Payoff Restructuring

type: archetype

source: generated

batch: First Iteration/Batch 09

Payoff Redesign

notes: Plain-language wording variant.

Incentive Restructuring

notes: Near name in organizational and policy contexts; can be narrower if it only concerns rewards.

Incentive Adjustment

notes: Component-level name extracted in reconciliation controls; included for retrieval but not drafted as a separate archetype.

Reward Redesign

notes: Usually points to the positive-incentive variant rather than the whole archetype.

Penalty Design

notes: Usually points to deterrence-oriented payoff restructuring.

slug: feedback

slug: externality

slug: accountability

slug: screening

slug: signaling

components:
Payoff Map
Actor Strategy Profile
Desired Equilibrium Description
Incentive Adjustment
Penalty or Reward Rule
Monitoring and Verification Signal
Enforcement Capacity
Distributional Fairness Review
Gaming and Adaptation Review
Feedback Update Cadence
Outside Option Review
Risk Transfer Boundary
Appeal or Exception Path

mechanisms:
Targeted Subsidy or Bonus
Penalty, Tax, or Fee
Performance Bond or Deposit
Contract Incentive Clause
Reputation Score or Public Rating
Escrow or Holdback
Shared Savings or Gainsharing
Liability Shift or Warranty
Access Priority Rule
Clawback or Recovery Clause

essence_one_line: Change the rewards, costs, penalties, or risks in a strategic interaction so rational choices move toward a better outcome.

compression_statement: When actors choose harmful, unstable, or low-value behavior because the current payoff structure rewards it or fails to reward the alternative, map the strategic choices and alter expected rewards, costs, risks, access, or consequences so the preferred behavior becomes more attractive and stable.

core_intuition: People and organizations often do what the situation pays them to do, even when they say they value something else. Payoff restructuring changes the situation rather than only asking actors to be better. It works by making the desired action less costly, more rewarding, safer, more credible, more visible, or more protected than the action that currently dominates.

canonical_formula: strategic actors + undesirable payoff surface + feasible alternative behavior + credible reward/cost/risk change + monitoring and update loop → improved choice pattern or equilibrium

structural_problem: Multiple actors or decision units make interdependent choices, and the current distribution of rewards, costs, effort, risk, status, or opportunity makes a harmful or suboptimal action rational from the actor perspective. The system may be asking for one behavior while paying for another.

condition: Actors repeatedly choose an undesired behavior despite awareness, training, or stated agreement.

diagnostic_question: What payoff makes the unwanted behavior rational or easier than the desired behavior?

condition: The benefits of good behavior accrue to someone other than the actor who must bear the cost.

diagnostic_question: Who pays, who benefits, who controls the lever, and who carries the risk?

condition: Punishment, rewards, metrics, or access rules unintentionally favor the wrong action.

diagnostic_question: Which rule or metric currently converts bad behavior into local advantage?

condition: A coordination, compliance, safety, quality, or contribution problem persists because defecting or shirking has a better private payoff.

diagnostic_question: Would an actor be worse off if they complied while others did not?

condition: Actors appear to game a metric, exploit a loophole, shift harm to others, or optimize visible output while degrading the underlying outcome.

diagnostic_question: What proxy is being rewarded instead of the real target?

symptoms: Repeated exhortations, training, or awareness campaigns produce little change because the underlying payoff remains unchanged.

symptoms: Actors comply symbolically while preserving the behavior that benefits them.

symptoms: High-quality, cooperative, or preventive work is underproduced because it is costly to the actor and rewarded poorly by the system.

symptoms: Harmful behavior persists because its cost is delayed, hidden, externalized, or borne by someone else.

symptoms: Actors focus on the measured or rewarded proxy rather than the intended outcome.

symptoms: Good actors are penalized for cooperation because competitors, peers, or counterparties can free ride or defect.

symptoms: The system oscillates between weak incentives and heavy enforcement because neither has been aligned with real choice conditions.

root_tension: The system wants actors to choose one behavior, but the local payoff surface makes another behavior more attractive, safer, easier, cheaper, more prestigious, or less risky. The intervention must alter consequences without destroying fairness, autonomy, trust, or the system goal.

case: Actors already prefer the desired behavior but lack capability, information, or access.

why_not: Capability building, information design, or resource allocation may be more central than payoff restructuring.

case: The problem is a one-actor optimization problem with no strategic interdependence.

why_not: Use process redesign, constraint adjustment, or direct optimization rather than game-theoretic payoff design.

case: The desired behavior is ethically unacceptable even if incentives could produce it.

why_not: The archetype changes choice architecture; it does not legitimize coercion, exploitation, or deceptive manipulation.

case: The payoff change cannot be observed, enforced, funded, or maintained.

why_not: An unenforceable incentive can create cynicism, gaming, or arbitrary punishment.

case: The behavior is driven primarily by identity, trauma, culture, legal prohibition, physical constraint, or moral commitment rather than payoff.

why_not: Payoff changes may still matter, but they should not be treated as the primary explanatory structure.

structural_intervention: Map the strategic interaction, define the desired behavior or equilibrium, identify the payoff terms that currently reward the wrong behavior, and change rewards, costs, penalties, risks, access, or future opportunity so the desired behavior becomes comparatively rational and credible.

Identify actors and choices

logic: List the actors whose decisions matter and the choices they can realistically make, including exit, delay, evasion, symbolic compliance, and substitutes.

Map current payoffs

logic: Represent the material, reputational, effort, risk, timing, and opportunity payoffs associated with each choice for each actor.

Define the desired stable behavior

logic: Specify the behavior or joint outcome that should become more attractive, not only the behavior to suppress.

Locate the perverse or missing payoff

logic: Find where the current system rewards defection, neglect, low quality, risk shifting, gaming, or undercontribution.

Choose a payoff lever

logic: Select whether to add rewards, reduce burdens, add penalties, change risk allocation, shift timing, grant access, expose reputation, or share gains.

Tie the payoff to verifiable behavior

logic: Use monitoring and verification signals close enough to the target behavior to avoid rewarding proxies or punishing noise.

name: Check gaming, fairness, and outside options

logic: Anticipate strategic adaptation, burden shifting, exclusion, loopholes, and alternative behaviors created by the new payoff surface.

Implement with feedback

logic: Apply the redesigned payoff, monitor behavior and side effects, and recalibrate as actors learn and the environment changes.

rule: Change payoffs only after identifying the current rationality of the undesired behavior.

why_it_matters: Otherwise the design may punish symptoms while leaving the true incentive untouched.

rule: Prefer positive or burden-reducing incentives when actors lack resources or control.

why_it_matters: Punishing constrained actors often creates injustice without improving behavior.

rule: Use penalties when harm is preventable, observable, and linked to actor control.

why_it_matters: Deterrence requires credible detection and fair attribution.

rule: Use risk reallocation when one actor controls a risk currently borne by someone else.

why_it_matters: Moving expected losses or liability can align care with control.

rule: Treat every new incentive as a new target for gaming.

why_it_matters: Actors respond to the rule as written, not to the designer’s intention.

rule: Include sunset, review, or recalibration rules for incentives that may outlive their purpose.

why_it_matters: A repaired payoff gap can become a new distortion after behavior, markets, or institutions adapt.

invariant: Actor-control fit

preserve: Consequences should track choices actors can actually influence.

invariant: Outcome proximity

preserve: Rewards and penalties should attach to behavior or outcomes close enough to the real target to discourage proxy gaming.

invariant: Credibility

preserve: The redesigned payoff must be reliable, enforceable, and expected by actors before it can change strategy.

invariant: Proportionality

preserve: Payoff changes should be large enough to shift behavior but not so large that they become coercive, punitive, or distortionary.

invariant: Distributional fairness

preserve: The design should not offload costs onto actors with the least power or least ability to change.

invariant: Strategic adaptivity

preserve: The design must remain monitorable and adjustable as actors discover new responses.

outcome: Improved behavioral alignment

description: Desired behavior becomes easier, safer, more rewarded, or less costly relative to the harmful alternative.

outcome: Reduced defection or free riding

description: The payoff advantage of shirking, exploiting, or noncontribution falls.

outcome: Better risk ownership

description: Actors who can prevent harm bear more of the expected cost of not preventing it.

outcome: More credible cooperation

description: Other actors can trust the new structure because it makes cooperation rational rather than merely promised.

outcome: Lower reliance on exhortation

description: The system no longer depends primarily on reminders, moral appeals, or heroic effort.

outcome: Inspectable tradeoff surface

description: Designers can see which costs, rewards, and risks were intentionally moved and why.

prose_essence:
Payoff Restructuring is the pattern of changing what a strategic situation pays actors to do. It applies when the system asks for one behavior but rewards another: quality is requested while speed is rewarded, cooperation is praised while free riding is cheaper, prevention is valued rhetorically while repair is funded, or safe behavior is expected while risk is borne by someone else.

The archetype does not assume that actors are greedy or immoral. It assumes that people and organizations respond to consequences, constraints, risk, status, opportunity, and effort. The practical move is to redesign those consequences so the desired behavior becomes a better response to the situation.

prose_when_to_use:
Use this archetype when undesirable behavior persists even after awareness, communication, or training has improved. That persistence is a clue that the behavior may be locally rational. The current payoff surface may reward defecting, rushing, hiding risk, delaying maintenance, shifting costs, or optimizing a proxy.

It is especially useful when several actors affect one another. A single actor may prefer cooperation, quality, or contribution in principle, but if others can defect, free ride, or avoid costs, unilateral good behavior may be punished. Payoff restructuring changes the strategic environment so the better action is not merely admirable but viable.

Do not reach for this archetype first when actors mainly lack skill, tools, access, information, or authority. In those cases, capability building or resource provision may come before incentives. Payoff restructuring works best when actors can act differently and the central blocker is that the current consequences make the wrong action more attractive.

prose_structural_problem:
The structural problem is a mismatch between system-level value and local actor payoff. The larger system may benefit from prevention, cooperation, truthfulness, contribution, quality, or restraint, while the actor who must choose those behaviors bears more cost than benefit. Over time, actors learn the actual payoff structure, not the declared values.

This is why purely moral language often fails. If the organization says “prioritize safety” but promotes only speed, speed is the real payoff. If a platform says “be reliable” but gives the same visibility to unreliable participants, reliability is under-rewarded. If a shared resource benefits everyone but contribution is optional and invisible, noncontribution becomes rational.

The problem becomes more difficult when actors can game measurements. A poorly designed payoff can reward the appearance of the target behavior while degrading the real outcome. The archetype therefore treats monitoring, verification, and adaptation as part of the structure, not as administrative details.

prose_intervention_logic:
The intervention begins by mapping the current game. Who chooses? What can they choose? What do they gain or lose from each option? What risks, status effects, future opportunities, effort costs, and outside options matter? This map reveals why the unwanted behavior is rational.

The designer then defines the desired stable behavior. The aim is not simply to make bad behavior costly; it is to make a better pattern viable. That may mean rewarding contribution, reducing the cost of preventive work, tying access to reliability, moving liability toward the risk controller, sharing savings with the actor who creates them, or adding credible consequences for avoidable harm.

Finally, the design is tested against strategic adaptation. Actors will respond to the rule as implemented, not to the intention behind it. A good payoff restructure therefore includes verification, fairness review, appeal paths, gaming analysis, and a cadence for recalibration.

prose_key_components:
### Payoff Map

**component:** `payoff_map`

Represents the current rewards, costs, penalties, risks, status gains, effort burdens, and opportunity costs each actor faces under each plausible choice. The payoff map is the diagnostic base of the archetype. Without it, the intervention becomes guesswork or moral exhortation rather than structural redesign.

### Actor Strategy Profile

**component:** `actor_strategy_profile`

Identifies the relevant actors, the strategies available to them, and how each actor expects others to respond. Payoff restructuring is strategic: the same change can fail if it ignores substitutes, retaliation, coordination expectations, or hidden decision-makers.

### Desired Equilibrium Description

**component:** `desired_equilibrium_description`

Defines the behavior pattern or joint state the redesigned payoff structure is supposed to make stable or more attractive. This keeps the design from merely punishing an unwanted action without specifying what rational alternative should replace it.

### Incentive Adjustment

**component:** `incentive_adjustment`

Changes expected rewards, costs, penalties, subsidies, privileges, liabilities, or risk exposure so the preferred action becomes comparatively more attractive. The adjustment can add rewards, remove perverse rewards, impose costs, lower costs for desired behavior, or rebalance risk. It is a component, not the entire archetype.

### Penalty or Reward Rule

**component:** `penalty_or_reward_rule`

Specifies exactly what behavior triggers a reward, cost, privilege, loss, sanction, rebate, bond release, or other payoff change. A clear rule protects the design from ambiguity, favoritism, weak enforcement, and accidental reward of proxy behavior rather than target behavior.

### Monitoring and Verification Signal

**component:** `monitoring_and_verification_signal`

Provides evidence that the rewarded or penalized behavior actually occurred and that actors are not merely gaming a visible proxy. Because actors adapt to payoff rules, verification must be hard enough to fake and close enough to the target behavior to preserve incentive quality.

### Enforcement Capacity

**component:** `enforcement_capacity`

Ensures the promised payoff changes are applied consistently, credibly, and soon enough to affect future expectations. A weak or delayed consequence may become symbolic. Credibility is part of the payoff, not an administrative afterthought.

### Distributional Fairness Review

**component:** `distributional_fairness_review`

Checks who bears the new costs, who receives the benefits, and whether vulnerable or low-control actors are being punished for constraints they cannot change. Payoff restructuring can be effective and still unjust. The fairness review helps distinguish legitimate incentive repair from coercive burden-shifting.

### Gaming and Adaptation Review

**component:** `gaming_and_adaptation_review`

Anticipates how actors may exploit, evade, arbitrage, or symbolically satisfy the new payoff structure while preserving the old behavior. The review should be repeated after implementation because payoff changes create new strategies, not just new compliance.

### Feedback Update Cadence

**component:** `feedback_update_cadence`

Sets how often outcomes, side effects, compliance quality, and strategic responses are reviewed and the payoff design is recalibrated. Payoff restructuring is rarely one-and-done. As actors learn, the payoff surface can drift away from the intended behavioral effect.

Optional components such as the Outside Option Review, Risk Transfer Boundary, and Appeal or Exception Path become important when actors can exit the interaction, when risks are being reassigned, or when rigid rules could punish exceptional cases.

prose_common_mechanisms:
Mechanisms implement the archetype, but none of them is the archetype by itself. A tax, bonus, contract clause, or rating system is only payoff restructuring when it deliberately changes the expected consequences of strategic choice.

### Targeted Subsidy or Bonus

**mechanism:** `targeted_subsidy_or_bonus`
**type:** `institution`

Adds a positive payoff for desired behavior, usually when the behavior creates system value but actors would otherwise underinvest in it. This implements the archetype by increasing the relative attractiveness of a chosen action; the subsidy itself is not the archetype.

### Penalty, Tax, or Fee

**mechanism:** `penalty_tax_or_fee`
**type:** `institution`

Adds a cost to behavior that imposes risk, waste, congestion, external harm, or strategic defection. Works only when the rule is measurable, enforceable, and not so blunt that it penalizes actors who lack viable alternatives.

### Performance Bond or Deposit

**mechanism:** `performance_bond_or_deposit`
**type:** `institution`

Requires an actor to place value at risk and releases or forfeits it depending on whether promised behavior occurs. Often used when future performance must be credible. It overlaps with commitment mechanisms but functions here as a payoff-restructuring implementation.

### Contract Incentive Clause

**mechanism:** `contract_incentive_clause`
**type:** `document`

Builds bonuses, penalties, retainage, clawbacks, service credits, warranties, or shared-savings provisions into an agreement. The contract turns the desired payoff map into a legally or institutionally durable commitment.

### Reputation Score or Public Rating

**mechanism:** `reputation_score_or_public_rating`
**type:** `metric_or_dashboard`

Changes future opportunities, trust, or status by making behavior visible and comparable to others. Reputational payoffs can be powerful but can also create gaming, stigma, measurement injustice, or herd behavior.

### Escrow or Holdback

**mechanism:** `escrow_or_holdback`
**type:** `institution`

Temporarily withholds value until performance, delivery, verification, or dispute resolution conditions are met. This mechanism restructures the timing of payoffs so delayed verification does not leave one side exposed.

### Shared Savings or Gainsharing

**mechanism:** `shared_savings_or_gainsharing`
**type:** `institution`

Splits the benefits of improved performance so the party able to change behavior receives part of the system-level gain. Useful when one actor controls the operational lever but another actor receives the original benefit.

### Liability Shift or Warranty

**mechanism:** `liability_shift_or_warranty`
**type:** `institution`

Moves downside risk toward the actor best positioned to prevent it, changing expected costs of low-quality or risky action. This should be calibrated to control, evidence, and insurance capacity; otherwise it becomes unfair risk dumping.

### Access Priority Rule

**mechanism:** `access_priority_rule`
**type:** `protocol`

Grants faster access, preferred queue position, capacity, visibility, or scarce resources to actors who meet desired behavior conditions. This uses non-monetary payoffs such as time, status, reliability, or opportunity access.

### Clawback or Recovery Clause

**mechanism:** `clawback_or_recovery_clause`
**type:** `document`

Recovers a previously granted payoff when later evidence shows misconduct, underperformance, misrepresentation, or failure to satisfy conditions. This reduces the benefit of short-term gaming when evidence arrives after the original reward.

prose_failure_modes:
Common failures include proxy gaming, overpaying for baseline behavior, perverse substitution, unfair burden shifting, weak credibility, intrinsic motivation crowd-out, enforcement escalation, distributional backlash, and overcorrection. These failures happen because payoff restructuring creates a new strategic environment; it does not eliminate strategy.

The remedy is not to abandon consequences, but to design them as living structures. Use downstream outcome checks, fairness review, exception paths, and recalibration. When actors evade a rule, treat the evasion as diagnostic evidence about the new payoff surface.

prose_neighbor_distinctions:
Payoff Restructuring is close to Incentive Alignment, but incentive alignment is a broader goal state. Payoff restructuring is the concrete pattern of changing rewards, costs, risks, access, or future opportunity.

It is close to Mechanism Design and Incentive-Compatible Rule Design, but those can involve creating an entire rule system for allocation, truthfulness, or coordination. Payoff restructuring can operate within an existing interaction by modifying its consequences.

It overlaps with Externality Internalization when the payoff problem is spillover harm. But payoff restructuring also covers contribution, quality, risk allocation, reputation, access, and cooperation problems where the issue is not simply an externality.

It differs from Feedback Loop Redirection because feedback is not enough unless it changes consequences or decisions. It differs from Elasticity-Based Leverage because elasticity asks where sensitivity is greatest, while payoff restructuring asks what consequence structure would make the desired behavior rational.

Coordination Equilibrium Shift remains on merge-review hold in this batch. It changes expectations and focal points; payoff restructuring changes the payoff matrix. The two can be combined, but they should not be collapsed here.

prose_cross_domain_examples:
In environmental policy, fees and rebates can make harmful resource use more costly and cleaner alternatives more attractive. In software organizations, promotion criteria can reward reliability work rather than only feature delivery. In procurement, service credits and uptime bonuses can align supplier payoff with buyer outcomes. In healthcare, payment models can reward prevention and quality while guarding against patient selection. In community governance, access privileges can be tied to contribution to shared resources.

Across these examples, the common pattern is not the specific instrument. The common pattern is changing what the situation pays actors to do.

prose_non_examples: A dashboard that merely displays behavior is not payoff restructuring unless consequences, decisions, access, or status change. A motivational speech is not payoff restructuring. A punishment for outcomes actors cannot control is not valid payoff restructuring; it is unfair sanctioning. A process improvement that reduces friction may support payoff restructuring, but it is a separate pattern unless effort cost is being deliberately treated as part of the strategic payoff.

prose_tradeoffs:
The main tradeoff is effectiveness versus legitimacy. Strong consequences can shift behavior, but they can also feel coercive, especially when actors have few alternatives. Precision is another tradeoff: accurate incentives require measurement, audits, exception handling, and maintenance. Simple incentives are easier to understand, but they may be unfair when actors have different capacities or constraints.

Positive incentives often preserve goodwill but cost resources and may pay for behavior that would have happened anyway. Penalties can deter harm but may also produce concealment, adversarial compliance, or avoidance of hard cases. Reputational incentives can be powerful but can create stigma, bias, and long recovery paths after failure.

prose_invariants_to_preserve: The first invariant is actor-control fit: consequences should track choices actors can influence. The second is outcome proximity: the rewarded or penalized signal should remain close to the real outcome. The third is credibility: actors must believe that the payoff will actually be applied. The fourth is proportionality: consequences should be strong enough to shift behavior without becoming coercive or punitive. The fifth is distributional fairness: the design should not transfer burdens to those with the least power. The sixth is adaptability: the design must be revisited as actors learn how to respond.

prose_target_outcomes:
A successful payoff restructure makes the desired behavior more rational, not merely more requested. It reduces the advantage of defection, gaming, shirking, risk shifting, or free riding. It helps actors who want to do the right thing avoid being punished for doing it. It also makes cooperation more credible because actors can see that others now face consequences aligned with the shared outcome.

The best result is a quieter system: fewer heroic interventions, fewer repeated reminders, fewer adversarial audits, and fewer situations where the formal goal and actual reward pull in opposite directions.

prose_parameter_dimensions:
Payoff restructuring has to be tuned. A tiny incentive may be ignored; a huge incentive may distort the system. Immediate consequences are easier to learn from, but delayed consequences may better match long-term outcomes. Individual rewards can improve attribution while damaging teamwork; group rewards can support cooperation while creating free-rider risk.

The main tuning dimensions are payoff magnitude, timing, target granularity, verification burden, symmetry across actors, reversibility, visibility, and enforcement intensity. Each dimension changes the strategic meaning of the intervention. For example, a public rating creates reputational stakes; a private bonus creates financial stakes; a bond creates downside exposure; a shared-savings contract creates upside participation.

prose_variants_and_near_names:
Important variants include Reward or Subsidy Rebalancing, Penalty-Based Deterrence, Risk Reallocation Payoff Design, Free-Rider Payoff Rebalancing, and Reputational Payoff Restructuring. These variants differ in which part of the payoff surface they change: upside reward, downside cost, risk-bearing, contribution leakage, or future reputation.

Near names include Incentive Restructuring, Payoff Redesign, Reward Redesign, Penalty Design, and Incentive Adjustment. Taxes, subsidies, deposits, escrow, warranties, clawbacks, and ratings should usually be treated as mechanisms unless the design question is the broader payoff structure.

Reward or Subsidy Rebalancing

short_definition: Increase the payoff for a desired behavior when actors underinvest because the private gain is lower than the system value.

distinctive_feature: The decisive move is adding or improving positive payoff rather than imposing punishment.

distinct_from_parent: The parent covers all payoff changes; this variant focuses specifically on positive incentives and under-rewarded behavior.

still_same_parent_because: It still changes strategic choice by changing the payoff surface rather than by redefining the task, adding information alone, or changing expectations alone.

use_when: Desired behavior is socially or systemically valuable but privately under-rewarded.

use_when: Actors have viable alternatives and will not change through duty, persuasion, or information alone.

use_when: The sponsor can fund, allocate, or grant benefits without creating a larger distortion than the one being repaired.

Payoff Map

role: Identifies the private/system payoff gap.

Penalty or Reward Rule

role: Defines the condition for receiving the positive payoff.

Distributional Fairness Review

role: Checks who receives support and who is excluded.

Targeted Subsidy or Bonus

role: Adds a direct positive payoff.

Shared Savings or Gainsharing

role: Shares system gains with the actor who can change behavior.

example_domains:
public goods funding
sales compensation
energy efficiency
healthcare quality improvement

domain: Energy

example: A utility shares savings with customers who reduce peak demand instead of only asking for voluntary conservation.

domain: Workplace

example: A team bonus rewards cross-functional quality improvements that individual productivity metrics previously ignored.

failure_modes: Overpaying for behavior that would have happened anyway.

failure_modes: Rewarding a proxy that actors can game while the target outcome does not improve.

Penalty-Based Deterrence

short_definition: Increase the expected cost of harmful behavior so avoidance, compliance, or safer substitution becomes the rational choice.

distinctive_feature: The decisive move is raising expected cost for harmful or defective behavior.

distinct_from_parent: The parent includes rewards, risk-sharing, access rules, and reputational payoffs; this variant concentrates on deterrent costs.

still_same_parent_because: It restructures payoffs by changing the cost side of strategic choice.

source_primes: game_theory accountability externality

use_when: An unwanted action creates harm, risk, congestion, waste, or unfair advantage.

use_when: The action is observable enough to detect or credibly audit.

use_when: Actors have real alternatives, and the penalty will not simply punish unavoidable need.

Monitoring and Verification Signal

role: Detects the penalized behavior.

Enforcement Capacity

role: Makes the penalty credible.

Appeal or Exception Path

role: Prevents unjust punishment in edge cases.

Penalty, Tax, or Fee

role: Raises expected cost.

Clawback or Recovery Clause

role: Removes gains after later evidence.

example_domains:
regulation
platform governance
contract management
safety policy

domain: Platform governance

example: Repeated policy violations reduce seller privileges and impose escalating fees.

domain: Construction

example: A contract imposes liquidated damages for avoidable delay while preserving force-majeure exceptions.

failure_modes: Excessive penalties drive actors out of the system or into concealment.

failure_modes: Unequal enforcement turns the penalty into selective punishment.

Risk Reallocation Payoff Design

short_definition: Move expected gains, losses, liabilities, or uncertainty toward actors whose choices control those risks.

distinctive_feature: The payoff change occurs through allocation of downside or upside risk, not only through immediate payment.

distinct_from_parent: The parent includes any payoff change; this variant focuses on expected-value and risk-bearing structure.

still_same_parent_because: Risk-bearing changes the payoff surface and thereby changes strategic choice.

use_when: One actor controls the risky behavior while another actor bears the downside.

use_when: Risk-bearing, liability, insurance, or guarantee structure changes expected behavior.

use_when: The actor receiving risk has enough control to influence the outcome.

Risk Transfer Boundary

role: Defines legitimate risk allocation.

Distributional Fairness Review

role: Prevents unfair dumping of risk.

Monitoring and Verification Signal

role: Links outcomes to risk-bearing rules.

Liability Shift or Warranty

role: Places downside with the actor best positioned to prevent it.

Performance Bond or Deposit

role: Places value at risk before performance.

example_domains:
insurance
procurement
healthcare payment
product warranties
safety management

domain: Procurement

example: A supplier warranty makes defects costly to the party best able to prevent them.

domain: Healthcare

example: A bundled payment changes incentives from more procedures to fewer avoidable complications.

failure_modes: Risk is shifted to actors who cannot control it.

failure_modes: Actors avoid hard cases to protect metrics or liability exposure.

Free-Rider Payoff Rebalancing

short_definition: Reduce the private advantage of benefiting without contributing so contribution becomes rational, expected, or easier than noncontribution.

distinctive_feature: The key problem is asymmetric contribution to a shared benefit.

distinct_from_parent: The parent changes payoffs generally; this variant targets public-goods and collective-action leakage.

still_same_parent_because: It still changes contribution incentives through rewards, costs, access, or benefit allocation.

use_when: A shared resource, collective effort, or common standard benefits noncontributors.

use_when: Voluntary contribution is unstable because contributors subsidize noncontributors.

use_when: Participation can be measured or tied to access, benefit, status, obligation, or future claims.

Payoff Map

role: Shows contribution versus noncontribution payoffs.

Penalty or Reward Rule

role: Links contribution to benefit, cost, or access.

Access Priority Rule

role: Links access to contribution.

Targeted Subsidy or Bonus

role: Rewards contribution when exclusion is undesirable.

example_domains:
open-source maintenance
team collaboration
public infrastructure
standards consortia

domain: Open source

example: A consortium grants roadmap influence and support priority to organizations that fund maintainers.

failure_modes: Contribution requirements exclude actors with less capacity.

failure_modes: Actors perform visible contribution rituals while avoiding substantive work.

Reputational Payoff Restructuring

short_definition: Change behavior by making future trust, status, access, or opportunity depend on visible past behavior.

distinctive_feature: The changed payoff is social, reputational, relational, or opportunity-based rather than direct money or liability.

distinct_from_parent: The parent includes all payoff forms; this variant emphasizes indirect reputational payoffs.

still_same_parent_because: Reputation changes the expected future payoff of current behavior.

source_primes: signaling feedback accountability social_norms

use_when: Material penalties or rewards are unavailable, insufficient, or undesirable.

use_when: Actors value reputation, status, trust, or future opportunity.

use_when: Behavior can be measured in a way that is fair enough to publish, compare, or feed into access decisions.

Monitoring and Verification Signal

role: Supplies evidence for reputational judgment.

Distributional Fairness Review

role: Checks for stigma, bias, or unequal visibility.

Reputation Score or Public Rating

role: Makes behavior visible to future counterparties.

Access Priority Rule

role: Connects reputation to future opportunity.

example_domains:
marketplaces
peer review
professional credentials
community moderation

domain: Marketplace

example: Sellers with reliable fulfillment receive better placement and lower dispute friction.

failure_modes: Ratings become biased, stale, or strategically manipulated.

failure_modes: Low-reputation actors cannot recover even after behavior changes.

near_names:
Incentive Restructuring
Incentive Adjustment
Reward Redesign
Penalty Design
Payoff Redesign
Strategic Incentive Design


Peer Sanctioning Governance

batch: Batch 10

canonical_family: Norms, Legitimacy & Compliance

slug: reputation

slug: reciprocity

slug: internalization

slug: legitimacy

slug: proportionality

slug: trust

slug: cooperation

slug: monitoring

slug: boundary

slug: consent

slug: transparency

slug: normativity

slug: authority

components:
Enforced Norm Definition
Reference Group Boundary
Behavior Observability Channel
Violation Interpretation Rule
Distributed Witness Network
Social Sanction Repertoire
Proportionality and Graduation Ladder
Reputational Memory Channel
Repair and Reentry Path
Anti-Abuse and Retaliation Guardrail
Formal Escalation Boundary
Legitimacy and Consensus Signal
Privacy and Discretion Boundary
Positive Reinforcement Channel
Trusted Mediator or Elder Role
Sanction Impact Monitor

mechanisms:
Peer Accountability Check-In
Graduated Peer Sanction Protocol
Reputation Record or Standing Ledger
Community Moderation Process
Norm Reminder and Private Warning
Restorative Conference or Repair Circle
Membership Standing Review
Trusted Intermediary Warning
Counterparty Rating and Feedback
Access Restriction or Exclusion Rule
Non-Retaliation Reporting Channel

essence_one_line: Use legitimate peer visibility, reputational memory, graduated social sanctions, and repair paths to sustain norm compliance when formal enforcement is absent, incomplete, or too costly.

compression_statement: Peer Sanctioning Governance applies when a group, network, market, profession, or community needs members to follow shared norms but cannot or should not rely primarily on central authority. The intervention defines the norm, identifies the reference group, makes relevant behavior observable, calibrates decentralized sanctions, carries reputational memory, enables repair and reentry, and constrains abuse so social pressure stabilizes compliance rather than becoming mob punishment, exclusion, or factional control.

core_intuition: People often comply because others notice, remember, approve, trust, trade with, include, or avoid them. That pressure can be constructive when it is bounded by shared norms, proportional response, evidence discipline, and repair. It becomes destructive when every observer becomes a judge without context, appeal, or limits.

canonical_formula: shared_norm + bounded_reference_group + observable_behavior + distributed_witnessing + graduated_social_sanction + reputational_memory + repair_path + anti_abuse_guardrails -> sustained_compliance_without_primary_formal_authority

structural_problem: A norm matters for cooperation, safety, exchange, trust, identity, or collective viability, but formal enforcement is unavailable, too slow, too expensive, not trusted, outside the relevant jurisdiction, or unable to see everyday conduct. If no informal enforcement exists, violations become cheap and compliant actors feel exploited. If informal enforcement exists without design, it can become gossip, scapegoating, clique power, vigilantism, discriminatory exclusion, or permanent reputational harm.

trigger_conditions: Compliance depends on everyday behavior that formal authority cannot continuously observe or efficiently police.

trigger_conditions: Participants care about future relationships, reputation, belonging, access, referrals, or exchange opportunities.

trigger_conditions: The relevant group has enough shared expectation to distinguish norm compliance from norm violation.

trigger_conditions: Peer response can change incentives or standing before problems require formal adjudication.

trigger_conditions: The costs of noncompliance are diffuse, delayed, relational, or collective, so victims may not be able to enforce alone.

trigger_conditions: Formal escalation remains possible for severe, contested, unsafe, or rights-sensitive cases but should not be the first line for every breach.

symptoms: People say “everyone knows the norm,” but repeated violations continue because no credible social consequence follows.

symptoms: Compliant members reduce effort, exit, or retaliate because free riders or norm breakers face no meaningful cost.

symptoms: The group relies on gossip, pile-ons, or informal blacklists rather than proportionate and reviewable response.

symptoms: Formal rules exist on paper, but actual compliance is governed by unofficial expectations that are invisible or unevenly enforced.

symptoms: Reputation matters, but there is no correction, decay, repair, or appeal path for outdated or false reputational signals.

symptoms: High-status actors evade consequences while low-status actors are punished for similar conduct.

symptoms: Minor breaches escalate socially because the group lacks a lower-intensity reminder, warning, or repair mechanism.

root_tension: The system needs decentralized social pressure strong enough to make norms real, but decentralized pressure is itself a form of power. It must remain socially embedded enough to work without formal authority while becoming explicit and constrained enough to avoid arbitrary punishment, domination, surveillance, and permanent stigma.

anti_signatures: The main task is to create, change, or translate shared norms rather than enforce already legible norms; use Norm Shaping or Ethical Context Translation.

anti_signatures: The issue is governance of a shared resource with quotas, replenishment, access rules, and resource monitoring; use Commons Governance, with informal enforcement as a component.

anti_signatures: The primary design problem is formal authority, mandate, consent, and legitimate decision rights; use Authority Legitimacy and Consent Foundations.

anti_signatures: The case requires impartial evidence handling, hearing, remedy, and appeal for a dispute; use Procedural Fairness Design or Adjudication Process Design.

anti_signatures: The only missing element is visibility of individual effort in group work; use Contribution Visibility Design.

anti_signatures: The key pattern is long-run reciprocal cooperation in repeated games; use Iterative Reciprocity and Repeated Interaction unless broader social sanction governance is needed.

anti_signatures: The target is to design reputation as an aggregate signal independent of norm enforcement; use or draft a reputation-signal archetype rather than this one.

anti_signatures: The proposed intervention would expose people to harassment, discrimination, vigilantism, or retaliation without safeguards; do not use this archetype in that form.

structural_intervention: Convert informal social pressure into a bounded governance pattern: define the norm, bound the reference group, make relevant behavior appropriately observable, specify how peers interpret violations, select graduated sanctions, preserve reputational memory with correction and decay, create repair and reentry paths, monitor legitimacy, and reserve severe or contested cases for formal escalation.

action_logic: Identify the norm whose compliance must be sustained and test whether the group actually recognizes it.

action_logic: Define the reference group whose judgment, trust, access, or cooperation matters to participants.

action_logic: Map where norm-relevant behavior is visible, hidden, ambiguous, or distorted by status and context.

action_logic: Set interpretation rules so peers distinguish breach from mistake, incapacity, conflicting duties, exception, and malicious violation.

action_logic: Create a graduated response ladder that begins with low-intensity reminders and moves only as needed toward stronger consequences.

action_logic: Attach reputational memory to behavior, but include time limits, correction channels, context, and proof of repair.

action_logic: Define repair, apology, restitution, behavior change, monitoring, and reentry conditions so enforcement restores cooperation when possible.

action_logic: Install anti-abuse safeguards for discrimination, mobbing, retaliation, factional capture, gossip cascades, and disproportionate exclusion.

action_logic: Specify the formal escalation boundary for serious harm, safety risk, legal rights, evidence disputes, or power imbalance.

action_logic: Review whether sanctions actually improve compliance and trust rather than creating fear, concealment, exit, or domination.

decision_rules: Use informal enforcement only when the norm is sufficiently legible to the affected group and the group has legitimate standing to respond.

decision_rules: Prefer private reminder, clarification, or repair before public sanction when harm is low and the breach may be correctable.

decision_rules: Escalate sanction intensity by severity, repetition, intent, harm, refusal to repair, and ongoing risk, not by outrage alone.

decision_rules: Do not rely on reputation unless there is a way to correct error, add context, observe repair, and allow reputational decay.

decision_rules: Do not let the same clique define the norm, interpret the violation, apply the sanction, and control reentry without checks.

decision_rules: Use formal process when the allegation is severe, evidence-sensitive, legally consequential, safety-critical, or involves major power imbalance.

decision_rules: Treat exit, silence, or hidden noncompliance as a possible signal that informal enforcement has become coercive or illegitimate.

target_invariants: The enforced norm is explicit enough to be contestable and teachable.

target_invariants: The reference group has recognizable standing to evaluate and respond.

target_invariants: Sanctions are proportionate, graduated, and tied to observed conduct rather than identity or status.

target_invariants: Reputational memory remains revisable, contextual, and capable of decay after repair.

target_invariants: Repair and reentry are possible unless ongoing risk makes them unsafe.

target_invariants: Anti-abuse safeguards protect against harassment, discrimination, retaliation, and clique domination.

target_invariants: Formal escalation remains available and mandatory for cases that exceed the competence of informal enforcement.

expected_outcomes: Norm violations become less attractive because social consequences are credible and predictable.

expected_outcomes: Compliant members retain trust in the group because free-riding, exploitation, or misconduct is not ignored.

expected_outcomes: Minor breaches are corrected earlier through reminders and repair before formal conflict is needed.

expected_outcomes: Reputational information helps future cooperation while avoiding permanent unreviewable stigma.

expected_outcomes: Groups can govern everyday behavior without converting every problem into formal bureaucracy.

expected_outcomes: The risk of mob punishment, gossip cascades, and discriminatory sanctioning is reduced by explicit safeguards.

prose_essence:
Peer Sanctioning Governance is the archetype for making informal norms consequential without turning every violation into a formal case. It applies when a group depends on shared behavior but formal authority is unavailable, slow, too expensive, illegitimate, or unable to observe everyday conduct. The intervention is not simply “punish people socially.” It is a governed loop: define the norm, bound the reference group, observe behavior, interpret breaches, choose proportionate social response, preserve reputational memory, enable repair, and constrain abuse.

The target prime, **Informal Enforcement**, names the core abstraction: norm compliance sustained by decentralized social sanction rather than formal authority. This draft turns that abstraction into a reusable solution pattern.

prose_tradeoffs: The archetype trades formal bureaucracy for local speed and contextual sensitivity. That gain comes with risks: weak evidence discipline, clique capture, public overreaction, status bias, and permanent reputational damage. The design should therefore use the least public effective response, reserve strong sanctions for repeated or severe cases, and preserve correction and repair.

Reputational Enforcement Loop

short_definition: Sustain compliance by making past behavior visible enough that future trust, access, or exchange depends on reputation.

distinctive_feature: The sanction operates mainly through future treatment by others rather than immediate punishment by a designated authority.

distinct_from_parent: The parent includes all decentralized sanctions; this variant specifically uses reputational memory as the enforcement channel.

still_same_parent_because: It still depends on shared norms, peer observation, and decentralized treatment changes rather than formal authority.

use_when: Actors expect repeated or future interactions and care about being chosen, trusted, included, hired, promoted, or traded with.

use_when: Formal enforcement is too costly, slow, unavailable, or incomplete, but social memory can travel through the relevant network.

use_when: The system can correct false or outdated reputational information rather than freezing stigma permanently.

example_domains:
online platforms
professional networks
markets
open-source communities

domain: online marketplace

example: Repeated failure to fulfill orders lowers seller standing and reduces future buyer trust.

domain: professional network

example: A contractor known not to honor commitments receives fewer referrals until repair is demonstrated.

failure_modes:
false reputation cascades
permanent stigma
gaming reviews
status-based double standards

Private-Ordering Enforcement

short_definition: Use repeat dealings, private association rules, reputation, and access to sustain commitments where formal legal enforcement is unavailable or too expensive.

distinctive_feature: The enforcement setting is exchange or association rather than general community behavior.

distinct_from_parent: The parent is cross-domain; this variant emphasizes transaction, membership, and private institutional ordering.

still_same_parent_because: Compliance is still sustained by decentralized social and relational consequences rather than state authority.

use_when: Actors can choose future partners, grant or deny access, and remember past conduct.

use_when: Formal contract enforcement is slow, costly, jurisdictionally difficult, or incomplete.

use_when: The informal regime has enough legitimacy and safeguards to avoid cartel, discrimination, or coercive exclusion.

example_domains:
commerce
professional associations
supply networks
clubs and guilds

domain: supply network

example: A partner who repeatedly violates delivery norms loses access to preferred deals before litigation is considered.

domain: professional association

example: Members who breach conduct expectations lose referrals until they make repair.

failure_modes:
collusive exclusion
opaque blacklisting
barriers to entry
private coercion

Community Peer Sanctioning

short_definition: Sustain local norms through neighbor, member, or peer response in a community where ongoing belonging matters.

distinctive_feature: Belonging and standing inside a community are the primary enforcement levers.

distinct_from_parent: The parent includes markets and platforms; this variant emphasizes community membership and relational legitimacy.

still_same_parent_because: It is still decentralized norm compliance through social sanction.

use_when: The relevant group has repeated interaction, shared identity, or mutual dependence.

use_when: Sanctions must be understood as community accountability rather than external command.

use_when: Repair, reintegration, and protection from factional punishment are necessary.

example_domains:
neighborhood associations
mutual aid groups
religious communities
volunteer organizations

domain: mutual aid group

example: Members who repeatedly take without contributing are first reminded, then lose access to coordination channels until they repair.

domain: volunteer organization

example: Unsafe conduct triggers peer intervention and a repair plan before formal removal.

failure_modes:
clique dominance
scapegoating
conformity pressure
exclusion of outsiders

Platform Norm Moderation

short_definition: Use user reports, moderator judgment, reputation, warnings, and access limits to enforce community norms in digital spaces.

distinctive_feature: The sanction infrastructure is partly technical, but the enforced norm remains socially generated and socially recognized.

distinct_from_parent: The parent is not platform-specific; this variant adds scale, anonymity, moderation queues, and appeal requirements.

still_same_parent_because: Compliance is still shaped by distributed observation, community standing, and social access consequences.

use_when: The group interacts through a shared platform or communication channel.

use_when: User reporting and peer expectations help detect norm breaches.

use_when: Formal legal enforcement is irrelevant or reserved for serious harms.

example_domains:
online communities
open-source projects
forums
workplace chat systems

domain: open-source project

example: Maintainers use warnings, temporary moderation, and contribution review to enforce conduct norms.

domain: online forum

example: Repeated harassment reduces posting privileges after notice and appeal opportunity.

failure_modes:
brigading
opaque moderation
context collapse
false reports

near_names:
Informal Enforcement Design
Decentralized Sanctioning
Social Sanctioning
Norm Enforcement
Reputational Enforcement
Private Ordering
Peer Sanctioning


Perceived-Consensus Calibration

batch: Batch 12

canonical_family: Shared Awareness & Identity Alignment

aliases:
False-Consensus Correction
Consensus Projection Audit
Assumed Majority Check
Perceived Agreement Calibration
Social Projection Correction
Consensus Assumption Check
User-Like-Me Bias Guardrail

slug: consensus

slug: projection

slug: calibration

slug: groupthink

slug: identification

slug: anchoring

slug: feedback

slug: metacognition

slug: uncertainty

slug: randomness

slug: diversity

slug: evidence

slug: validation

slug: framing

components:
Consensus Claim Boundary
Self-Anchor Assumption Record
Target Population Map
Nonrandom Sample Exposure
Independent Belief Estimate Window
Representative Evidence Requirement
Belief Distribution View
Silent or Absent Segment Guardrail
Projection Risk Trigger
Minority Exception Channel
Consensus Confidence Annotation
Post-Decision Reality Check

mechanisms:
Anonymous Belief Pre-Poll
Representative Consensus Survey
Nonrandom Sample Audit
Belief Distribution Dashboard
Silent-Start Estimation Round
Outgroup or Edge-Case Interview
Consensus Claim Evidence Log
Minority Report Prompt
False-Consensus Premortem

essence_one_line: Before acting on “everyone thinks this,” separate the speaker’s local anchor from the target population and replace perceived consensus with representative, independent, and distributional evidence.

compression_statement: Perceived-consensus calibration governs claims that a belief, behavior, preference, norm, risk tolerance, or interpretation is widely shared. It first bounds the population and proposition, records the local self-anchor and visible sample, exposes nonrandom sampling, obtains independent or representative evidence where stakes justify it, displays distributions and unknowns instead of a single imagined majority, preserves minority exceptions, and feeds later reality checks back into the consensus prior.

core_intuition: People often mistake familiar agreement for broad agreement. The solution is not to distrust every intuition, but to ask: whose consensus, about what, based on which sample, with which missing voices, and how confident should we be?

canonical_formula: Perceived consensus claim C is usable only when self-anchor A and visible sample S are separated from target population P, evidence E covers P well enough for stakes K, and confidence is annotated as f(E, representativeness, independence, nonresponse, subgroup variance).

structural_problem: A person, team, leader, designer, expert, or institution believes that its own view, local peer group, meeting room, customer sample, online audience, or visible supporters represent what most people think or do. Because the evidence is self-anchored and nonrandom, the system overestimates consensus, underestimates minority or absent views, and commits to decisions, messages, designs, policies, or norms that later meet surprise resistance or miss real variation.

trigger_conditions: Someone uses language such as “everyone,” “users want,” “the team agrees,” “normal people,” “the community,” or “we all know” without population evidence.

trigger_conditions: The visible sample comes from friends, insiders, power users, meeting attendees, vocal supporters, online followers, experts, or respondents with high motivation to speak.

trigger_conditions: A decision depends on the prevalence of a belief, behavior, preference, norm, or acceptance level.

trigger_conditions: The decision-maker shares identity, expertise, incentives, or environment with the visible sample.

trigger_conditions: Quiet, absent, low-power, inconvenient, nonresponding, or hard-to-reach groups may disagree or behave differently.

trigger_conditions: Public, employee, customer, patient, student, or stakeholder legitimacy depends on distinguishing actual prevalence from local projection.

trigger_conditions: The group is under time pressure and tempted to treat anecdotal agreement as enough evidence.

symptoms: Surprise backlash after a policy, product, message, or organizational change that leaders expected would be broadly accepted.

symptoms: Meeting participants appear aligned, but anonymous or later feedback reveals wider disagreement.

symptoms: Designers or experts repeatedly overestimate how intuitive, desirable, or acceptable their own preferences are for users.

symptoms: Majority claims are based on anecdotes, social media, support tickets, friends, or internal discussion rather than target-population evidence.

symptoms: The group compresses a belief distribution into a single “what people think” story.

symptoms: Minority or edge cases are framed as exceptions discovered too late.

symptoms: Nonresponse, absence, or politeness is interpreted as support.

symptoms: After outcomes arrive, decision-makers say they “had no idea people felt that way.”

root_tension: Fast social inference needs cheap local cues, but legitimate or reliable action requires estimating what a broader and often nonrandomly visible population actually thinks or does.

anti_signatures: The claim is about the speaker’s own preference only and does not generalize to others.

anti_signatures: A representative, independent, current evidence base already supports the population estimate.

anti_signatures: The primary problem is suppressing known dissent under group pressure rather than overestimating prevalence from local projection.

anti_signatures: The primary problem is forming consensus, not estimating whether consensus already exists.

anti_signatures: The decision does not depend on belief or behavior prevalence.

anti_signatures: The cost of being wrong about prevalence is negligible.

structural_intervention: Insert a consensus-calibration layer before action: state the consensus claim and target population, record the local self-anchor, expose nonrandom samples, gather independent or representative evidence proportional to stakes, show the belief distribution, protect silent and minority segments, annotate confidence, and update the prior after outcomes.

action_logic: Bound the consensus claim by population, proposition, behavior, time, and decision use.

action_logic: Record the speaker or group’s own belief, behavior, peer sample, and rationale as a possible projection anchor.

action_logic: Map the target population and identify missing, quiet, or overrepresented segments.

action_logic: Audit the visible evidence source for nonrandom sampling, homophily, status, channel, or response bias.

action_logic: Collect independent estimates or representative evidence when the stakes justify it.

action_logic: Display belief or behavior distributions, subgroup differences, unknowns, and uncertainty.

action_logic: Preserve minority exceptions and avoid treating silence or absence as agreement.

action_logic: Attach confidence and evidence notes to the consensus claim, then compare against outcomes later.

decision_rules: Escalate from intuition to evidence when the decision is high-stakes, public, irreversible, costly to reverse, or affects absent stakeholders.

decision_rules: Never accept “everyone thinks” claims without a target population and evidence source.

decision_rules: Use independent estimates before discussion when group anchoring or authority influence is likely.

decision_rules: Use representative or stratified evidence when population legitimacy or product-market fit matters.

decision_rules: Treat nonresponse as unknown unless absence-as-information conditions are explicitly satisfied.

decision_rules: Report distributions and subgroups when variance matters; do not collapse disagreement into a majority label.

decision_rules: Keep minority and edge-case evidence visible even when the majority estimate is credible.

target_invariants: Population and proposition boundaries must be explicit.

target_invariants: The local self-anchor must be separated from the population estimate.

target_invariants: Visible agreement must not be equated with representativeness.

target_invariants: Silence, absence, or politeness must not count as support by default.

target_invariants: Distribution, uncertainty, and minority views must remain visible.

target_invariants: Outcome feedback must update future perceived-consensus priors.

expected_outcomes: Fewer decisions based on imagined agreement.

expected_outcomes: More accurate estimates of belief, behavior, preference, and norm prevalence.

expected_outcomes: Earlier discovery of minority, quiet, or out-of-sample views.

expected_outcomes: Reduced surprise backlash and adoption failure.

expected_outcomes: More legitimate stakeholder, employee, public, or user claims.

expected_outcomes: Better calibration of leaders’, experts’, and designers’ social intuitions.

prose_tradeoffs: The archetype trades speed for accuracy and legitimacy. Evidence collection can slow decisions, and distributional displays are harder to communicate than simple majority stories. But the cost is often lower than launching a product, policy, message, or change program on a false assumption about what people believe or will accept.

Team-Norm Projection Check

short_definition: Checks whether a team is mistaking the loud or local norm for the beliefs of the full group.

distinctive_feature: Centers small-group norm inference and meeting dynamics.

distinct_from_parent: The parent covers any perceived-consensus calibration; this variant focuses on teams and local group norms.

still_same_parent_because: It still tests self-anchored or local-sample consensus claims against independent and distributional evidence.

use_when: A small group claims “we all agree” or “everyone knows” without independent evidence.

use_when: Status, meeting dynamics, or silence may suppress visible disagreement.

use_when: A decision depends on whether the norm is truly shared.

example_domains:
teams
boards
classrooms
clinical units

domain: project team

example: A silent-start poll reveals that only half the team supports the supposedly obvious deadline.

failure_modes:
polite silence treated as agreement
manager view anchors the room
minority concerns recorded too late

User-Like-Me Projection Guardrail

short_definition: Prevents designers, founders, researchers, or experts from treating their own preferences or convenient users as representative of the target population.

distinctive_feature: The local self-anchor is the builder or insider, and the target is a user or stakeholder population.

distinct_from_parent: The parent is cross-domain; this variant specializes to design, product, service, and policy user assumptions.

still_same_parent_because: It still recalibrates perceived consensus using population boundaries, sampling evidence, and distribution views.

use_when: Builders are demographically, experientially, or contextually unlike the user population.

use_when: Evidence comes from insiders, enthusiasts, friends, or easily reached users.

use_when: Product, policy, or service decisions depend on presumed user agreement.

example_domains:
product design
public service
policy design
education technology

domain: product design

example: A team tests whether its own enthusiasm for a feature appears in novice and low-frequency users before shipping.

failure_modes: founder intuition presented as market consensus

Public-Opinion Projection Check

short_definition: Tests whether leaders, advocates, or communities are projecting their local political or cultural views onto a broader public.

distinctive_feature: The perceived consensus has legitimacy and representation consequences in public or institutional decisions.

distinct_from_parent: The parent covers perceived consensus generally; this variant focuses on public, political, community, and institutional claims.

still_same_parent_because: It still challenges self-anchored consensus estimates through population mapping and representative evidence.

use_when: A public-facing decision rests on what “people want,” “the community believes,” or “the public will accept.”

use_when: Visible voices may be organized, online, high-status, or self-selected.

use_when: Legitimacy depends on distinguishing public sentiment from local echo.

example_domains:
public policy
community engagement
advocacy
organizational governance

domain: local policy

example: A council tests whether meeting attendees reflect the broader affected community before claiming public support.

failure_modes: organized attendees treated as the public

failure_modes: online outrage mistaken for majority opinion

near_names:
False-Consensus Correction
Consensus Projection Audit
Assumed Majority Check
“Everyone Thinks That” Guardrail
User-Like-Me Bias Check


batch: Batch 14

aliases:
dynamic situation awareness loop
situational awareness maintenance
situation awareness loop design
SA refresh cycle
perceive-comprehend-project loop
common operating awareness loop
moving-system awareness design
operational situation model loop

source_primes: situation_awareness alertness foresight readiness_window scenario_planning observability sensemaking monitoring state_and_state_transition feedback decision attention

slug: alertness

slug: foresight

slug: observability

slug: sensemaking

slug: monitoring

slug: decision

slug: attention

slug: metaplasticity

slug: interpretation

slug: context

slug: time

slug: risk

slug: resilience

slug: controllability

slug: latency

slug: threshold

slug: salience

slug: representation

slug: agency

slug: coordination

slug: transparency

slug: kairos

slug: satisficing

components:
Element Perception Field
Signal Relevance Filter
Situation Meaning Model
Projection Horizon
Trajectory Hypothesis Set
Update Cadence and Expiry Rule
Action-Linked Awareness State
Uncertainty and Confidence Markers
Shared Awareness Interface
Degradation and Handoff Rule
Specialist Watch Role
Projection Trigger Threshold
Retrospective Awareness Recalibration

essence_one_line: Keep action aligned with a moving situation by continuously refreshing what is seen, what it means, what is likely next, and what decision it now supports.

compression_statement: Perception-Comprehension-Projection Loop Design applies when competent action depends on a situation that changes faster than static plans, dashboards, or initial briefings can remain valid. The intervention designs a live awareness loop: define the perception field, filter relevant signals, interpret them into situation meaning, project plausible near-future trajectories, mark uncertainty, refresh the assessment on cadence and triggers, and bind the result to decisions, handoffs, and action thresholds.

core_intuition: Situation awareness fails when actors either see without understanding, understand without projecting, or project from a stale picture. The remedy is not simply more data or more discussion; it is a disciplined loop that keeps perception, meaning, projection, and action coupled as the world moves.

canonical_formula: action_ready_awareness = scoped_perception_field + relevance_filter + meaning_model + projection_horizon + uncertainty_markers + refresh_rule + decision_link + handoff_guardrail

structural_problem: Actors must act in a changing system, but their awareness state is fragmented across raw signals, local interpretations, stale assumptions, private mental models, and untested projections, so action is based on an incomplete or outdated picture of what is happening and what is likely to happen next.

trigger_conditions: The environment changes while decisions, handoffs, or interventions are still unfolding.

trigger_conditions: Available information is distributed across people, instruments, locations, roles, or time windows.

trigger_conditions: Raw observations exist but their operational meaning is ambiguous or contested.

trigger_conditions: Near-future trajectory matters more than a static current-state snapshot.

trigger_conditions: Actors must coordinate under time pressure, risk, uncertainty, or partial observability.

trigger_conditions: Status displays, reports, or briefings show many facts but do not say what those facts imply for action.

trigger_conditions: Prior plans or expectations are likely to remain active after the situation has changed.

trigger_conditions: Critical cues can expire, be missed, or be misunderstood during shift changes, incident escalation, or rapid context switching.

symptoms: People can recite data points but disagree about what the situation means.

symptoms: A team keeps acting from an earlier picture after indicators have changed.

symptoms: Alerts, dashboards, or reports multiply while decision clarity does not improve.

symptoms: Near-future implications are discovered only after the window for response has closed.

symptoms: Handoffs lose assumptions, open uncertainties, pending triggers, or projection horizons.

symptoms: Operators notice anomalies but lack a channel to convert them into shared meaning and action.

symptoms: Plans are updated slowly because perception, interpretation, and projection are handled by separate routines.

symptoms: Teams overreact to salient local cues or underreact to subtle trajectory shifts.

symptoms: After the event, people say the signs were present but were not assembled into a useful situation picture.

root_tension: A moving system requires awareness that is both stable enough to coordinate action and fluid enough to update when new cues change the meaning or trajectory of the situation.

anti_signatures: The problem is only a lack of instrumentation; use Observability Instrumentation if hidden state must first be made inferable.

anti_signatures: The problem is primarily group interpretation of an ambiguous event without a near-term action/projection loop; use Structured Sensemaking.

anti_signatures: The problem is long-range strategic uncertainty rather than immediate moving-system action; use Scenario Portfolio Planning or foresight methods.

anti_signatures: The current state and future trajectory are already known, and the task is simply to execute a plan.

anti_signatures: The problem is only team role alignment or shared mental model maintenance, not live perception-comprehension-projection refresh.

anti_signatures: The needed intervention is a one-time retrospective explanation rather than an ongoing awareness loop.

structural_intervention: Design a refreshable awareness loop that turns current cues into interpreted meaning, projected trajectory, explicit uncertainty, and action-linked decisions before the situation picture goes stale.

action_logic: Define the perception field: name which elements, actors, resources, constraints, hazards, opportunities, and environmental changes must be noticed.

action_logic: Separate signals from noise: create relevance rules so alertness has a target and routine data does not overwhelm action.

action_logic: Build the meaning layer: connect observations to goals, constraints, causal structure, risks, and available action.

action_logic: Set the projection horizon: state how far forward the current situation assessment must remain useful for decisions.

action_logic: Maintain trajectory hypotheses: hold a small set of plausible near-future paths rather than treating one forecast as certain.

action_logic: Mark uncertainty and confidence: distinguish observed facts, inferences, assumptions, delays, disputes, missing data, and projected claims.

action_logic: Attach refresh rules: specify cadence, expiry, and change triggers that force a new perception-comprehension-projection pass.

action_logic: Bind awareness to action: connect the current awareness state to thresholds, decisions, handoffs, escalation, de-escalation, and no-go rules.

action_logic: Preserve handoff continuity: transfer not only status but also meaning, projection horizon, unresolved uncertainties, and pending triggers.

action_logic: Recalibrate after reality arrives: compare what was perceived, understood, and projected against outcomes to improve the next loop.

decision_rules: Do not accept a current-state display as situation awareness unless it also states meaning, projection, uncertainty, and action relevance.

decision_rules: Do not let a situation assessment guide action after its expiry rule has been crossed or a material change trigger has fired.

decision_rules: When signals conflict, preserve competing trajectory hypotheses until evidence resolves them or action requires a bounded choice.

decision_rules: Escalate when perception confidence, meaning confidence, or projection confidence falls below the threshold required for the intended action.

decision_rules: For each handoff, transfer the perception field, current interpretation, projection horizon, assumptions, open uncertainties, and trigger watchlist.

decision_rules: Prefer a smaller high-relevance awareness picture over an exhaustive picture that cannot be refreshed or used in time.

decision_rules: If the projection horizon extends beyond near-term action, route long-range uncertainty to scenario planning rather than overloading the awareness loop.

decision_rules: If actors have data but no shared meaning, insert a sensemaking mechanism before action continues.

target_invariants: The awareness state remains current enough for the action it supports.

target_invariants: Perception, comprehension, and projection remain explicitly connected rather than split into isolated artifacts.

target_invariants: Uncertainty, missingness, and confidence are visible instead of hidden behind a smooth narrative.

target_invariants: Awareness updates are triggered by material change, not only by scheduled meetings.

target_invariants: Handoffs preserve assumptions, trajectory expectations, and pending triggers.

target_invariants: The loop supports timely action without collapsing into either alarm fatigue or analysis paralysis.

target_invariants: Later outcomes are used to recalibrate cues, interpretations, horizons, and thresholds.

expected_outcomes: Actors notice relevant change earlier and ignore irrelevant noise more deliberately.

expected_outcomes: Teams share not just facts but an action-ready interpretation of what those facts mean.

expected_outcomes: Decisions are better timed because near-future trajectories are projected before the response window closes.

expected_outcomes: Handoffs lose fewer assumptions and pending uncertainties.

expected_outcomes: Stale situation pictures are detected before they control action.

expected_outcomes: Operational learning improves because perception, comprehension, and projection failures can be separated during review.

expected_outcomes: Coordination improves in fast, distributed, or safety-sensitive environments.

prose_essence:
Perception-Comprehension-Projection Loop Design is the pattern of keeping a usable awareness state alive while the world is still moving. It prevents a common failure in operations, care, incident response, strategy execution, and field work: people have observations, reports, or dashboards, but the observations are not assembled into a current meaning, a near-future projection, and a decision-ready action posture.

The archetype is built around three cognitive products from the target prime. First, actors must **perceive** the relevant current elements. Second, they must **comprehend** what those elements mean for goals, risks, constraints, and available action. Third, they must **project** the near-future trajectory far enough to act before the response window closes. The solution is not complete until those three products are refreshed, uncertainty-marked, handed off, and linked to action.

Individual Dynamic Awareness Loop

short_definition: A single actor maintains current cues, meaning, projection, and action readiness under changing conditions.

distinctive_feature: Centers one actor’s awareness maintenance rather than a shared team picture.

distinct_from_parent: Narrower in scale and usually lighter in mechanism; may use checklists, cue prompts, or personal update rhythms rather than formal boards.

still_same_parent_because: It still uses the perceive-comprehend-project-refresh-action structure.

source_primes: situation_awareness alertness attention

use_when: A person must operate under time pressure, partial observability, or changing context.

use_when: The main risk is personal attention tunneling, stale assumptions, or missed trajectory change.

domain: aviation

example: A pilot keeps aircraft state, weather, traffic, and next workload in a refreshed mental model.

failure_modes: overconfidence without confidence markers

Team Common Operating Awareness

short_definition: A team maintains a shared, updateable situation model across roles, sensors, and handoffs.

distinctive_feature: Emphasizes shared interface, handoff preservation, and explicit disagreement/uncertainty management.

distinct_from_parent: Adds coordination and common-operating-picture concerns to the general loop.

still_same_parent_because: The core loop remains perception, comprehension, projection, refresh, and action linkage.

use_when: Multiple actors must coordinate from distributed observations.

use_when: Handoffs, role boundaries, or cross-functional decisions can lose the situation picture.

example: An operations center maintains a shared board of current hazards, projected needs, and pending triggers.

failure_modes:
shared display without shared meaning
handoff loss
group overconfidence

Crisis / Incident Situation Awareness

short_definition: A high-velocity incident loop that keeps hazards, resources, constraints, projected escalation, and response posture current.

distinctive_feature: Emphasizes velocity, risk thresholds, role handoffs, and rapid projection refresh.

distinct_from_parent: More formal about incident roles, escalation triggers, and safety-critical expiry.

still_same_parent_because: It is an intensified incident-domain instance of the same awareness loop.

source_primes: situation_awareness risk resilience

use_when: Events unfold under time pressure and high consequence.

use_when: Actors must escalate, allocate, or de-escalate based on live trajectory.

domain: cybersecurity

example: An incident cell updates attacker stage, containment status, likely next moves, and action thresholds.

failure_modes:
alert flood
command picture lag
projection fixation

Automated Operator Awareness Panel

short_definition: A dashboard or software interface that assists perception, comprehension, projection, and refresh for human operators.

distinctive_feature: Uses automation and interface design to support awareness without replacing the cognitive loop.

distinct_from_parent: Implementation-heavy and vulnerable to automation bias.

still_same_parent_because: It supports the same awareness loop rather than being the archetype itself.

use_when: Sensors or logs can automatically gather high-volume cues.

use_when: Humans need display support but remain responsible for meaning and action.

example: A control-room display combines sensor status, inferred risk state, next projected threshold, and operator action prompts.

failure_modes:
automation bias
data-rich meaning-poor display
false precision

near_names:
Situational Awareness
SA
Common Operating Picture
Situation Assessment


Perceptual Texture Modulation

batch: Batch 25

canonical_family: Visual Composition & Aesthetic Reduction

source_primes: texture figure_ground composition

slug: texture

slug: composition

slug: contrast

slug: emphasis

slug: attention

slug: phenomenology

slug: scale

slug: rhythm

slug: affordance

slug: constraint

slug: iteration

slug: feedback

components: Comparative Texture Evaluation and Stop Rule

mechanisms:
Controlled Surface Swatch Series
Granularity Scale Ladder
Roughness and Relief Gradient
Stochastic Microvariation Field
Directional Mark or Grain Map
Layered Texture Mask
Cross-Lighting Surface Probe
Visual-Tactile Calibration Panel
Blind Affective Response Probe
Distance and Reproduction Stress Test

essence_one_line: Tune fine-grained surface variation so form gains controlled depth, tactile expectation, sensory rhythm, and emotional tone without turning texture into a code or mere decoration.

compression_statement: When a surface feels flat, inert, noisy, generic, or affectively mismatched, define the intended perceptual and experiential character; map the carrier surface, viewing and touch conditions, and modality constraints; select a bounded texture vocabulary; calibrate granularity, relief, density, direction, regularity, contrast, and edge behavior; prototype controlled variations; test across distance, lighting, reproduction, touch, accessibility, fabrication, and maintenance conditions; guard against accidental semantic coding and material deception; and stop at the least textural intervention that produces the intended character without obscuring gross form or function.

core_intuition: Texture operates below gross form yet is never neutral. Small variations change how a surface catches light, predicts touch, organizes figure and ground, sustains attention, implies age or softness, and sets emotional temperature. Treating those variations as an adjustable system makes sensory character reviewable rather than leaving it to arbitrary finish or decorative noise.

canonical_formula: experience_target + carrier_context + bounded_texture_vocabulary + calibrated_microvariation -> comparative_surface_studies -> perceptual_tactile_context_tests -> revise | stop

structural_problem: Gross form and layout are adequate, but the surface still reads as flat, sterile, visually noisy, falsely tactile, affectively inappropriate, or disconnected from the desired experience. Texture choices accumulate as finish, filter, or ornament without declared targets, controlled parameters, comparative studies, contextual tests, or a stop rule.

trigger_conditions: Fine-grained variation should change depth, softness, age, calm, energy, intimacy, resistance, or sensory richness.

trigger_conditions: The same gross form produces different readings under different grain, relief, density, direction, or regularity.

trigger_conditions: A visual texture predicts a tactile quality that must be intentionally confirmed or contradicted.

trigger_conditions: A surface must retain character across lighting, distance, scale, reproduction, and wear.

trigger_conditions: Uniform finish erases useful local variation while uncontrolled variation competes with form and content.

trigger_conditions: Designers need a reviewable vocabulary for comparing surface character rather than selecting one arbitrary swatch.

symptoms: Texture is added late as a generic overlay and detaches from form, hierarchy, and material behavior.

symptoms: Microvariation disappears at intended distance or becomes coarse pattern at another scale.

symptoms: Relief, grain, or noise overwhelms edges, labels, affordances, or accessibility cues.

symptoms: A surface looks warm, soft, rough, aged, or natural in ways that conflict with actual touch and use.

symptoms: Reproduction, compression, lighting, fabrication, cleaning, or wear destroys the intended character.

symptoms: Review relies on taste adjectives without parameter changes, comparison artifacts, or audience evidence.

root_tension: Texture must be strong enough to create sensory and affective character while remaining subordinate to gross form, legibility, safe use, honest material conditions, and any semantic distinctions that belong to other owners.

anti_signatures: The purpose is to reveal the literal material as itself; use material_literalness_foregrounding.

anti_signatures: Texture differences must encode status, category, data quality, or affordance; use texture_as_signal_encoding.

anti_signatures: Decorative surface treatment must carry function or reveal structure; use ornament_function_integration_and_structural_expression.

anti_signatures: The work primarily removes literal depiction; use representational_reduction_and_formal_abstraction.

anti_signatures: Empty field rather than surface variation carries structure; use negative_space_as_structural_element.

anti_signatures: A repeated macro-unit grammar is the main intervention; use the relevant pattern or primitive-vocabulary owner.

structural_intervention: Establish a governed texture-modulation sequence that declares the intended sensory character, maps the surface and encounter context, selects a bounded microvariation vocabulary, calibrates scale and distribution parameters, prototypes adjacent alternatives, tests visual and tactile readings under real conditions, checks semantic leakage and material honesty, and iterates until the minimum sufficient texture produces the target character without damaging form or function.

action_logic: State the perceptual, tactile, rhythmic, and affective character the surface should produce.

action_logic: Identify the carrier surface, gross form, hierarchy, edge conditions, material facts, audience, and encounter modes.

action_logic: Separate nonsemantic sensory character from discrete codes, literal-material revelation, and functional ornament.

action_logic: Define a bounded vocabulary of grain, relief, mark, pore, weave, roughness, softness cue, irregularity, or layered variation.

action_logic: Set granularity, amplitude, density, direction, regularity, contrast, and transition ranges at intended scales.

action_logic: Map where texture should intensify, recede, align, interrupt, or remain absent in relation to figure, ground, and affordances.

action_logic: Produce adjacent swatches or surface studies that vary one parameter at a time and several parameters in controlled combinations.

action_logic: Test at intended distance, scale, lighting, motion, reproduction, compression, touch, and wear conditions.

action_logic: Probe perceptual depth, tactile expectation, sensory rhythm, emotional tone, legibility, fatigue, and aversion with representative users.

action_logic: Check accessibility, hygiene, durability, fabrication, maintenance, cultural association, accidental code, and material-deception risks.

action_logic: Record comparisons, revise the smallest responsible parameter set, and stop when added variation no longer improves the declared character.

decision_rules: Change one texture dimension at a time before evaluating coupled combinations.

decision_rules: Keep gross form, critical edges, text, controls, and required semantic cues legible.

decision_rules: Use the least texture intensity that reliably produces the target reading across intended contexts.

decision_rules: Route stable category or status mappings to texture_as_signal_encoding rather than hiding them in an aesthetic palette.

decision_rules: Route intrinsic material revelation to material_literalness_foregrounding when substrate truth is the primary criterion.

decision_rules: Reject visual texture that creates unsafe or materially false tactile expectations unless contradiction is explicit and ethically justified.

decision_rules: Treat discomfort, fatigue, sensory overload, and touch aversion as evidence, not as taste disagreement.

decision_rules: Stop adding microvariation when it becomes macro-pattern, decoration without role, semantic noise, or loss of form.

target_invariants: The intended sensory character remains explicit and testable.

target_invariants: Gross form, hierarchy, critical edges, labels, controls, and safety cues remain legible.

target_invariants: Texture parameters and their ranges remain traceable across prototypes.

target_invariants: Visual and tactile expectations are either aligned or deliberately and safely contrasted.

target_invariants: Material, fabrication, cleaning, durability, and wear constraints remain represented.

target_invariants: Accessibility includes alternatives where touch or fine visual detail is unavailable or aversive.

target_invariants: Semantic mappings remain absent unless deliberately handed to the signal-encoding owner.

target_invariants: Comparison evidence and the stop rationale remain attached to the selected treatment.

expected_outcomes: Surfaces gain controlled depth, warmth, softness, energy, calm, age, intimacy, or resistance without changing gross form.

expected_outcomes: Fine variation supports attention and figure-ground articulation without becoming clutter.

expected_outcomes: Visual texture produces more reliable tactile expectation under intended conditions.

expected_outcomes: Surface character survives realistic lighting, distance, reproduction, fabrication, and maintenance conditions.

expected_outcomes: Teams can compare texture alternatives using declared parameters and evidence rather than taste labels alone.

expected_outcomes: Neighbor lifecycles remain clear when texture becomes material revelation, semantic code, or functional ornament.

Visual Surface Texture Modulation

short_definition: Tunes visible grain mark relief and microcontrast when touch is unavailable or irrelevant

distinctive_feature: Visual depth and atmospheric character are evaluated without direct touch

distinct_from_parent: Narrows the active modality to sight

still_same_parent_because: Uses the same brief vocabulary parameter context and stop lifecycle

source_primes: texture representation

use_when:
The encounter is primarily visual
Physical relief cannot be produced

failure_modes: Implying a tactile property that the surface contradicts

Tactile Surface Texture Modulation

short_definition: Tunes relief roughness compliance and spacing when touch is a primary experience channel but not a discrete code

distinctive_feature: Actual tactile response controls selection

distinct_from_parent: Gives touch priority over visual reading

still_same_parent_because: It retains the same continuous character objective rather than semantic mapping

source_primes: texture affordance

use_when: Users intentionally handle or traverse the surface

failure_modes: Abrasion contamination exclusion or false safety confidence

Stochastic Microvariation Modulation

short_definition: Uses bounded irregularity to avoid mechanical repetition and create naturalistic or lively surface character

distinctive_feature: Distribution and outlier limits replace a fixed repeating unit

distinct_from_parent: Privileges controlled randomness within the texture vocabulary

still_same_parent_because: Randomness changes parameter generation not the parent purpose or evaluation

source_primes: texture randomness pattern_in_design

use_when: Regular repetition looks synthetic or produces artifacts

failure_modes: Unbounded noise clustering and nonreproducible fabrication

Directional Texture Flow

variant_type: subtype

short_definition: Aligns grain marks or relief so microvariation reinforces movement tension or orientation

distinctive_feature: Orientation and interruption dominate the texture field

distinct_from_parent: Narrows parameter emphasis to directional rhythm

still_same_parent_because: It remains one texture-parameter regime under the same context and stop tests

source_primes: texture rhythm flow

use_when: Direction should shape the sensed flow of a surface

example_domains: drawing architecture product_design

failure_modes: Accidental navigation cue or conflict with gross form

Atmospheric Texture Tuning

short_definition: Calibrates texture primarily for comparative calm energy warmth intimacy distance age or tension

distinctive_feature: Comparative affective and sensory response is the main evaluation evidence

distinct_from_parent: Prioritizes atmosphere over depth or tactile prediction

still_same_parent_because: Uses the same parameterized surface studies and guards against universal emotion claims

source_primes: texture phenomenology

use_when: Experiential atmosphere is the declared target

failure_modes: Cultural overgeneralization manipulative mood claims and sensory overload

near_names:
Sensory Texture Tuning
Surface Character Modulation
Textural Atmosphere Design
Nonsemantic Texture Design