Whole-System Alignment

batch: First Iteration/Batch 04

source_primes: holism feedback composition

related_primes: boundary boundary_critique constraint representation

Holism

slug: holism

role: The archetype treats the system as a whole whose emergent behavior can be degraded even when each part performs locally.

Feedback

role: Feedback connects local choices to system-level consequences so parts can adjust before suboptimization accumulates.

Composition

slug: composition

role: The intervention concerns how parts compose into a functioning whole rather than how each part performs in isolation.

Boundary

slug: boundary

role: System boundaries determine which parts, outcomes, stakeholders, and feedback paths count as part of the whole being aligned.

Boundary Critique

role: Boundary critique is needed when the chosen whole excludes affected parties, externalities, or downstream consequences.

Constraint

slug: constraint

role: Constraints define the local autonomy envelopes and guardrails that keep local action compatible with whole-system viability.

Representation

slug: representation

role: Maps, dashboards, scorecards, and models represent whole-system relations so alignment can be reasoned about and governed.

components:
Whole-System Map
Part-Whole Interaction Map
Shared Outcome Metric
Local Metric Crosswalk
Incentive Alignment Rule
Feedback Path Adjustment
Coordination Protocol
Tradeoff Resolution Rule
Integration Owner
Alignment Review Cadence
System Health Dashboard
Local Autonomy Envelope
Externality Scan

mechanisms:
Balanced Scorecard
Shared OKRs or Cross-Functional Goals
Systems Engineering Review
Integrated Planning Process
Cross-Silo Governance Forum
System Health Review
Ecological Management Plan
Patient Care Team Conference

essence_one_line: Align local parts and incentives with the behavior of the whole system so local optimization does not undermine global viability.

compression_statement: When locally rational actions produce globally poor outcomes, map part-whole interactions and realign metrics, incentives, feedback, constraints, interfaces, or coordination rules with shared whole-system outcomes so local action supports system-level viability rather than suboptimization.

core_intuition: A part can be doing exactly what it was asked to do and still damage the whole if its goal, feedback, or reward system is too local. Whole-System Alignment works by making the larger consequences visible and consequential enough that local actors can preserve local usefulness while also protecting the system that makes their work meaningful.

canonical_formula: whole_system_alignment = whole_system_map + part_whole_interaction_map + shared_outcome_metric + local_metric_crosswalk + incentive_alignment_rule + feedback_path_adjustment + coordination_protocol + tradeoff_resolution_rule

structural_problem: Parts of a system optimize locally, act independently, or follow fragmented metrics in ways that degrade emergent whole-system behavior, shift costs across boundaries, or make the larger system less viable even though each part may appear successful on its own terms.

trigger_conditions: Local units, teams, agencies, modules, species, markets, or subsystems can improve their own metrics while worsening end-to-end outcomes.

trigger_conditions: The system has interdependencies, feedback loops, shared resources, cumulative effects, or delayed consequences that local actors do not fully see.

trigger_conditions: Responsibility is fragmented across parts, but outcomes are produced by the interaction among those parts.

trigger_conditions: A local optimization transfers cost, risk, maintenance burden, delay, or harm to another part of the system.

trigger_conditions: Metrics, incentives, budgets, or accountability rules reward part-level performance more strongly than whole-system viability.

trigger_conditions: Cross-boundary coordination is necessary, but existing interfaces, governance, or review rituals are too weak to reconcile tradeoffs.

trigger_conditions: A proposed improvement in one area threatens system resilience, equity, safety, long-term sustainability, user experience, or lifecycle value.

trigger_conditions: The system boundary is sufficiently defined that alignment can be attempted, but still broad enough to include the consequences that matter.

symptoms: Every unit reports success while customers, patients, users, residents, ecosystems, or downstream teams experience deteriorating outcomes.

symptoms: Throughput increases in one segment create backlog, defects, rework, risk, or overload elsewhere.

symptoms: Teams optimize for utilization, cost, speed, compliance, publication, test scores, or local revenue while system quality declines.

symptoms: Important feedback arrives too late, too locally, or only after harm has propagated through the system.

symptoms: Handoffs are formally correct but fail because no one owns the end-to-end outcome.

symptoms: Local dashboards look green while system-level indicators are red, ambiguous, or not measured.

symptoms: Units resist cooperation because their incentive structures make cooperation personally or locally costly.

symptoms: Escalation happens only after crises because routine governance cannot resolve part-whole tensions.

root_tension: The system needs local actors to make tractable, context-sensitive decisions, but those actors often see only local signals and are rewarded for local success. The whole depends on interactions among parts, yet responsibility, measurement, incentives, and authority are partitioned. The archetype resolves this tension by preserving useful local autonomy while making whole-system consequences visible, actionable, and consequential.

anti_signatures: The main problem is that the system boundary itself is undefined; use System Scope Definition first.

anti_signatures: The main intervention is redrawing what counts inside the system so excluded causes or stakeholders become visible; use Boundary Reframing first.

anti_signatures: The main task is regulating what crosses a boundary; use Boundary Permeability Control or Gateway Mediation instead.

anti_signatures: The main intervention is a bounded trial or protected test environment; use Sandboxing or Scoped Experimentation instead.

anti_signatures: The main intervention is only changing one feedback loop; use Feedback Loop Redirection when part-whole alignment is not the central structure.

anti_signatures: The main issue is composing parts into a larger artifact or architecture; use Compositional Assembly when local incentives and whole-system viability are not the problem.

anti_signatures: The proposed solution is only a systems-thinking conversation, diagram, or diagnosis with no change to metrics, incentives, coordination, constraints, or governance.

anti_signatures: The apparent whole excludes people, harms, costs, or environments that materially determine viability; perform boundary critique before aligning the wrong whole.

structural_intervention: Map how parts interact to produce whole-system outcomes, then adjust local metrics, incentives, feedback paths, constraints, interfaces, coordination protocols, and tradeoff rules so local decisions remain useful while preserving system-level viability.

action_logic: Define the whole being protected or improved, including the relevant system boundary, stakeholders, time horizon, and viability outcomes.

action_logic: Map the parts, their responsibilities, their local goals, and the interactions through which their choices affect the whole.

action_logic: Identify local optimization patterns: where parts improve their own measures by shifting cost, risk, delay, harm, or complexity elsewhere.

action_logic: Name the whole-system invariants that cannot be sacrificed, such as safety, resilience, equity, reliability, ecosystem health, lifecycle value, or user outcome.

action_logic: Crosswalk local measures to shared outcomes so each part can see which local behaviors support or damage the whole.

action_logic: Realign incentives, budgets, approval rules, status signals, or accountability paths where local success currently conflicts with system viability.

action_logic: Add or redirect feedback so local actors receive timely signals about downstream, cross-boundary, and cumulative consequences.

action_logic: Create coordination protocols and escalation rules for tradeoffs that no single part should resolve alone.

action_logic: Preserve bounded local autonomy where local optimization is harmless or beneficial, and constrain or coordinate choices that affect shared invariants.

action_logic: Review system health over time and revise alignment controls as the system, environment, and failure modes change.

decision_rules: Use this archetype when local success and system success are observably diverging.

decision_rules: Start with the smallest whole that includes the major consequences of local action; expand the boundary if important externalities remain outside the frame.

decision_rules: Do not impose a single global metric when the whole-system outcome is multi-dimensional or ethically contested.

decision_rules: Preserve local autonomy for decisions whose consequences are local, reversible, or within agreed guardrails.

decision_rules: Require cross-boundary coordination for decisions that affect shared resources, irreversible harm, long-term viability, safety, equity, or end-to-end user outcomes.

decision_rules: Treat incentives as suspect when they reward local throughput, cost reduction, utilization, or speed without tracking downstream burden.

decision_rules: Prefer feedback that is timely enough to change behavior, specific enough to guide action, and broad enough to include cross-system effects.

decision_rules: Escalate unresolved tradeoffs to a governance layer that can represent the whole, not merely the loudest or most powerful part.

decision_rules: Revise metrics before blaming actors when actors are following rules that rationally produce system harm.

decision_rules: Audit for excluded stakeholders or externalities before declaring the system aligned.

target_invariants: Whole-system viability remains visible and influential in local decision-making.

target_invariants: Local parts retain enough autonomy to use domain knowledge, adapt to context, and avoid unnecessary central bottlenecks.

target_invariants: System-level metrics do not erase critical local realities, minority harms, safety constraints, or long-term consequences.

target_invariants: Feedback from downstream and adjacent parts reaches the actors whose choices create the effects.

target_invariants: Tradeoffs are explicit, reviewable, and governed rather than hidden inside local metrics.

target_invariants: Alignment controls are maintained as the system changes; they are not treated as one-time planning artifacts.

target_invariants: The boundary of the whole includes the effects that materially determine viability.

target_invariants: Coordination costs remain proportionate to the risk of misalignment.

expected_outcomes: Reduced suboptimization and fewer local wins that create global losses.

expected_outcomes: Improved end-to-end reliability, service quality, safety, resilience, equity, sustainability, or lifecycle value.

expected_outcomes: Clearer relationships between local actions and system-level consequences.

expected_outcomes: Better cross-boundary coordination, escalation, and tradeoff resolution.

expected_outcomes: Metrics and incentives that reward contributions to the whole rather than isolated performance.

expected_outcomes: Earlier detection of emergent harm from fragmented decisions.

expected_outcomes: More legitimate governance because system-level decisions can be traced to shared outcomes and explicit tradeoffs.

expected_outcomes: Healthier balance between local autonomy and whole-system coherence.

prose_essence:
Whole-System Alignment is the intervention pattern for cases where parts of a system are doing locally sensible things that add up to a globally poor result. It does not merely ask people to “think systemically.” It changes the structure around local action: what actors see, what they are rewarded for, what feedback they receive, how tradeoffs are escalated, and which outcomes count as success.

The core question is: **what must change so that each part can remain locally competent while also preserving the viability of the whole?**

prose_when_to_use:
Use this archetype when local optimization is visibly diverging from system-level health. The pattern is especially relevant when teams, modules, agencies, departments, suppliers, disciplines, or ecological actors each improve their own metrics while the end-to-end outcome worsens.

It is useful when the whole has real emergent properties: reliability, resilience, safety, ecosystem health, patient outcomes, learning, user experience, lifecycle value, or public trust. It is weak when the system boundary is still undefined, when a one-time impact map is enough, or when the only intended change is a conversation or diagram.

prose_structural_problem:
The structural problem is **suboptimization**: parts optimize for local goals that are not equivalent to the health of the whole. This can happen even when no actor is malicious. A team rewarded for speed may create operational fragility. A department rewarded for throughput may create downstream rework. A supplier rewarded for unit cost may weaken resilience. A school rewarded for one score may narrow learning.

The underlying tension is that local decision-making is necessary, but local signals are incomplete. The whole is produced by interactions among parts, yet responsibility, measurement, incentives, and authority are often partitioned.

prose_intervention_logic:
The intervention begins by naming the whole whose viability matters. Then it maps parts, dependencies, feedback paths, metrics, and local incentives. The draft then asks where local success creates system harm. Alignment is created by modifying the local environment of action: shared outcome metrics, local metric crosswalks, incentive rules, feedback paths, coordination protocols, and tradeoff resolution rules.

The goal is not total centralization. Good alignment preserves local autonomy inside guardrails and reserves system-level coordination for decisions that affect shared invariants.

prose_key_components:
### Whole-System Map

A whole-system map shows the relevant whole, its parts, boundaries, flows, dependencies, feedback paths, and emergent behaviors. It prevents alignment from being based on one part’s view of the system.

### Part-Whole Interaction Map

A part-whole interaction map focuses on the causal channels through which local actions shape system-level outcomes. It shows where local wins become global losses.

### Shared Outcome Metric

A shared outcome metric names the system-level result that multiple parts must jointly preserve. It might be reliability, patient outcome, ecological viability, safety, resilience, equity, or lifecycle value.

### Local Metric Crosswalk

A local metric crosswalk connects local measures to system outcomes. It helps each part see which local behaviors support the whole and which create hidden burden elsewhere.

### Incentive Alignment Rule

An incentive alignment rule adjusts rewards, penalties, budgets, approval criteria, or accountability paths so local actors are not rationally pushed to harm the whole.

### Feedback Path Adjustment

Feedback path adjustment ensures that downstream, delayed, or cross-boundary consequences reach the actors who can change behavior. Without feedback, alignment decays into aspiration.

### Coordination Protocol

A coordination protocol defines how parts share information, make joint decisions, resolve tensions, and update plans. It gives alignment a repeatable operating structure.

### Tradeoff Resolution Rule

A tradeoff resolution rule specifies how conflicts among local efficiency, safety, equity, resilience, cost, speed, and long-term viability should be judged. Whole-system alignment exposes tradeoffs; it does not magically remove them.

prose_common_mechanisms:
### Balanced Scorecard

A balanced scorecard can implement this archetype by representing multiple dimensions of system health. It becomes Whole-System Alignment only when it affects decisions, incentives, and review behavior.

### Shared OKRs or Cross-Functional Goals

Shared objectives can align interdependent units around common outcomes. They fail when they remain slogans while local metrics still reward conflicting behavior.

### Systems Engineering Review

A systems engineering review checks whether component choices, interfaces, requirements, risks, and lifecycle decisions support system-level performance. It is a mechanism for the archetype in engineered systems.

### Integrated Planning Process

Integrated planning coordinates budgets, schedules, dependencies, risks, and capabilities across parts. It should surface real tradeoffs rather than merely assemble separate local plans.

### Cross-Silo Governance Forum

A cross-silo forum gives interdependent units a recurring place to revise shared constraints, resolve tradeoffs, and coordinate decisions. It is effective only if it has authority to change rules or commitments.

### System Health Review

A system health review compares local performance with whole-system outcomes at a recurring cadence. It helps detect local improvements that create global harm.

### Ecological Management Plan

An ecological management plan aligns actions across species, habitats, land uses, stakeholders, and time horizons. It shows the archetype where whole-system viability depends on many delayed and interacting effects.

### Patient Care Team Conference

A patient care conference aligns specialized roles around whole-person outcomes. It works when it changes care plans, responsibilities, handoffs, and metrics rather than merely sharing updates.

prose_failure_modes:
Common failure modes include aligning the wrong whole, reducing system health to one simplistic metric, centralizing too much, creating alignment theater, encouraging metric gaming, receiving feedback too late, overloading coordination forums, and allowing powerful actors to define “the whole” in self-serving ways.

The most important mitigation is to tie every alignment artifact to action: a decision rule, an owner, a cadence, a feedback path, or a tradeoff procedure.

prose_neighbor_distinctions:
Whole-System Alignment is distinct from **System Scope Definition**, which defines the system boundary. It is distinct from **Boundary Reframing**, which redraws the boundary to reveal different causes or responsibilities. It is distinct from **Whole-System Impact Mapping**, which maps possible consequences before action. It is distinct from **Feedback Loop Redirection**, which changes a feedback loop itself. It is broader than **Incentive Alignment**, because incentives are only one lever among metrics, feedback, coordination, constraints, and governance.

It is also distinct from generic systems thinking. Systems thinking may motivate the intervention, but this archetype requires enacted structural changes.

prose_cross_domain_examples: In software reliability, feature teams can be aligned around user-visible reliability so feature velocity does not degrade the platform. In healthcare, specialists can coordinate around whole-patient outcomes rather than departmental throughput. In supply chains, procurement, production, logistics, and service can align around lifecycle value and resilience instead of unit cost alone. In watershed management, municipalities, farms, industry, and conservation actors can align around water quality and flood resilience. In education, accountability can balance test performance with attendance, wellbeing, equity, and long-term learning.

prose_non_examples: A dashboard that nobody uses for decisions is not Whole-System Alignment. A holistic workshop without changed incentives or feedback is not this archetype. A centralized mandate that suppresses local knowledge is not automatically alignment. A clean modular architecture is not this archetype unless local behavior is being realigned with whole-system outcomes. A one-time impact map is a neighbor method unless it leads to ongoing alignment controls.

prose_tradeoffs: The main tradeoff is between local autonomy and system coherence. Alignment also trades speed for coordination, simplicity for plural metrics, and local accountability for shared responsibility. The pattern can improve viability, but it adds governance overhead and can be misused as a rationale for excessive central control.

prose_invariants_to_preserve: The archetype should preserve whole-system viability, legitimate local autonomy, transparent tradeoffs, timely feedback, plural outcome representation, stakeholder legitimacy, and proportional coordination cost. It should also preserve the boundary discipline that keeps “the whole” from being defined in a way that exports harm.

prose_target_outcomes: Successful Whole-System Alignment reduces suboptimization, connects local action to global consequences, improves end-to-end outcomes, clarifies tradeoffs, strengthens coordination, and makes shared system health visible. It helps parts stop winning locally by shifting burden elsewhere.

prose_parameter_dimensions:
Important tuning dimensions include the scale of the whole, the time horizon, the number of outcome dimensions, the degree of local autonomy, the strength of shared incentives, feedback latency, escalation thresholds, governance cadence, metric granularity, and the acceptable cost of coordination.

A narrow whole is easier to govern but may exclude externalities. A broad whole is more realistic but harder to measure and coordinate. Stronger central governance may improve coherence but can suppress local knowledge. Looser governance preserves autonomy but may let suboptimization persist.

prose_variants_and_near_names:
Recognized variants include **Metric and Incentive Alignment**, which emphasizes the measurement and reward structure, and **Cross-Silo Whole-System Coordination**, which emphasizes fragmentation across organizational or jurisdictional boundaries. **Whole-Part Reintegration** is captured as a collapsed variant because Batch 004 treats it as merge-or-defer material and its current structure fits inside this parent.

Near names include holistic alignment, local-global alignment, suboptimization correction, systems thinking, balanced scorecards, and cross-functional goals. Some are aliases, some are mechanisms, and some are lenses rather than archetypes.

Metric and Incentive Alignment

short_definition: Realigns local performance measures and incentives with shared whole-system outcomes.

distinctive_feature: The main lever is a traceable connection between local metrics, incentives, and system-level outcomes.

distinct_from_parent: The parent includes mapping, coordination, feedback, and governance; this variant emphasizes measurement and reward structure.

still_same_parent_because: The purpose remains aligning local behavior with the whole rather than merely improving measurement.

use_when: Local units meet their own targets while damaging system-level outcomes.

use_when: Fragmented metrics reward speed, cost reduction, throughput, or utilization in ways that shift harm elsewhere.

use_when: The system needs a practical bridge from whole-system goals to local decision criteria.

example_domains:
organizations
platform engineering
healthcare
public administration

domain: platform engineering

example: Team metrics include user-visible reliability and incident recovery, not only feature velocity.

domain: healthcare

example: Departmental targets are crosswalked to patient outcomes across the full care journey.

failure_modes: A shared metric becomes a simplistic score that hides safety, equity, or long-term harm.

failure_modes: Incentive changes reward gaming rather than genuine system health.

Cross-Silo Whole-System Coordination

short_definition: Aligns units separated by organizational, jurisdictional, disciplinary, or functional boundaries around shared system outcomes.

distinctive_feature: The main friction is cross-boundary fragmentation among parts that must jointly preserve a whole.

distinct_from_parent: The parent can apply to any part-whole relation; this variant specifically addresses siloed or cross-boundary coordination.

still_same_parent_because: The intervention still realigns parts with whole-system outcomes rather than only inserting a bridge or interface.

source_primes: holism boundary composition

use_when: Separate units optimize handoffs, budgets, priorities, or accountability in incompatible ways.

use_when: No single unit owns the end-to-end outcome even though each unit affects it.

use_when: Local autonomy is valuable but uncoordinated choices create fragmentation.

example_domains:
enterprise governance
public services
healthcare
infrastructure delivery

domain: public services

example: Housing, health, and social-service agencies coordinate around stable household outcomes instead of agency-specific throughput.

domain: infrastructure delivery

example: Engineering, procurement, construction, and operations revise plans jointly around lifecycle reliability.

failure_modes: The coordination forum becomes a status meeting without authority to change tradeoffs.

failure_modes: Local expertise is suppressed by a one-size-fits-all central plan.

Whole-Part Reintegration

short_definition: Reconnects specialized or decomposed parts to whole-system purpose after fragmentation or siloing weakens coherence.

distinctive_feature: The intervention is triggered by prior decomposition or specialization rather than by any local-global mismatch.

distinct_from_parent: This name emphasizes repair after fragmentation; the parent also covers initial alignment and ongoing alignment maintenance.

still_same_parent_because: The components and mechanisms are largely the same: system map, shared outcome metric, feedback adjustment, coordination protocol, and review cadence.

source_primes: holism composition feedback

use_when: Parts have lost sight of the whole after decomposition, specialization, outsourcing, or silo formation.

use_when: Local units maintain their own operations but no longer understand their role in system viability.

example_domains:
organizations
systems engineering
integrated care
whole-of-government work

domain: organization design

example: Specialized teams reconnect their roadmaps to customer journey outcomes and shared operating constraints.

failure_modes: Reintegration becomes symbolic alignment language while local metrics continue to reward fragmentation.

near_names:
Holistic Alignment
Local-Global Alignment
Suboptimization Correction
Balanced Scorecard
Cross-Functional Goals
Systems Thinking


Wild-Card Contingency Mapping

type: archetype

source: generated

batch: First Iteration/Batch 34/Wave 2

Wild-Card Planning

notes: Use only when planning maps disruption pathways to maintained contingency options.

Disruption Contingency Mapping

notes: A plain-language near name for the same pattern.

Black-Swan Contingency Planning

notes: Use cautiously: true black swans are not predicted; the draft maps plausible disruption classes and readiness options.

Low-Probability/High-Impact Planning

notes: Descriptive near name when low probability and high impact are the defining trigger.

slug: resilience

slug: uncertainty

slug: robustness

slug: threshold

slug: feedback

components:
Wild-Card Event Class
Plausibility Boundary
Impact Pathway
Exposure and Vulnerability Map
Precursor Signal
Contingency Option
Readiness Threshold
Activation and Retirement Rule
Readiness Review Cadence
Wild-Card Owner
Response Capacity Boundary
Ethical Scope Guardrail
Post-Event Learning Loop

mechanisms:
Wild-Card Workshop
Contingency Map
Crisis Scenario Catalog
Precursor Watchlist
Contingency Option Register
Disruption Playbook
Tabletop Exercise
Readiness Drill
Strategic Reserve Plan
Red-Team Disruption Challenge

essence_one_line: Map low-probability, high-impact disruptions and predefine flexible response options before the disruption becomes urgent.

compression_statement: When rare disruptions could radically change conditions but cannot be reliably forecast, define plausible wild-card event classes, trace their impact pathways, identify possible precursors, and maintain proportionate contingency options and readiness thresholds.

core_intuition: The point is not to predict the shock. The point is to make the system less blind, less brittle, and less improvised if a disruptive possibility moves from imagination toward reality.

canonical_formula: wild_card_event_class + impact_pathway + exposure_map + precursor_or_no_warning_logic + contingency_option + readiness_review -> proportionate preparedness under rare-event uncertainty

structural_problem: A system ignores low-probability disruptive events because they are hard to forecast, uncomfortable to discuss, politically inconvenient, or outside normal planning assumptions.

trigger_conditions: A disruption could have high consequence even if its probability is low or hard to estimate.

trigger_conditions: Existing strategy assumes normal continuity or only models expected scenarios.

trigger_conditions: Preparedness requires lead time, authority, resources, contracts, reserves, or coordination that cannot be improvised during crisis.

trigger_conditions: Some wild-card possibilities are socially or politically difficult to name, so they are excluded from planning until too late.

trigger_conditions: Potential precursors exist but are weak, ambiguous, or not connected to any readiness decision.

trigger_conditions: The system has resilience rhetoric but no concrete mapping from event classes to response options and readiness thresholds.

symptoms: Rare disruptions are dismissed as implausible until they become obvious.

symptoms: Contingency plans exist as documents but are not tied to impact pathways, triggers, owners, or maintained capabilities.

symptoms: Risk registers list events without explaining how they propagate or what option would be activated.

symptoms: Teams react to vivid shocks with panic planning while ignoring quieter but plausible wild cards.

symptoms: Preparedness decays because the organization never reviews whether options, contacts, reserves, or authorities remain valid.

symptoms: Decision-makers confuse imagination of a disruption with prediction of that disruption.

root_tension: Rare-event preparedness must imagine enough to act before crisis, but not so much that it turns speculation into panic, false certainty, or expensive overfitting.

anti_signatures: The event is already likely, imminent, and well characterized; ordinary operational planning or incident response is more appropriate.

anti_signatures: The main problem is choosing a robust strategy across several plausible futures, not preparing for rare disruptive event classes.

anti_signatures: The focal object is a plan assumption being broken or reversed; Assumption Stress Testing is likely a better fit.

anti_signatures: There is no meaningful action, option, capacity, or readiness threshold that can be changed by mapping the wild card.

anti_signatures: The exercise is only a creative brainstorming session with no pathway, contingency, or review logic.

structural_intervention: Generate bounded wild-card event classes, map how they could affect the system, identify exposure and possible precursors, define proportionate contingency options and readiness thresholds, then review and update preparedness as context changes.

action_logic: Define the decision context, time horizon, critical functions, and kinds of disruption worth considering.

action_logic: Generate low-probability/high-impact wild-card event classes using scenario imagination, expert challenge, historical analogues, weak signals, cross-impact analysis, or adversarial review.

action_logic: Apply a plausibility boundary so the map includes disruptive possibilities without becoming arbitrary fantasy or fear amplification.

action_logic: Trace impact pathways from each selected event class through direct, indirect, delayed, cascading, and recovery consequences.

action_logic: Map exposure and vulnerability so the organization can see which assets, actors, functions, dependencies, and decisions would be under pressure.

action_logic: Identify any precursor signals, trigger conditions, or no-warning assumptions that should shape readiness strategy.

action_logic: Define contingency options that preserve response capacity, buy time, reduce irreversible exposure, or enable rapid activation when signals change.

action_logic: Assign activation, escalation, retirement, ownership, and review rules so options remain usable rather than symbolic.

action_logic: Test the map with drills, tabletop exercises, red-team review, or small readiness checks, then revise pathways and options.

action_logic: Periodically review the map as external conditions, dependencies, weak signals, and response capacities change.

decision_rules: Map only wild-card classes whose potential impact and response lead time justify attention; do not turn every remote possibility into a planning burden.

decision_rules: Separate plausibility, impact, evidence, and response cost; low probability does not automatically mean ignore, and high impact does not automatically mean full mobilization.

decision_rules: Use precursors when they are plausible, but do not invent false indicators for no-warning disruptions.

decision_rules: Choose contingency options that are maintainable, reversible, reusable, or capacity-preserving whenever exact event probability is uncertain.

decision_rules: Escalate preparedness when impact is high, warning time is short, exposure is severe, and a feasible low-regret option exists.

decision_rules: Retire or downgrade maps and options when the event class becomes irrelevant, the option decays, costs dominate, or better preparedness pathways emerge.

decision_rules: Treat workshops, catalogs, risk registers, and plans as mechanisms; the archetype succeeds only when they change readiness, options, or decision authority.

target_invariants: The archetype does not claim to predict exact shocks.

target_invariants: Wild-card imagination remains tied to plausible impact pathways and usable response options.

target_invariants: Preparedness is proportionate to impact, plausibility, lead time, reversibility, and maintenance cost.

target_invariants: Uncertainty is preserved rather than hidden behind false probability precision.

target_invariants: Each selected wild-card class has an owner, review cadence, or explicit reason for not being maintained.

target_invariants: Mechanisms remain subordinate to the mapping, option, and readiness logic.

target_invariants: Ethical and safety guardrails prevent fear-based, discriminatory, or exploitative uses of rare-event scenarios.

expected_outcomes: Earlier recognition of rare but consequential disruption pathways.

expected_outcomes: More credible preparedness because options, owners, authorities, and thresholds are defined before crisis.

expected_outcomes: Less brittle strategy because critical dependencies and irreversible exposures are visible.

expected_outcomes: Better connection between horizon scanning, weak-signal triage, scenario planning, and response readiness.

expected_outcomes: Reduced panic improvisation when a disruptive event starts to materialize.

expected_outcomes: More disciplined use of imagination: neither dismissive normalcy bias nor runaway catastrophizing dominates the planning process.

prose_essence:
Wild-Card Contingency Mapping prepares a system for rare but consequential disruptions without pretending to forecast their exact timing or form. It names plausible low-probability/high-impact event classes, traces how they could affect the system, identifies possible precursors or no-warning conditions, and links the map to maintained contingency options.

The archetype is useful because many systems are optimized around expected futures. They may have scenario workshops, risk registers, or resilience language, but no clear bridge from an uncomfortable disruption possibility to concrete options, authority, resources, and readiness review.

prose_when_to_use:
Use this archetype when a disruption could be rare, hard to forecast, or politically awkward to discuss, yet severe enough that improvised response would be unacceptable. It is especially relevant when the system needs lead time to arrange backup capacity, decision authority, supply alternatives, communication protocols, recovery routes, or reserves.

Do not use it as a catch-all for every uncertainty. If the work is about comparing broad futures and choosing robust strategy, use Scenario Portfolio Planning. If the work is about triaging ambiguous evidence that already exists, use Weak Signal Triage. If the work is about breaking a plan's assumptions, use Assumption Stress Testing.

prose_structural_problem:
The structural problem is rare-event neglect. Because a disruption is hard to estimate or uncomfortable to name, it is treated as outside planning. This creates a gap between imagination and readiness: people may be able to describe a shocking event, but they have not mapped how it would propagate, what capacities would fail, who would decide, or what options would still be available.

The opposite failure is panic overreaction. Once a vivid disruption is imagined, teams may treat it as inevitable and overfit planning around it. The archetype therefore needs both imaginative reach and plausibility discipline.

prose_intervention_logic:
The intervention starts by selecting bounded wild-card event classes. Each event class is passed through a plausibility boundary, not to calculate exact probability, but to separate credible disruption classes from fantasy. The team then maps impact pathways, exposure, and vulnerability. Where precursors exist, they are connected to scanning or weak-signal triage. Where no reliable precursor exists, the map focuses on response capacity and readiness thresholds instead.

The decisive step is translating the map into contingency options. These may include alternate suppliers, emergency authority, fallback procedures, strategic reserves, communication protocols, recovery paths, or reversible commitments. Each option needs activation and retirement rules so the system neither ignores rising evidence nor remains permanently mobilized around stale possibilities.

prose_key_components:
### Wild-Card Event Class

A wild-card event class defines the rare disruption being considered. It should be specific enough to reason about but not so narrow that preparedness depends on exact prediction. “Sudden port closure” is usually more useful than “the port closes on a specific date for a specific reason.”

### Plausibility Boundary

The plausibility boundary keeps the exercise disciplined. It asks why the event class could occur, what constraints or analogues support it, and what would make it too speculative to retain. This boundary prevents both normalcy bias and fantasy planning.

### Impact Pathway

The impact pathway traces how the disruption could propagate through functions, dependencies, stakeholders, and time. Without this component, wild-card planning remains a list of scary labels rather than a map of actionable consequences.

### Exposure and Vulnerability Map

The exposure map identifies which assets, people, operations, rights, dependencies, or decisions are at risk. It turns a general event into a concrete preparedness problem.

### Precursor Signal

A precursor signal is a possible early cue that a wild-card pathway is becoming more relevant. It may come from horizon scanning, weak-signal triage, expert judgment, or monitoring dashboards. Some wild cards have no reliable precursor; the archetype should admit that rather than invent false triggers.

### Contingency Option

A contingency option is a response move preserved before the event becomes urgent. It can be a backup route, reserve capacity, pre-authorized decision right, emergency communication path, alternate supplier, contractual option, or recovery move.

### Readiness Threshold

The readiness threshold defines what must be true for the system to claim credible preparedness. It may cover time to respond, resources, authority, trained roles, updated contacts, tested procedures, or accessible reserves.

### Activation and Retirement Rule

Activation and retirement rules define when to escalate, revise, pause, or retire options. They keep preparedness from becoming either panic activation or permanent emergency posture.

### Readiness Review Cadence

The review cadence keeps the map alive. Wild-card maps decay as dependencies, suppliers, policies, personnel, and technical systems change. Review makes the map a maintained intervention rather than a one-time artifact.

prose_common_mechanisms:
Wild-card workshops are useful for generating and challenging event classes, but the workshop is not the archetype. It is only one mechanism for initiating the mapping logic.

Contingency maps and crisis scenario catalogs preserve the reasoning in visible form. They help teams connect event classes to pathways, vulnerabilities, signals, options, and owners. These artifacts become valuable only when they affect readiness decisions.

Precursor watchlists connect the archetype to horizon scanning and weak-signal triage. They should track evidence without implying certainty. A watchlist is a way to update attention, not a prediction engine.

Contingency option registers and disruption playbooks operationalize the response side. They specify what can be activated, by whom, under what conditions, and with what resources.

Tabletop exercises, readiness drills, and red-team disruption challenges test whether the mapped options are real. They reveal missing authority, brittle assumptions, unavailable resources, and confusing communication paths.

Strategic reserve plans preserve capacity for selected wild-card classes. They are useful when response capacity cannot be created instantly, but they require careful calibration so rare-event preparedness does not consume all available resources.

prose_failure_modes:
The most common failure mode is a shock catalog without action. Teams list dramatic possibilities but never map pathways, options, thresholds, or owners.

A second failure is false precision. Rare-event uncertainty is forced into numerical certainty that the evidence cannot support. This can create either false reassurance or unjustified panic.

A third failure is generic resilience substitution. The team says “build resilience” but never connects a disruption class to concrete preparedness.

Other failures include stale contingency options, risk-register capture, politically convenient dismissal of uncomfortable scenarios, and fear-based misuse against vulnerable groups.

prose_neighbor_distinctions:
Wild-Card Contingency Mapping differs from Scenario Portfolio Planning because it does not primarily compare broad plausible futures to choose robust strategy. It focuses on rare disruption classes and the readiness options attached to them.

It differs from Surprise Preparedness because Surprise Preparedness is broader and remains under merge review toward the resilience family. This draft is narrower and therefore safer to draft: event class, pathway, precursor or no-warning logic, option, threshold, and review.

It differs from Weak Signal Triage because weak-signal triage starts with ambiguous evidence. This archetype starts with a disruption class and may later attach signals.

It differs from Assumption Stress Testing because assumption stress testing targets the assumptions embedded in a plan. Wild-card contingency mapping targets disruptive event classes and response readiness.

It differs from a contingency plan because a plan is a mechanism. The archetype is the structured conversion of rare-event imagination into mapped impact, options, activation rules, and maintained readiness.

prose_cross_domain_examples:
In supply-chain strategy, a manufacturer maps a sudden export ban and prepares alternate sourcing, customer prioritization, and trigger thresholds.

In public health, an agency maps a rare pathogen emergence scenario and links it to surveillance cues, lab-capacity readiness, communication authority, and drills.

In cybersecurity, a platform maps compromise of a critical dependency and prepares exposure checks, emergency patch authority, customer communication, and fallback procedures.

In urban infrastructure, a city maps an extreme heat plus grid-failure event and prepares cooling-center contingencies, backup power priorities, and mutual aid agreements.

prose_non_examples: A list of dramatic disaster ideas is not this archetype if it lacks impact pathways and maintained options. A routine continuity checklist for frequent outages is not this archetype because the event is already familiar and operational. A standard scenario portfolio is not this archetype unless rare disruption classes and contingency readiness are central.

prose_tradeoffs:
The main tradeoff is preparedness versus attention cost. Every wild-card class consumes time, imagination, and maintenance effort. Another tradeoff is specificity versus flexibility: exact playbooks are easier to execute, while flexible options survive a wider range of disruptions.

There is also a governance tradeoff. Some rare-event plans need confidentiality, but too much secrecy can hide overreach, discriminatory assumptions, or unaccountable emergency powers.

prose_invariants_to_preserve:
The archetype must preserve the distinction between preparedness and prediction. It must keep wild-card imagination tied to plausible impact pathways and maintained options. It must also preserve proportionality: high impact justifies attention, but it does not automatically justify intrusive, costly, or permanent emergency measures.

The map must remain actionable. If no option, threshold, owner, review, or capacity decision changes, the work is probably a scenario artifact rather than this archetype.

prose_target_outcomes:
A successful draft of this archetype produces earlier recognition of disruptive pathways, clearer exposure and vulnerability understanding, maintained contingency options, more credible readiness thresholds, and less panic improvisation when conditions change.

It should also improve the connection among other foresight archetypes. Horizon scanning can supply signals, weak-signal triage can calibrate evidence, scenario portfolio planning can place wild cards among broader futures, cross-impact mapping can expose cascades, and resilience work can strengthen response capacity.

prose_parameter_dimensions:
Important tuning dimensions include how broad the wild-card event classes are, how strict the plausibility boundary is, how much response capacity is preserved, and how often readiness is reviewed. The map can be lightweight for exploratory strategy or formal for safety-critical infrastructure.

Another key parameter is the balance between event-specific and reusable options. Highly specific options can be easier to activate but more likely to decay or overfit. Reusable options, such as communication authority, backup capacity, decision rights, or supplier diversity, can support multiple wild-card classes.

A final tuning dimension is public visibility. Some maps require confidentiality, especially in security or continuity contexts, but ethical guardrails are needed so rare-event planning does not become opaque fear governance.

prose_variants_and_near_names:
Recognized variants include Precursor-Triggered Wild-Card Mapping, No-Warning Wild-Card Readiness, Cascading Wild-Card Mapping, and Wild-Card Recovery Pathway Mapping.

Near names include wild-card planning, wild-card scenario planning, disruption contingency mapping, black-swan contingency planning, and low-probability/high-impact planning. The black-swan name should be used carefully because the archetype does not claim to predict true black swans.

Several names should collapse into mechanisms rather than archetypes: contingency plan, wild-card workshop, crisis scenario catalog, disruption playbook, tabletop exercise, and risk register.

Precursor-Triggered Wild-Card Mapping

variant_type: subtype

short_definition: A subtype that links rare disruption classes to weak signals, leading indicators, or trigger thresholds that increase readiness before the event becomes obvious.

distinctive_feature: The center of gravity is signal-to-readiness movement: precursors change preparedness state before the disruption fully arrives.

distinct_from_parent: The parent can map wild cards with or without useful warning signs; this variant depends on monitored precursors and activation thresholds.

still_same_parent_because: It still begins with a wild-card disruption class and maps contingencies; the signal system is a readiness trigger rather than the whole intervention.

use_when: The wild-card class has plausible precursors, such as regulatory signals, early technical anomalies, adversary preparation, social unrest cues, supply-chain anomalies, or epidemiological indicators.

use_when: The system can increase readiness gradually without committing to a full emergency response.

use_when: Scanning and weak-signal triage systems already exist or can be connected to the contingency map.

Precursor Signal

role: tracks early warning cues

Activation and Retirement Rule

role: moves options as evidence changes

Readiness Threshold

role: defines minimum preparedness state

Precursor Watchlist

role: monitors trigger conditions

Contingency Option Register

role: links options to triggers

example_domains:
cybersecurity
public health
supply-chain resilience
policy foresight

domain: cybersecurity

example: A security team maps a novel exploit class and connects dark-web chatter, exploit proof-of-concepts, and vendor advisories to escalating detection and patch-readiness options.

domain: public health

example: A health agency maps rare pathogen emergence scenarios and links unusual syndromic reports to lab-capacity and communication-readiness thresholds.

failure_modes: Treating weak precursors as proof and triggering panic response.

failure_modes: Monitoring signals without maintaining any option that can be activated.

No-Warning Wild-Card Readiness

short_definition: A subtype for wild-card classes where reliable precursors are absent, so readiness depends on generic response capacity, slack, rehearsed authority, and fast sensemaking.

distinctive_feature: It intentionally avoids trigger fantasy and focuses on capacities that matter when there is no useful warning.

distinct_from_parent: The parent may include precursor signals; this variant marks cases where precursor tracking should not be overemphasized.

still_same_parent_because: It still maps a wild-card class to impact pathways and contingency options rather than becoming generic resilience capacity building.

use_when: The event could arrive with little or no reliable warning.

use_when: The exact form of the disruption is uncertain, but response functions and critical dependencies can still be prepared.

use_when: Preparedness depends more on response capacity than on forecast precision.

Response Capacity Boundary

role: calibrates preserved capacity

Readiness Threshold

role: defines minimum standby capability

Contingency Option

role: preserves response moves

Readiness Drill

role: tests standby capacity

Strategic Reserve Plan

role: preserves slack

example_domains:
critical infrastructure
organizational crisis management
emergency response
financial continuity

domain: critical infrastructure

example: An operator maps sudden control-system failure and prepares fallback authority, manual procedures, and communications even though there may be no warning.

domain: financial continuity

example: A treasury team prepares liquidity access and decision rights for abrupt market closure rather than pretending to forecast the closure date.

failure_modes: Collapsing into generic resilience advice.

failure_modes: Maintaining excessive reserve capacity for events with no bounded relevance.

Cascading Wild-Card Mapping

short_definition: A variant that emphasizes how a rare disruption could cascade through coupled systems, producing secondary and tertiary failures that matter more than the initial event.

distinctive_feature: The main analytic work is tracing cascade propagation and dependent response failures.

distinct_from_parent: The parent can map any wild-card event; this variant specializes in events whose danger lies in cascading effects.

still_same_parent_because: The trigger remains a wild-card event class and the output remains contingency and readiness design.

use_when: The disruption is likely to propagate across dependencies or sectors.

use_when: Independent risk lists understate the compound effect.

use_when: Preparedness depends on sequencing, chokepoints, and cross-system coordination.

Impact Pathway

role: traces cascade paths

Exposure and Vulnerability Map

role: finds dependency fragility

Readiness Threshold

role: sets cross-system readiness levels

Contingency Map

role: shows propagation paths

Tabletop Exercise

role: tests cascade response

example_domains:
supply chains
climate adaptation
public infrastructure
geopolitical risk

domain: supply chains

example: A manufacturer maps how a sudden port closure cascades through inventory, customer commitments, cash flow, and supplier prioritization.

failure_modes: Every dependency becomes a worst-case cascade.

failure_modes: Maps become too complex for readiness decisions.

Wild-Card Recovery Pathway Mapping

short_definition: A recovery-centered variant that maps how a system can move from disruption through stabilization, adaptation, and restored or transformed operation.

distinctive_feature: Emphasizes post-disruption pathways and recovery decisions rather than pre-event response activation.

distinct_from_parent: The parent covers impact, signals, options, and readiness; this variant foregrounds recovery path design.

still_same_parent_because: The recovery pathways are attached to wild-card event classes and contingency options.

use_when: The main concern is not preventing the wild card but preserving recovery options after it occurs.

use_when: Several recovery states are possible and some are more desirable or ethical than others.

use_when: The system must decide what to restore, what to redesign, and what to abandon after shock.

Impact Pathway

role: identifies recovery obstacles

Contingency Option

role: supports stabilization

Post-Event Learning Loop

role: updates readiness after disruption

Disruption Playbook

role: coordinates recovery moves

Readiness Drill

role: tests recovery assumptions

example_domains:
disaster recovery
organizational continuity
digital infrastructure
community resilience

domain: digital infrastructure

example: A platform maps how it would stabilize service, communicate, rebuild trust, and change architecture after a major data-integrity shock.

failure_modes: Recovery pathway becomes a promise unsupported by resources.

failure_modes: The plan restores a harmful prior state by default.

near_names:
Wild-Card Planning
Wild-Card Scenario Planning
Disruption Contingency Mapping
Black-Swan Contingency Planning
Low-Probability/High-Impact Planning


name: Windfall Discipline and Capacity Preservation

batch: Batch 12

canonical_family: Shared Resources & Boundary Spillover

slug: accountability

slug: incentive

slug: dependency

slug: maintenance

slug: scarcity

slug: bottleneck

slug: reserve

slug: autonomy

slug: allocation

essence_one_line: When easy value arrives without being earned by current performance, partition the windfall, preserve accountability and practice signals, reinvest in endogenous capacity, and test viability without the windfall.

compression_statement: Windfall Discipline and Capacity Preservation applies when a system receives abundant value through a channel weakly coupled to the performance signals that would normally build capability. The intervention treats the inflow as a temporary, volatile, and potentially corrupting condition: it isolates the source, measures performance coupling, protects accountability, invests in durable capacity and diversification, maintains selected scarcity-like practice constraints, and rehearses post-windfall operation before prosperity becomes brittle dependence.

core_intuition: A windfall can make a system look strong while removing the pressures that kept it capable. The goal is not to reject abundance; it is to make abundance serve capability rather than replace it.

canonical_formula: Observed strength S_obs = endogenous capacity C_endogenous + windfall W - atrophy A - accountability decay D. Intervene when W is large, weakly performance-coupled, volatile, or capacity-substituting: partition W, preserve feedback F, reinvest in C_endogenous, diversify sources, and stress-test W → 0.

structural_problem: A system’s apparent strength, solvency, growth, legitimacy, or comfort is sustained by an easy inflow that is not sufficiently coupled to the performance and feedback loops that would ordinarily build or maintain capacity. As the inflow substitutes for earning, learning, maintenance, accountability, diversification, or adaptive effort, the system becomes brittle in proportion to the windfall it appears to enjoy.

trigger_conditions: A large resource, revenue, attention, legitimacy, subsidy, inheritance, grant, royalty, monopoly rent, or growth-capital inflow arrives faster than endogenous capability grows.

trigger_conditions: The inflow is weakly tied to current user value, citizen accountability, customer willingness, operating discipline, or mission performance.

trigger_conditions: The system uses windfall to fund ordinary commitments, expansion, staffing, consumption, or prestige before post-windfall viability is tested.

trigger_conditions: Stakeholders treat windfall-supported output as proof of durable capability.

trigger_conditions: The inflow is volatile, politically fragile, externally controlled, depletable, or concentrated in one source.

trigger_conditions: Capabilities that scarcity, feedback, competition, or maintenance pressure formerly exercised begin to weaken.

symptoms: Budgets, services, headcount, expectations, or infrastructure expand faster than durable revenue or capability.

symptoms: Learning, maintenance, customer/citizen feedback, or operating discipline declines because money or support covers errors.

symptoms: Decision makers defend weak performance by pointing to aggregate wealth, growth, or reserves.

symptoms: Rent capture, patronage, low-productivity consumption, corruption, or prestige spending increases.

symptoms: Alternative revenue, skill, local ownership, or capability development is postponed because the windfall is easier.

symptoms: A modest drop in inflow causes disproportionate service collapse, legitimacy crisis, austerity, or organizational panic.

root_tension: The windfall is real value and may be ethically or strategically useful, but because it bypasses ordinary performance coupling, it can replace the pressures that produced capability. The system must use abundance without letting abundance dissolve the mechanisms needed to survive without it.

anti_signatures: The inflow is small, brief, explicitly one-time, and cannot materially substitute for capability.

anti_signatures: The system already has strong drawdown rules, independent feedback loops, diversification, and post-windfall stress tests.

anti_signatures: The core issue is immediate scarcity allocation rather than abundance-induced atrophy.

anti_signatures: The support relation is primarily about a donor/source and recipient/sink boundary rather than internal windfall discipline; use donor_coupled_capacity_governance first.

anti_signatures: The issue is protected risk-taking alone without capacity atrophy, diversification, or accountability decay; use moral_hazard_mitigation first.

structural_intervention: Convert a weakly performance-coupled windfall from a hidden substitute for capability into a bounded, auditable, capacity-building input governed by allocation partitions, accountability guards, reinvestment floors, diversification paths, shadow constraints, and post-windfall stress tests.

action_logic: Name the windfall source, reliability, volatility, and performance-coupling level.

action_logic: Separate observed strength from endogenous capacity by building a baseline of what the system can sustain without the windfall.

action_logic: Partition windfall use into operating drawdown, reserve, maintenance, capability-building, diversification, restitution/equity, and restricted-use buckets.

action_logic: Preserve accountability and feedback links to the stakeholders or outcomes that define legitimate performance.

action_logic: Require capability reinvestment and diversification before windfall-funded expansion becomes permanent.

action_logic: Maintain selected scarcity-like practice constraints or drills so essential capacities stay exercised.

action_logic: Stress-test lower-inflow and no-windfall scenarios and revise commitments, reserves, and taper triggers.

action_logic: Review at a cadence tied to inflow volatility, capacity milestones, accountability signals, and dependency growth.

decision_rules: If windfall-funded commitments cannot pass post-windfall viability thresholds, keep them temporary, reversible, or paired with replacement capacity.

decision_rules: If unearned inflow bypasses accountability, add independent outcome feedback, transparency, or stakeholder review before expanding discretionary spending.

decision_rules: If the windfall is volatile or depletable, smooth drawdowns through reserve/stabilization rules rather than matching spending to peak inflow.

decision_rules: If core capabilities stop being practiced, impose shadow scarcity, drills, maintenance floors, or performance-linked drawdown rules.

decision_rules: If concentration on one inflow source rises, prioritize diversification over further consumption or prestige expansion.

target_invariants: Do not treat windfall-supported performance as proof of endogenous capability.

target_invariants: Do not punish beneficiaries by withdrawing support abruptly merely to create discipline.

target_invariants: Keep accountability to mission, users, citizens, customers, or affected stakeholders visible.

target_invariants: Convert part of the windfall into durable capacity, not only temporary consumption.

target_invariants: Preserve enough reserve and reversibility to survive inflow decline.

expected_outcomes: Observed performance is decomposed into earned capability and windfall support.

expected_outcomes: Windfall use becomes auditable and strategically partitioned.

expected_outcomes: Endogenous capacity, maintenance, and diversification improve while inflow is available.

expected_outcomes: Accountability links remain intact despite unearned or weakly earned resources.

expected_outcomes: The system can tolerate lower windfall levels with less brittle collapse.

Natural-Resource Revenue Governance

short_definition: Govern royalties, extraction rents, or commodity windfalls so they build durable capacity instead of replacing accountability, taxation discipline, diversification, and institutional learning.

distinctive_feature: Specializes the parent to commodity or extraction windfalls where inflow volatility, governance opacity, and accountability bypass are central.

distinct_from_parent: The parent covers any abundant value inflow; this variant focuses on natural-resource and commodity revenue institutions.

still_same_parent_because: The intervention still partitions windfall, preserves accountability, reinvests in capacity, diversifies sources, and tests post-windfall viability.

use_when: A region or institution receives large extraction or commodity inflows.

use_when: Revenue is weakly coupled to citizen/customer value creation or ordinary performance feedback.

use_when: Volatility, corruption, institutional atrophy, or diversification failure is plausible.

example_domains:
resource-rich states
municipal royalties
commodity-exporting regions
extractive industries

domain: public finance

example: A resource-rich region places royalties into a stabilization fund with public dashboards, capability investment floors, and non-resource revenue targets.

failure_modes: Fund rules become symbolic while operating budgets quietly depend on volatile royalties.

name: Grant or Donor Windfall Dependency Control

short_definition: Govern large grants, philanthropic surges, emergency aid, or donor-backed expansion so temporary support does not displace local capability, revenue learning, or accountable practice.

distinctive_feature: Bridges resource-curse dynamics with cross-boundary support: the inflow is external and often mission-justified, yet still risks capacity atrophy.

distinct_from_parent: The parent does not require a donor/recipient relation; this variant does.

still_same_parent_because: The core problem remains unearned or weakly earned abundance weakening capacity and feedback.

use_when: External support rapidly expands a program, organization, or community service.

use_when: The recipient may confuse donor-backed scale with autonomous viability.

use_when: Exit, localization, diversification, or endogenous funding capacity matters.

example_domains:
nonprofits
humanitarian response
public health grants
research labs

domain: nonprofit operations

example: A large restricted grant includes an exit readiness threshold and local revenue-learning milestones before headcount becomes permanent.

failure_modes: Donor reporting metrics reward scale while local capability, maintenance, or feedback discipline declines.

Venture or Platform Subsidy Discipline

short_definition: Use abundant venture funding, platform subsidy, or growth capital without letting subsidized demand, unprofitable habits, or weak unit economics masquerade as durable market capability.

distinctive_feature: Specializes the parent to growth-subsidy settings where measured demand and operating discipline may be artifactually high.

distinct_from_parent: The parent is broader than venture/platform economics; this variant focuses on capital-subsidized growth and product-market signal distortion.

still_same_parent_because: It still requires windfall partitioning, performance coupling, shadow scarcity, and post-windfall stress tests.

use_when: A product, marketplace, team, or platform can buy growth or activity through subsidy.

use_when: Performance signals such as demand, retention, reliability, or unit economics are distorted by easy capital.

use_when: Post-subsidy viability must be tested before scale hardens.

example_domains:
startups
platform marketplaces
subsidized growth programs
internal innovation funds

domain: marketplace strategy

example: A platform phases down subsidies by cohort and checks whether retention, matching quality, and supply reliability persist without subsidy.

failure_modes: Subsidized scale creates infrastructure, staffing, or user expectations that cannot survive unsubsidized conditions.

Scarcity-Practice Preservation

short_definition: Preserve selected scarcity-like practice signals, maintenance habits, and earned-performance constraints even while genuine abundance is available.

distinctive_feature: Focuses on maintaining adaptive practice and maintenance discipline rather than on revenue governance alone.

distinct_from_parent: The parent includes allocation, accountability, diversification, and stress testing; this variant emphasizes practice-preserving constraints.

still_same_parent_because: Practice preservation is one implementation family for preventing abundance-induced capacity atrophy.

use_when: Abundance is real and useful, but it has eliminated the practice that maintained capability.

use_when: The system must remain competent under future scarcity, volatility, or withdrawal.

use_when: Artificial or shadow constraints can be used safely without needless deprivation.

example_domains:
organizational capability
military logistics
education
family wealth governance

domain: operations

example: A team with abundant emergency funding still runs low-resource drills to preserve prioritization, repair, and improvisation skill.

failure_modes: Artificial scarcity becomes punitive theater instead of a capability-preserving rehearsal.


Winner-Conditioned Valuation Correction

batch: Batch 14

aliases: overpayment prevention in common-value contests

alias_records: overpayment prevention in common-value contests

slug: competition

slug: risk

slug: probability

slug: screening

slug: signaling

slug: evidence

slug: optionality

components:
Common-Value Exposure Map
Unconditional Estimate Trace
Winner Signal Model
Competitor Information Assumption
Selection-Adjusted Valuation
Bid-Shading or Commitment Rule
Downside Exposure Cap
Independent Challenge Review
Post-Win Verification Gate
Walkaway and Renegotiation Path
Outcome Calibration Record
Auction or Contest Rule Boundary
Seller Incentive Scan
Reputation Pressure Filter
Correlated Signal Warning
Portfolio-Level Loss Budget

mechanisms: Earnout, Holdback, or Contingent Contract

essence_one_line: When winning a common-value contest would reveal that your estimate was probably too high, condition the valuation on winning before bidding, committing, or celebrating.

compression_statement: Winner-Conditioned Valuation Correction applies when actors compete for an uncertain common-value object and selection favors the most optimistic estimate. The intervention treats winning as an adverse information signal, then revises the bid, price, commitment, exposure cap, and verification path using independent evidence, rival-information assumptions, reference classes, bid shading, and walkaway rules.

core_intuition: In a common-value contest, the winner is often the actor whose estimate overshot the true value most. The same signal that says “you won” also says “everyone else stopped below you.” This archetype works by making that selection signal explicit before the actor becomes trapped by pride, sunk costs, financing pressure, or contractual irreversibility.

canonical_formula: corrected_commitment = E[value | winning_signal, evidence, rival_estimate_model] - downside_margin; proceed only if corrected_commitment ≥ required_price and exposure ≤ cap.

structural_problem: An actor uses an unconditional value estimate to win a contested allocation even though winning is itself evidence that the actor may have overestimated a shared, uncertain value. The contest selects the most optimistic estimate and can convert selection success into overpayment, operational loss, reputational damage, or irreversible commitment.

trigger_conditions: The contested object has a substantial common-value component: the winner and losers would face broadly similar true value, cost, risk, or downstream obligation.

trigger_conditions: Participants hold noisy, incomplete, asymmetric, or strategically biased estimates of that common value.

trigger_conditions: The allocation rule tends to award the object to the highest bidder, most aggressive applicant, most optimistic buyer, or most confident estimator.

trigger_conditions: Winning commits the actor to a price, obligation, delivery promise, employment contract, acquisition, lease, project, or exposure before the true value is known.

trigger_conditions: The number, expertise, or information quality of rivals makes their refusal to continue bidding meaningful evidence.

trigger_conditions: Prestige, mandate pressure, fear of losing, escalation, or identity attachment makes the team eager to treat winning as proof of correctness.

trigger_conditions: Post-win verification, renegotiation, or walkaway options are weak, costly, or socially illegitimate unless designed in advance.

trigger_conditions: Past wins have produced disappointing returns, write-downs, delivery losses, overruns, or buyer’s remorse despite appearing rational at the time.

symptoms: The winning bid, acquisition price, hire, grant, project estimate, or lease repeatedly looks sensible before selection and excessive after realization.

symptoms: Teams celebrate winning but later discover that rivals stopped because they saw lower value, higher risk, or worse costs.

symptoms: Decision-makers explain losses as bad luck while ignoring the selection process that made overestimation likely.

symptoms: Bidders increase offers in small increments after crossing their own earlier valuation boundary.

symptoms: Post-win due diligence becomes a search for justification rather than a real gate.

symptoms: Forecasts are compared against the object in isolation, not against the fact that the actor had to outbid or outperform other estimators to obtain it.

symptoms: The organization has strong pre-bid enthusiasm but weak post-outcome learning about bid discipline.

symptoms: The same experts who want to win are also the people who approve the final estimate.

symptoms: A seller, platform, organizer, or agent controls information release in a way that amplifies optimistic competition.

root_tension: The actor must bid high enough to win, but the higher the bid must be to win a common-value contest, the stronger the evidence that the actor may be the most overoptimistic participant. Selection success and valuation accuracy pull in opposite directions.

anti_signatures: The object is almost entirely private value, such as a purely personal preference or unique strategic fit that rivals cannot meaningfully value for the actor.

anti_signatures: The actor is designing the market rule to elicit truth or allocate efficiently; use Incentive-Compatible Rule Design or auction-based mechanism work instead.

anti_signatures: The main problem is hidden low-quality entrants selecting into a pool; use Adverse Selection Filtering.

anti_signatures: The central risk is generic uncertainty without competitive selection; use Uncertainty Explicitness, Robust Solution Selection, or Assumption Stress Testing.

anti_signatures: The contest creates winner-take-all concentration or market power after victory; use Bounded Rivalry Governance, Bottleneck Power Governance, Network Effect Governance, or the Winner-Take-All Market Governance variant.

anti_signatures: The actor can fully verify value before commitment, and winning adds no information about estimate bias.

anti_signatures: The task is only to negotiate a lower price after known value is established; use transaction, bargaining, or price-mechanism tools instead.

anti_signatures: The proposed correction would be used as a pretext for exploitative lowballing, bad-faith renegotiation, or discriminatory exclusion rather than genuine selection-bias control.

structural_intervention: Transform an unconditional bid, offer, or commitment into a winner-conditioned valuation by modeling the selection signal, discounting for likely overestimation, capping downside exposure, and adding independent review plus verification or walkaway gates before irreversible commitment.

action_logic: Define the contested object, allocation rule, commitment point, true-value horizon, and who bears post-win downside.

action_logic: Separate common value from private value, option value, prestige value, strategic complementarity, and nonfinancial constraints.

action_logic: Trace the original estimate, evidence quality, uncertainty band, assumptions, and organizational pressure to win.

action_logic: Model why winning would occur: high estimate, better private information, risk tolerance, strategic need, financing difference, or rival constraint.

action_logic: Condition the valuation on winning by asking what the actor should infer if every rival stops below the actor’s bid or commitment level.

action_logic: Adjust the maximum bid, price, promise, or participation threshold using reference classes, simulations, lower-bound scenarios, or explicit bid shading.

action_logic: Set downside caps, financing limits, walkaway thresholds, renegotiation triggers, and post-win verification gates before submitting the bid or offer.

action_logic: Route the corrected valuation through an independent reviewer or red team that does not own the prestige of winning.

action_logic: Design post-win due diligence, holdbacks, earnouts, staged delivery, reserve clauses, or contingent commitments where the arena permits them.

action_logic: After the outcome, compare estimates and realized value to update the correction rule and distinguish real opportunity from repeated overpayment.

decision_rules: Use this archetype when the event of being selected is itself informative about possible overestimation.

decision_rules: Increase correction strength when there are more qualified rivals, higher estimate noise, less post-win verification, stronger common-value exposure, or greater irreversible downside.

decision_rules: Decrease correction strength when private value, unique complementarity, or verifiable strategic fit dominates the common-value component.

decision_rules: Do not override the walkaway limit merely because the actor is near winning; proximity to winning often increases, rather than reduces, the need for discipline.

decision_rules: Treat rival withdrawal, nonparticipation, and silence as possible information, not only as weakness or lack of ambition.

decision_rules: Require a specific reason to believe the actor has better information than rivals before rejecting the winner signal.

decision_rules: Prefer contingent payment, staged commitment, or post-win verification when uncertainty cannot be resolved before bidding.

decision_rules: Prefer market-rule redesign when many participants are systematically harmed by winner’s-curse dynamics rather than only one actor needing discipline.

decision_rules: Escalate to ethics and fairness review when the correction could become manipulative lowballing or bad-faith renegotiation.

target_invariants: The valuation is explicitly conditioned on the event of winning, not only on the object’s apparent value.

target_invariants: Common-value and private-value components remain separated enough to avoid overcorrecting genuine private fit.

target_invariants: The corrected maximum commitment is set before rivalry, sunk cost, or identity pressure peaks.

target_invariants: Independent evidence and challenge are protected from the team’s desire to win.

target_invariants: Downside exposure, financing, and operational capacity are capped before commitment becomes irreversible.

target_invariants: Post-win verification gates are real decision points, not ceremonial due-diligence rituals.

target_invariants: The process records both wins and near-misses so calibration does not learn only from visible failures.

target_invariants: The correction is not used to deceive, exclude, or exploit weaker counterparties.

expected_outcomes: Actors avoid bids, acquisitions, contracts, hires, leases, or projects whose expected value becomes negative once conditioned on winning.

expected_outcomes: Winning becomes a disciplined decision rather than a status signal that overrides valuation evidence.

expected_outcomes: Common-value contests produce fewer write-downs, overruns, post-win regrets, and emergency renegotiations.

expected_outcomes: Teams become better at distinguishing real private advantage from ordinary overoptimism.

expected_outcomes: Bid discipline improves because walkaway limits, exposure caps, and verification gates are explicit before competitive pressure escalates.

expected_outcomes: Sellers, platforms, and organizers face clearer pressure to disclose material information or use formats that reduce pathological overpayment.

expected_outcomes: The organization learns from both won and lost contests, improving future calibration.

prose_essence:
Winner-Conditioned Valuation Correction is the intervention pattern behind the winner's curse. It applies when an actor wins an uncertain common-value contest and that win is not pure good news. It is also evidence that the actor's estimate may have been the most optimistic estimate in the field.

The archetype does not say “never win.” It says: **do not value the object as if you had not won yet.** Before bidding, buying, hiring, acquiring, leasing, or promising, ask what the event of winning would tell you about your estimate. Then convert that inference into a maximum commitment, downside cap, independent challenge review, and post-win verification path.

Common-Value Auction Bid Shading

short_definition: A bidder-side variant where the maximum bid is reduced below the unconditional value estimate because winning a common-value auction selects for optimism.

distinctive_feature: Centers the numerical bid-shading rule applied before bidding.

distinct_from_parent: The parent covers broader contested commitments; this variant is the classic auction/bid implementation.

still_same_parent_because: It uses the same winner signal model, selection-adjusted valuation, exposure cap, and calibration record.

use_when: The allocation is auction-like and awards the object to the highest or most aggressive bidder.

use_when: The object has common value and bidders hold noisy estimates.

use_when: Bidder-side discipline is possible even if the auction format is fixed.

Acquisition Overpayment Guard

short_definition: A deal-making variant that prevents the highest bidder for an asset, company, lease, or contract from converting deal enthusiasm into value-destroying overpayment.

distinctive_feature: Combines winner-conditioned valuation with contractual exposure control.

distinct_from_parent: The parent is cross-domain; this variant focuses on deals where post-win due diligence and contingent pricing are available.

still_same_parent_because: It still applies the same conditional-on-winning correction and post-win verification logic.

use_when: A buyer competes for an acquisition, lease, asset, or concession with uncertain future value.

use_when: Rivals’ withdrawal is meaningful evidence about price, risk, or hidden cost.

use_when: Earnouts, holdbacks, diligence gates, financing caps, or walkaway terms can still be designed.

Low-Bid Delivery-Curse Guard

short_definition: A contractor or applicant variant where winning by promising the lowest cost, fastest schedule, or highest performance may reveal that the winner underestimated delivery burden.

distinctive_feature: The curse appears as underpricing or overpromising rather than paying too much for an asset.

distinct_from_parent: The parent includes all common-value overpayment or overcommitment; this variant centers delivery-cost underestimation.

still_same_parent_because: Winning still selects the most optimistic estimate and requires condition-on-winning correction.

source_primes: winner_s_curse risk competition

use_when: Selection favors the lowest price, shortest timeline, or most optimistic promise.

use_when: The winner must deliver after selection and absorbs overrun risk.

use_when: Rivals’ higher bids or refusals may indicate real delivery costs.

near_names:
Winner’s Curse Control
Winners Curse Control
Common-Value Overpayment Guard
Winner Signal Adjustment
Bid Shading Against Winner’s Curse