A service expansion uses early adopter communities to seed nearby regions while sending staff to the next adoption edge.

failure_mode: Front outruns support

cause: Acceleration is pursued without enough training, capacity, or local legitimacy.

mitigation: Use readiness gates and behind-front stabilization before expanding.

failure_mode: Champion overfitting

cause: The design optimizes for early adopters and fails at the mainstream front.

mitigation: Monitor friction at each new cohort, not only aggregate adoption numbers.

Preparation Ahead of Front

short_definition: Uses front forecasts to prepare downstream areas before the wave reaches them.

distinctive_feature: The main move is anticipatory readiness ahead of the wave rather than direct blocking or acceleration.

distinct_from_parent: The parent includes intervention at the front; this variant emphasizes pre-arrival preparation based on front forecasts.

still_same_parent_because: It depends on estimating the moving front and acting in the timing window before arrival.

source_primes: wave foreseeing resilience

condition: The front is expected to arrive before ordinary mobilization can respond.

notes: Advance warning is valuable only if it changes readiness.

condition: The appropriate response is not necessarily to stop the wave.

notes: The goal may be evacuation, capacity staging, communication, staffing, or protective configuration.

condition: Downstream exposure varies by place, cohort, subsystem, or dependency.

notes: Preparation should be targeted rather than uniformly broadcast.

Response Timing Window

role: Defines how far ahead preparation must occur.

notes: Lead time determines whether prepositioning is practical.

Prepositioned Response Capacity

role: Places resources before arrival.

notes: The resource may be material, informational, computational, staffing, or authority.

Front Priority Rule

role: Selects which downstream areas prepare first.

notes: Prioritization matters under resource constraints.

Flood Wave Preparation

role: Prepares downstream locations before a crest arrives.

Prepositioned Resource Cache

mechanism_type: artifact

role: Stages resources at likely arrival points.

Leading Edge Dashboard

role: Shows forecasted arrival timing and exposure.

domain: Flood response

example: Communities downstream of a rising river stage sandbags and evacuation routes before the crest reaches them.

domain: Customer operations

example: A support team prepares help articles and staffing for regions about to receive a product release.

failure_mode: False-front mobilization fatigue

cause: Repeated forecasts trigger preparations for waves that never arrive.

mitigation: Represent uncertainty and use tiered preparation levels.

failure_mode: Preparedness gap

cause: Resource staging occurs in easy-to-reach areas rather than exposed areas.

mitigation: Use vulnerability and arrival-time criteria in front priorities.

Front Redirection or Channeling

short_definition: Changes the path of a propagating wave so it travels through safer, more absorbent, or more useful channels.

distinctive_feature: The aim is path change rather than attenuation or acceleration alone.

distinct_from_parent: The parent manages any front; this variant specifically redirects the path or channel of propagation.

still_same_parent_because: It still uses front location, front timing, and propagation-medium design.

source_primes: wave flow boundary

condition: Stopping the wave is impossible or undesirable.

notes: The better move is to steer it toward a safer path or prepared medium.

condition: The medium has alternate paths with different costs and vulnerabilities.

notes: Channeling requires knowledge of topology and downstream consequences.

condition: Burden shifting can be ethically evaluated and governed.

notes: Redirection must not create invisible sacrifice zones.

Propagation Map

role: Identifies alternate channels and side effects.

notes: Topology matters more than in simple containment.

Front Priority Rule

role: Chooses which redirection path is acceptable.

notes: Should include exposure, fairness, and reversibility.

Traffic Rerouting at Front

role: Moves congestion or disruption around vulnerable points.

Safe Channel Design

mechanism_type: method

role: Creates a lower-risk path for the wave to follow.

domain: Infrastructure

example: Flood channels redirect a crest away from a populated area toward a designated basin.

failure_mode: Unexamined burden transfer

cause: The redirected front harms a less visible downstream group.

mitigation: Use explicit ethical review and downstream monitoring.

near_names:
Leading Edge Response
Spread-Edge Intervention
Advancing Front Management
Propagation Front Management
Wavefront Control


Weak Signal Triage

type: archetype

source: generated

batch: First Iteration/Batch 34

Weak Signal Assessment

notes: Usable when the assessment includes response tiering rather than only descriptive interpretation.

Signal Triage

notes: Broader plain-language name; keep bounded to ambiguous early signals rather than ordinary alert routing.

Emerging Issue Triage

notes: Useful subtype when the signal may become a policy, stakeholder, or legitimacy issue.

Weak Signal Scoring

notes: Treat as a mechanism unless scoring is embedded in triage logic and linked to calibrated responses.

slug: uncertainty

slug: screening

slug: feedback

slug: observability

slug: satisficing

components:
Weak Signal Record
Ambiguity Context
Plausibility Assessment
Impact Estimate
Evidence Trajectory
Response Cost Assessment
Response Tier
Follow-Up Probe
Escalation Boundary
Review Cadence
Counter-Signal Check
Source Diversity Map
Signal Owner
Retirement Rule
Stakeholder Sensitivity Note

mechanisms:
Weak Signal Log
Emerging Issue Triage Board
Signal Scoring Rubric
Watchlist Review
Probe Experiment
Early Warning Forum
Uncertainty Dashboard
Escalation Playbook

essence_one_line: Evaluate ambiguous early signals without ignoring them or overreacting to them.

compression_statement: When early signals are faint, ambiguous, or uncertain, triage them by plausibility, potential impact, evidence trajectory, and response cost.

core_intuition: Weak signals are valuable precisely because they appear before certainty. Triage keeps the system in a middle state: attentive enough to learn and prepare, but disciplined enough not to confuse possibility with proof.

canonical_formula: weak signal record + ambiguity context + plausibility assessment + impact estimate + evidence trajectory + response cost + response tier + review cadence -> calibrated early attention without panic or neglect

structural_problem: Subtle indicators of change are either dismissed as noise or amplified prematurely into certainty.

condition: A signal appears before the underlying change is confirmed.

diagnostic_question: Is there a faint observation that could matter if real, but is still too ambiguous to treat as established fact?

condition: The cost of dismissing the signal and the cost of overreacting are both meaningful.

diagnostic_question: Would ignoring the signal create avoidable surprise, while acting too strongly would waste resources or create harm?

condition: Evidence is sparse, indirect, contested, or source-dependent.

diagnostic_question: Does interpretation depend on provenance, context, mechanism plausibility, or whether similar signals accumulate?

condition: Response lead time matters.

diagnostic_question: Would waiting for certainty eliminate the opportunity to prepare, probe, preserve options, or shape the outcome?

condition: Existing routines polarize signals into noise or crisis.

diagnostic_question: Does the organization lack intermediate categories between ignore and full escalation?

symptom: Premature dismissal

description: Faint evidence is discarded because it is not yet statistically strong, mainstream, officially confirmed, or aligned with current assumptions.

symptom: Panic amplification

description: Anecdotes, rumors, or small anomalies are treated as proof, producing overreaction, reputational harm, or resource distortion.

symptom: Watchlist graveyard

description: Signals are collected but never reviewed, probed, retired, or connected to decision pathways.

symptom: Single-score distortion

description: Plausibility, impact, urgency, source quality, response cost, and confidence are collapsed into one number that hides the relevant tradeoff.

symptom: Narrative capture

description: Signals are interpreted mainly through the preferred story of a leader, department, ideology, vendor, or expert group.

symptom: Late escalation

description: Decision-makers only act after weak evidence becomes obvious, by which time options are narrower and response costs are higher.

root_tension: Early evidence is most actionable when it is least certain. The system must preserve attention, learning, and optionality without turning weak evidence into false certainty.

condition: The signal is already confirmed and operational response rules are clear.

why_not: Incident management, crisis response, or standard operations may be primary once ambiguity has resolved.

condition: The problem is building a continuous scanning function.

why_not: Horizon Scanning System is primary when the task is to design the ongoing intake loop; weak signal triage is primary when ambiguous signals must be evaluated and assigned response tiers.

condition: The task is to amplify a validated signal so others notice it.

why_not: Signal amplification is a neighboring pattern; triage comes before amplification and may decide not to amplify.

condition: The evidence can be modeled directly with stable data and known variables.

why_not: Forecasting, statistical monitoring, or sensitivity analysis may be more appropriate when the uncertainty is quantitative and well specified.

condition: The output is only a log, dashboard, or meeting.

why_not: Those are mechanisms; the archetype requires an explicit assessment-to-response logic.

structural_intervention: Capture weak signals, assess plausibility and impact, track evidence accumulation, and assign watch, probe, prepare, or ignore actions.

name: Capture the signal without over-framing it

logic: Record what was observed, where it came from, what it might indicate, and why the interpretation remains uncertain.

Separate plausibility from impact

logic: Evaluate whether the signal could be real separately from what would follow if it were real; low confidence and high consequence require different treatment than low confidence and low consequence.

name: Characterize ambiguity and alternative explanations

logic: Ask whether the signal could be noise, reporting artifact, source bias, normal fluctuation, strategic deception, seasonal pattern, or early evidence of genuine change.

Track evidence trajectory

logic: Define what confirming, disconfirming, or diversifying evidence would look like and review whether the signal strengthens, fades, shifts, or fragments over time.

Compare response cost and reversibility

logic: Match evidence strength to possible responses by considering cost, reversibility, lead time, and opportunity value.

Assign a response tier

logic: Place the signal in a tier such as ignore, archive, watch, probe, prepare, escalate, or act, with ownership and next review conditions.

Update or retire the signal

logic: Signals should move as evidence changes; stale signals are retired, persistent ambiguity is bounded, and escalating evidence opens stronger decision pathways.

rule: Never use plausibility alone as the triage result.

reason: A low-plausibility high-impact signal may justify cheap preparedness, while a high-plausibility low-impact signal may only need monitoring.

rule: Require an explicit response tier for each retained signal.

reason: Without a tier, weak-signal work becomes collection theater.

rule: Prefer reversible probes when evidence is weak but response lead time matters.

reason: Probes create information without forcing premature strategic commitment.

rule: Escalate on a combination of impact, evidence trajectory, proximity, source diversity, and response lead time.

reason: Single-factor escalation either misses slow-building risk or overreacts to salient anecdotes.

rule: Retire signals deliberately.

reason: A watched signal that never changes can consume attention indefinitely unless retirement criteria exist.

rule: Seek counter-signals before major escalation.

reason: Ambiguous evidence is vulnerable to confirmation bias, groupthink, and narrative capture.

invariant: Ambiguity remains visible

why_it_matters: The system must not translate weak evidence into false certainty just to make decision-making feel easier.

invariant: Response remains proportionate

why_it_matters: The archetype fails if every signal becomes either ignored or treated as a crisis.

invariant: Evidence can accumulate

why_it_matters: The record and review cadence must allow faint signals to mature, fade, or change meaning over time.

invariant: Impact is considered separately from confidence

why_it_matters: This preserves attention to high-consequence possibilities without pretending they are probable.

invariant: Triage changes action

why_it_matters: If no watch, probe, preparation, escalation, or retirement action follows, the process is only documentation.

outcome: Earlier calibrated awareness

description: The system notices and evaluates emerging change before it becomes obvious or urgent.

outcome: Lower false-positive harm

description: Weak signals are not allowed to become automatic panic, blame, or irreversible commitment.

outcome: Lower false-negative neglect

description: Important early cues are less likely to disappear because they were initially faint or inconvenient.

outcome: Better option preservation

description: Watch, probe, and prepare tiers preserve time and optionality before commitment is necessary.

outcome: Improved learning under uncertainty

description: Evidence trajectories and review cadence turn ambiguity into structured inquiry rather than rumor or denial.

prose_essence:
Weak Signal Triage is the discipline of taking faint early evidence seriously without pretending it is already proof. It creates a middle zone between dismissal and alarm: a signal can be watched, probed, prepared for, escalated, retired, or archived based on plausibility, possible impact, evidence movement, and response cost.

The archetype matters because early evidence is often most useful before it is clean. By the time a signal is certain, many options may already be gone. The intervention preserves learning time while protecting the system from rumor-driven overreaction.

prose_when_to_use:
Use this archetype when a system has noticed something that could matter but cannot yet be interpreted with confidence. It is especially useful when the cost of waiting for certainty is high, but the cost of acting too strongly is also high. The pattern fits scattered public-health indicators, cybersecurity anomalies, weak market signals, social or regulatory issue emergence, supplier stress cues, and early legitimacy concerns.

Do not use it merely because information is incomplete. The signal needs enough structure to be recorded, interpreted, reviewed, and connected to a possible response. Pure speculation belongs outside the process unless a bounded probe can turn it into evidence.

prose_structural_problem:
The structural problem is polarization of attention. Ambiguous early signals are usually pushed into one of two bins: noise or crisis. In the first case, faint evidence disappears because it is not yet strong enough. In the second case, weak evidence is amplified into certainty because someone wants action, publicity, or justification.

Weak Signal Triage introduces intermediate states. A signal can be retained without being believed, probed without being acted on, prepared for without being announced as likely, and retired without shame when evidence fades.

prose_intervention_logic:
The intervention starts by preserving the original signal and its ambiguity. It then separates plausibility from impact. A low-plausibility signal with severe possible consequences is not the same as a low-plausibility signal with trivial consequences. The process then looks at evidence trajectory: is the signal strengthening, diversifying, repeating from independent sources, fading, or contradicted by better data?

The final move is assignment to a response tier. Typical tiers include ignore, archive, watch, probe, prepare, escalate, and act. The key is that every retained signal has a next review condition and a response owner. Otherwise the process becomes a symbolic list rather than an intervention.

prose_key_components:
- **Weak Signal Record (`weak_signal_record`): Stores an ambiguous observation without forcing it to become either noise or confirmed evidence too early. A good record preserves source, date, context, signal description, uncertainty, possible interpretation, affected assumptions, and follow-up needs.
- **Ambiguity Context (`ambiguity_context`): Explains why the signal is hard to interpret and what kinds of error are plausible. Weak signals are not merely small signals; they are often sparse, indirect, early, unfamiliar, politically inconvenient, or embedded in noisy environments.
- **Plausibility Assessment (`plausibility_assessment`): Estimates whether the signal could indicate a real emerging change rather than coincidence, rumor, artifact, or normal fluctuation. Plausibility should be expressed with uncertainty and rationale, not as a false binary; it may use source diversity, mechanism plausibility, precedent, counterevidence, and expert challenge.
- **Impact Estimate (`impact_estimate`): Surfaces the consequence of being wrong in either direction by asking what changes if the signal is real. Impact can include harm, opportunity, legitimacy, cost, safety, strategic position, operational capacity, or irreversibility; high impact does not automatically imply high certainty.
- **Evidence Trajectory (`evidence_trajectory`): Tracks whether evidence is accumulating, fading, diversifying, accelerating, or contradicting the initial interpretation. The trajectory prevents one-time anecdotes from becoming permanent beliefs while also preventing faint early evidence from being lost before it can mature.
- **Response Cost Assessment (`response_cost_assessment`): Compares the cost, reversibility, and opportunity cost of possible responses before committing resources. Cheap, reversible probes may be justified with much weaker evidence than costly, irreversible commitments; this component helps calibrate action to evidence strength.
- **Response Tier (`response_tier`): Assigns the signal to an action category such as ignore, archive, watch, probe, prepare, escalate, or act. The tier is the triage output. It should carry review conditions, owner, and next check date so signals do not drift into unattended lists.
- **Follow-Up Probe (`follow_up_probe`): Creates a small, bounded information-gathering or learning action when evidence is too weak for commitment but too important to ignore. Probes can include expert interviews, data pulls, user research, limited experiments, tabletop discussion, field observation, or source triangulation.
- **Escalation Boundary (`escalation_boundary`): Defines when a signal must move from local watchfulness into broader review, contingency preparation, scenario revision, or decision attention. Boundaries may combine plausibility, impact, acceleration, proximity, source diversity, reversibility, stakeholder sensitivity, and response lead time.
- **Review Cadence (`review_cadence`): Prevents weak signals from being forgotten, over-discussed, or escalated only by whoever is most anxious. Cadence sets when watchlists, probes, evidence trajectories, and escalation thresholds are revisited; cadence should become faster when proximity or potential impact increases.

These components work as a chain. The record prevents memory distortion; the ambiguity context prevents false certainty; the plausibility and impact assessments prevent one-dimensional scoring; the evidence trajectory keeps the signal alive or retires it; the response tier converts interpretation into action.

prose_common_mechanisms:
- **Weak Signal Log (`weak_signal_log`): A artifact that records weak signals in a durable form so later evidence can be compared against the original observation rather than reconstructed from memory.
- **Emerging Issue Triage Board (`emerging_issue_triage_board`): A workflow that reviews new weak signals, assigns response tiers, identifies owners, and schedules follow-up without requiring every signal to become an emergency.
- **Signal Scoring Rubric (`signal_scoring_rubric`): A test_or_assessment that separates plausibility, impact, evidence direction, response cost, and response lead time so ambiguous signals are not flattened into one misleading score.
- **Watchlist Review (`watchlist_review`): A ritual that revisits signals assigned to watch or prepare status and decides whether to maintain, probe, escalate, retire, or archive them.
- **Probe Experiment (`probe_experiment`): A method that uses a low-cost reversible action to learn whether an ambiguous signal is real, relevant, accelerating, or strategically actionable.
- **Early Warning Forum (`early_warning_forum`): A ritual that creates a regular cross-functional venue where weak signals can be interpreted from multiple perspectives without premature consensus.
- **Uncertainty Dashboard (`uncertainty_dashboard`): A metric_or_dashboard that displays watched signals, evidence movement, confidence bands, decision triggers, and response owners so uncertainty remains visible.
- **Escalation Playbook (`escalation_playbook`): A document that specifies who is notified, what decisions are opened, and what actions become available when a signal crosses an escalation boundary.

These mechanisms implement the archetype, but none of them is the archetype by itself. A weak signal log without response tiers is only a repository. A dashboard without evidence movement and escalation logic is only a display. A scoring rubric that collapses confidence and consequence into one number can actively undermine the archetype.

prose_failure_modes:
Common failure modes include signal theater, premature certainty, false-negative dismissal, false-positive amplification, watchlist accumulation, source homophily, anxiety-driven escalation, and suppression of inconvenient signals. Most of these failures happen when one part of the process dominates the others: collection without action, scoring without interpretation, urgency without evidence, or skepticism without follow-up.

The strongest mitigation is to require every retained signal to have a response tier, owner, review condition, and rationale. The second mitigation is to deliberately seek alternative explanations before major escalation.

prose_neighbor_distinctions:
Weak Signal Triage is closest to Horizon Scanning System, but the two patterns answer different questions. Horizon scanning asks how a system keeps looking outward and forward. Weak Signal Triage asks what to do with a faint signal once it has been noticed.

It also differs from Remote Signal Early Warning. Early warning tends to focus on precursor detection and alert thresholds. Weak Signal Triage lives earlier in the interpretive chain, when the signal is not yet reliable enough to become a warning. It differs from Signal Amplification because triage may decide not to amplify a signal. It differs from Scenario Portfolio Planning because scenarios organize plausible futures; triaged signals may update scenarios but are not scenarios themselves.

prose_cross_domain_examples:
In public health, a small cluster of unusual symptoms may trigger targeted data pulls rather than public alarm. In cybersecurity, scattered exploit chatter may justify detection-rule testing before confirmed attack. In product strategy, faint user behavior shifts may justify interviews or small experiments before roadmap change. In supply chains, weak vendor stress cues may justify alternate-supplier probes. In governance, early complaints and legal developments may justify policy review before a public controversy hardens.

Across all examples, the same logic transfers: preserve the signal, keep uncertainty visible, assess impact separately from confidence, and assign a proportionate next action.

prose_non_examples: A confirmed emergency with known response rules is not Weak Signal Triage; it is incident response. A trend report with no assessment or action tier is not Weak Signal Triage; it is an artifact. A rumor used to justify a predetermined decision is not Weak Signal Triage; it is narrative capture. A dashboard that marks everything urgent is not Weak Signal Triage; it is alarm amplification.

prose_tradeoffs:
Weak Signal Triage trades certainty for lead time. It also trades attention bandwidth for optionality. A broad process will catch more possible signals but may exhaust reviewers. A narrow process will be cleaner but may reproduce blind spots. Formal scoring improves consistency, but too much scoring can hide judgment calls behind pseudo-precision.

Transparency is another tradeoff. Sharing weak signals can improve collective sensemaking, but publicizing them can also amplify rumor, stigmatize groups, reveal vulnerabilities, or create pressure for premature action.

prose_invariants_to_preserve:
The first invariant is visible uncertainty. Weak signals must remain labeled as weak until evidence changes. The second invariant is proportionality: the response must fit the combination of plausibility, impact, response cost, and reversibility. The third invariant is movement: signals cannot sit forever without being strengthened, probed, escalated, retired, or archived.

A fourth invariant is separation of confidence and consequence. Low confidence does not always mean ignore, and high consequence does not always mean act. The archetype exists to reason inside that tension.

prose_target_outcomes: The target outcomes are earlier calibrated awareness, fewer missed early warnings, fewer rumor-driven overreactions, better option preservation, and more disciplined learning under uncertainty. In practice, a working triage process should make the system less brittle: it can notice faint evidence, test it, and prepare proportionately without reorganizing around every anomaly.

prose_parameter_dimensions:
The most important tuning dimension is sensitivity: how faint a signal must be before it enters the process. A very sensitive process catches more early change but creates noise load. A restrictive process protects attention but misses weak evidence until late.

Other tuning dimensions include review cadence, source diversity, response-tier granularity, escalation thresholds, probe budget, retirement rules, and tolerance for high-impact low-confidence possibilities. High-stakes settings usually need explicit safeguards, documentation, and privacy limits; lighter strategy settings may use a simpler board or review ritual.

prose_variants_and_near_names:
Important variants include **Emerging Issue Triage**, which handles weak signals that may become policy or legitimacy issues; **Probe-Based Signal Triage**, which uses bounded learning actions to test uncertain signals; **High-Impact Low-Confidence Watch**, which preserves attention for severe but uncertain possibilities; and **Counter-Signal Review**, which actively seeks alternative explanations before escalation.

Near names include weak signal assessment, signal triage, weak signal scoring, issue radar, uncertainty watchlist, and early signal review. Most of these should point back to the parent unless they include a distinct structural emphasis.

Emerging Issue Triage

variant_type: subtype

short_definition: A policy, strategy, or governance-oriented subtype that evaluates weak signals as possible emerging issues requiring watch, probe, preparation, or agenda placement.

distinctive_feature: Focuses on whether a faint signal deserves issue status and agenda attention.

distinct_from_parent: The parent can triage any ambiguous early signal; this variant specializes in agenda-setting and governance implications.

still_same_parent_because: It still uses the same core logic of plausibility, impact, evidence trajectory, and calibrated response tiering.

use_when: Signals may become public issues, regulatory questions, social expectations, reputational risks, or policy windows.

use_when: The main question is whether to place the signal on an issue agenda rather than whether to act operationally immediately.

use_when: Several stakeholders interpret the same signal differently and premature framing could create avoidable conflict.

Weak Signal Record

role: documents the issue cue

Impact Estimate

role: estimates governance or stakeholder consequence

Response Tier

role: assigns agenda treatment

Emerging Issue Triage Board

role: reviews issue status

example_domains:
public policy
corporate affairs
higher education governance

domain: public policy

example: A regulator watches scattered complaints about a new platform practice before deciding whether to open formal inquiry.

domain: corporate affairs

example: A firm tracks early stakeholder objections to an AI feature before assigning a policy response.

failure_modes: Every issue becomes urgent, exhausting attention.

failure_modes: Signals are framed through institutional self-protection rather than open inquiry.

Probe-Based Signal Triage

short_definition: A subtype that resolves ambiguous signals primarily by assigning bounded probes rather than by waiting for passive evidence accumulation.

distinctive_feature: The triage output is often a designed probe rather than a watchlist status.

distinct_from_parent: The parent includes watch, ignore, prepare, and escalate tiers; this variant emphasizes active learning through probes.

still_same_parent_because: The probe is selected after the same weak-signal assessment, not as a separate experimentation archetype.

use_when: The signal is too uncertain for full commitment but consequential enough to justify learning investment.

use_when: A small reversible test can improve the evidence trajectory.

use_when: Waiting passively would lose response lead time or strategic option value.

Follow-Up Probe

role: tests the signal

Response Cost Assessment

role: keeps the probe proportionate

Evidence Trajectory

role: updates after the probe

Probe Experiment

role: generates evidence

Signal Scoring Rubric

role: selects probe priority

example_domains:
product discovery
public health preparedness
supply-chain strategy

domain: product discovery

example: A team runs five interviews and a mock landing page after seeing faint user behavior shifts.

domain: supply-chain strategy

example: A procurement group tests an alternate supplier after early reports of regional capacity constraints.

failure_modes: Probes are designed to confirm the preferred story rather than learn.

failure_modes: The organization runs too many tiny probes without committing to decisions when evidence becomes strong.

High-Impact Low-Confidence Watch

short_definition: A subtype for signals that remain low-confidence but would be costly to miss if real, requiring watchfulness or cheap preparedness without panic.

distinctive_feature: Separates confidence from consequence so low confidence does not automatically imply dismissal.

distinct_from_parent: The parent covers the full triage space; this variant specializes in the quadrant where uncertainty is high and potential consequence is large.

still_same_parent_because: It still produces calibrated tiers such as watch, probe, and prepare instead of pretending to predict a shock.

use_when: The evidence is weak, but the plausible consequence is severe or irreversible.

use_when: A low-cost safeguard, option, or monitoring action is available.

use_when: Escalating to full response would be disproportionate or politically destabilizing.

Impact Estimate

role: keeps consequence visible

Response Cost Assessment

role: prevents panic response

Escalation Boundary

role: defines when preparedness must increase

Watchlist Review

role: maintains vigilance

Escalation Playbook

role: predefines response movement

example_domains:
cybersecurity
biosecurity
financial risk
critical infrastructure

domain: cybersecurity

example: A security team watches scattered indicators of a novel exploit class while preparing detection rules.

domain: critical infrastructure

example: An operator monitors faint sensor anomalies that could imply rare cascading failure while checking backup readiness.

failure_modes: Low-confidence warnings become a rationale for permanent alarm.

failure_modes: Decision-makers dismiss the signal because probability is low while ignoring response lead time.

Counter-Signal Review

short_definition: A debiasing subtype that deliberately seeks disconfirming or alternative explanations before escalating a weak signal.

distinctive_feature: Builds counterevidence search into the triage process before response tiers harden.

distinct_from_parent: The parent requires calibrated assessment; this variant foregrounds bias control and alternative explanations.

still_same_parent_because: It remains an assessment variant inside weak-signal triage rather than a general debiasing archetype.

use_when: The signal is emotionally salient, politically useful, or aligned with a favored narrative.

use_when: False positives could cause costly overreaction, scapegoating, or strategic drift.

use_when: The evidence base is narrow, anecdotal, or source-homogeneous.

Ambiguity Context

role: exposes alternative readings

Plausibility Assessment

role: tests competing interpretations

Evidence Trajectory

role: tracks confirming and disconfirming evidence

Signal Scoring Rubric

role: forces separate evidence categories

Early Warning Forum

role: brings multiple interpretations into view

example_domains:
intelligence analysis
executive risk review
public health surveillance

domain: public health surveillance

example: Analysts test whether a small case rise is reporting artifact, seasonal noise, or a genuine emerging cluster.

domain: executive risk review

example: A leadership team asks for rival explanations before treating a social-media spike as a market shift.

failure_modes: Counter-signal review becomes a veto that neutralizes all inconvenient warnings.

failure_modes: Debiasing is applied only to signals that leaders dislike.

near_names:
Weak Signal Assessment
Signal Triage
Emerging Issue Triage
Weak Signal Scoring
Issue Radar
Uncertainty Watchlist
Early Signal Review


name: Welfare Analysis and Distributional Effects Assessment

batch: Second Iteration/Batch 01

aliases:
Distributional Welfare Assessment
Distributional Effects Assessment
Welfare Impact and Incidence Review
Equity-Sensitive Welfare Analysis
Winners-and-Losers Assessment
Pareto-plus-Distribution Review
Distributional Impact Analysis

proposed_primes: fairness welfare distributional_effects redistribution incidence

slug: equity

slug: externality

slug: accountability

slug: proportionality

slug: legitimacy

slug: transparency

components: Decision Trace and Accountability Record

mechanisms:
Distributional Incidence Matrix
Counterfactual Welfare Comparison
Subgroup Disaggregation Audit
Value-Weight Sensitivity Analysis
Compensation Adequacy Review
Externality and Spillover Inventory
Equity Guardrail Test
Public Reason Disclosure Protocol

essence_one_line: Do not accept an aggregate improvement claim until the gains, losses, burdens, remedies, and fairness constraints are visible by affected group.

compression_statement: This archetype applies when a proposed intervention appears efficient, Pareto-improving, cost-beneficial, or welfare-positive in aggregate, but its consequences are unevenly distributed. The intervention is to map affected parties, establish a baseline, allocate benefits and burdens, make welfare metrics and value weights explicit, test equity guardrails, examine compensation or mitigation, and record the final judgment with uncertainty and accountability.

core_intuition: Aggregate surplus can rise while some groups become worse off, carry transition burdens, or lose access. A decision is more trustworthy when it shows not only that value is created, but where that value and burden go, which losses are acceptable, and what remedies make the claim legitimate.

canonical_formula: welfare_claim := aggregate_delta + incidence_map + equity_guardrails + mitigation_path + sensitivity_trace

structural_problem: A decision is justified by aggregate improvement, efficiency, or Pareto-style reasoning while the distribution of benefits, burdens, risks, and remedies across affected parties remains hidden, under-specified, or normatively unexamined.

trigger_conditions: A policy, design, restructuring, pricing change, or allocation is defended as efficient, cost-beneficial, Pareto-improving, or welfare-positive.

trigger_conditions: The affected population is heterogeneous and different groups experience different benefits, costs, risks, or transition burdens.

trigger_conditions: The formal decision boundary excludes parties who may absorb spillovers, unpaid work, degraded access, or delayed costs.

trigger_conditions: The decision involves public goods, externalities, infrastructure, essential services, employment, access, or rights-sensitive outcomes.

trigger_conditions: A compensation, mitigation, or transfer claim is used to justify a change that would otherwise create concentrated losses.

symptoms: The proposal reports total surplus, net value, average satisfaction, or overall efficiency without subgroup incidence.

symptoms: Stakeholders disagree because each side is implicitly using a different baseline, time horizon, or fairness standard.

symptoms: The same decision appears efficient in aggregate but unfair, illegitimate, or destabilizing to affected groups.

symptoms: Costs appear in one budget while burdens are shifted to users, workers, communities, future maintainers, or third parties.

symptoms: Mitigation promises are vague, unfunded, inaccessible, or not assigned to an accountable owner.

root_tension: Efficiency reasoning compresses multiple effects into an aggregate claim, while legitimacy and fairness require knowing how the change is distributed across different people, roles, places, and time horizons.

anti_signatures: The decision has no meaningful heterogeneity of impact and no plausible indirect effects beyond the decision owner.

anti_signatures: The goal is simply to choose among non-dominated technical tradeoffs, with no welfare or distributional stakes; Pareto frontier selection is a closer neighbor.

anti_signatures: The main issue is whether the decision process is fair rather than whether the outcome distribution is acceptable; use procedural fairness design first.

anti_signatures: The intervention has already specified binding rights floors or duties that prohibit the tradeoff, making welfare balancing inappropriate.

anti_signatures: Only a narrow implementation artifact is needed, such as a table or dashboard, without a distributional judgment or decision consequence.

structural_intervention: Convert an aggregate improvement claim into a distributionally accountable welfare assessment by explicitly mapping incidence, value criteria, equity guardrails, uncertainty, and mitigation responsibilities before the decision is accepted.

action_logic: Name the proposal, alternatives, and no-action path so the assessment has a clear comparison set.

action_logic: Define the baseline, counterfactual, time horizon, and affected-party map before estimating impacts.

action_logic: Estimate benefits, burdens, risks, access changes, externalities, and transition costs for each affected group or role.

action_logic: State the welfare metric, value weights, discount assumptions, and non-negotiable guardrails rather than hiding them inside an aggregate score.

action_logic: Check whether aggregate gains depend on concentrated, uncompensated, or rights-sensitive losses.

action_logic: Design or evaluate compensation, mitigation, phasing, support, or redesign options when losses are material.

action_logic: Run sensitivity analysis over uncertain estimates and contested value weights.

action_logic: Record the welfare judgment, unresolved disputes, monitoring requirements, and accountable owners for follow-through.

decision_rules: Use the archetype whenever an aggregate efficiency or welfare claim affects multiple groups with different baselines, exposures, or bargaining power.

decision_rules: Do not treat a decision as Pareto-improving unless no affected party is made worse off or credible compensation makes that claim operational rather than hypothetical.

decision_rules: Escalate to equity adjustment or redesign when a protected group, minimum floor, or proportionality guardrail is violated.

decision_rules: Escalate to externality internalization when welfare gains depend on shifting costs outside the decision boundary.

decision_rules: Require human review when value weights, compensation credibility, or protected-group impacts are contested.

target_invariants: The affected-party map remains visible and revisable throughout the decision.

target_invariants: Aggregate gains cannot erase identifiable losses without explicit justification and mitigation review.

target_invariants: The baseline and time horizon are stable enough that conclusions are not changed by opportunistic framing.

target_invariants: Value weights, equity constraints, and exclusions are documented rather than implied.

target_invariants: Mitigation commitments are assigned to accountable owners and monitored after implementation.

expected_outcomes: More credible welfare claims because gains and losses are disaggregated and traceable.

expected_outcomes: Reduced risk that efficiency improvements create hidden inequity, legitimacy loss, or backlash.

expected_outcomes: Clearer distinction between true Pareto improvements, compensated improvements, and tradeoffs requiring value judgment.

expected_outcomes: Better mitigation design because losses are identified early and tied to accountable remedies.

expected_outcomes: Improved learning after implementation because predicted distributional effects can be compared with observed outcomes.

prose_essence:
Welfare Analysis and Distributional Effects Assessment is the discipline of refusing to let aggregate improvement claims stand alone. It asks whether an intervention that appears efficient, Pareto-improving, or welfare-positive also remains acceptable once benefits, burdens, risks, access changes, and remedies are assigned to the people and groups who actually experience them.

The archetype is not a synonym for fairness in general. It is a structured assessment pattern: define the baseline, map affected parties, estimate who gains and loses, state the welfare metric and value weights, test equity guardrails, review compensation or mitigation, and preserve an accountable decision trace.

prose_when_to_use:
Use this archetype when a decision is justified by total surplus, net benefit, efficiency, average improvement, or Pareto-style reasoning, but the affected population is heterogeneous. It is especially useful when costs can be shifted to users, workers, communities, future maintainers, or third parties outside the decision boundary.

It is also useful when a proposal claims that losers can be compensated. The archetype asks whether compensation is credible, funded, accessible, timely, and assigned to an accountable owner rather than merely possible in theory.

prose_structural_problem:
The structural problem is aggregate masking. A decision can create real total gains while concentrating losses in places that are politically weak, operationally hidden, or normatively important. Average outcomes can improve while access, dignity, exposure, transition cost, or opportunity worsen for a subgroup.

The problem becomes sharper when baseline choice, time horizon, and value weights are left implicit. A proposal can seem welfare-positive under one comparison and unfair under another. This archetype makes those choices visible before the decision is accepted.

prose_intervention_logic:
The intervention logic is to convert an aggregate claim into a distributionally accountable assessment. First define the proposal, alternatives, baseline, and no-action path. Then map affected parties and assign expected effects to each party or subgroup. Next state the welfare metric, value weights, and equity guardrails. Finally, test whether losses are mitigated, compensated, justified, or unacceptable.

The output is not merely a score. It is a reasoned welfare judgment: what is gained, what is lost, who carries which burden, what assumptions matter, which guardrails are binding, and who remains accountable after implementation.

prose_key_components:
### Affected Party Map

The affected party map names everyone whose welfare can change: direct users, indirect third parties, workers, administrators, maintainers, future cohorts, communities, and groups that may lack representation. This prevents the analysis from assuming that the formal decision owner is the only relevant party.

### Baseline and Counterfactual Frame

Distributional effects are always relative to a comparison. The baseline and counterfactual frame defines whether the proposal is compared against the current state, a no-action decline path, an alternative policy, or a future scenario. Without this component, gains and losses can be created by framing rather than by the intervention itself.

### Benefit-Burden Incidence Model

The incidence model allocates benefits, costs, risks, service changes, externalities, and transition burdens across affected parties. It asks where effects actually land, not only where they formally originate. This matters when costs are passed through, unpaid work is shifted, or public-good effects are displaced outside a budget.

### Welfare Metric and Value Weights

The welfare metric specifies what counts as welfare in the decision: money, time, safety, access, wellbeing, dignity, opportunity, surplus, or a mixed measure. Value weights disclose how different dimensions are compared. The point is not to eliminate judgment, but to make judgment visible.

### Equity Guardrail Set

Equity guardrails define minimum floors, protected constraints, proportionality checks, and burden limits that aggregate gains cannot override silently. They are crucial when affected groups differ in vulnerability, bargaining power, baseline access, or ability to exit.

### Compensation and Mitigation Pathway

This component asks what happens to identified losses. Are they offset, reduced, phased, shared, compensated, or accepted as unavoidable? A welfare-positive claim is stronger when remedies are concrete and accountable rather than hypothetical.

### Uncertainty and Sensitivity Frame

Distributional conclusions often depend on uncertain estimates, discount rates, pass-through assumptions, and value weights. Sensitivity analysis shows whether the conclusion is robust or whether it depends on a fragile assumption.

### Decision Trace and Accountability Record

The decision trace records the final judgment, excluded effects, unresolved disputes, mitigation commitments, monitoring plan, and accountable owners. It turns the assessment from a one-time argument into a revisable decision control.

prose_common_mechanisms:
### Distributional Incidence Matrix

A distributional incidence matrix is a template that lists affected groups against benefits, burdens, risks, and remedies. It implements the archetype by making hidden allocation patterns visible. The matrix is not the archetype itself; it is one tool for operationalizing the affected-party map and incidence model.

### Counterfactual Welfare Comparison

Counterfactual welfare comparison tests the proposal against credible alternatives and the no-action path. It implements the archetype by protecting the conclusion from baseline manipulation.

### Subgroup Disaggregation Audit

A subgroup audit breaks aggregate effects into meaningful groups or exposure categories. It implements the archetype by checking whether averages hide concentrated harm, exclusion, or unequal access.

### Value-Weight Sensitivity Analysis

Value-weight sensitivity analysis varies welfare weights, thresholds, and discount assumptions. It implements the archetype by showing whether the decision remains acceptable under different reasonable normative choices.

### Compensation Adequacy Review

Compensation adequacy review asks whether remedies are credible, reachable, funded, proportionate, and monitored. It implements the archetype when aggregate gains depend on claims that losses can be offset.

### Externality and Spillover Inventory

The externality inventory looks outside the formal decision boundary for displaced costs or benefits. It implements the archetype by preventing a welfare claim from excluding third parties or public-good effects.

### Equity Guardrail Test

An equity guardrail test checks whether any affected group falls below a minimum floor or receives a burden that violates declared proportionality or fairness constraints. It implements the archetype by converting values into reviewable decision rules.

### Public Reason Disclosure Protocol

A public reason disclosure protocol documents the welfare rationale, distributional evidence, weights, mitigation commitments, and unresolved disputes. It implements the archetype by making the assessment contestable and monitorable.

prose_failure_modes:
A common failure mode is aggregate masking: the analysis reports net value but omits subgroup incidence. Another is baseline manipulation, where a comparison point makes one group’s loss look smaller or inevitable. Hypothetical compensation is also dangerous: a decision is justified because losers could be compensated even though no credible compensation pathway exists.

Other failures include externality displacement, value-weight laundering, tokenistic participation, and over-balancing rights-sensitive harms. The strongest mitigations are explicit incidence matrices, multiple counterfactuals, guardrail tests, compensation adequacy review, and public reason disclosure.

prose_neighbor_distinctions:
This archetype is distinct from **Equity Adjustment** because it assesses distributional effects before deciding whether adjustment is needed. It is distinct from **Procedural Fairness Design** because fair process does not by itself show whether outcomes and burdens are acceptable. It is distinct from **Externality Internalization** because revealing displaced costs is not the same as redesigning incentives or liabilities.

It is also distinct from **Deadweight Loss Reduction**, which aims to remove efficiency wedges; this archetype checks whether the resulting efficiency gains are distributionally acceptable. It differs from **Objective Weighting Governance**, which legitimates value weights, because this archetype also requires incidence mapping, mitigation review, and accountability.

prose_cross_domain_examples:
In tax reform, a rule may increase total revenue and reduce compliance cost while imposing new burdens on small businesses. In organizational automation, productivity gains may coincide with layoffs, support-team overload, or reduced access for unusual cases. In transit planning, average travel time may fall while specific neighborhoods lose essential service.

In platform governance, a ranking or moderation policy may improve average user experience while harming new creators or increasing appeal burdens. In environmental regulation, public health gains may be real but transition costs and local labor impacts still require mitigation.

prose_non_examples: A benefit-cost summary table with no subgroup incidence is not this archetype. A fairness slogan added after a decision is not this archetype. A technical optimization with no meaningful distributional consequences is not this archetype. A public hearing that does not change evidence, guardrails, or mitigation is not this archetype. A rights violation justified by aggregate gains is not this archetype; it is a case where guardrails should block welfare balancing.

prose_tradeoffs:
The archetype increases transparency but also increases decision overhead. It exposes normative disagreement that a single metric might hide. It can make efficient interventions more expensive by requiring mitigation or compensation. It can also generate privacy and complexity risks when subgroup analysis becomes very granular.

These tradeoffs are not defects. They are the cost of refusing to treat welfare as a placeless aggregate.

prose_invariants_to_preserve: The affected-party map must remain visible. Aggregate gains must not erase identifiable losses. The baseline and time horizon must be stable enough to prevent opportunistic reframing. Value weights and exclusions must be disclosed. Mitigation commitments must be assigned to accountable owners. These invariants preserve the assessment function even when the specific methods vary.

prose_target_outcomes:
The target outcome is a decision that is not only efficient in aggregate but distributionally legible and normatively accountable. A successful use of the archetype clarifies who gains, who loses, what is redistributed, which harms are mitigated, and which remaining tradeoffs require explicit judgment.

Second-order outcomes include reduced backlash, better mitigation design, stronger legitimacy, more accurate post-implementation learning, and clearer distinction between true Pareto improvements, compensated improvements, and contested tradeoffs.

prose_parameter_dimensions:
Important tuning dimensions include the granularity of affected groups, the time horizon, the baseline, the welfare metric, the value weights, the strictness of equity guardrails, the credibility threshold for compensation, and the uncertainty tolerance.

A coarse assessment may be enough for low-stakes internal decisions. High-stakes decisions involving essential services, employment, public goods, environment, health, or vulnerable groups require finer subgroup analysis, stronger guardrails, and a more explicit accountability record.

prose_variants_and_near_names:
Important variants include **Distributional Incidence Assessment**, **Equity-Sensitive Cost-Benefit Review**, **Compensation Design Review**, **Intertemporal Distributional Assessment**, and **Externality-Inclusive Welfare Assessment**.

Near names include distributional impact analysis, welfare impact assessment, winners-and-losers mapping, equity-sensitive benefit-cost analysis, and distributional effects review. Some near names are only mechanisms. A winners-and-losers table, fairness dashboard, or public hearing can support the archetype, but none of them replaces the full welfare-distributional assessment.

Distributional Incidence Assessment

short_definition: A variant focused on tracing where benefits, costs, risks, and service changes actually land across groups or roles.

distinctive_feature: The main object is incidence mapping: locating the landing points of effects before judging acceptability.

distinct_from_parent: The parent includes incidence, welfare metrics, guardrails, mitigation, and decision accountability; this variant narrows attention to the distribution map itself.

still_same_parent_because: It remains a way of making aggregate welfare claims distributionally visible rather than a separate intervention logic.

use_when: Aggregate welfare or surplus appears positive but the real burden may fall on different parties than the formal decision owner.

use_when: Pass-through, displacement, eligibility, or access differences could change who gains and who loses.

use_when: The decision affects groups with different exposure, bargaining power, or ability to exit.

Affected Party Map

role: Defines the rows of the incidence analysis.

notes: Include indirect and future parties when relevant.

Benefit-Burden Incidence Model

role: Assigns each effect to parties and time windows.

notes: Treat pass-through and hidden labor as first-class effects.

Distributional Incidence Matrix

mechanism_type: template

role: Operationalizes the variant through tabular effect mapping.

Subgroup Disaggregation Audit

role: Checks whether aggregate rows hide meaningful subgroup differences.

failure_modes: The assessment lists formal stakeholders but omits those who absorb indirect costs.

failure_modes: Incidence is treated as static even though implementation shifts burdens over time.

Equity-Sensitive Cost-Benefit Review

short_definition: A cost-benefit review variant that disaggregates costs and benefits and applies declared equity constraints before accepting the aggregate conclusion.

distinctive_feature: It modifies the interpretation of cost-benefit analysis by requiring transparent equity and distribution checks.

distinct_from_parent: The parent is broader and can be used without a formal cost-benefit model; this variant begins from cost-benefit analysis and adds distributional controls.

still_same_parent_because: The core move is still to test aggregate welfare claims against who gains, who loses, and what safeguards are required.

use_when: A benefit-cost ratio or net-present-value result is being used as a decision justification.

use_when: The decision affects populations with unequal baseline conditions or access.

use_when: The decision maker needs to show why aggregate value does or does not override distributional concerns.

Welfare Metric and Value Weights

role: Defines how benefit and burden measures are compared.

notes: Weights must be explicit.

Equity Guardrail Set

role: Prevents net value from overriding protected floors.

notes: Guardrails should be stated before the conclusion is chosen.

Value-Weight Sensitivity Analysis

role: Tests whether conclusions survive alternative reasonable weights.

Equity Guardrail Test

role: Checks protected floors and burden limits.

failure_modes: Equity weights are tuned after the fact to rationalize a preferred choice.

Compensation Design Review

short_definition: A variant that asks whether those harmed by an efficiency-improving decision can be credibly compensated, protected, or supported.

distinctive_feature: The review centers on the credibility and sufficiency of mitigation, not merely on the existence of aggregate gains.

distinct_from_parent: The parent evaluates welfare and distributional effects generally; this variant focuses on the remedy and compensation layer after losses are identified.

still_same_parent_because: It is triggered by the same welfare-distributional tension and uses the same affected-party and incidence components.

source_primes: pareto_efficiency equity accountability

use_when: A policy, reorganization, or allocation creates aggregate gains but localized losses.

use_when: The decision is justified as potentially Pareto-improving if compensation occurs.

use_when: Transition costs, displacement, or loss of access require a concrete remedy path.

Compensation and Mitigation Pathway

role: Defines offsets and supports.

notes: Credibility matters more than theoretical possibility.

name: Decision Trace and Accountability Record

role: Assigns responsibility for promised mitigation.

notes: Unfunded or unenforceable compensation should be recorded as unresolved.

Compensation Adequacy Review

role: Tests whether remedies are proportionate and reachable.

Public Reason Disclosure Protocol

role: Records mitigation commitments and unresolved burden disputes.

failure_modes: Possible compensation is cited to justify harms even though no party is accountable for delivering it.

Intertemporal Distributional Assessment

short_definition: A variant that separates welfare effects by time horizon, generation, transition period, or maintenance burden.

distinctive_feature: The key distributional axis is time rather than only group identity.

distinct_from_parent: The parent includes temporal windows as a component; this variant makes intertemporal distribution the main assessment frame.

still_same_parent_because: It still evaluates welfare claims by making hidden winners and losers visible, only across time cohorts.

source_primes: discounting equity uncertainty

use_when: Near-term and long-term welfare effects differ strongly.

use_when: Future groups, maintainers, debt bearers, or downstream users may inherit costs.

use_when: Discounting choices could change the distributional judgment.

Temporal Distribution Window

role: Separates time horizons and affected cohorts.

notes: Avoid mixing transition harm with steady-state benefit without disclosure.

Value-Weight Sensitivity Analysis

role: Tests discounting and time-weight assumptions.

failure_modes: Long-run gains are used to bury immediate noncompensated harms, or short-run savings ignore future maintenance costs.

short_definition: A variant that expands the welfare boundary to include spillovers, public goods, and third-party costs that are outside the main transaction.

distinctive_feature: Boundary expansion is the central move: the analysis refuses to treat displaced costs as external to the welfare claim.

distinct_from_parent: The parent covers all distributional effects; this variant specializes in spillovers and decision-boundary critique.

still_same_parent_because: It still asks whether the welfare story remains valid once gains and losses are allocated to affected parties.

source_primes: externality public_goods accountability

use_when: The decision creates spillovers outside the budget, contract, platform, or organizational boundary.

use_when: Public goods, common resources, or environmental effects are part of the welfare impact.

use_when: The main actors can gain by shifting burdens to unrepresented parties.

Affected Party Map

role: Adds third parties and public-good users.

notes: Boundary critique begins by expanding the map.

Externality and Spillover Inventory

mechanism_type: checklist

role: Identifies displaced effects.

failure_modes: The inventory names spillovers but never integrates them into the welfare judgment.

near_names:
Distributional Impact Analysis
Welfare Impact Assessment
Equity-Sensitive Benefit-Cost Analysis
Winners-and-Losers Mapping
Social Welfare Assessment
Distributional Effects Review