Anticipatory Forecasting

batch: First Iteration/Batch 27/Wave 2

aliases:
forecast preparation
future-state preparation forecast
lead-time forecasting
forecast-to-action loop

source_primes: foreseeing uncertainty horizon_scanning

related_primes: probability approximation boundedness adaptation feedback environmental_scanning scenario_planning

slug: foreseeing

slug: probability

slug: approximation

slug: boundedness

slug: adaptation

components:
Forecast Target
Decision Horizon
Lead-Time Requirement
Signal Basis
Uncertainty Range
Preparation Action
Trigger Threshold
Update Rule
Abort or De-escalation Rule
Forecast Owner
Forecast Error Memory
Hedge Portfolio
Forecast Communication Protocol

mechanisms:
Demand Forecasting
Capacity Forecast
Early Warning Forecast
Trend Projection
Reference-Class Forecast
Rolling Forecast Review
Forecast Trigger Dashboard
Scenario-Informed Preparation
Forecast After-Action Review

essence_one_line: Use plausible forecasts to prepare before future states arrive.

compression_statement: When future conditions can be partially anticipated, forecast likely states with enough lead time to prepare, hedge, allocate capacity, or adapt before the event or demand actually arrives.

core_intuition: A forecast becomes a solution archetype only when it changes action before the future state arrives. The point is not prediction for its own sake, but a disciplined loop that turns uncertain future-state estimates into timely preparation while remaining ready to update or de-escalate.

canonical_formula: forecast target + lead-time requirement + uncertainty range + preparation action + update/de-escalation rule -> earlier, better-calibrated readiness

structural_problem: The system acts reactively even though future conditions are partially knowable and preparation requires lead time. Signals exist, but they are not converted into timely, uncertainty-aware action.

trigger_conditions: A future demand, risk, failure, shortage, overload, opportunity, or environmental condition can be estimated before it arrives.

trigger_conditions: Preparation actions need lead time, such as staffing, procurement, capacity reservation, public warning, hedging, staging, redesign, or scheduling.

trigger_conditions: Waiting for certainty would make the response late, expensive, brittle, or impossible.

trigger_conditions: The cost of modest preparation is justified by the risk of being unprepared, but overreaction remains possible.

trigger_conditions: The decision can tolerate approximate forecasts if uncertainty and update rules are explicit.

symptoms: Repeated surprise by seasonal, cyclical, or trend-driven conditions.

symptoms: Capacity shortages, queues, stockouts, emergency spending, or rushed interventions after warning signals were available.

symptoms: Forecasts are produced but not connected to specific preparation actions or decision thresholds.

symptoms: Plans are anchored to a single expected future and break when the forecast is wrong.

symptoms: Teams delay action until evidence is certain, even though certainty arrives too late.

symptoms: Forecasts become stale because no update cadence or de-escalation rule exists.

root_tension: Preparation must begin before certainty, but premature or overconfident preparation can waste resources, cause alarm, or lock the system into the wrong future.

anti_signatures: The future state is not meaningfully forecastable and only surprise preparedness or resilience planning is possible.

anti_signatures: The forecast does not change any action, allocation, timing, capacity, or hedge.

anti_signatures: The task is broad exploration of multiple futures rather than preparing for a forecasted target with lead time.

anti_signatures: The work is only a forecast model, forecast report, or dashboard with no preparation loop.

anti_signatures: The needed action can be taken instantly after the event arrives, so lead-time anticipation adds little value.

structural_intervention: Define a forecast target tied to an actionable future state, estimate it with explicit uncertainty and lead time, connect forecast thresholds to preparation actions, and update or de-escalate the response as evidence changes.

action_logic: Choose the future state that matters for action, not merely what is easiest to predict.

action_logic: Determine the lead time required for useful preparation and work backward to the forecast cadence.

action_logic: Select signals, reference classes, models, or expert estimates that support the forecast target.

action_logic: Represent uncertainty as ranges, probabilities, confidence labels, scenario envelopes, or explicit assumptions.

action_logic: Define preparation actions and trigger thresholds before the future condition fully arrives.

action_logic: Maintain update and de-escalation rules so the system can revise or stop preparation when the forecast changes.

action_logic: Compare realized outcomes to forecasts so future thresholds and preparation choices improve.

decision_rules: Use this archetype only when the forecast can change preparation before the future state arrives.

decision_rules: Prefer a rough forecast with enough lead time over a precise forecast that arrives too late for action.

decision_rules: Scale preparation to uncertainty; do not treat the point forecast as the whole decision basis.

decision_rules: Use staged or reversible preparation when forecast confidence is moderate but action delay would be costly.

decision_rules: Escalate when forecast uncertainty, consequence severity, or lead-time compression crosses a pre-defined threshold.

decision_rules: Abort, de-escalate, or redirect preparation when signal quality deteriorates or the forecasted condition fails to materialize.

target_invariants: Forecasts remain attached to explicit action implications.

target_invariants: Uncertainty is visible and affects preparation scale.

target_invariants: Lead-time requirements are respected rather than discovered after the deadline has passed.

target_invariants: Preparation remains revisable as evidence changes.

target_invariants: Mechanisms such as forecast models or dashboards do not replace the action loop.

target_invariants: The system avoids both passive waiting for certainty and reckless commitment to weak forecasts.

expected_outcomes: Earlier and better-calibrated preparation for demand, risk, disruption, opportunity, or capacity stress.

expected_outcomes: Fewer avoidable surprises where signals were available but not operationalized.

expected_outcomes: More resilient allocation of staffing, supplies, capacity, time, reserves, and attention.

expected_outcomes: Reduced emergency response costs caused by late reaction.

expected_outcomes: Clearer accountability for forecast updates, trigger thresholds, and preparation decisions.

expected_outcomes: Improved learning from forecast error and preparation outcomes.

prose_essence:
Anticipatory Forecasting turns partial knowledge about future conditions into preparation before those conditions arrive. Its core move is not simply predicting the future. It defines a future-state target, estimates it with visible uncertainty, preserves enough lead time for action, and links forecast thresholds to preparation, update, and de-escalation rules.

This archetype is useful when waiting for certainty would make the response too late, but acting as if the forecast were certain would create waste or lock-in. It helps a system prepare under uncertainty without pretending uncertainty has disappeared.

prose_when_to_use:
Use Anticipatory Forecasting when a future demand, risk, shortage, overload, opportunity, or environmental condition can be estimated before it arrives and when preparation takes time. The archetype is especially appropriate when staffing, supply, capacity, public warning, hedging, routing, scheduling, or design adaptation must begin before the future state is fully observable.

Do not use it merely because a forecast can be produced. A forecast belongs here only if it changes action. When the work is broad exploration of possible futures, use scenario planning or horizon scanning instead. When the work is just showing uncertainty around a current estimate, use Uncertainty Explicitness.

prose_structural_problem:
The structural problem is reactive operation in a partially forecastable environment. Signals exist, but the system does not convert them into timely preparation. By the time the future condition is certain, the useful preparation window has closed.

This creates avoidable surprise, emergency spending, capacity overload, rushed decisions, fragile plans, and repeated late responses. It can also create the opposite failure: overconfident preparation for a point forecast with no update or de-escalation path.

prose_intervention_logic:
The intervention begins by choosing an action-relevant forecast target. It then works backward from the lead-time requirement: how early must the system know enough to act? It selects signals, reference classes, models, or expert estimates that support the forecast target, expresses uncertainty, defines preparation actions, and sets trigger thresholds.

The final step is a live update loop. As new evidence arrives, the forecast and preparation action are revised. If the forecast weakens or becomes invalid, the system de-escalates or redirects preparation rather than staying locked to stale assumptions.

prose_key_components:
- **Forecast Target** (`forecast_target`): Defines the specific future state to anticipate. Without a target, forecasting collapses into vague future talk.
- **Decision Horizon** (`decision_horizon`): Sets the time window over which the forecast must matter for action.
- **Lead-Time Requirement** (`lead_time_requirement`): Specifies how much warning is needed for useful preparation.
- **Signal Basis** (`signal_basis`): Identifies evidence, trends, reference classes, weak signals, or causal indicators supporting the forecast.
- **Uncertainty Range** (`uncertainty_range`): Represents plausible forecast error and spread so preparation is scaled rather than brittle.
- **Preparation Action** (`preparation_action`): Links forecast output to a concrete change in staffing, supply, capacity, timing, warning, or hedging.
- **Trigger Threshold** (`trigger_threshold`): Defines when preparation, escalation, or monitoring should activate.
- **Update Rule** (`update_rule`): Determines when and how the forecast and preparation plan are revised.
- **Abort or De-escalation Rule** (`abort_or_deescalation_rule`): Defines when preparation should stop, shrink, or redirect if the forecast changes.

Optional components include a forecast owner, forecast error memory, hedge portfolio, and forecast communication protocol. These become important when responsibility, learning, optionality, or communication quality determines whether the forecast changes action.

prose_common_mechanisms:
Demand forecasting, capacity forecasts, early-warning forecasts, trend projections, reference-class forecasts, rolling forecast reviews, forecast trigger dashboards, scenario-informed preparation, and forecast after-action reviews can all implement the archetype.

These mechanisms should not be confused with the archetype itself. A forecast model estimates a future state. A dashboard displays signals. A trend projection extends an observed pattern. Anticipatory Forecasting is the broader intervention that connects those mechanisms to lead-time preparation, uncertainty handling, update, and de-escalation.

prose_failure_modes:
Common failure modes include forecast without action, late precision, false certainty, stale forecast lock-in, overreaction to weak signals, alarm fatigue, forecast target mismatch, and no post-event learning.

A particularly common failure is producing forecast artifacts for reporting rather than operational preparation. Another is treating the forecast as a promise, which hides uncertainty and makes plans brittle. A third is continuing preparation after evidence has changed because no abort or de-escalation rule was defined.

prose_neighbor_distinctions:
Anticipatory Forecasting differs from **Uncertainty Explicitness** because it uses uncertainty-bearing future estimates to trigger preparation, not merely to communicate confidence. It differs from **Bounded Approximation** because the key issue is lead-time preparation, not just good-enough simplification. It differs from **Scenario Portfolio Planning** because it centers a forecast target and preparation rule rather than a portfolio of alternative futures. It differs from **Horizon Scanning** because scanning detects change, while this archetype converts selected signals into forecast-triggered action.

It also differs from **Capacity Reservation**, which may be a preparation action but does not itself create the forecast loop. A **Forecast Model** is only a mechanism unless it is connected to lead time, triggers, preparation, update, and de-escalation.

prose_cross_domain_examples: In healthcare operations, a clinic forecasts winter respiratory demand and adjusts staffing, outreach, and supplies before the peak. In cloud infrastructure, a platform forecasts launch-week traffic and stages capacity before overload. In emergency management, flood forecasts trigger staged shelter, pump, and communication readiness. In supply chains, disruption forecasts activate alternate suppliers before production stops. In education, enrollment forecasts support classroom, transport, and hiring decisions before the school year begins.

prose_non_examples: A forecast presentation that no one uses is not Anticipatory Forecasting. A retrospective explanation of last month’s demand is not anticipatory. A general scenario workshop with no forecast target or preparation trigger belongs elsewhere. An always-on reserve for unknown shocks is capacity reservation or surprise preparedness rather than forecast-triggered preparation.

prose_tradeoffs:
Anticipatory Forecasting trades certainty for lead time. It may require acting before evidence is complete. That can preserve options and prevent crisis, but it can also waste resources or create alarm if thresholds are too sensitive. It also trades stability for adaptability: frequent updates can keep plans current, but excessive updates can cause churn.

The archetype works best when preparation can be staged, reversible, or scaled to uncertainty. It is riskier when preparation is irreversible, costly, or socially consequential and the forecast signal is weak.

prose_invariants_to_preserve: The forecast must remain tied to action. Uncertainty must remain visible. Lead-time requirements must drive cadence and escalation. Preparation must be revisable as evidence changes. Forecast mechanisms must not substitute for the preparation loop. The system must avoid both passive waiting for certainty and reckless commitment to weak forecasts.

prose_target_outcomes: The desired outcomes are earlier preparation, fewer avoidable surprises, better-calibrated capacity and resource allocation, lower emergency response costs, clearer forecast accountability, and improved learning from forecast error. The archetype also helps simplify future uncertainty into action-relevant readiness decisions without pretending that prediction is perfect.

prose_parameter_dimensions:
Important tuning dimensions include forecast horizon, update cadence, acceptable false-alarm rate, acceptable miss rate, preparation cost, reversibility of action, trigger thresholds, uncertainty display format, escalation levels, and de-escalation criteria.

A short horizon may improve accuracy but reduce usefulness. A long horizon may preserve preparation time but widen uncertainty. Conservative thresholds reduce false alarms but increase late responses. Sensitive thresholds improve readiness but can produce alarm fatigue or wasted preparation.

prose_variants_and_near_names:
Recognized variants include Demand Anticipation, Capacity-Stress Forecasting, Early-Warning Preparation, Rolling Forecast Preparation Loop, and Forecast-Triggered Hedging. Near names and implementation names include forecast preparation, lead-time forecasting, demand forecasting, capacity forecasting, early-warning forecast, trend projection, planning estimate, and forecast communication.

The key policy is conservative: keep models, reports, dashboards, and projections as mechanisms unless they include the full forecast-to-preparation structure.

Demand Anticipation

short_definition: Forecasts future demand with enough lead time to adjust capacity, supply, staffing, or outreach before load arrives.

distinctive_feature: The forecast target is expected load or demand rather than a general future condition.

distinct_from_parent: The parent covers any future-state preparation loop; this variant focuses on demand-load matching.

still_same_parent_because: It still depends on a forecast target, lead-time requirement, uncertainty range, preparation action, and update rule.

source_primes: foreseeing uncertainty flow

use_when: Demand surges, seasonal patterns, user growth, or recurring cycles can be partially anticipated.

use_when: Late response would create shortages, queues, waste, overload, or missed service obligations.

use_when: The system can change capacity, inventory, staffing, scheduling, or communication before demand materializes.

example_domains:
retail operations
healthcare staffing
cloud infrastructure
public transit
food systems

domain: healthcare

example: Forecasting winter appointment demand early enough to adjust clinic staffing and vaccine outreach.

domain: cloud operations

example: Estimating traffic growth before a product launch so compute capacity is provisioned before saturation.

failure_modes: forecast optimized for accuracy rather than action lead time

Capacity-Stress Forecasting

short_definition: Forecasts future stress on constrained capacity so reserves, rate limits, staffing, or rerouting can be prepared before overload.

distinctive_feature: The focal future state is capacity stress rather than demand level alone.

distinct_from_parent: The parent anticipates future states broadly; this variant turns forecast output into capacity protection.

still_same_parent_because: It still implements the same forecast-to-preparation structure and should not be separated from update and abort rules.

source_primes: foreseeing constraint boundedness

use_when: A resource, queue, team, facility, budget, network, or supply chain has hard or soft capacity limits.

use_when: Capacity additions or protective controls require lead time.

use_when: The cost of overload is higher than the cost of measured preparation.

example_domains:
infrastructure
software reliability
hospital operations
supply chains
public administration

domain: transportation

example: Forecasting holiday ridership stress early enough to add service and reroute maintenance windows.

domain: public administration

example: Forecasting benefit-application surges before a deadline so temporary review capacity can be staged.

failure_modes: using average demand while ignoring peaks

failure_modes: preparing only after overload is visible

failure_modes: locking in excess capacity after risk passes

Early-Warning Preparation

short_definition: Uses early signals to forecast an emerging threat or disruption and activate preparation before the full event arrives.

distinctive_feature: The distinctive focus is acting on early warning under uncertainty, not merely forecasting expected demand.

distinct_from_parent: The parent includes routine planning forecasts; this variant handles emerging risk and weak-signal escalation.

still_same_parent_because: It still uses a forecast target, signal basis, uncertainty representation, trigger threshold, and preparation action.

use_when: Signals are ambiguous but consequential if they indicate an emerging disruption, safety issue, or risk escalation.

use_when: Waiting for certainty would eliminate useful preparation time.

use_when: Preparation can be staged, reversible, or scaled as confidence improves.

example_domains:
public health
cybersecurity
safety engineering
disaster operations
financial risk

domain: public health

example: Using rising syndromic surveillance signals to stage testing capacity before a confirmed outbreak peak.

domain: cybersecurity

example: Escalating monitoring and patch prioritization when threat intelligence forecasts exploitation of a vulnerable system.

failure_modes:
alarm fatigue
false certainty from weak signals
preparing for every low-quality warning
no de-escalation after signal reversal

Rolling Forecast Preparation Loop

short_definition: Continuously refreshes forecast, uncertainty, thresholds, and preparation actions as new evidence arrives.

distinctive_feature: The distinctive focus is the repeated update loop rather than the original forecast estimate.

distinct_from_parent: The parent may use one forecast cycle; this variant emphasizes rolling recalibration and adaptive preparation.

still_same_parent_because: It remains centered on anticipation for preparation, not generic feedback or monitoring.

source_primes: foreseeing feedback adaptation

use_when: Future conditions are changing fast enough that one-time forecasts become stale.

use_when: Preparation actions can be incrementally adjusted rather than committed all at once.

use_when: The organization needs a standing cadence for forecast review and action revision.

example_domains:
finance
operations planning
software reliability
public health
supply chains

domain: supply chain

example: Refreshing demand and lead-time forecasts weekly so procurement commitments can be scaled before shortages or overstock.

example: Updating storm staffing forecasts as the weather track changes and de-escalating when risk falls.

failure_modes: moving thresholds to justify prior action

failure_modes: update cadence too slow for the decision

Forecast-Triggered Hedging

short_definition: Uses forecast thresholds to activate hedges, option-preserving moves, reserves, or staged commitments under uncertain futures.

distinctive_feature: The preparation action is a hedge or option rather than a direct capacity or operations change.

distinct_from_parent: The parent includes many preparation actions; this variant focuses on uncertainty-resilient hedging.

still_same_parent_because: It still depends on forecast target, uncertainty, trigger, preparation, update, and de-escalation components.

source_primes: foreseeing uncertainty adaptation

use_when: The forecast is uncertain but strong enough to justify partial protection or optionality.

use_when: Full commitment would be costly if the forecast is wrong.

use_when: Preparation can be staged through reversible or diversified actions.

example_domains:
finance
supply chains
climate adaptation
public-sector budgeting
project management

example: Activating alternate supplier contracts when forecasts show rising disruption probability but not enough certainty for full switching.

domain: climate adaptation

example: Staging reversible flood protections when seasonal forecasts cross a risk threshold.

failure_modes: hedges that become hidden full commitments

failure_modes: overpaying for protection against low-quality forecasts

near_names:
Forecast
Forecast Model
Demand Forecasting
Capacity Forecasting
Early Warning Forecast
Trend Projection
Planning Estimate
Forecast Communication


Anticipatory Offset Governance

type: archetype

source: generated

batch: Batch 01

canonical_family: Anticipation & Forward Models

aliases:
Anticipatory Offset Control
Strategic Response Offset Governance
Behavioral Compensation Anticipation
Preemptive Neutralization Control
Forward-Agent Response Governance

slug: feedforward

slug: feedback

slug: observability

slug: equilibrium

essence_one_line: Treat strategic pre-response as part of the intervention, not as noise after implementation.

compression_statement: Anticipatory Offset Governance is the intervention pattern of modeling who can foresee a planned rule, incentive, constraint, or signal; estimating how they can pre-adjust; measuring early offset indicators; and redesigning timing, information, incentives, monitoring, and recalibration rules so the realized net effect survives adaptive neutralization.

core_intuition: An intervention aimed at adaptive agents lands in a system that starts moving before impact. If the design assumes behavior is fixed, it overstates its effect. The archetype makes the anticipated response set part of the design object.

canonical_formula: net_effect = engineered_effect − anticipatory_offset ± adaptation_error; govern when |anticipatory_offset| threatens the intended effect size.

structural_problem: A planned intervention is evaluated as if targets will hold behavior fixed, but forward-looking agents can infer the intervention and adjust in advance, partially or fully neutralizing the intended effect.

trigger_conditions: The affected actors receive advance notice, can infer an upcoming policy, or can predict a recurring enforcement or incentive regime.

trigger_conditions: Actors have feasible pre-adjustment paths that reduce the intended effect, shift costs, accelerate activity before the rule changes, or move behavior outside the observed channel.

trigger_conditions: The measured outcome depends on strategic behavior rather than only physical constraints or passive response.

trigger_conditions: Past interventions show smaller-than-modeled effects after announcement, delayed rollout, or repeated exposure.

trigger_conditions: The intervention changes expectations, prices, enforcement probabilities, access rules, reporting incentives, or timing rather than simply changing a material capability.

trigger_conditions: The population contains sophisticated subgroups that can move sooner or more effectively than the designer can update controls.

symptoms: Large pre-period movement appears between announcement and implementation.

symptoms: A modeled treatment effect disappears, shrinks, or reverses after actors learn the rule.

symptoms: Compliance appears high while underlying behavior moves to substitute channels.

symptoms: Targets bunch activity just before a deadline, threshold, ban, audit, or price change.

symptoms: A safety, subsidy, quota, tax, or monitoring intervention is followed by compensating behavior that preserves prior exposure or payoff.

symptoms: Post-implementation metrics look successful only because the response moved outside the measured boundary.

symptoms: Teams repeatedly explain failed interventions as poor execution even when implementation matched the plan.

root_tension: The designer needs advance planning and communication, but advance visibility gives adaptive agents time and information to neutralize the planned effect.

anti_signatures: The relevant system is passive and cannot pre-adjust strategically; use ordinary forecasting, buffering, or feedforward design instead.

anti_signatures: The core problem is hidden action after protection from downside, not anticipatory offset before effect; use moral_hazard_mitigation.

anti_signatures: The task is only to make participants choose the desired strategy by payoff design; use incentive_compatible_rule_design or payoff_restructuring.

anti_signatures: The problem is a feedback loop already operating after outcomes occur; use feedback_loop_redirection or adaptive_response_recalibration.

anti_signatures: The task is simple preparation for an external future state rather than countering strategic target response; use anticipatory_forecasting.

structural_intervention: Make anticipated response part of the design: identify who can foresee the intervention, map their feasible offset actions, estimate the counterfactual net effect, govern information release and timing, align incentives where possible, monitor leading indicators, and recalibrate or randomize when offset pressure appears.

action_logic: Define the intended effect and the minimum net effect that would count as success.

action_logic: List actors, intermediaries, and affected subgroups that can observe, infer, or anticipate the intervention.

action_logic: Map each actor’s feasible offset actions, substitution channels, timing shifts, reporting changes, and arbitrage paths.

action_logic: Separate mechanical effect, announcement effect, expectation effect, and behavioral compensation in the causal model.

action_logic: Specify leading indicators that reveal pre-adjustment before the intervention fully lands.

action_logic: Choose information-release, rollout, randomization, or commitment rules that reduce harmful pre-adjustment without undermining legitimacy or preparation needs.

action_logic: Redesign payoffs, constraints, eligibility, monitoring, or fallback rules so neutralization is less attractive or less feasible.

action_logic: Run pre-implementation simulations, sentinel pilots, or red-team exercises against strategic response pathways.

action_logic: Measure net effect against the offset-adjusted counterfactual rather than the static baseline.

action_logic: Recalibrate, pause, or redesign when offset indicators exceed the intervention’s tolerance band.

decision_rules: If actors cannot know or infer the intervention before behavior changes, treat the issue as ordinary adaptation rather than anticipatory neutralization.

decision_rules: If disclosure is ethically, legally, or operationally required, preserve disclosure and use incentive alignment, phased rollout, monitoring, or randomization rather than secrecy.

decision_rules: If offset channels are cheaper than compliance, redesign the payoff or constraint structure before rollout.

decision_rules: If pre-period behavior changes materially after announcement, include the announcement window in evaluation.

decision_rules: If the response is heterogeneous, segment by response capacity rather than averaging away the offset.

decision_rules: If neutralization appears through metric gaming, replace the metric or add ungameable corroborating signals.

decision_rules: If counter-response is expected to improve welfare or autonomy, do not suppress it; redesign the policy objective instead.

target_invariants: The intended net effect remains explicit and separately measured from the engineered intervention input.

target_invariants: Adaptive response pathways are documented before rollout rather than discovered only after failure.

target_invariants: Required transparency, fairness, and due-process constraints are preserved.

target_invariants: Evaluation includes anticipation windows, substitution channels, and heterogeneous response capacity.

target_invariants: Recalibration rules are triggered by offset evidence, not by blame narratives about implementation.

expected_outcomes: Interventions are less likely to be neutralized by preemptive behavioral compensation.

expected_outcomes: Forecasts of policy or rule impact become more realistic because they include strategic response.

expected_outcomes: Announcement effects, anticipatory pull-forward, substitution, and metric gaming are detected earlier.

expected_outcomes: Design teams can distinguish weak intervention logic from strong implementation undermined by adaptive offset.

expected_outcomes: Legitimate preparation is preserved while harmful neutralization pathways are constrained or monitored.


name: Appearance vs. Reality Distinction Audit

batch: Second Iteration/Batch 02

aliases:
Appearance/Reality Audit
Phenomenal Claim Warrant Audit
Experience–Reality Boundary Check
Subjective/Object Claim Split
Perception-Limited Claim Audit

slug: phenomenalism

slug: phenomenology

slug: representation

slug: ontology

slug: uncertainty

slug: schema

slug: validation

components:
Claim Inventory
Appearance–Reality Classifier
Evidential Source Trace
Bridge Assumption Register
Ontology Commitment Map
Warrant Boundary Statement
Language Calibration Rule
Decision Implication Map
Experience Condition Set
Phenomenological Report Channel
Measurement Corroboration Plan
Revision Trigger

essence_one_line: Separate what is warranted by experience, perception, report, or instrumented appearance from what is being claimed about underlying or mind-independent reality.

compression_statement: Appearance vs. Reality Distinction Audit is the intervention pattern of inventorying claims, classifying their evidential footing, exposing bridge assumptions between appearances and asserted realities, and recalibrating language and decisions so an experience, observation, or measurement is neither dismissed nor overextended.

core_intuition: We usually meet the world through appearances: reports, sensations, instrument readings, dashboards, behavior, and social presentations. The problem is not that appearances are worthless; it is that they do not automatically license every stronger claim about causes, objects, internal states, or reality. The audit protects both sides: it preserves the reality of experience as experience while preventing unsupported leaps from appearance to object-level certainty.

canonical_formula: calibrated_claim = classify(claim, evidence_source, appearance_type, bridge_assumptions, ontology_commitments, warrant_boundary, decision_implications)

structural_problem: A person or system treats appearances, reports, perceptions, instrument readings, or socially presented facts as if they directly established underlying reality, or conversely dismisses appearances because they are not already objective measurements. The result is confused evidence, overconfident claims, and decisions that fail to distinguish experience, observation, inference, and ontology.

trigger_conditions: A decision depends on evidence that is mediated by perception, testimony, instrumentation, interface display, measurement model, or social convention.

trigger_conditions: Participants disagree about whether a claim describes an experience, an observation, an inferred cause, an institutional fact, or mind-independent reality.

trigger_conditions: Subjective reports and objective measurements diverge, and neither can responsibly be ignored.

trigger_conditions: A theory, report, dashboard, diagnosis, product evaluation, or policy argument uses language that silently shifts from “appears” to “is.”

trigger_conditions: The domain has high stakes and a risk of either dismissing lived experience or overclaiming from limited observations.

trigger_conditions: A claim needs different action implications depending on whether it is a report, observation, inference, or established reality claim.

symptoms: Reports use objective-sounding language for what is only perceived, self-reported, sampled, or instrument-displayed.

symptoms: Decision-makers argue past each other because one side is defending the reality of an experience while the other is questioning the inferred cause.

symptoms: A dashboard, benchmark, or test result is treated as the system state rather than as a measurement produced through a model and interface.

symptoms: User or patient experience is dismissed because it is not immediately corroborated by external measurement.

symptoms: A philosophical or methodological debate stalls because participants have not named their assumptions about appearances, possible experiences, and mind-independent objects.

symptoms: Communication alternates between “it seems,” “it is,” “we know,” and “users feel” without a clear warrant boundary.

root_tension: Action requires making inferences from appearances, but appearances are mediated and partial; the system must preserve what experience and observation legitimately show without treating them as automatic access to all underlying reality.

anti_signatures: The task is mainly to collect first-person reports or stakeholder experience rather than calibrate claim status; lived_experience_capture may be the better fit.

anti_signatures: The task is mainly to include marginalized knowers or repair credibility injustice; epistemic_inclusion_design may be the better fit.

anti_signatures: The task is mainly to select the best representation format or model for a target; representation_fit_selection may be the better fit.

anti_signatures: The task is mainly to define the entities and relations in a domain ontology; ontology_clarification may be the better fit.

anti_signatures: The task is mainly to trace source reliability, provenance, or chain of custody; source_provenance_triangulation may be the better fit.

anti_signatures: The task is mainly to separate emotional reaction from evidence; affect_evidence_separation may be the better fit.

anti_signatures: The claim is already well classified and the only remaining question is whether the evidence is statistically strong enough; hypothesis_testing_frame or validation patterns may be more relevant.

structural_intervention: Create an explicit claim-warrant boundary by inventorying claims, classifying each claim’s appearance/reality status, tracing evidence sources, naming bridge assumptions, mapping ontology commitments, and recalibrating language and decisions to the level of warrant actually available.

action_logic: Collect the claim set before editing language, including informal statements, dashboard labels, findings, diagnoses, user quotes, metric interpretations, and proposed conclusions.

action_logic: Identify the immediate evidence source for each claim: first-person report, third-person observation, instrument reading, telemetry, model output, institutional rule, or inference from other claims.

action_logic: Classify the claim as an appearance/report claim, measurement claim, inference claim, social-reality claim, mind-independent reality claim, or mixed claim.

action_logic: For each movement from appearance to reality, write the bridge assumption that justifies the stronger claim.

action_logic: Name ontology commitments: what kinds of entities, properties, states, or social facts must exist for the claim to be true as worded.

action_logic: Calibrate wording so the claim form matches warrant: “appears,” “is reported,” “is measured,” “is consistent with,” “we infer,” or “is established under stated conditions.”

action_logic: Attach decision implications: what can be acted on now, what requires corroboration, what should remain provisional, and what would change the classification.

action_logic: Review the audit with affected stakeholders or domain experts when misclassification could dismiss experience, overclaim objectivity, or distort high-stakes action.

decision_rules: Use appearance/report language when the evidence is a first-person experience, perception, interface impression, or local observation whose underlying cause is not established.

decision_rules: Use measurement language when an instrument, test, telemetry stream, or metric produces the claim, and keep the measurement model visible.

decision_rules: Use inference language when a claim moves from appearance or measurement to a hidden state, cause, object, intention, disease, risk, or system condition.

decision_rules: Use stronger reality language only when the bridge assumptions are warranted by convergent evidence, reliable measurement, stable theory, or domain-appropriate validation.

decision_rules: Do not dismiss an experience claim merely because it is first-person; classify it accurately and assess what it does and does not establish.

decision_rules: Do not treat objective-looking measurements as complete reality; preserve limits from sampling, instruments, models, observability, and frame of reference.

decision_rules: When a claim concerns socially constructed reality, distinguish private appearance, institutional status, and physical or causal reality rather than forcing a binary subjective/objective split.

decision_rules: Escalate to review when language calibration would materially change diagnosis, blame, safety action, policy, access, or public communication.

target_invariants: Claim-warrant alignment: every claim must be worded at the level its evidence supports.

target_invariants: Experience preservation: first-person or phenomenal reports remain valid as reports of experience even when their causes are uncertain.

target_invariants: Reality-claim discipline: object-level, causal, or mind-independent claims require explicit bridge assumptions and corroboration.

target_invariants: Evidence-source traceability: reviewers can reconstruct how a claim moved from observation to conclusion.

target_invariants: Ontology transparency: hidden assumptions about what exists, what counts, and what kind of thing is being discussed are visible.

target_invariants: Decision relevance: the audit changes permissible action, inquiry, or communication rather than producing only philosophical labels.

target_invariants: Revisability: claim classifications can change when new evidence changes warrant.

expected_outcomes: Clearer separation between user experience, instrumented measurement, inferred cause, and established reality claim.

expected_outcomes: Reduced overclaiming from perception, telemetry, anecdote, dashboards, symptoms, or partial observations.

expected_outcomes: Reduced dismissal of lived or phenomenal experience when it is real and actionable as experience.

expected_outcomes: More precise research reports, diagnoses, product evaluations, policy arguments, and incident reviews.

expected_outcomes: Better disagreement resolution because parties can locate whether they dispute the report, the measurement, the bridge inference, or the ontology.

expected_outcomes: Improved trust in communication because uncertainty and warrant boundaries are visible.

prose_essence:
Appearance vs. Reality Distinction Audit is a claim-calibration pattern. It does not say that appearances are false, nor that reality claims are impossible. It says that a report, perception, interface display, measurement, or social presentation must be kept in the correct evidential lane until the bridge to a stronger claim is made explicit.

The practical move is simple: write down the claims, identify what each claim is immediately grounded in, classify the claim type, expose the assumption that would upgrade it to a stronger claim, and then decide what language and action are justified.

Perception-Limited Claims

variant_type: subtype

short_definition: Limit a claim to what available modes of perception, observation, or instrumentation can actually support.

distinctive_feature: The variant emphasizes claim scope: what the observer is licensed to say given the observation channel.

distinct_from_parent: The parent audits and separates appearance, reality, and bridge claims across an argument or decision context; this variant focuses on limiting individual claims to observational warrant.

still_same_parent_because: It uses the same appearance-versus-reality boundary and bridge-assumption discipline, but at the scale of claim wording and evidential scope.

source_primes: observability phenomenalism uncertainty

use_when: A statement is being inferred from observable appearances, reports, or outputs rather than directly established internal or mind-independent structure.

use_when: The available observation channel is partial, noisy, biased, or perspective-bound.

use_when: Users need wording that prevents an observation from becoming an overextended reality claim.

example_domains:
scientific reporting
monitoring dashboards
incident review
social research

domain: System monitoring

example: Say “the dashboard shows elevated latency” before claiming “the database is failing.”

domain: Clinical review

example: Say “the patient reports pain” and separately assess what, if anything, biomarkers show about underlying pathology.

failure_modes: Over-limiting claims can become artificial skepticism when convergent evidence is strong.

failure_modes: Under-limiting claims can turn local appearances into asserted reality without bridge evidence.

Sense-Experience Reduction Protocol

short_definition: Rewrite or analyze object-directed claims in terms of actual and possible experience conditions.

distinctive_feature: It is more reductive and formal than the parent: it attempts a translation from object claims into experience-condition claims.

distinct_from_parent: The parent can simply classify and separate claim types; this variant actively translates reality-claims into sense-experience conditions where possible.

still_same_parent_because: The translation still depends on the parent boundary between appearances, possible appearances, and object-level assertions.

use_when: The task is to expose what sensory or experiential content would make an object claim meaningful or testable.

use_when: A philosophical, methodological, or research design context needs a disciplined reduction of object language to experience language.

use_when: Participants disagree about whether a claim refers to mind-independent objects or to patterns of experience.

example_domains:
philosophy of perception
research-methods design
scientific interpretation
quality assurance

domain: Philosophy

example: Translate “the object is solid” into conditions about possible tactile, visual, and instrumental experiences.

domain: Testing

example: Define what observations would count as evidence that a hidden system state is present.

failure_modes: The reduction may erase explanatory structure that is not directly experiential but remains inferentially legitimate.

Symptom–Biomarker Distinction Audit

short_definition: Separate first-person symptom reports from objective or instrumented clinical indicators while preserving the evidential value of both.

distinctive_feature: It applies the appearance/reality distinction to clinical evidence streams where subjective experience and measured pathology can diverge.

distinct_from_parent: The parent is cross-domain; this variant is specialized to medicine and clinical reasoning.

still_same_parent_because: It still inventories claims, separates evidence modes, and names bridge assumptions between experience and underlying condition.

use_when: Patients report experiences that are not fully explained by available tests.

use_when: Clinical decisions risk privileging either subjective report or objective measurement too absolutely.

use_when: Care teams need to distinguish experience claims, measurement claims, and causal-diagnostic inferences.

example_domains:
diagnosis
chronic pain care
mental health assessment

domain: Medicine

example: Record “patient reports severe fatigue” separately from “lab results are within reference range” and “we infer no acute infection.”

failure_modes: Symptoms can be dismissed as unreal because they are not biomarker-confirmed, or biomarker readings can be ignored because reported experience is salient.

Perceived-vs-Measured Performance Split

short_definition: Separate what users or operators perceive from task-performance, telemetry, or outcome measurements.

distinctive_feature: It treats perception and measurement as parallel evidence streams rather than forcing one to override the other.

distinct_from_parent: The parent applies to any appearance/reality claim boundary; this variant is specialized to product, operations, and UX evaluation.

still_same_parent_because: It preserves the same separation between appearance claims, measured claims, bridge assumptions, and action consequences.

use_when: A system appears usable, fast, fair, safe, or reliable to users, but measured outcomes tell a different story.

use_when: Telemetry or benchmark scores look good, but users report confusion, anxiety, distrust, or friction.

use_when: A design decision requires both perceived experience and externally measured performance.

example_domains:
UX design
service operations
AI system evaluation
safety culture

domain: UX design

example: Track task completion rate separately from “users feel lost,” then decide whether the feature is objectively effective but experientially confusing.

failure_modes: Teams optimize telemetry while ignoring experienced friction, or overreact to perception while ignoring task outcomes.

Constructed Reality Boundary Audit

short_definition: Distinguish claims about socially stabilized realities from claims about private appearance or mind-independent physical reality.

distinctive_feature: It adds a third category: intersubjectively constructed reality, not just private appearance versus physical reality.

distinct_from_parent: The parent uses a basic appearance/reality distinction; this variant handles cases where “reality” is institutionally constructed and socially enforced.

still_same_parent_because: It still prevents unsupported category shifts among appearance, claim warrant, and asserted reality.

use_when: Participants treat institutional categories, roles, rankings, or statuses as objective facts.

use_when: An audit must distinguish private experience, social fact, and mind-independent fact.

use_when: The analysis risks collapsing social reality into either mere appearance or physical objectivity.

example_domains:
law
organizational status
identity categories
credentialing systems

domain: Organizations

example: Distinguish “employees experience the title as high-status,” “the company formally grants authority,” and “the person has causal competence.”

failure_modes: The audit can over-philosophize social categories or deny the practical force of institutional facts.

near_names:
Appearance/Reality Audit
Phenomenal Claim Warrant Audit
Subjective/Object Claim Split
Perception-Limited Claims
Sense-Experience Reduction Protocol
Experience–Reality Boundary Check
Appearance Claim Tagging


Approach–Avoidance Decomposition

batch: First Iteration/Batch 39/Wave

Goal Ambivalence Decomposition

notes: Useful when the salient feature is repeated ambivalence around a valued goal rather than the ontology term approach-avoidance.

Mixed-Valence Goal Redesign

notes: Highlights that the goal carries both positive and aversive valence and that the path can be redesigned rather than simply accepted or rejected.

Ambivalence Mapping

notes: A mapping method that may implement this archetype, but the archetype requires redesign and commitment logic beyond mapping.

Approach–Avoidance Mapping

notes: Near-name for the diagnostic part of the archetype. It should point here when paired with value preservation and commitment-path design.

slug: reactance

slug: controllability

slug: framing

components:
Approach Driver
Avoidance Driver
Mixed-Valence Map
Value Preservation
Aversion Reduction
Safeguard Boundary
Commitment Step
Identity Meaning Check
Social Cost Check
Reversibility Test
Support Requirement
Review Cadence

mechanisms:
Barrier Decomposition
Pros/Cons Mapping
Graduated Commitment Path
Risk Safeguard Design
Path Redesign Workshop
Commitment Ladder
Ambivalence Interview Guide
Pilot with Exit Criteria

essence_one_line: Separate the attractive and aversive components of a goal so ambivalent action can be redesigned.

compression_statement: When a valued goal produces both pull and resistance, decompose the approach drivers and avoidance drivers, preserve the desired value, reduce modifiable aversion, protect against legitimate downside, and define a bounded commitment step that can generate evidence rather than more oscillation.

core_intuition: Ambivalence becomes stuck when attraction and aversion are treated as one fused yes/no choice. By separating what is worth moving toward from what makes the path threatening, costly, or identity-challenging, the system can redesign the path instead of forcing a false choice between full commitment and avoidance.

canonical_formula: approach_driver + avoidance_driver -> value_preservation + aversion_reduction + safeguards -> bounded_commitment_step -> learning_or_decision_update

structural_problem: Actors repeatedly move toward and away from a valued goal because the reward, threat, cost, uncertainty, identity meaning, or effort load are fused into a single undifferentiated decision. The goal is attractive enough to keep returning to, but aversive enough to prevent stable commitment.

trigger_conditions: A person, team, user, or stakeholder group repeatedly says the goal matters but delays, reverses, under-commits, or reopens the decision.

trigger_conditions: The same option carries a clear benefit and a clear aversive element, such as status risk, effort burden, identity threat, loss exposure, social conflict, or uncertainty.

trigger_conditions: Risk calibration, autonomy support, or self-efficacy scaffolding explains part of the pattern but not the full coexistence of pull and resistance.

trigger_conditions: Discussion alternates between selling the goal and defending against the cost, without decomposing which pieces can be preserved, reduced, bounded, or staged.

trigger_conditions: The decision environment encourages all-or-nothing commitment even though a smaller test, redesigned path, or safeguard could produce useful evidence.

symptoms: Oscillation between enthusiasm and avoidance.

symptoms: Repeated planning without a next commitment step.

symptoms: Overgeneral labels such as not ready, too risky, not worth it, or I want it but cannot do it, without identifying which aversive part is decisive.

symptoms: Stakeholders argue as if one side is pro-goal and the other is anti-goal, when both sides may value the goal but weight the aversive path differently.

symptoms: The desired outcome is watered down until it no longer carries the original value, or the aversive barrier is ignored until resistance returns.

symptoms: Avoidance is treated as irrational resistance even when part of it signals legitimate risk, constraint, or unresolved cost.

root_tension: The system needs to honor the attractive value of the goal and the protective information contained in avoidance. If attraction dominates, real risks and costs are suppressed. If avoidance dominates, valuable goals remain unreachable. The archetype resolves the tension by decomposing both sides and redesigning the path so value, safety, and learning can coexist long enough for action.

anti_signatures: The issue is only objective downside risk and the goal is not otherwise ambivalent; Risk Aversion Calibration is closer.

anti_signatures: The issue is primarily threatened freedom or coercive restriction; Autonomy-Supportive Constraint Design is closer.

anti_signatures: The issue is only low confidence in capability without a valued-but-aversive goal structure; Self-Efficacy Scaffolding is closer.

anti_signatures: The issue is past uncontrollability and passivity rather than active oscillation around a valued goal; Helplessness Reversal is closer.

anti_signatures: The decision can be resolved by ordinary tradeoff scoring because both sides are already explicit and stable; a general tradeoff surface may be sufficient.

anti_signatures: The situation requires clinical treatment, trauma processing, or exposure therapy; this archetype should remain nonclinical and structural.

structural_intervention: Create a decomposition-and-redesign sequence that explicitly names the pull side and the avoidance side, distinguishes modifiable aversion from legitimate safeguards, preserves the core value of the goal, redesigns the path where possible, and ends with a bounded commitment step or principled non-commitment.

action_logic: Name the goal or option in a neutral form so the intervention is not framed as persuading, shaming, or forcing commitment.

action_logic: Identify the approach drivers: desired benefits, values, needs, opportunities, identity gains, learning gains, or social goods that make the goal attractive.

action_logic: Identify the avoidance drivers: risks, costs, effort loads, uncertainty, identity threats, social consequences, loss exposures, autonomy concerns, or capability doubts that make the same goal aversive.

action_logic: Separate legitimate protective signals from avoidable frictions. Some avoidance should become a safeguard; some should become a redesign target; some may reveal that the goal should be declined or re-scoped.

action_logic: Preserve the value by asking what outcome, principle, or benefit must remain intact for pursuit to still count as pursuing the goal.

action_logic: Reduce modifiable aversion through path redesign, support, sequencing, simplification, participation, or smaller exposure to cost, while keeping real constraints visible.

action_logic: Add safeguards for non-modifiable or legitimate downside: caps, opt-outs, reversibility, review points, fallback plans, consent boundaries, or escalation conditions.

action_logic: Define a bounded commitment step that is small enough to be tolerable and real enough to produce evidence about the next decision.

action_logic: Review the result so the system learns whether the redesigned path preserved value, reduced aversion, and clarified whether stronger commitment is warranted.

rule: Use this archetype when pull and resistance both attach to the same goal and neither side can be dismissed without losing important information.

rationale: The archetype is about mixed valence, not generic motivation or persuasion.

rule: If avoidance is legitimate and cannot be bounded, decline, defer, or redesign the goal rather than forcing commitment.

rationale: The archetype preserves safeguards; it does not treat avoidance as a defect to overcome.

rule: If the approach value disappears during redesign, stop and re-evaluate rather than celebrating reduced aversion around a now-empty goal.

rationale: A less aversive path that no longer delivers the value is a false resolution.

rule: If decomposition produces more analysis but no next step, add a commitment-step criterion or hand the issue to a decision-closure archetype.

rationale: The intervention must convert oscillation into evidence-generating action or principled non-action.

rule: If one avoidance driver dominates, route to the more specific neighboring archetype without losing the decomposition record.

rationale: Dominant risk, autonomy threat, capability doubt, or uncontrollability may need its own first-wave archetype.

invariant: Do not erase the value that made the goal worth approaching.

why_it_matters: Otherwise the redesign solves resistance by sacrificing the reason for action.

invariant: Do not dismiss avoidance signals as irrational by default.

why_it_matters: Avoidance may carry valid information about downside, identity threat, autonomy, workload, capability, or ethics.

invariant: Do not let analysis replace action indefinitely.

why_it_matters: The pattern is resolved by bounded commitment, principled refusal, or evidence-gathering, not by endless mapping.

invariant: Keep the frame nonclinical unless the domain explicitly belongs to clinical care.

why_it_matters: The archetype is a structural decision and design pattern, not therapy advice.

invariant: Preserve consent, authority, and risk boundaries.

why_it_matters: No mixed-valence redesign should push actors into exposure, loss, or obligation they lack authority or consent to accept.

outcome: Reduced oscillation around valued goals.

observable_signal: Actors can describe what they are moving toward, what they are protecting against, and what next step will test the path.

outcome: Better path design.

observable_signal: Avoidable aversive elements are reduced, while legitimate safeguards remain explicit.

outcome: More proportionate commitment.

observable_signal: Commitment size matches evidence, value, downside, and reversibility rather than swinging between overcommitment and avoidance.

outcome: Clearer stakeholder conversation.

observable_signal: Participants stop arguing as if the only options are full pursuit or total refusal, and can negotiate value-preserving alternatives.

outcome: Improved learning from ambivalent choices.

observable_signal: Small commitments produce evidence that updates both approach value and avoidance estimates.

prose_essence:
Approach–Avoidance Decomposition is for situations where a goal is not simply wanted or unwanted. The same goal pulls actors forward and pushes them back. The useful move is not to persuade harder, ignore fear, or abandon the goal immediately. The useful move is to split the goal into the value that attracts action and the aversive conditions that block commitment, then redesign the path so the value can be tested or pursued with appropriate safeguards.

The archetype is especially useful when people say some version of: “I want this, but…” The “want” and the “but” both carry information. The approach side identifies value. The avoidance side identifies cost, risk, identity threat, social exposure, loss of control, workload, uncertainty, or capability doubt. The intervention works by preserving the value while reducing avoidable aversion and protecting against legitimate downside.

prose_when_to_use:
Use this archetype when a person, team, stakeholder group, user population, or institution repeatedly circles a valued goal without making stable progress. The goal has a real attraction, but the path also carries threat, cost, effort, status risk, uncertainty, or identity disruption. The pattern is not simple indecision; it is mixed valence.

Good use cases include a learner who wants a challenge but avoids public mistakes, a team that wants the benefits of a new process but resists migration pain, a user who wants a product’s outcome but distrusts the onboarding path, or a professional who wants a leadership opportunity but resists the identity shift. In each case, the goal is valuable enough to approach and aversive enough to avoid.

Do not use it when the actor does not value the goal at all, when a hard ethical or safety boundary should stop action, or when the issue is better explained by a single neighboring pattern such as risk calibration, autonomy threat, low self-efficacy, or learned helplessness.

prose_structural_problem:
The structural problem is fused ambivalence. The attractive and aversive parts of the goal are bundled together, so the decision appears to be a binary choice: pursue the whole package or avoid the whole package. This bundling creates oscillation. Actors move toward the desired value, encounter the aversive side, retreat, then return because the value remains attractive.

This pattern often gets mislabeled. Advocates may call it resistance, procrastination, lack of courage, or lack of buy-in. Skeptics may call it risk, realism, or not being ready. Both labels can be incomplete. The issue is that different forces are acting at the same time, and the decision environment has not separated them.

The unresolved tension is that the system needs both movement and protection. If movement dominates, real risks and costs may be ignored. If protection dominates, valuable opportunities may remain unreachable. The archetype creates a way to keep both kinds of information in view.

prose_intervention_logic:
The intervention starts by naming the goal without pressuring commitment. Then it identifies the approach drivers: what makes the goal valuable, attractive, useful, meaningful, or necessary. Next it identifies the avoidance drivers: what makes the same goal costly, threatening, uncertain, identity-challenging, socially risky, or effort-heavy.

Once both sides are visible, the intervention classifies avoidance. Some avoidance is legitimate and should become a safeguard. Some is modifiable friction and should become a redesign target. Some reveals that the goal is not worth pursuing in its current form. That classification prevents the process from treating all avoidance as irrational resistance.

The design work then asks: what value must be preserved, what aversion can be reduced, what safeguards must remain, and what bounded next step will produce evidence? The result is not always full commitment. It may be a pilot, rehearsal, private first step, staged commitment, redesigned path, or principled refusal. What matters is that the system exits endless oscillation.

prose_key_components:
### Approach Driver

The approach driver names why the goal is attractive. It may be growth, safety, learning, belonging, revenue, justice, influence, mastery, efficiency, autonomy, or another concrete value. Without this component, the process becomes only barrier analysis and may redesign away the reason the goal mattered.

### Avoidance Driver

The avoidance driver names what pushes actors away from the same goal. It can include downside risk, effort burden, identity threat, public evaluation, social conflict, migration cost, loss of control, uncertainty, or capability doubt. This component should be treated as information, not as a personal flaw.

### Mixed-Valence Map

The mixed-valence map places the approach and avoidance drivers side by side. It prevents false simplification. The map helps a group see that someone can support the value of a goal and still resist the current path. This is often the turning point in stakeholder conversations because it changes the question from “Who is for or against this?” to “Which parts are valuable, which parts are aversive, and what can be redesigned?”

### Value Preservation

Value preservation defines what must remain intact for the redesigned path to still count as pursuing the goal. For example, a training challenge may need to preserve real skill growth; a product migration may need to preserve better analytics; a leadership path may need to preserve meaningful influence. Without value preservation, the intervention can make action easier by hollowing out the goal.

### Aversion Reduction

Aversion reduction redesigns avoidable barriers. It may reduce workload, sequence exposure, add support, simplify first steps, create privacy, invite participation, reduce unnecessary status threat, or make uncertainty learnable. It should not remove safeguards that protect against real harm.

### Safeguard Boundary

The safeguard boundary converts legitimate avoidance into explicit protection. A safeguard may be a cap, opt-out, fallback plan, review point, reversible pilot, consent boundary, escalation condition, or stop rule. This component prevents the archetype from becoming pressure to “push through” all resistance.

### Commitment Step

The commitment step defines the next bounded action. It should be small enough to be tolerable and real enough to produce evidence. A commitment step can be a pilot, prototype, first conversation, rehearsal, limited adoption, test migration, or decision to decline with reasons. Without this component, the archetype risks becoming endless ambivalence analysis.

prose_common_mechanisms:
### Barrier Decomposition

Barrier decomposition is an analysis mechanism that splits avoidance into categories such as risk, cost, identity, social exposure, capability, autonomy, uncertainty, and effort. It implements the archetype by giving different avoidance drivers different treatments. It is not the archetype by itself because it does not necessarily preserve value or define a commitment step.

### Pros/Cons Mapping

Pros/cons mapping can be a useful entry point because it makes pull and resistance visible. But a pros/cons list is only a mechanism. The archetype requires the additional work of deciding which value must be preserved, which aversion can be reduced, which downside needs safeguards, and what commitment step comes next.

### Graduated Commitment Path

A graduated commitment path sequences action from low commitment to stronger commitment. It is useful when all-or-nothing commitment intensifies avoidance. Each step should generate evidence about whether the goal remains valuable and whether the aversive side has become manageable.

### Risk Safeguard Design

Risk safeguard design adds protections where avoidance reflects real downside. This mechanism overlaps with risk calibration, but here it is used inside a broader mixed-valence pattern. It should not crowd out identity, social, effort, autonomy, or capability drivers when those are also active.

### Path Redesign Workshop

A path redesign workshop is a facilitated mechanism for groups. It helps stakeholders preserve the desired outcome while changing sequencing, support, workload, participation, or safeguards. It works best when the facilitator avoids framing one group as pro-change and another as anti-change.

### Commitment Ladder

A commitment ladder is a planning artifact that arranges progressively stronger steps. It is useful when the goal is meaningful but the first required commitment feels too large. The ladder should include review criteria, not just smaller steps, so it does not become permanent delay.

### Ambivalence Interview Guide

An ambivalence interview guide is a nonclinical coaching or discovery procedure. It asks structured questions about what pulls actors toward the goal and what pushes them away. It must remain non-shaming and non-coercive. It implements the archetype only if it leads to redesign or a bounded decision.

### Pilot with Exit Criteria

A pilot with exit criteria turns a conflicted goal into an evidence-generating test. The exit criteria matter because they preserve legitimacy for stopping, redesigning, or escalating. Without exit criteria, a pilot can become disguised pressure for full commitment.

prose_failure_modes:
A common failure mode is avoidance dismissal. The designer treats resistance as irrational and tries to remove it without examining whether it signals real downside. The mitigation is to classify avoidance before reducing it.

Another failure mode is value dilution. The path becomes easier, but the goal no longer delivers the original benefit. The mitigation is to define value preservation before redesign begins.

A third failure mode is endless analysis. The group keeps mapping reasons for and against the goal but never chooses a test, refusal, or next step. The mitigation is to require a commitment step or decision point after the decomposition.

A fourth failure mode is coercive exposure. Someone is pushed into discomfort, risk, disclosure, or public commitment without consent or safeguards. The mitigation is to preserve opt-out, reversibility, authority, and nonclinical boundaries.

A fifth failure mode is single-driver collapse. The process reduces the entire pattern to risk, autonomy, confidence, or workload. Sometimes that is correct, but if the goal remains mixed-valence, a single-driver solution will miss part of the structure.

prose_neighbor_distinctions:
**Risk Aversion Calibration** is the closest neighbor when the avoidance driver is downside risk. If risk is the whole issue, use risk calibration. Use Approach–Avoidance Decomposition when risk is only one aversive driver inside a broader pattern that also includes value, identity, social cost, effort, or commitment structure.

**Autonomy-Supportive Constraint Design** is closer when resistance is mainly caused by threatened freedom or coercive constraint. Approach–Avoidance Decomposition can include autonomy concerns, but it is not limited to restrictions or reactance.

**Self-Efficacy Scaffolding** is closer when the actor mainly doubts their capability. Approach–Avoidance Decomposition can use efficacy supports, but its signature is the coexistence of attraction and aversion around the same goal.

**Helplessness Reversal** is closer when past uncontrollability has produced passivity. Approach–Avoidance Decomposition applies when there is active oscillation rather than generalized futility.

**Tradeoff Surface Mapping** is an analytic neighbor. It compares dimensions of value, but this archetype adds motivational dynamics, aversion reduction, safeguards, and commitment design.

prose_cross_domain_examples:
In learning, a student may want an advanced project but avoid the public difficulty of participating. The archetype separates mastery value from shame threat and time burden, then creates a low-stakes first contribution with feedback.

In organizational change, a team may want a new workflow’s coordination benefits but avoid migration work and loss of local control. The archetype preserves coordination value, reduces migration friction, adds participation and rollback safeguards, and pilots the workflow.

In career design, a contributor may want influence but resist a leadership role because it threatens craft identity. The archetype preserves influence and craft value while creating a temporary technical-lead step.

In product adoption, customers may want analytics but avoid setup and migration. The archetype preserves analytics value, redesigns onboarding, adds data safeguards, and offers a reversible pilot.

In community action, residents may want improvement but avoid organizing because of conflict and possible disappointment. The archetype maps the shared value and social-cost aversion, then defines a small public action with feedback.

prose_non_examples:
A simple pros/cons worksheet is not the archetype. It may be one mechanism, but it lacks value preservation, aversion classification, safeguards, and commitment design.

A motivational speech is not the archetype. It may amplify the approach side while leaving the aversive side unchanged.

A hard safety, legal, or ethical boundary is not something to decompose away. If avoidance reflects a non-negotiable boundary, the correct response is to respect or redesign around that boundary, not to reduce resistance.

A clinical exposure protocol is not this archetype. This draft remains nonclinical and should not be used as therapy advice.

A disliked goal with no real approach driver is not a mixed-valence case. If there is no value to preserve, the question is not how to reduce avoidance but whether the goal should exist.

prose_tradeoffs:
The archetype increases clarity but can increase deliberation time. It should therefore be scaled to the stakes. Low-stakes situations may need only a quick map and a next step; high-stakes transitions may need a facilitated process.

It can reduce aversion but may also make pursuit easier in ways that weaken the goal. This is why value preservation is a core component. If the path becomes comfortable but meaningless, the intervention has failed.

It can protect actors from coercion, but too many safeguards can become disguised avoidance. This is why review criteria and escalation conditions matter.

It can make identity and social concerns discussable, but that can also feel exposing. Use privacy, consent, and non-shaming language when those concerns are active.

prose_invariants_to_preserve:
The first invariant is respect for both sides of the ambivalence. Attraction and avoidance are both treated as signals. The process fails if it assumes the attractive side is always right or the avoidance side is always irrational.

The second invariant is value preservation. The intervention must keep the core reason for approaching the goal visible throughout the redesign process.

The third invariant is legitimate protection. Real downside, consent boundaries, safety limits, and ethical constraints must remain intact.

The fourth invariant is movement toward evidence. The process should end in a bounded step, redesigned path, principled refusal, or evidence-gathering action. Mapping alone is not enough.

The fifth invariant is nonclinical framing. This archetype can support learning, design, organizational change, and decision processes, but it should not be presented as therapy advice or clinical exposure guidance.

prose_target_outcomes:
The target outcome is not simply more action. It is better-proportioned action. The actor or group should be able to say what they value, what they are protecting against, what has been redesigned, what remains guarded, and what the next evidence-generating step is.

A successful application reduces oscillation, improves path design, turns vague resistance into addressable drivers, and creates commitment steps that are neither reckless nor avoidant. It also improves communication: stakeholders can stop treating one another as either courageous supporters or obstructive resisters and instead discuss the structure of the mixed-valence goal.

prose_parameter_dimensions:
The first tuning dimension is commitment size. If the step is too large, avoidance remains high. If it is too small, it may not test the goal. The right step is large enough to produce evidence and small enough to preserve safety, consent, and reversibility.

The second dimension is value preservation. Some redesigns reduce aversion by reducing the desired value. This may be acceptable if the goal is being deliberately re-scoped, but it is a failure if the original value silently disappears.

The third dimension is safeguard strength. Too few safeguards create coercion or reckless exposure. Too many safeguards can preserve avoidance and prevent learning. Safeguards should match legitimate downside.

The fourth dimension is privacy versus visibility. Some mixed-valence goals need private rehearsal before public commitment. Others need visible commitment to produce coordination or accountability. The tuning depends on social cost, identity threat, and stakeholder trust.

The fifth dimension is review cadence. Ambivalent commitments need review points because both the approach and avoidance estimates may change after action. Review criteria should be explicit before the commitment step begins.

prose_variants_and_near_names:
**Identity-Conflicted Goal Redesign** is a recognized variant where the aversive side is tied to role, identity, belonging, status, or self-concept. A person may want the opportunity but resist who they would have to become to pursue it.

**Adoption Ambivalence Decomposition** is a domain variant for product, service, technology, or organizational adoption. Users or teams want the promised value but avoid the transition path. The design task is to preserve the value while reducing switching pain and adding safeguards.

**Commitment Ladder Design** is an implementation-oriented variant where the main lever is staged commitment. It remains a variant unless future evidence shows a broader archetype that applies beyond mixed-valence goals.

Near names include goal ambivalence decomposition, mixed-valence goal redesign, approach–avoidance mapping, and ambivalence mapping. Pros/cons mapping should remain a mechanism name, not a separate archetype.

Identity-Conflicted Goal Redesign

short_definition: A variant where the goal is attractive but aversive because pursuing it threatens role identity, belonging, status, or self-concept.

distinctive_feature: The aversive side is identity meaning rather than only risk, cost, or effort.

distinct_from_parent: The parent covers any mixed-valence goal; this variant specifically preserves value while lowering unnecessary identity threat or redesigning role transition.

still_same_parent_because: It still decomposes approach and avoidance drivers and converts the result into a bounded commitment path.

use_when: The action is technically possible but feels like becoming a different kind of person or betraying an existing role.

use_when: Avoidance is expressed as not me, people like us do not do that, or I would lose standing if I tried.

Identity Meaning Check

role: Examines whether the aversive side is tied to role identity, belonging, status, or self-concept.

Value Preservation

role: Keeps the desired value intact while the path is redesigned.

Commitment Step

role: Defines the bounded next action that produces evidence without forcing all-or-nothing commitment.

Private Rehearsal

role: Tests the action privately before public commitment when social or identity exposure is a major avoidance driver.

Role Model Signal

role: Shows credible examples of people preserving identity value while pursuing the goal.

Graduated Commitment Path

role: Moves from lower to stronger commitment as evidence and tolerance improve.

example: A technical expert wants leadership influence but resists manager identity; the path begins with a bounded mentorship role.

failure_modes: Treating identity concern as irrational vanity

failure_modes: Reducing the role transition so much that the desired growth disappears

Adoption Ambivalence Decomposition

short_definition: A product, process, or organizational adoption variant where actors want the promised benefit but avoid the switching path.

distinctive_feature: The approach value is the adopted capability, while the avoidance drivers concentrate in the transition path.

distinct_from_parent: The parent is domain-general; this variant is specific to adoption, switching, onboarding, and migration contexts.

still_same_parent_because: It uses the same decomposition and value-preserving path redesign logic.

use_when: Users or teams agree with the value proposition but delay uptake.

use_when: The aversive side includes migration cost, trust, workload, loss of control, training burden, or reversibility concerns.

Support Requirement

role: Specifies assistance, authority, time, or resources needed to make the next step viable.

Safeguard Boundary

role: Protects against legitimate downside with caps, reversibility, fallback plans, or review points.

Review Cadence

role: Defines when evidence will be reviewed and whether to continue, redesign, escalate, or exit.

Pilot with Exit Criteria

role: Creates a bounded trial with pre-agreed continuation, redesign, or exit criteria.

Migration Sandbox

role: Lets actors test an adoption path without exposing core operations to full switching risk.

Onboarding Commitment Ladder

role: Stages adoption steps so users can build evidence and support before full transition.

example: A team wants automation benefits but avoids migration until a sandbox, rollback path, and staged handoff are created.

failure_modes: Selling benefits harder while leaving switching pain intact

Commitment Ladder Design

short_definition: A variant that resolves mixed valence by arranging progressively stronger commitments with evidence, support, and exit criteria at each step.

distinctive_feature: The main redesign lever is commitment granularity over time.

distinct_from_parent: The parent may use many forms of aversion reduction; this variant relies specifically on staged commitment.

still_same_parent_because: The ladder works only after approach and avoidance drivers have been decomposed and value preservation has been defined.

source_primes: scaffolding threshold uncertainty

use_when: All-or-nothing commitment is intensifying avoidance.

use_when: The goal can be tested, staged, or reversed without destroying its value.

Reversibility Test

role: Determines whether action can be piloted, staged, reversed, or contained.

Commitment Ladder

role: Defines progressively stronger commitments with review criteria at each step.

example: A company tests a market with a reversible pilot before scaling investment.

failure_modes: Making steps so small they do not test the goal

failure_modes: Letting staged commitment become permanent delay

near_names:
Ambivalence Mapping
Approach–Avoidance Mapping
Pros/Cons Mapping
Goal Ambivalence Decomposition


Approximation-Target Divergence Mapping

batch: Second Iteration/Batch 03

slug: refinement

slug: approximation

slug: iteration

slug: optimization

slug: convergence

slug: calibration

slug: threshold

slug: overfitting

slug: robustness

essence_one_line: Refine an approximation by mapping where it diverges from the target, then focus improvement effort on the most consequential gaps.

compression_statement: Approximation-Target Divergence Mapping is the pattern of treating the difference between a current approximation and an intended target as a structured object. Instead of refining everywhere or reacting to the most visible defects, it specifies the target, snapshots the approximation, decomposes their divergence by dimension, distinguishes acceptable approximation error from material mismatch, and directs refinement effort toward the gaps whose correction most improves fit.

core_intuition: An approximation is useful because it is not the full target, but refinement fails when nobody knows which differences matter. A visible map of target divergence turns vague improvement into selective correction: keep harmless simplifications, repair consequential mismatches, and avoid polishing irrelevant details.

canonical_formula: refinement_priority_i := consequence(target_i - approximation_i) × tractability_i × confidence_i; refine gaps above the action threshold while preserving acceptable approximation bounds

structural_problem: A model, prototype, plan, performance, forecast, classification, or artifact is recognized as approximate, but the divergence between the approximation and the target is implicit, unevenly perceived, or poorly prioritized. Teams then refine by habit, salience, stakeholder pressure, available tools, or aesthetic polish rather than by the gap structure that determines whether the approximation is becoming more fit for purpose.

condition: A current approximation is serviceable but known to miss the target in several ways.

diagnostic_use: Create a divergence map before choosing the next refinement action.

condition: Some deviations are harmless simplifications while others threaten validity, safety, value, or acceptance.

diagnostic_use: Separate acceptable approximation error from material mismatch.

condition: Refinement effort is scarce and not all gaps can be closed at once.

diagnostic_use: Rank divergence dimensions by consequence, tractability, and evidence quality.

condition: Different stakeholders disagree about what “closer to target” means.

diagnostic_use: Make target dimensions and tolerance bands explicit.

condition: A prototype, model, or performance keeps improving locally without improving overall fit.

diagnostic_use: Check whether refinement is polishing low-value details while structural gaps remain.

condition: The target itself is measurable only through proxies, benchmarks, or qualitative criteria.

diagnostic_use: Record proxy validity and uncertainty in the divergence map.

symptom: Refinement backlogs contain many fixes but no explicit link to target-state fit.

interpretation: The team is improving artifacts without a target-divergence model.

symptom: High-effort polishing changes do not materially improve performance, validity, acceptance, or usefulness.

interpretation: Refinement is being allocated to visible but low-consequence deviations.

symptom: Different reviewers keep identifying different “main problems” with the approximation.

interpretation: The target dimensions and gap criteria are not shared.

symptom: A simplified model is rejected wholesale because its acceptable approximation limits are not documented.

interpretation: Harmless simplification and material divergence are being conflated.

symptom: A design or model passes average metrics but fails important edge cases or target regimes.

interpretation: Divergence has not been mapped by region, case type, or operating condition.

symptom: The project oscillates between broad redesign and minor patching.

interpretation: The gap map is missing; no one can tell which mismatches require local refinement versus target or architecture revision.

root_tension: Refinement requires comparison to a target, but approximations intentionally differ from targets. The archetype manages the tension between preserving useful simplification and correcting consequential mismatch.

case: The goal is open-ended exploration with no target state, benchmark, or evaluation frame.

reason: Use divergent exploration, discovery, or ideation patterns before target-divergence mapping.

case: Exact replication is mandatory and any deviation is unacceptable.

reason: Use compliance verification, quality control, or tolerance enforcement rather than approximation refinement.

case: The target is still politically or conceptually contested and cannot be provisionally specified.

reason: Use alignment governance, requirements negotiation, or scenario framing first.

case: The approximation is already good enough and further refinement has low marginal value.

reason: Use a refinement stop rule, minimum sufficient solution, or bounded approximation boundary.

case: The issue is runaway divergence from a dynamic path rather than static or staged target mismatch.

reason: Use divergence_detection_and_correction or control-loop stabilization.

structural_intervention: Transform refinement from an undirected improvement loop into a target-relative gap map: define the target, snapshot the approximation, decompose divergence by dimension and context, score the materiality of each gap, choose refinement actions, and verify whether the gap structure has improved.

action_logic: Specify the target state, benchmark, score, tolerance band, reference artifact, or qualitative acceptance criteria.

action_logic: Capture the current approximation in comparable terms, including known simplifications and assumptions.

action_logic: Decompose divergence into dimensions such as function, structure, timing, scale, cost, safety, user experience, aesthetics, fidelity, or edge-case behavior.

action_logic: Label each divergence as acceptable approximation, material defect, uncertain mismatch, or target-specification ambiguity.

action_logic: Score gaps by consequence, tractability, uncertainty, dependency, and regression risk.

action_logic: Translate high-priority gaps into refinement actions with verification criteria.

action_logic: Re-evaluate the approximation after refinement and update the divergence map rather than treating fixes as final by default.

rule: Do not refine a deviation unless it is linked to a target dimension or explicitly logged as exploratory.

rationale: Prevents decorative or habit-driven improvement from consuming scarce effort.

rule: Preserve deviations that are inside the accepted approximation envelope.

rationale: Maintains the value of abstraction and bounded simplification.

rule: Prioritize gaps that are high consequence, high confidence, and tractable, unless a low-tractability gap blocks all downstream fit.

rationale: Directs effort toward improvements that materially change target alignment.

rule: When repeated refinement fails to close the same gap, review the target, representation, measurement method, or architecture.

rationale: Persistent divergence may indicate the approximation frame is wrong rather than merely incomplete.

rule: Treat edge-case divergence separately from average divergence when the target includes robustness or safety.

rationale: Average closeness can hide target violations in critical regimes.

target_invariants: The target dimensions are explicit enough to compare against the approximation.

target_invariants: Acceptable approximation error is distinguished from material divergence.

target_invariants: Refinement effort is traceable to gap priority rather than salience alone.

target_invariants: The approximation remains useful and not over-refined beyond its purpose.

target_invariants: Verification checks whether the target gap changed, not merely whether work was done.

expected_outcomes: Improvement effort concentrates on consequential mismatches.

expected_outcomes: Teams can explain why some deviations are corrected while others are tolerated.

expected_outcomes: Refinement loops converge faster and with less wasted polish.

expected_outcomes: Approximation limits become visible to users, reviewers, or decision makers.

expected_outcomes: Persistent divergence triggers model, target, or architecture review earlier.

expected_outcomes: Quality, validation, and feedback activities are tied to a shared gap structure.

Metric Residual Divergence Mapping

short_definition: Map quantitative residuals between a model or process output and target values to guide selective refinement.

distinctive_feature: Divergence is expressed as measurable residual structure rather than narrative review alone.

distinct_from_parent: Narrower because the target gap is primarily quantitative.

still_same_parent_because: It retains the parent logic of target-relative gap decomposition and prioritized refinement.

source_primes: refinement approximation

use_when: Target dimensions can be measured or benchmarked.

use_when: Residuals vary by region, case type, subgroup, or operating regime.

example_domains:
simulation refinement
machine-learning error analysis
quality control

failure_modes: Average residuals hide high-consequence local divergence.

Qualitative Target-Gap Mapping

short_definition: Map divergence from a qualitative, interpretive, aesthetic, or experiential target using criteria rather than pure metrics.

distinctive_feature: Uses rubric, exemplar, stakeholder interpretation, or expert judgment to structure target divergence.

distinct_from_parent: Narrower because comparison depends on qualitative criteria.

still_same_parent_because: The intervention remains gap-directed refinement of an approximation toward a target.

use_when: The target includes style, meaning, user experience, judgment, or interpretive fit.

use_when: Numeric scores are insufficient or misleading.

example_domains:
musical performance
design review
policy implementation

failure_modes: Hidden preferences masquerade as target criteria.

failure_modes: Rubrics become too vague to guide refinement.

Benchmark-Anchored Divergence Mapping

short_definition: Use benchmark cases or reference examples to decide which approximation-target divergences require refinement.

distinctive_feature: Benchmark cases serve as the target anchors for divergence scoring.

distinct_from_parent: Narrower because the target is represented through test cases or exemplars.

still_same_parent_because: The core action is still mapping divergence and using it to prioritize refinement.

source_primes: refinement validation

use_when: Direct target measurement is difficult but benchmark cases exist.

use_when: The approximation must perform across known regimes before being trusted.

example_domains:
scientific model validation
software acceptance testing
engineering calibration

failure_modes: Systems overfit benchmarks while missing the real target.

Regression-Guarded Refinement

short_definition: Close one target gap while explicitly checking that already acceptable dimensions do not regress.

distinctive_feature: The divergence map includes protected satisfied dimensions, not only unsatisfied gaps.

distinct_from_parent: Narrower because regression control is the dominant constraint.

still_same_parent_because: It remains a target-divergence-guided refinement process.

use_when: Refinement actions interact across dimensions.

use_when: Local improvement can degrade prior target fit.

example_domains:
software releases
model tuning
service redesign

failure_modes: Guard set excludes newly important target dimensions.

near_names:
Target Divergence Mapping
Approximation Gap Analysis
Residual Gap Mapping
Gap-Directed Refinement