Attenuated Threat Inoculation

batch: Batch 01

canonical_family: Adaptation Under Adversarial Pressure

aliases:
Inoculation Theory Application
Resistance Inoculation
Preemptive Refutation Training
Counterargument Inoculation
Prebunking by Refutation
Cognitive Inoculation
Weak-Threat Exposure Training
Persuasion-Resistance Pretraining

slug: learning

slug: reactance

slug: trust

slug: framing

slug: coevolution

slug: kairos

slug: risk

slug: legitimacy

slug: authority

slug: attention

components:
Threat Family Model
Attenuated Challenge Sample
Refutation Scaffold
Recognition Cue Map
Resistance Response Repertoire
Dose Timing and Readiness Window
Safe Exposure Boundary
Autonomy and Trust Boundary
Transfer Probe
Refresh and Decay Rule
Nonamplification Guard
Trusted Context or Messenger
Audience Vulnerability Map
Messenger Legitimacy Plan
Mutation Watch
Repair After Overdose Path

mechanisms: Social Engineering Simulation with Debrief

essence_one_line: Prepare a receiver for a future attack by giving it a safe weak dose of the attack, showing why that dose fails, and rehearsing how to recognize and resist stronger variants later.

compression_statement: A system that first meets a threat at full strength often responds too late, too emotionally, too credulously, or too narrowly. Attenuated Threat Inoculation turns the first encounter into a controlled rehearsal: name the threat family, present a weakened but recognizable specimen, refute it successfully, extract transfer cues, and practice a response repertoire. The goal is not familiarity alone; it is anticipatory defensive generalization.

core_intuition: Resistance forms more reliably when the receiver has already seen the shape of the attack and survived a small version of it with an explanation. The weakened exposure wakes up the defensive machinery; the refutation tells it what to do; transfer practice prevents it from mistaking the lesson for a single memorized case.

canonical_formula: future_resistance ≈ weakened_exposure × refutation_quality × cue_transfer × response_rehearsal × trust ÷ (overdose + amplification + reactance + decay)

structural_problem: A foreseeable threat will arrive later in a stronger, faster, more persuasive, or more adversarial form than the receiver can safely learn from on first contact. If the receiver remains naive until then, the first full-strength exposure may shape beliefs, trigger panic, exploit trust, bypass verification, or overwhelm action before correction can work.

trigger_conditions: A threat family is predictable enough to preview without needing to know the exact future instance.

trigger_conditions: The receiver has some defensive capacity, but it is dormant, unpracticed, poorly cued, or too abstract to activate under pressure.

trigger_conditions: Reactive correction after full exposure is likely to be late, distrusted, socially costly, or less memorable than the original threat.

trigger_conditions: A weakened example can be constructed without causing the same harm as the full threat.

trigger_conditions: Successful resistance depends on recognizing a pattern, not merely memorizing one prohibited claim or action.

trigger_conditions: The receiver must retain agency and trust; coercive or manipulative preparation would undermine the intervention.

symptoms: Receivers believe, share, obey, or freeze when the first strong threat appears because no recognition cues have been practiced.

symptoms: Corrections arrive after the hostile frame has already anchored attention, identity, or group interpretation.

symptoms: Training warns that threats exist but does not let receivers practice detecting and refuting a realistic weak example.

symptoms: People can reject an exact training example but fail when wording, messenger, timing, or emotional tone changes.

symptoms: Defensive messaging uses fear or censorship rather than demonstrable refutation, causing reactance or distrust.

symptoms: Organizations repeat generic awareness advice while attackers keep reusing structurally similar tactics.

root_tension: The system must expose the receiver to enough of the threat to activate resistance, while withholding enough intensity, vividness, repetition, or operational detail to avoid teaching, spreading, legitimizing, traumatizing, or recruiting for the threat.

anti_signatures: The main task is testing whether a plan survives stressed assumptions; Assumption Stress Testing is closer.

anti_signatures: The main task is deliberately injecting operational failures to reveal system weaknesses; Chaos Exposure Testing is closer.

anti_signatures: The threat has already circulated at full strength and the work is belief correction; Belief Revision Workflow, Knowledge-Warrant Audit, or Moral Panic De-escalation may be closer.

anti_signatures: The intervention relies on repeated pleasant familiarity rather than defensive refutation; Negative-Mere-Exposure Reversal or Fluency-Based Preference Exploitation may be closer.

anti_signatures: The receiver cannot safely encounter even a weakened version of the material.

anti_signatures: The future problem is unknown in kind, requiring general adaptive capacity rather than a recognizable threat-family inoculation.

structural_intervention: Create a controlled first encounter with a weakened representative of the future threat, pair it with a clear refutation and response path, then test transfer against mutated examples under safety, trust, and nonamplification constraints.

action_logic: Define the threat family by tactic, structure, incentive, or failure mode rather than by one exact specimen.

action_logic: Identify who needs resistance, what they already believe, where they are vulnerable, and what level of challenge they can safely handle.

action_logic: Construct an attenuated challenge sample that preserves the recognizable tactic while reducing intensity, harm, credibility, or operational detail.

action_logic: Deliver the sample before the likely full threat and in a context where attention, trust, and readiness are sufficient.

action_logic: Immediately provide or co-construct a successful refutation that explains why the challenge fails and what cues revealed the failure.

action_logic: Connect the refutation to a practical response repertoire: verify, pause, ask, refuse, report, seek corroboration, reframe, or escalate.

action_logic: Probe transfer with mutated examples so the receiver learns a threat family, not a phrase to memorize.

action_logic: Monitor for backfire, reactance, overconfidence, distress, curiosity amplification, and decay of resistance over time.

action_logic: Refresh the inoculation before high-risk windows or after threat mutation, but avoid overexposing receivers until the training material becomes noise.

decision_rules: If the weakened sample is not recognizable as the future threat family, increase fidelity; if it is persuasive, harmful, or too vivid, reduce dose and strengthen safeguards.

decision_rules: If receivers can repeat the refutation but fail on mutated examples, add transfer cues and varied practice rather than more of the same explanation.

decision_rules: If the audience distrusts the source, repair messenger legitimacy before increasing message intensity.

decision_rules: If the pre-exposure could amplify the hostile claim or tactic, inoculate against the tactic rather than repeating the full claim, and use nonshareable or contextualized samples.

decision_rules: If the threat family mutates rapidly, use principle-level cues and response routines, not fixed scripts alone.

decision_rules: If the intervention creates learned helplessness, reduce threat intensity, increase agency, and add visible successful responses.

decision_rules: If a full threat has already dominated the receiving context, switch from inoculation to correction, de-escalation, or belief-revision work before trying future inoculation.

target_invariants: The first exposure remains bounded, safe, and ethically justified.

target_invariants: The refutation succeeds at the level of principle, not only against a strawman specimen.

target_invariants: The receiver gains agency and response options rather than only fear or dependence on an authority.

target_invariants: The intervention does not needlessly spread, legitimize, sensationalize, or operationalize the threat.

target_invariants: Resistance is tested against changed forms of the threat.

target_invariants: Refresh cycles preserve vigilance without creating habituation, panic, or contempt for the warning source.

expected_outcomes: Receivers recognize the structure of future threats faster and with less emotional capture.

expected_outcomes: False or manipulative claims lose anchoring power because a counterframe is already available.

expected_outcomes: People can explain why a threat fails and choose a legitimate response under pressure.

expected_outcomes: Training transfer improves from exact-example recognition to family-level resistance.

expected_outcomes: Defensive communication becomes less reactive, less panicked, and less dependent on censorship or after-the-fact correction.

Persuasion-Resistance Inoculation

short_definition: A rhetoric and communication variant that prepares an audience to resist future persuasive attacks by exposing them to weakened claims plus refutations.

distinctive_feature: Audience belief, trust, reactance, messenger credibility, and rhetorical framing are central rather than incidental.

distinct_from_parent: The parent can apply to scams, cyber attacks, training, and adaptive systems; this variant focuses on communicative persuasion and counterargument formation.

still_same_parent_because: The intervention still uses a weakened challenge and successful refutation to build transferable resistance before full exposure.

use_when: The future threat is a persuasive message, ideology, rumor, sales tactic, political attack, or manipulative frame.

use_when: The receiver is likely to encounter the message later under social pressure, affective arousal, or time pressure.

domain: media literacy

example: A course presents a simplified conspiracy claim, labels the tactic, and rehearses why the evidence standard fails before students meet stronger versions online.

failure_modes: The message feels patronizing or coercive, creating reactance instead of resistance.

Misinformation Prebunking

short_definition: A public-information variant that prepares people for likely false or misleading claims by explaining the tactic or false frame before it circulates widely.

distinctive_feature: Nonamplification, public trust, source credibility, and claim mutation are unusually important.

distinct_from_parent: The parent covers any weakened-threat inoculation; this variant centers public misinformation and rumor ecology.

still_same_parent_because: It still depends on prior exposure to a weakened claim/tactic plus successful refutation and transfer cues.

use_when: A misleading claim, rumor, scam, or manipulation tactic is predictable enough to warn about before peak circulation.

use_when: Reactive correction is likely to arrive too late, be distrusted, or amplify the false claim.

example: Before a campaign, a health agency warns that false “hidden danger” posts may appear and shows how to verify evidence and source claims.

failure_modes: A vivid prebunk repeats the false claim so memorably that it becomes the first exposure for some people.

Social-Engineering Resistance Training

short_definition: A security variant that prepares people to resist phishing, impersonation, urgency pressure, or credential-harvesting attempts through bounded simulations and debriefed refutations.

distinctive_feature: The threat is adaptive, adversarial, and often designed to exploit attention, authority, urgency, or trust cues.

distinct_from_parent: The parent is domain-general; this variant uses security simulations, reporting channels, and operational consequences.

still_same_parent_because: The key move remains pre-exposure to a bounded attack plus debriefed refutation and response practice.

use_when: Human judgment is a critical boundary in an adversarial security environment.

use_when: Attackers use recurring tactics that can be safely previewed and practiced against.

domain: security training

example: Employees receive a safe simulated phishing email, then a debrief names the urgency cue, verification step, and reporting path.

failure_modes: Punitive or deceptive training reduces trust and makes users hide mistakes rather than learn from them.

Adaptive-System Preconditioning

short_definition: A non-human or organizational variant in which a system is exposed to bounded challenges and corrected feedback so it develops a response repertoire before harsher conditions arrive.

distinctive_feature: The receiver may be an organization, model, team, or adaptive process rather than a human audience.

distinct_from_parent: The parent includes communication inoculation; this variant generalizes to adaptive systems and preconditioning regimes.

still_same_parent_because: It retains bounded exposure, corrective feedback, transfer cues, and future resistance as the core structure.

use_when: A system has learning or adaptation machinery that can be activated by bounded challenge.

use_when: Full-strength exposure would be too costly, too late, or too risky as the first training episode.

domain: organizational learning

example: A crisis team practices against simplified rumor and supply-disruption scenarios before a high-risk launch window.

failure_modes: The challenge set becomes too artificial and creates brittle confidence rather than adaptive resistance.

near_names:
Inoculation Theory Application
Prebunking
Counterargument Inoculation
Resistance Pretraining
Preemptive Refutation Training
Cognitive Inoculation


type: archetype

source: generated

name: Attractor Landscape Shaping and Basin Steering

batch: Batch 19

aliases:
Attractor Basin Steering
Basin-of-Attraction Control
Stable-State Landscape Shaping
Attractor Selection Control
Dynamical Basin Design

slug: equilibrium

slug: controllability

slug: hysteresis

slug: feedback

slug: resilience

slug: perturbation

slug: constraint

slug: stability

slug: convergence

slug: optimization

slug: nonlinearity

components: Prohibited and Competing Attractor Register

components: Basin Migration and Emergent-Attractor Monitor

components: Distributional Welfare and Legitimacy Boundary

essence_one_line: Select a viable attractor, reshape its basin or steer state into it, and maintain capture without creating a more dangerous stable pattern elsewhere.

compression_statement: Attractor Landscape Shaping and Basin Steering begins by defining the state variables, time scale, and viable operating envelope; identifying candidate stable states, cycles, or bounded recurrent regimes; estimating basin boundaries and uncertainty; evaluating which attractor is desirable for whom; selecting control levers that change state, transition rules, feedback, constraints, delays, incentives, or noise; widening the target basin, narrowing harmful basins, or applying a bounded state kick; guiding the trajectory through a safe capture corridor; supporting settling; testing robustness to perturbation; and monitoring escape, basin migration, hidden lock-in, and the emergence of new attractors.

core_intuition: In nonlinear systems, telling the system where to go is not enough. Several stable patterns can coexist, nearby starting points can end differently, and a temporary push may decay back into the original pattern. Durable change can come from moving the current state across a basin boundary, changing the boundary itself, altering the depth or shape of the landscape, or changing feedback so the desired state becomes easier to enter and harder to leave. Every such intervention can also create lock-in, suppress useful diversity, or stabilize a harmful state, so selection and safety are inseparable.

canonical_formula: Given dynamics x_next = F(x, u, theta, disturbance), candidate attractors A_i and estimated basins B_i(theta), select a viable target A_star; choose bounded controls u and parameter changes delta theta that place or keep x within a robust capture region of B_star while respecting forbidden states, transition costs, reversibility, and affected-party constraints; verify convergence, perturbation recovery, and absence of unacceptable competing-attractor growth.

structural_problem: A system repeatedly settles into an undesirable but self-reinforcing state, oscillation, norm, operating mode, ecological regime, queue pattern, local optimum, or coordination equilibrium. Direct correction changes the state briefly but not the dynamics that pull it back. Alternatively, several desirable and harmful stable patterns coexist and ordinary actions cannot reliably select among them. Basin boundaries may be narrow, uncertain, path dependent, moving, or hidden behind sparse observations. A strong intervention can cross the boundary but pass through unsafe states, overshoot into another attractor, or create brittle dependence on continuous control.

trigger_conditions: repeated resets or reforms decay back into the same pattern

trigger_conditions: small differences in starting state produce qualitatively different long-run outcomes

trigger_conditions: an organization or population coordinates on an inferior but stable convention

trigger_conditions: recovery succeeds only after a different threshold than the one that caused decline

trigger_conditions: a system has multiple local optima, stable cycles, regimes, or self-reinforcing equilibria

trigger_conditions: a desired state exists but its basin is too small or inaccessible under ordinary trajectories

trigger_conditions: a harmful state is easy to enter, hard to leave, or enlarged by existing feedback

trigger_conditions: transition policy focuses on the destination while ignoring capture and settling dynamics

consequences_of_non_intervention: relapse into a harmful pattern after costly change

consequences_of_non_intervention: failed coordination and persistent inferior equilibrium

consequences_of_non_intervention: overshoot, chaotic transient, or capture by an unintended attractor

consequences_of_non_intervention: excessive continuous control effort and operator fatigue

consequences_of_non_intervention: hidden lock-in that reduces resilience and adaptive capacity

consequences_of_non_intervention: distributional harm concealed by system-level stability

consequences_of_non_intervention: false confidence from a phase-space map that is never converted into control

intervention_summary: Define the dynamical scope and viable envelope; identify and validate candidate attractors; estimate basin membership, boundaries, and uncertainty; evaluate desirability, distribution, reversibility, and resilience; select a target and prohibited attractors; identify levers that act on state, parameters, feedback, constraints, delays, topology, incentives, or stochasticity; choose landscape shaping, boundary shifting, capture steering, or a hybrid; stage a safe trajectory; support settling; test perturbation recovery; and adapt control as basins move.

operating_sequence: define state variables, observation model, time scale, control authority, and viable envelope

operating_sequence: collect trajectories across representative starting conditions, disturbances, and operating contexts

operating_sequence: identify fixed points, cycles, recurrent regimes, metastable states, and plausible hidden attractors

operating_sequence: estimate basin membership and boundaries with uncertainty rather than a single crisp map

operating_sequence: evaluate each attractor for function, welfare, safety, distribution, resilience, reversibility, and control burden

operating_sequence: select the target attractor, prohibited attractors, acceptable alternatives, and abort conditions

operating_sequence: diagnose whether to change current state, basin boundary, landscape depth, feedback, constraint, topology, delay, incentive, or noise

operating_sequence: select bounded levers and predict transient paths, control effort, spillover, and competing-attractor response

operating_sequence: run safe-to-fail probes near informative boundaries before irreversible large-scale steering

operating_sequence: widen or deepen the target basin, narrow harmful basins, or steer the state through a capture corridor

operating_sequence: maintain temporary scaffolding until settling and recovery criteria hold

operating_sequence: taper control gradually while watching for rebound, hysteresis, oscillation, and hidden dependency

operating_sequence: test robustness with bounded perturbations and cross-context starting states

operating_sequence: monitor basin migration, emergent attractors, external change, distributional harm, and loss of adaptability

operating_sequence: revise or retire the intervention when the target is no longer viable or legitimate

sense: Observe state, trajectory, local stability, control effort, disturbance, and basin-membership confidence.

interpret: Update attractor and boundary hypotheses; distinguish settling from temporary slowing or forced compliance.

decide: Select state kick, parameter change, feedback redesign, constraint change, incentive shift, or exploration schedule.

act: Apply bounded steering with capture, stabilization, and abort controls.

verify: Test convergence, perturbation recovery, control taper, competing attractors, welfare, and adaptability.

decision_rules: if the desired state is stable but the current state lies outside its basin, use bounded trajectory steering or a state kick

decision_rules: if the target basin is too narrow for expected disturbances, reshape feedback or constraints rather than relying on repeated rescue

decision_rules: if a harmful attractor is deep because of reinforcing loops, weaken its regeneration points and strengthen viable alternatives

decision_rules: if the boundary is highly uncertain, prioritize probes and reversible controls over a large deterministic push

decision_rules: if transition passes through forbidden states, redesign the capture corridor or intermediate scaffold

decision_rules: if the target requires permanent high control effort, reclassify it as forced operation rather than self-maintaining attraction

decision_rules: if stability suppresses necessary exploration or minority viability, preserve multiple acceptable basins or scheduled exploration

decision_rules: if affected parties disagree about desirability, resolve governance before optimizing the landscape

prose_essence:
Attractor Landscape Shaping and Basin Steering directs a dynamic system toward a selected stable state, cycle, or recurrent regime by changing where the system starts, how it moves, or what patterns are self-maintaining. It is used when direct correction does not last because the system’s own feedback pulls it back, or when several long-run patterns coexist and ordinary action cannot reliably select among them.

The archetype treats an attractor as more than a desired endpoint. A target must have a defined state representation, time scale, convergence or recurrence criterion, and evidence that nearby trajectories remain or return. Its basin is the set of starting conditions that tend toward that attractor under the relevant dynamics. Control can move the current state across a basin boundary, widen or deepen a target basin, narrow a harmful basin, change feedback or constraints, or adjust exploration so a viable alternative is found.

This makes capture and settling central. A system may cross a boundary but rebound before the new pattern is self-maintaining. A temporary scaffold, guarantee, resource reserve, feedback change, or coordination device may be required until trajectories are well inside the target basin. Control is then tapered while recovery and escape are tested.

Stability is not automatically good. A harmful institution, ecological collapse, unsafe operating mode, addiction loop, inferior convention, or poor local optimum may be an attractor. The target selection record must evaluate function, welfare, distribution, legitimacy, resilience, reversibility, and continued control burden. A stable aggregate can coexist with severe local harm.

The mature intervention therefore combines map, selection, shaping, steering, capture, stabilization, taper, perturbation testing, and adaptive monitoring. It also preserves alternatives when plural stable states provide resilience or legitimate diversity.

prose_when_to_use:
Use this archetype when the system repeatedly relapses after repair, reform, reset, or rescue. Recurrent return suggests that state correction left the feedback, incentives, constraints, topology, delays, or expectations that regenerate the old pattern intact.

Use it when small differences in starting state lead to qualitatively different outcomes, when a desired regime exists but is difficult to enter, when a harmful regime is easy to enter and hard to leave, or when actors remain coordinated on an inferior convention because unilateral switching is costly. It also fits optimization trapped in local minima, ecosystems with alternative stable regimes, operations with recurrent overload cycles, and organizations whose routines re-form after reorganization.

The archetype is appropriate only when attractor claims are operational. Define state variables or defensible proxies, the relevant time scale, observations that distinguish transient from recurrent behavior, and a testable relation between initial state and long-run pattern. In social systems, “culture is an attractor” is not enough; specify observable routines, incentives, information flows, participation, and recovery behavior.

Use it when there is meaningful control authority. Levers may act on state, feedback gain or sign, constraints, defaults, topology, incentives, delays, access, noise, or transition support. The lever must be bounded by viable and forbidden states, affected-party authority, and stop rules.

Do not use it for an ordinary one-time project transition whose destination does not need to become self-maintaining. Controlled Phase Transition is the clearer parent there. Do not use it when the only task is mapping states or installing a control interface. Phase-Space Mapping and Control Surface Creation cover those narrower needs.

Do not claim basin control when data are too sparse to distinguish an attractor from a long transient, external forcing, seasonality, or operator effort. A provisional hypothesis and safe boundary probes are appropriate; a precise landscape is not.

prose_structural_problem:
Nonlinear systems can have several stable outcomes under the same broad rules. Feedback amplifies some deviations and damps others. Constraints and topology channel trajectories. Delays create oscillations. Expectations make conventions self-fulfilling. Once the state enters a basin, ordinary fluctuations decay toward the associated attractor.

This defeats endpoint-only interventions. A team is told to collaborate, a service is reset, a habitat is restored superficially, or an optimizer is restarted, but the same incentives, thresholds, resource flows, and feedback remain. The observed state improves briefly and then returns. Repetition increases cost without increasing capture probability.

The opposite problem is lock-in. A stable pattern can be resilient to valuable evidence and correction. Deepening its basin may lower variance while reducing learning, diversity, mobility, or exit. In organizations and institutions, landscape shaping can become coercive when authorities manipulate payoffs, information, or sanctions so alternatives are technically possible but practically unreachable.

Basin boundaries are often uncertain and mobile. They can be fractal, history dependent, affected by hidden variables, or shifted by external conditions. A model estimated from normal operations may fail near critical boundaries. A lever that appears local can change a competitor’s basin or create a new attractor elsewhere.

Transient paths add risk. Even if the target is viable, reaching it may require crossing a region of instability, low service, ecological vulnerability, social conflict, or financial exposure. Strong control can overshoot. Slow control can leave the system exposed too long. The design must govern the corridor as carefully as the destination.

Finally, attractor desirability is a governance question. System-level output, order, or persistence cannot decide whose welfare counts, which variation is legitimate, or who may impose the control. Selection precedes optimization.

prose_intervention_logic:
Start by defining the dynamical scope. State what evolves, the boundary of the system, the time step or continuous horizon, exogenous drivers, and how state is observed. Distinguish fast transients from slow structural change and identify scales that may contain different attractors.

Collect trajectories across representative starting conditions, disturbances, contexts, and control settings. Existing history, simulation, safe experiments, and natural perturbations can all contribute. Mark missing regions rather than interpolating a confident basin from sparse paths.

Identify candidate attractors. These may be fixed points, bounded ranges, limit cycles, recurrent routines, metastable regimes, coordination equilibria, or local optima. Define entry, settling, recurrence, and escape criteria for each. Test whether continued external forcing is required.

Estimate basin membership and boundaries with uncertainty. Sample initial conditions, fit transition models, use reachability or continuation analysis, and perform small reversible probes near informative boundaries. Record alternative models, hidden-state risk, and areas where the boundary may be fractal or moving.

Evaluate candidate attractors before selecting one. Compare operational function, safety, welfare, distribution, legitimacy, resilience to expected disturbance, reversibility, adaptability, and continuing control cost. Name prohibited attractors and acceptable alternatives. Establish abort conditions.

Map control levers to dynamical effects. A state kick changes current coordinates. Feedback rewiring changes local stability. Constraint or topology changes alter reachable paths. Incentives and information shift coordination payoffs. Delay reduction can remove oscillation. Noise or exploration can help escape a poor basin. Each hypothesis must predict target and competing-attractor response.

Choose the intervention family. If the target already has a robust basin, steer the state into its interior. If the basin is narrow, reshape feedback or constraints. If a harmful attractor is deep, weaken the loops that regenerate it and strengthen a viable replacement. If boundaries are uncertain, learn through safe probes before committing.

Plan a capture corridor. Define intermediate states, thresholds, authority, resources, monitoring, and abort actions. Protect forbidden regions and affected parties. Use temporary scaffolds such as guarantees, parallel capacity, facilitation, reserves, or stabilizing feedback until settling criteria hold.

Taper control rather than switching it off abruptly. Monitor rebound, hysteresis, oscillation, and operator effort. If the target disappears when support declines, report forced operation honestly and decide whether permanent control is acceptable.

Validate with bounded perturbations and varied starting states. A robust target recovers under expected disturbance without excessive effort and does not enlarge a harmful competitor. Continue monitoring basin migration, external drivers, emergent attractors, and distributional harm.

prose_key_components:
### Dynamical Scope and Time Scale (`dynamical_scope_and_time_scale`)

The system boundary, evolution interval, fast and slow processes, exogenous drivers, and horizon on which attraction is claimed.

Semantic canonical mapping retained the complete legacy component record: {"slug":"dynamical_scope_and_time_scale","name":"Dynamical Scope and Time Scale","component_type":"specification","maturity":"reusable","definition":"The system boundary, evolution interval, fast and slow processes, exogenous drivers, and horizon on which attraction is claimed.","required_fields":["boundary","actors_or_units","time_step","fast_processes","slow_processes","exogenous_drivers","horizon","owner"],"invariants":["stability_claim_is_time_bounded","external_forcing_is_explicit"],"validation_checks":["boundary_review","time_scale_separation_check"],"failure_signatures":["seasonality_called_attractor","slow_variable_omitted","open_system_treated_closed"],"domain_examples":["service_operations","lake_regime","organizational_routine"]}

### State Variable and Observation Model (`state_variable_and_observation_model`)

State coordinates, proxies, sampling, uncertainty, hidden variables, and transformations used to infer trajectories.

Semantic canonical mapping retained the complete legacy component record: {"slug":"state_variable_and_observation_model","name":"State Variable and Observation Model","component_type":"model","maturity":"reusable","definition":"State coordinates, proxies, sampling, uncertainty, hidden variables, and transformations used to infer trajectories.","required_fields":["state_variables","units","proxies","sampling","observation_error","hidden_state","transformations","version"],"invariants":["proxy_is_not_equated_with_state_silently","units_and_scales_are_stable"],"validation_checks":["observability_check","proxy_validity_test","reconstruction_error"],"failure_signatures":["metric_substitution","hidden_state_aliasing","scale_mismatch"],"domain_examples":["queue_state","ecosystem_state","optimization_population"]}

### Viable and Forbidden State Envelope (`viable_and_forbidden_state_envelope`)

Acceptable operating regions, transition constraints, hard prohibitions, and affected-party harm thresholds.

Semantic canonical mapping retained the complete legacy component record: {"slug":"viable_and_forbidden_state_envelope","name":"Viable and Forbidden State Envelope","component_type":"safety_boundary","maturity":"reusable","definition":"Acceptable operating regions, transition constraints, hard prohibitions, and affected-party harm thresholds.","required_fields":["viable_region","forbidden_regions","transient_limits","affected_parties","measurement","authority","breach_action"],"invariants":["safe_endpoint_does_not_excuse_unsafe_path","aggregate_viability_does_not_hide_local_harm"],"validation_checks":["trajectory_constraint_test","subgroup_harm_review"],"failure_signatures":["corridor_harm","hidden_local_collapse","moving_limit_untracked"],"domain_examples":["grid_frequency","dissolved_oxygen","service_latency"]}

### Candidate Attractor Inventory (`candidate_attractor_inventory`)

A versioned inventory of fixed points, cycles, recurrent regimes, metastable patterns, and hidden-attractor hypotheses.

Semantic canonical mapping retained the complete legacy component record: {"slug":"candidate_attractor_inventory","name":"Candidate Attractor Inventory","component_type":"dynamical_record","maturity":"reusable","definition":"A versioned inventory of fixed points, cycles, recurrent regimes, metastable patterns, and hidden-attractor hypotheses.","required_fields":["identifier","attractor_class","state_signature","entry","settling","recurrence","escape","evidence","uncertainty"],"invariants":["forced_states_are_labeled","multiple_candidate_classes_are_allowed"],"validation_checks":["recurrence_test","forcing_dependency_check","alternate_model_review"],"failure_signatures":["long_transient_mislabeled","hidden_competitor","one_observation_attractor"],"domain_examples":["clear_turbid_lake","overload_cycle","local_optimum"]}

### Basin Membership and Boundary Model (`basin_membership_and_boundary_model`)

Estimated mapping from initial states and contexts to attractors, including boundary uncertainty and nonstationarity.

Semantic canonical mapping retained the complete legacy component record: {"slug":"basin_membership_and_boundary_model","name":"Basin Membership and Boundary Model","component_type":"state_space_model","maturity":"reusable","definition":"Estimated mapping from initial states and contexts to attractors, including boundary uncertainty and nonstationarity.","required_fields":["initial_state_domain","context","attractor_labels","membership_method","boundary","uncertainty","path_dependence","update_rule"],"invariants":["uncertainty_is_preserved","unsampled_regions_are_marked"],"validation_checks":["held_out_trajectory_test","perturbation_probe","calibration_check"],"failure_signatures":["crisp_boundary_from_sparse_data","context_omitted","fractal_boundary_smoothed"],"domain_examples":["simulation_grid","empirical_transition_map","coordination_threshold"]}

### Target Attractor Selection Record (`target_attractor_selection_record`)

Comparison and authorization of candidate attractors by function, safety, welfare, distribution, resilience, reversibility, adaptability, and control burden.

Semantic canonical mapping retained the complete legacy component record: {"slug":"target_attractor_selection_record","name":"Target Attractor Selection Record","component_type":"governance_decision","maturity":"reusable","definition":"Comparison and authorization of candidate attractors by function, safety, welfare, distribution, resilience, reversibility, adaptability, and control burden.","required_fields":["candidates","criteria","evidence","affected_parties","tradeoffs","selected_target","acceptable_alternatives","authority","review_date"],"invariants":["persistence_is_not_desirability","affected_party_impacts_are_visible"],"validation_checks":["criteria_trace","legitimacy_review","alternative_comparison"],"failure_signatures":["stable_harm_optimized","aggregate_only_selection","authority_missing"],"domain_examples":["organizational_norm","ecological_regime","operating_mode"]}

### Prohibited and Competing Attractor Register (`prohibited_and_competing_attractor_register`)

Harmful and unintended stable patterns, their basin indicators, emergence pathways, and escalation rules.

Semantic canonical mapping retained the complete legacy component record: {"slug":"prohibited_and_competing_attractor_register","name":"Prohibited and Competing Attractor Register","component_type":"hazard_register","maturity":"reusable","definition":"Harmful and unintended stable patterns, their basin indicators, emergence pathways, and escalation rules.","required_fields":["attractor","harm","indicators","entry_paths","interactions","threshold","owner","response"],"invariants":["global_competitors_are_not_ignored","cross_scale_patterns_are_considered"],"validation_checks":["scenario_scan","indicator_backtest","cross_scale_review"],"failure_signatures":["new_harmful_basin","remote_competitor_growth","hidden_oscillation"],"domain_examples":["service_thrash","invasive_regime","gaming_equilibrium"]}

### Control Lever and Authority Map (`control_lever_and_authority_map`)

Authorized levers acting on state, parameters, feedback, constraints, delays, topology, incentives, information, or noise.

Semantic canonical mapping retained the complete legacy component record: {"slug":"control_lever_and_authority_map","name":"Control Lever and Authority Map","component_type":"control_map","maturity":"reusable","definition":"Authorized levers acting on state, parameters, feedback, constraints, delays, topology, incentives, information, or noise.","required_fields":["lever","control_locus","operator","authority","range","latency","reversibility","side_effects","audit"],"invariants":["operator_authority_is_explicit","lever_range_is_bounded"],"validation_checks":["actuator_response_test","authority_check","side_effect_review"],"failure_signatures":["unauthorized_control","lever_saturation","delayed_response_ignored"],"domain_examples":["admission_threshold","nutrient_loading","switching_guarantee"]}

### Landscape-Shaping Hypothesis (`landscape_shaping_hypothesis`)

A falsifiable prediction of how a lever changes target and competing basin geometry, stability, and transients.

Semantic canonical mapping retained the complete legacy component record: {"slug":"landscape_shaping_hypothesis","name":"Landscape-Shaping Hypothesis","component_type":"causal_control_hypothesis","maturity":"reusable","definition":"A falsifiable prediction of how a lever changes target and competing basin geometry, stability, and transients.","required_fields":["lever","causal_path","target_effect","competitor_effect","transient_effect","assumptions","uncertainty","discriminating_test"],"invariants":["target_and_competitors_are_modeled","local_effect_is_not_generalized_silently"],"validation_checks":["sensitivity_analysis","safe_probe","counterfactual_review"],"failure_signatures":["leverage_story_without_dynamics","sign_error","unmodeled_spillover"],"domain_examples":["feedback_rewiring","payoff_change","constraint_shift"]}

### Capture Corridor and Transition Plan (`capture_corridor_and_transition_plan`)

Safe intermediate states, control actions, monitoring, resources, and abort rules from current state to target-basin interior.

Semantic canonical mapping retained the complete legacy component record: {"slug":"capture_corridor_and_transition_plan","name":"Capture Corridor and Transition Plan","component_type":"transition_plan","maturity":"reusable","definition":"Safe intermediate states, control actions, monitoring, resources, and abort rules from current state to target-basin interior.","required_fields":["start","target_capture_region","path","intermediate_states","controls","limits","monitoring","abort","owner"],"invariants":["forbidden_states_are_excluded","capture_margin_is_declared"],"validation_checks":["trajectory_simulation","resource_check","abort_rehearsal"],"failure_signatures":["overshoot","corridor_viability_loss","no_abort"],"domain_examples":["migration_path","ecosystem_recovery_sequence","coordinated_switch"]}

### Stabilization and Settling Support (`stabilization_and_settling_support`)

Temporary resources, feedback, guarantees, redundancy, facilitation, or scaffolds maintained until settling and recovery criteria hold.

Semantic canonical mapping retained the complete legacy component record: {"slug":"stabilization_and_settling_support","name":"Stabilization and Settling Support","component_type":"temporary_support_plan","maturity":"reusable","definition":"Temporary resources, feedback, guarantees, redundancy, facilitation, or scaffolds maintained until settling and recovery criteria hold.","required_fields":["support","purpose","activation","duration","settling_criteria","dependencies","owner","taper_link"],"invariants":["support_is_time_or_state_bounded","dependency_is_measured"],"validation_checks":["settling_check","dependency_review","resource_sufficiency"],"failure_signatures":["support_removed_early","permanent_scaffold_hidden","stabilization_masks_harm"],"domain_examples":["transition_guarantee","restoration_support","parallel_capacity"]}

### Control Effort and Taper Policy (`control_effort_and_taper_policy`)

Measurement and staged withdrawal of the energy, money, authority, attention, or enforcement that maintains target capture.

Semantic canonical mapping retained the complete legacy component record: {"slug":"control_effort_and_taper_policy","name":"Control Effort and Taper Policy","component_type":"withdrawal_policy","maturity":"reusable","definition":"Measurement and staged withdrawal of the energy, money, authority, attention, or enforcement that maintains target capture.","required_fields":["effort_metrics","baseline","taper_stages","entry_criteria","rebound_threshold","pause","restore","owner"],"invariants":["forced_operation_is_reported","taper_follows_state_evidence"],"validation_checks":["effort_accounting","taper_trial","rebound_monitor"],"failure_signatures":["premature_taper","invisible_operator_burden","permanent_emergency_control"],"domain_examples":["facilitation_taper","subsidy_stepdown","control_gain_reduction"]}

### Perturbation and Escape Test Plan (`perturbation_and_escape_test_plan`)

Bounded tests of target recovery, basin margin, escape, rebound, and competing-attractor capture.

Semantic canonical mapping retained the complete legacy component record: {"slug":"perturbation_and_escape_test_plan","name":"Perturbation and Escape Test Plan","component_type":"validation_plan","maturity":"reusable","definition":"Bounded tests of target recovery, basin margin, escape, rebound, and competing-attractor capture.","required_fields":["disturbances","starting_states","bounds","expected_response","recovery","escape","stop_rule","evidence"],"invariants":["tests_remain_inside_safety_bounds","absence_of_escape_is_uncertainty_qualified"],"validation_checks":["test_coverage","recovery_time_check","boundary_calibration"],"failure_signatures":["only_nominal_tested","destructive_probe","recovery_metric_gamed"],"domain_examples":["demand_shock","ecological_disturbance","adversarial_restart"]}

### Basin Migration and Emergent-Attractor Monitor (`basin_migration_and_emergent_attractor_monitor`)

Monitoring for changed parameters, boundary movement, critical slowing, new recurrent patterns, and model invalidation.

Semantic canonical mapping retained the complete legacy component record: {"slug":"basin_migration_and_emergent_attractor_monitor","name":"Basin Migration and Emergent-Attractor Monitor","component_type":"adaptive_monitor","maturity":"reusable","definition":"Monitoring for changed parameters, boundary movement, critical slowing, new recurrent patterns, and model invalidation.","required_fields":["signals","cadence","thresholds","model_residual","emergent_pattern_rule","owner","response"],"invariants":["model_failure_is_actionable","monitor_does_not_assume_fixed_landscape"],"validation_checks":["drift_backtest","alert_precision_review","model_update_trace"],"failure_signatures":["stale_control","unseen_new_attractor","alarm_fatigue"],"domain_examples":["climate_shift","market_change","organizational_turnover"]}

### Distributional Welfare and Legitimacy Boundary (`distributional_welfare_and_legitimacy_boundary`)

Rights, welfare, burden, consent, contestability, exit, and authority constraints on target selection and control.

Semantic canonical mapping retained the complete legacy component record: {"slug":"distributional_welfare_and_legitimacy_boundary","name":"Distributional Welfare and Legitimacy Boundary","component_type":"governance_safeguard","maturity":"reusable","definition":"Rights, welfare, burden, consent, contestability, exit, and authority constraints on target selection and control.","required_fields":["affected_parties","benefits","harms","control_burden","rights","consent","contestability","exit","authority","remedy"],"invariants":["aggregate_stability_does_not_erase_local_harm","social_lock_in_is_contestable"],"validation_checks":["distribution_review","rights_check","exit_test","independent_review"],"failure_signatures":["coercive_lock_in","minority_harm_hidden","target_selected_without_authority"],"domain_examples":["institutional_convention","land_management","workforce_change"]}

### Rollback, Escape, and Retirement Policy (`rollback_escape_and_retirement_policy`)

Authorized escape, rollback, containment, and retirement when the target or control becomes unsafe, illegitimate, obsolete, or invalid.

Semantic canonical mapping retained the complete legacy component record: {"slug":"rollback_escape_and_retirement_policy","name":"Rollback, Escape, and Retirement Policy","component_type":"recovery_policy","maturity":"reusable","definition":"Authorized escape, rollback, containment, and retirement when the target or control becomes unsafe, illegitimate, obsolete, or invalid.","required_fields":["triggers","authority","escape_path","rollback_state","containment","communication","evidence_preservation","closure"],"invariants":["exit_is_not_removed_by_target_capture","irreversible_steps_have_stronger_review"],"validation_checks":["escape_rehearsal","rollback_viability","retirement_trigger_test"],"failure_signatures":["no_exit","rollback_state_decayed","obsolete_target_persisted"],"domain_examples":["feature_retirement","policy_sunset","ecological_containment"]} Consolidated omitted legacy component records: [{"slug":"multi_attractor_portfolio_policy","name":"Multi-Attractor Portfolio Policy","component_type":"optional_resilience_policy","maturity":"reusable","definition":"A policy preserving several acceptable stable regimes and safe transitions when diversity and context adaptation matter.","required_fields":["acceptable_attractors","context_fit","transition_paths","interoperability","monitoring","selection_rule"],"invariants":["plurality_is_deliberate","each_attractor_remains_viable"],"validation_checks":["portfolio_stress_test","transition_check"],"failure_signatures":["fragmentation_without_interoperability","one_attractor_silently_dominates"],"domain_examples":["regional_practice","redundant_operating_modes","strategy_portfolio"]},{"slug":"stochastic_exploration_schedule","name":"Stochastic Exploration Schedule","component_type":"optional_exploration_policy","maturity":"reusable","definition":"Bounded noise, mutation, restart, or experimentation used to escape poor basins or discover alternatives.","required_fields":["operator","intensity","budget","viability_bounds","baseline","cooling","stop_rule","log"],"invariants":["viable_baseline_is_preserved","exploration_is_bounded"],"validation_checks":["budget_check","diversity_response","rollback_test"],"failure_signatures":["unbounded_randomness","elite_loss","exploration_never_cools"],"domain_examples":["optimizer_restart","pilot_variation","search_noise"]},{"slug":"boundary_probe_experiment_register","name":"Boundary-Probe Experiment Register","component_type":"optional_experiment_record","maturity":"reusable","definition":"Safe-to-fail perturbations near uncertain boundaries with hypotheses, results, and model updates.","required_fields":["hypothesis","state","perturbation","bounds","result","recovery","model_update","owner"],"invariants":["probe_is_reversible_within_bound","negative_results_are_retained"],"validation_checks":["safety_review","result_reproducibility","update_trace"],"failure_signatures":["probe_becomes_unbounded","selective_result","no_model_update"],"domain_examples":["capacity_probe","small_area_restoration","coordination_pilot"]},{"slug":"cross_scale_coupling_model","name":"Cross-Scale Coupling Model","component_type":"optional_multiscale_model","maturity":"reusable","definition":"Links local, meso, and aggregate attractors and identifies when stabilizing one scale destabilizes another.","required_fields":["scales","states","coupling","delays","aggregation","spillovers","uncertainty","monitoring"],"invariants":["local_success_is_not_assumed_global","aggregation_loss_is_disclosed"],"validation_checks":["cross_scale_sensitivity","spillover_test"],"failure_signatures":["local_stability_global_collapse","ecological_fallacy","hidden_delay"],"domain_examples":["team_enterprise","patch_ecosystem","node_network"]}]

prose_common_mechanisms:
### Basin-of-Attraction Mapping (`basin_of_attraction_mapping`)

**Type:** `modeling_and_analysis`

Estimate attractor membership and boundary uncertainty across initial states, parameters, contexts, and disturbances.

Selection constraints and retained implementation evidence: state_model; dynamics_or_trajectory_data; candidate_attractors; initial_state_sample; disturbances; Complete legacy mechanism record retained: {"slug":"basin_of_attraction_mapping","name":"Basin-of-Attraction Mapping","mechanism_type":"modeling_and_analysis","maturity":"reusable","purpose":"Estimate attractor membership and boundary uncertainty across initial states, parameters, contexts, and disturbances.","inputs":["state_model","dynamics_or_trajectory_data","candidate_attractors","initial_state_sample","disturbances"],"procedure":["sample_initial_states","simulate_or_observe","classify_long_run_behavior","estimate_boundaries","quantify_uncertainty","validate_holdout"],"outputs":["membership_map","boundary_band","unsampled_regions","calibration_report"],"safeguards":["do_not_smooth_hidden_fractal_risk","disclose_context_and_time_scale"],"verification":["held_out_trajectory_accuracy","probe_consistency","alternative_model_review"],"failure_signatures":["crisp_sparse_map","forced_state_mislabeled","unknown_region_colored_as_known"]}

### State Kick or Capture Pulse (`state_kick_or_capture_pulse`)

**Type:** `bounded_control_action`

Move current state across an estimated boundary into a robust target capture region.

Selection constraints and retained implementation evidence: current_state; boundary_model; target_region; actuator; pulse_profile; safety_bounds; Complete legacy mechanism record retained: {"slug":"state_kick_or_capture_pulse","name":"State Kick or Capture Pulse","mechanism_type":"bounded_control_action","maturity":"reusable","purpose":"Move current state across an estimated boundary into a robust target capture region.","inputs":["current_state","boundary_model","target_region","actuator","pulse_profile","safety_bounds"],"procedure":["verify_margin","stage_actuator","apply_pulse","track_trajectory","stop_on_abort","confirm_capture"],"outputs":["state_change","capture_evidence","effort_record","anomalies"],"safeguards":["overshoot_limit","forbidden_state_abort","rollback_ready"],"verification":["trajectory_match","post_pulse_settling","competitor_scan"],"failure_signatures":["undershoot_rebound","overshoot","transient_damage"]}

### Feedback Gain or Sign Rewiring (`feedback_gain_or_sign_rewiring`)

**Type:** `structural_control`

Change local and global stability by modifying reinforcing or balancing loop strength, sign, delay, or response rule.

Selection constraints and retained implementation evidence: feedback_map; target_behavior; candidate_loop; intervention; delay_model; bounds; Complete legacy mechanism record retained: {"slug":"feedback_gain_or_sign_rewiring","name":"Feedback Gain or Sign Rewiring","mechanism_type":"structural_control","maturity":"reusable","purpose":"Change local and global stability by modifying reinforcing or balancing loop strength, sign, delay, or response rule.","inputs":["feedback_map","target_behavior","candidate_loop","intervention","delay_model","bounds"],"procedure":["estimate_loop_effect","test_small_change","monitor_target_and_competitors","stage_scale_up","validate_taper"],"outputs":["revised_loop","stability_response","spillover_report"],"safeguards":["cross_scale_review","delay_margin","avoid_single_metric_feedback"],"verification":["perturbation_recovery","oscillation_check","causal_trace"],"failure_signatures":["sign_error","oscillation","remote_harmful_attractor"]}

### Constraint and Boundary Reshaping (`constraint_and_boundary_reshaping`)

**Type:** `rule_or_environment_change`

Change feasible trajectories and basin geometry through defaults, rules, topology, access, capacity, or physical boundaries.

Selection constraints and retained implementation evidence: reachable_state_model; constraint_set; proposed_change; affected_parties; safety_limits; Complete legacy mechanism record retained: {"slug":"constraint_and_boundary_reshaping","name":"Constraint and Boundary Reshaping","mechanism_type":"rule_or_environment_change","maturity":"reusable","purpose":"Change feasible trajectories and basin geometry through defaults, rules, topology, access, capacity, or physical boundaries.","inputs":["reachable_state_model","constraint_set","proposed_change","affected_parties","safety_limits"],"procedure":["map_current_paths","predict_reachability_change","pilot","monitor_substitution","revise_or_scale"],"outputs":["new_constraint","reachability_change","side_effect_register"],"safeguards":["preserve_legitimate_exit","prevent_hidden_displacement","check_cross_boundary_effects"],"verification":["reachability_test","substitution_monitor","distribution_review"],"failure_signatures":["workaround_attractor","coercive_lock_in","risk_displacement"]}

### Incentive Landscape Reconfiguration (`incentive_landscape_reconfiguration`)

**Type:** `institutional_mechanism`

Change payoffs, switching costs, guarantees, information, or expectations so a viable coordination equilibrium becomes reachable and durable.

Selection constraints and retained implementation evidence: actor_map; payoff_structure; convention; target_equilibrium; switching_risk; authority; Complete legacy mechanism record retained: {"slug":"incentive_landscape_reconfiguration","name":"Incentive Landscape Reconfiguration","mechanism_type":"institutional_mechanism","maturity":"reusable","purpose":"Change payoffs, switching costs, guarantees, information, or expectations so a viable coordination equilibrium becomes reachable and durable.","inputs":["actor_map","payoff_structure","convention","target_equilibrium","switching_risk","authority"],"procedure":["model_unilateral_cost","design_guarantee_or_signal","stage_critical_mass","protect_exit","monitor_distribution"],"outputs":["reconfigured_incentives","adoption_path","commitment_record","harm_report"],"safeguards":["consent_and_contestability","no_hidden_sanction","minority_viability"],"verification":["adoption_persistence","exit_test","welfare_distribution"],"failure_signatures":["manufactured_compliance","subsidy_dependency","unequal_switching_cost"]}

### Continuation or Homotopy Steering (`continuation_or_homotopy_steering`)

**Type:** `gradual_parameter_path`

Move parameters gradually so the state follows a stable branch toward the target without an unsafe jump.

Selection constraints and retained implementation evidence: parameter_path; branch_model; state_monitor; step_size; bifurcation_warnings; rollback; Complete legacy mechanism record retained: {"slug":"continuation_or_homotopy_steering","name":"Continuation or Homotopy Steering","mechanism_type":"gradual_parameter_path","maturity":"reusable","purpose":"Move parameters gradually so the state follows a stable branch toward the target without an unsafe jump.","inputs":["parameter_path","branch_model","state_monitor","step_size","bifurcation_warnings","rollback"],"procedure":["initialize_stable_branch","advance_small_step","verify_tracking","adapt_step","pause_near_instability","finish_and_taper"],"outputs":["parameter_trajectory","branch_tracking_evidence","warnings","final_state"],"safeguards":["bifurcation_abort","step_size_bound","preserve_fallback"],"verification":["branch_residual","recovery_test","reverse_path_probe"],"failure_signatures":["branch_loss","hidden_hysteresis","step_too_large"]}

### Annealing, Noise, or Random Restart (`annealing_noise_or_random_restart`)

**Type:** `exploration_control`

Escape a poor basin and search alternative attraction regions under bounded variation.

Selection constraints and retained implementation evidence: current_candidates; variation_operator; intensity_schedule; viability_bounds; protected_baseline; budget; Complete legacy mechanism record retained: {"slug":"annealing_noise_or_random_restart","name":"Annealing, Noise, or Random Restart","mechanism_type":"exploration_control","maturity":"reusable","purpose":"Escape a poor basin and search alternative attraction regions under bounded variation.","inputs":["current_candidates","variation_operator","intensity_schedule","viability_bounds","protected_baseline","budget"],"procedure":["preserve_baseline","inject_variation","evaluate","retain_viable_candidates","cool_or_stop","validate_new_basin"],"outputs":["candidate_trajectories","diversity_record","selected_capture","rollback_evidence"],"safeguards":["elite_protection","sandbox","mutation_budget","safety_filter"],"verification":["improvement_holdout","robustness_test","budget_audit"],"failure_signatures":["progress_destroyed","endless_exploration","unsafe_candidate_escape"]}

### Basin Boundary Probe (`basin_boundary_probe`)

**Type:** `safe_to_fail_experiment`

Apply a small reversible perturbation near uncertainty to learn boundary position, sensitivity, and recovery.

Selection constraints and retained implementation evidence: state; boundary_hypothesis; perturbation; safety_envelope; observation_plan; rollback; Complete legacy mechanism record retained: {"slug":"basin_boundary_probe","name":"Basin Boundary Probe","mechanism_type":"safe_to_fail_experiment","maturity":"reusable","purpose":"Apply a small reversible perturbation near uncertainty to learn boundary position, sensitivity, and recovery.","inputs":["state","boundary_hypothesis","perturbation","safety_envelope","observation_plan","rollback"],"procedure":["authorize","establish_baseline","perturb","observe_response","rollback_if_needed","update_model"],"outputs":["trajectory","recovery","boundary_update","anomaly"],"safeguards":["strict_magnitude_bound","abort_signal","affected_party_protection"],"verification":["repeatability_or_explanation","model_update_trace"],"failure_signatures":["probe_crosses_forbidden_state","no_recovery_capacity","result_ignored"]}

### Temporary Scaffold and Taper (`temporary_scaffold_and_taper`)

**Type:** `stabilization_protocol`

Maintain viability and target capture until internal dynamics can sustain the state, then withdraw support gradually.

Selection constraints and retained implementation evidence: capture_state; support; settling_criteria; effort_metrics; taper_schedule; rebound_threshold; Complete legacy mechanism record retained: {"slug":"temporary_scaffold_and_taper","name":"Temporary Scaffold and Taper","mechanism_type":"stabilization_protocol","maturity":"reusable","purpose":"Maintain viability and target capture until internal dynamics can sustain the state, then withdraw support gradually.","inputs":["capture_state","support","settling_criteria","effort_metrics","taper_schedule","rebound_threshold"],"procedure":["activate_support","monitor_settling","begin_small_taper","test_recovery","pause_or_restore","complete_or_reclassify"],"outputs":["support_history","taper_evidence","autonomous_stability_classification"],"safeguards":["resource_limit","dependency_disclosure","no_calendar_only_withdrawal"],"verification":["post_taper_recovery","control_effort_reduction","competitor_scan"],"failure_signatures":["premature_rebound","permanent_scaffold_hidden","support_lock_in"]}

### Competing-Attractor Early-Warning Monitor (`competing_attractor_early_warning_monitor`)

**Type:** `monitoring`

Detect escape, critical slowing, trajectory deflection, competitor growth, or model invalidation before loss of capture.

Selection constraints and retained implementation evidence: state_signals; trajectory_model; competitor_indicators; control_effort; disturbances; thresholds; Complete legacy mechanism record retained: {"slug":"competing_attractor_early_warning_monitor","name":"Competing-Attractor Early-Warning Monitor","mechanism_type":"monitoring","maturity":"reusable","purpose":"Detect escape, critical slowing, trajectory deflection, competitor growth, or model invalidation before loss of capture.","inputs":["state_signals","trajectory_model","competitor_indicators","control_effort","disturbances","thresholds"],"procedure":["collect","estimate_residual_and_recovery","compare_competitors","flag_uncertainty","route_response","recalibrate"],"outputs":["warnings","basin_confidence","response_queue","model_update"],"safeguards":["no_single_indicator_proof","false_alarm_review","monitor_distributional_harm"],"verification":["backtest","incident_recall","threshold_recalibration"],"failure_signatures":["critical_slowing_overclaim","alarm_fatigue","hidden_new_attractor"]}

prose_failure_modes:
**False attractor identification** occurs when a long transient, seasonal cycle, or externally forced state is mistaken for autonomous attraction. Support is withdrawn and relapse follows. Test recurrence, taper, perturbation recovery, and multiple time scales.

**Basin-boundary overconfidence** turns sparse trajectories into a crisp map. Steering then crosses into danger or misses the target. Preserve uncertainty bands and use adaptive, reversible probes.

**Undesirable target selection** treats persistence or aggregate output as welfare. A stable harmful pattern becomes optimized. Require distributional, legitimacy, reversibility, and affected-party review.

**Transient corridor harm** ignores states passed through on the way to the target. Model the entire path, define forbidden regions, and use intermediate scaffolds and aborts.

**Competing-attractor creation** occurs when a lever changes remote or cross-scale dynamics. Scan beyond local target metrics and monitor emergent stable patterns.

**Forced equilibrium mislabeled** hides continuous control effort. Measure resources and authority needed, test taper, and classify permanent control honestly.

**Premature taper and rebound** remove support before settling or adequate margin. Use state- and recovery-based withdrawal criteria.

**Adaptive-capacity collapse** suppresses variation and alternatives in pursuit of stability. Maintain exploration windows, diversity reserves, and a portfolio when viable.

**Model staleness** leaves old controls active after external conditions move the basin. Monitor drift and retire invalid policies.

**Coercive social lock-in** manipulates options, information, or sanctions so exit becomes practically impossible. Preserve rights, consent, contestability, transparency of levers, and independent review.

**Metric-induced attractor** makes the monitoring target itself a self-reinforcing gaming pattern. Combine measures, audit behavior, and revise incentives.

**Scale mismatch** stabilizes a local unit while destabilizing the larger system, or vice versa. Model coupling and test outcomes at relevant scales.

prose_neighbor_distinctions:
**Phase-Space Mapping** makes states, trajectories, stable regions, and intervention zones visible. It supplies the diagnostic map; the present archetype selects and changes attraction, governs capture, and validates settling.

**Controlled Phase Transition** moves a system deliberately from one regime to another while managing crossing and cutover. Basin steering focuses on why trajectories converge and remain, including changes that widen capture without one discrete phase transition.

**Equilibrium Restoration** returns a destabilized system toward a viable prior balance. Basin steering may select a novel attractor, alter multiple equilibria, or preserve several acceptable regimes.

**Control Surface Creation** creates actionable levers. Basin steering consumes those levers within a particular dynamic selection and capture lifecycle.

**Leverage-Point Intervention** chooses a strategic point with disproportionate effect. It does not necessarily characterize attractors, basins, capture margins, taper, or competing stable patterns.

**Hysteresis Management** handles path-dependent entry and exit thresholds. Hysteresis is a key basin property but not the whole landscape-shaping intervention.

**Adaptive Mutation Rate Management** tunes deliberate variation for exploration and stability. Noise scheduling is one mechanism for basin escape and search; many basin controls act through feedback, constraints, state, or incentives instead.

**Sequential Policy Optimization** chooses actions over time under uncertain transitions and reward. It can compute steering policies, but the present archetype centers attractor selection, basin geometry, autonomous stability, and legitimacy rather than a general reward policy.

**Cycle Breaking** interrupts a recurring harmful loop. Basin steering additionally designs the replacement attractor and capture path so interruption does not merely leave an unstable vacuum.

The frozen provisional **Attractor Basin Steering** candidate is not an accepted archetype. Its one-line seed is incorporated and matured here; it does not block direct drafting.

prose_cross_domain_examples:
### Organizational routines

An organization repeatedly returns to siloed escalation after integration programs. Teams map handoff states, incentives, authority, and feedback. Shared ownership, cross-team resolution defaults, fast feedback, and temporary facilitation widen the cooperative basin. Selected workflows are steered through a capture corridor. Facilitation tapers after shocks no longer recreate silos.

### Ecological restoration

A shallow lake has clear and eutrophic regimes with hysteresis. Managers estimate nutrient and vegetation boundaries, reduce loading, restore stabilizing plants, avoid transient oxygen collapse, and monitor recovery under heat and inflow shocks. The target is a resilient ecological envelope, not one chemically neat point.

### Computational optimization

An optimizer repeatedly converges to poor local solutions. A protected elite set preserves viability while controlled noise, random restarts, penalty reshaping, and continuation explore alternatives. Candidate solutions are tested under perturbation before the new basin is accepted.

### Service operations

A service oscillates between overload and aggressive shedding. Delay, admission, staffing, and feedback models reveal a cycle. Constraints and response gains are retuned, capacity is scaffolded during capture, and demand shocks test the wider stable operating basin.

### Social coordination

Actors remain in an inferior convention because unilateral switching is costly. Temporary guarantees, shared signals, interoperability, and staged critical mass change expectations and switching risk. Voluntary exit, distributional impact, and minority alternatives are protected so coordination does not become coercive lock-in.

### Recovery and relapse prevention

A harmful behavioral or operational loop repeatedly re-forms after interruption. Triggers and reinforcing feedback are weakened, a viable replacement routine is made easier to enter, temporary support continues through settling, and perturbation tests identify relapse margin. Domain expertise and consent remain necessary; attractor language does not replace clinical or human judgment.

prose_non_examples:
A team draws a phase portrait showing possible states but does not select or change any basin. That is Phase-Space Mapping.

A migration plan moves a system once from an old platform to a new one with parallel run and rollback. That is Controlled Phase Transition unless self-maintaining dynamic capture is the central problem.

An administrator adds a feature flag or control knob. That is Control Surface Creation without attractor selection or basin evidence.

A manager orders continuous compliance and calls the resulting state an attractor. Continuous command is forced operation, not autonomous stability.

A consultant calls an unpopular culture an “attractor” without state proxies, time scale, trajectory evidence, or falsifiable claims. That is metaphor, not this archetype.

A policy stabilizes aggregate output by imposing concentrated harm and blocking exit. Persistence does not make the target legitimate.

An optimizer performs one random restart without preserving a baseline, modeling capture, or validating robustness. That is a mechanism, not the full archetype.

A restoration project returns a system toward a known prior balance without alternative-regime selection or basin shaping. Equilibrium Restoration may be sufficient.

prose_tradeoffs:
Stability competes with adaptability. A deep basin resists disturbance but can suppress exploration and learning. Preserve viable alternative paths, diversity reserves, and scheduled probes when uncertainty is material.

Fast capture competes with transient safety. Strong pulses cross boundaries quickly but can overshoot or destabilize coupled systems. Use capture corridors, intermediate scaffolds, and hard abort thresholds.

Basin width can compete with peak performance. A broad robust regime may be less efficient than a narrow optimum. Prioritize viability and recovery when disturbance is expected.

Autonomous stability competes with control authority. Permanent support can produce good outcomes while centralizing power and burden. Report it as governed operation, not natural equilibrium, and preserve review and exit.

Single-attractor coordination competes with legitimate plurality. Standardization reduces friction but can erase local fit, minority practice, or experimentation. Interoperability and multiple acceptable basins may provide a better design.

Exploration competes with preservation. Noise and restart can escape poor basins but destroy accumulated value. Use protected baselines, sandboxing, bounded budgets, and rollback.

Model simplicity competes with hidden dynamics. A compact landscape supports decisions but may omit slow variables, cross-scale coupling, and hidden attractors. Maintain alternative models and monitor prediction failures.

prose_invariants_to_preserve:
Every attractor claim specifies state, dynamics, time scale, recurrence or convergence criterion, and evidence. A metaphor is not allowed to substitute for a model that could be contradicted.

The target remains inside a declared viable and legitimate envelope. Persistence, order, low variance, or productivity alone do not establish desirability.

Transient paths respect forbidden-state constraints. A safe destination does not excuse an unsafe corridor.

Basin boundaries retain uncertainty. Probes and interventions use margins appropriate to hidden state, nonstationarity, and consequence.

Target and competing attractors are evaluated together. A local improvement cannot hide a new harmful basin elsewhere or at another scale.

Control effort is measured. A state that requires permanent force is not reported as self-maintaining unless permanent governed control is part of the declared design.

Taper and perturbation recovery are required evidence before claiming capture. Temporary slowing is not settling.

Distributional outcomes remain visible. Aggregate equilibrium cannot erase subgroup instability, exclusion, or concentrated harm.

Adaptive capacity is preserved unless a legitimate safety case requires otherwise. Diversity reserves, exploration windows, or multiple acceptable regimes prevent brittle lock-in.

Affected parties have a contestable target-selection and exit process proportionate to the intervention’s power. Social stability is not manufactured by hidden coercion.

The landscape model is updated when external drivers, rules, populations, topology, or technology change. Stale control policies have retirement triggers.

prose_target_outcomes:
The primary outcome is reliable capture of representative starting states into a selected viable attractor with quantified uncertainty and acceptable transient risk.

The second outcome is reduced relapse. After temporary support is tapered, the system remains or returns under expected disturbance without repeated rescue.

The third is a wider or more robust target basin, or a narrower harmful basin, demonstrated through trajectories and perturbation evidence rather than narrative alone.

The fourth is bounded control burden. Energy, money, authority, attention, or enforcement required to maintain the target is visible and acceptable.

The fifth is protection from unintended attractors, overshoot, oscillation, and forbidden-state crossings.

The sixth is preserved adaptability: the system can still explore, learn, or move among acceptable regimes when context changes.

The seventh is legitimate distribution: benefits, harms, exit costs, and control authority are acceptable to affected parties and reviewable.

Useful metrics include basin-membership probability, capture time, boundary margin, settling time, recovery rate, control effort, taper success, escape frequency, competitor indicators, trajectory variance, welfare distribution, option preservation, and model calibration. Metrics must be interpreted at the stated time and scale.

prose_parameter_dimensions:
**State granularity** determines which variables and scales define attraction. Too coarse a model hides subgroup collapse and transient risk; too fine a model becomes unidentifiable. Use the smallest state sufficient to predict relevant long-run behavior.

**Attractor class** can be a fixed point, bounded range, cycle, recurrent pattern, metastable regime, coordination equilibrium, or local optimum. Stability and testing criteria follow the class.

**Basin confidence** ranges from qualitative hypothesis through sampled empirical region to analytically characterized boundary. Control aggressiveness should decrease as uncertainty rises.

**Control locus** may act on current state, parameters, feedback, constraints, delays, topology, information, incentives, noise, or time scale. Prefer levers whose system-wide effects and authority are inspectable.

**Steering strength and duration** trade capture speed against overshoot, corridor harm, and dependency. Strong pulses require abort capability; gradual shaping requires protection during exposure.

**Target-basin margin** defines how far inside the estimated boundary the state must settle before taper. High disturbance environments require larger margins.

**Exploration intensity** controls variation used to discover or reach alternatives. Exploration needs viability bounds, a preserved baseline, and a stopping or cooling rule.

**Taper rate** governs withdrawal of temporary support. It should respond to recovery evidence, not an arbitrary date alone.

**Portfolio breadth** ranges from a single target to several acceptable attractors linked by safe transitions. Greater breadth supports adaptability but increases coordination and monitoring cost.

**Governance reversibility** defines who may change the target, contest harms, authorize stronger control, and trigger retirement. Irreversible or coercive landscape changes require the strongest legitimacy burden.

prose_variants_and_near_names:
**Initial-Condition Capture Steering** uses a bounded state change when the target attractor already exists and has adequate margin. It does not claim to reshape the landscape.

**Feedback-Landscape Reshaping** changes reinforcing and balancing loops to alter target and competing stability. It is preferred when repeated correction relapses.

**Coordination-Equilibrium Basin Design** changes information, expectations, guarantees, switching costs, and interoperability so actors can reach a better convention. Consent and exit constraints are central.

**Ecological Regime-Basin Restoration** manages alternative ecosystem regimes with disturbance recovery, biodiversity, cross-scale coupling, and high uncertainty.

**Local-Optimum Escape and Recapture** uses bounded variation, restart, annealing, penalty shaping, or continuation while preserving viable progress.

**Multi-Attractor Resilience Portfolio** preserves several acceptable regimes and safe transitions rather than maximizing one basin. It fits changing environments and legitimate local diversity.

Near names such as attractor, basin of attraction, fixed-point stabilization, equilibrium selection, regime navigation, and convergence guidance name objects or narrower operations. They do not automatically supply target governance, landscape shaping, capture, taper, perturbation testing, and emergent-attractor monitoring.

The queue aliases `attractor`, `attractors`, and `basin_of_attraction` are object-level retrieval terms. They should point to this archetype only in a control or intervention context, not collapse mathematical definitions or descriptive analyses into a solution identity.

Initial-Condition Capture Steering

variant_type: other

short_definition: Move current state into an already viable target basin without claiming to change the landscape.

distinct_from_parent: It adds bounded specialization while retaining the complete parent control loop.

still_same_parent_because: Pulse and trajectory remain bounded and target capture survives withdrawal.

use_when: The specialization materially changes how Attractor Landscape Shaping and Basin Steering is configured while preserving its invariants.

failure_modes: undershoot overshoot rebound

Feedback-Landscape Reshaping

short_definition: Alter reinforcing and balancing loops so target stability grows or harmful stability weakens.

still_same_parent_because: Target, competitors, delays, and cross-scale effects are evaluated together.

Coordination-Equilibrium Basin Design

short_definition: Change expectations, payoffs, switching costs, or interoperability so actors can reach a better convention.

still_same_parent_because: Consent, distribution, contestability, and viable exit remain explicit.

Ecological Regime-Basin Restoration

short_definition: Restore or enlarge a viable ecological regime under alternative stable states and disturbance.

still_same_parent_because: Biodiversity, cross-scale coupling, uncertainty, and disturbance recovery constrain target selection.

Local-Optimum Escape and Recapture

short_definition: Escape a poor search basin and establish a robust better solution while preserving viable progress.

still_same_parent_because: Exploration is bounded, a baseline survives, and the new basin is validated out of sample.

Multi-Attractor Resilience Portfolio

short_definition: Preserve several acceptable regimes and safe transitions when contexts vary or diversity is protective.

still_same_parent_because: Each option remains viable and interoperability or transition rules prevent destructive fragmentation.

near_names:
Attractor Basin Steering
Basin Of Attraction Control
Stable State Landscape Shaping
Attractor Selection Control


Attrition and Dropout Monitoring

batch: Second Iteration/Batch 04

aliases:
differential attrition monitoring
dropout pattern surveillance
loss-to-follow-up bias monitoring
retention threat monitoring
participant withdrawal surveillance
study attrition diagnostics

slug: validation

slug: monitoring

slug: confounding

slug: randomization

components:
Attrition Event Register
Denominator Transition Frame
Differential Attrition Monitor
Withdrawal Reason Taxonomy
Completer–Non-Completer Contrast
Missingness Assumption Record
Attrition Response Trigger Policy
Participant Burden Audit
Endpoint Recovery Channel
Independent Attrition Review Gate

mechanisms:
Participant Flow Diagram
Attrition Dashboard
Withdrawal Reason Survey or Interview
Completer Balance Table
Missing-Data Sensitivity Analysis
Retention Outreach Protocol
Data Monitoring Review

essence_one_line: Track who leaves a study, when they leave, why they leave, and from which condition so dropout cannot silently distort causal or comparative conclusions.

compression_statement: A validity-protection pattern for experiments, evaluations, and longitudinal comparisons in which attrition is treated as an observed process to be logged, compared across groups, interpreted against baseline covariates and early outcomes, and routed into retention action, missing-data analysis, or claim qualification.

core_intuition: A study does not only compare treatments; it also compares the people or units who remain observable. If different kinds of units disappear from different groups, the final dataset may describe survivors rather than the assigned population. Monitoring attrition makes that disappearance visible while there is still time to respond.

canonical_formula: Inferential threat increases when P(dropout | assignment, time, baseline covariates, burden, early response) differs across comparison groups or is plausibly related to the unobserved outcome.

structural_problem: A comparison is designed around an eligible or assigned population, but some units stop contributing data before the endpoint. When dropout depends on treatment condition, baseline characteristics, burden, adverse experience, or emerging outcome status, the analyzed sample is no longer exchangeable with the intended comparison frame.

trigger_conditions: The study requires repeated contact, follow-up measurement, attendance, adherence, or continued platform use.

trigger_conditions: Treatment conditions differ in burden, risk, attractiveness, stigma, cost, inconvenience, or perceived benefit.

trigger_conditions: Final outcomes are observed only for completers or for units still reachable at endpoint.

trigger_conditions: Subgroups face unequal practical barriers to continued participation, such as transportation, device access, schedule flexibility, language, or trust.

trigger_conditions: Regulators, reviewers, or decision makers will need to know whether loss to follow-up threatens the study claim.

trigger_conditions: Preliminary dropout rates exceed planned assumptions or begin to diverge between comparison groups.

symptoms: One arm, site, subgroup, class, cohort, or implementation channel loses more units than another.

symptoms: People who leave differ from completers on baseline variables, early outcomes, risk scores, or engagement behavior.

symptoms: Withdrawal reasons cluster around intervention side effects, task burden, perceived inefficacy, or dissatisfaction.

symptoms: The final analysis denominator is smaller or less representative than the enrollment or assignment denominator.

symptoms: Analysts debate whether to analyze only completers, all assigned units, or imputed outcomes after data collection has already ended.

symptoms: Reported effects appear strong among completers but fragile under plausible assumptions about missing outcomes.

root_tension: Experimental design tries to hold comparison groups stable, but participation is itself a dynamic response to the intervention, context, incentives, and measurement process.

anti_signatures: The system is a complete census or automated log with no meaningful unit loss or endpoint missingness.

anti_signatures: Dropout is the primary intended outcome, rather than a threat to observing another outcome.

anti_signatures: Missingness is externally verified to be unrelated to assignment, covariates, and outcomes, and the remaining sample is still sufficient for the planned inference.

anti_signatures: The project is a one-time cross-sectional snapshot with no follow-up window and no attrition process to monitor.

anti_signatures: The response is purely operational retention management with no implication for comparative validity, denominator integrity, or inference.

structural_intervention: Instrument the study so every loss event is recorded, classified, compared, and routed to a pre-specified response. The response may include retention outreach, burden reduction, protocol adjustment, missing-data sensitivity analysis, endpoint qualification, or stopping escalation when attrition makes the planned inference unreliable.

action_logic: Define the population denominators before observation begins: eligible, enrolled, assigned, exposed, retained, measured, analyzed, and excluded.

action_logic: Log every attrition event with assignment, time, site or subgroup, available baseline covariates, reason code, and whether endpoint data can still be obtained.

action_logic: Monitor overall and differential attrition rates at scheduled intervals rather than waiting until final analysis.

action_logic: Compare completers and non-completers using pre-treatment covariates, early responses, burden indicators, and process measures.

action_logic: Classify the missingness assumption that the evidence currently supports, while explicitly flagging uncertainty when MNAR risk is plausible.

action_logic: Trigger retention, protocol, analysis, or reporting actions when attrition crosses pre-specified thresholds.

action_logic: Carry denominator and attrition evidence into the final claim, so the conclusion says what population and missingness assumptions it can actually support.

decision_rules: Escalate when between-group dropout differences exceed the pre-specified tolerance or when one subgroup has materially worse retention than the rest of the sample.

decision_rules: Escalate when reasons for withdrawal are plausibly caused by the intervention, measurement burden, or access barriers.

decision_rules: Treat attrition as a validity threat when lost units differ from retained units on baseline risk, early outcome, adherence, engagement, or prior exposure.

decision_rules: Prefer retention and endpoint recovery actions that do not reveal treatment assignment, coerce participation, or introduce new differential incentives.

decision_rules: Require sensitivity analysis when missingness could plausibly depend on unobserved outcomes or when the final effect changes materially under plausible imputation assumptions.

decision_rules: Qualify or defer claims when attrition prevents the study from supporting its originally intended population-level inference.

target_invariants: The analysis denominator remains transparent and traceable from eligibility through final reporting.

target_invariants: Comparison groups are not silently redefined by differential dropout after assignment or enrollment.

target_invariants: Participant autonomy, withdrawal rights, privacy, and safety are preserved even when retention is operationally desirable.

target_invariants: Attrition handling rules are defined before outcome interpretation whenever possible.

target_invariants: Missingness assumptions are stated as assumptions, not disguised as observed facts.

target_invariants: Retention interventions do not create new outcome-relevant differences between study arms.

expected_outcomes: Earlier detection of validity-threatening dropout patterns.

expected_outcomes: More credible causal or comparative claims because denominator loss is visible and bounded.

expected_outcomes: Better retention design when burdens or barriers are discovered during the study.

expected_outcomes: More defensible missing-data analysis because assumptions are tied to observed attrition evidence.

expected_outcomes: Clearer reporting for reviewers, decision makers, participants, and downstream users of the result.

prose_tradeoffs: Monitoring increases visibility, but it also creates operational load and privacy obligations. Retention interventions can improve endpoint capture, but they can become coercive or unequal if poorly governed. Strict pre-specified thresholds reduce post-hoc rationalization, but they must leave room for unexpected ethical concerns. Detailed reason codes improve diagnosis, but inferred reasons can create false precision. Sensitivity analysis improves honesty, but it may reveal that the planned study cannot support the claim the team hoped to make.

Differential Arm Attrition Monitoring

short_definition: Focuses on dropout imbalance between treatment, control, cohort, or policy arms.

Loss-to-Follow-Up Endpoint Recovery

short_definition: Separates discontinuation from endpoint missingness by attempting ethical final outcome collection even when normal participation stops.

Burden-Induced Dropout Monitoring

short_definition: Tracks whether measurement burden, intervention intensity, logistical friction, or psychological cost is causing selective exit.

Nonresponse Attrition Monitoring

short_definition: Monitors wave-to-wave survey or panel nonresponse and its relationship to observed covariates or previous responses.

Administrative Censoring Audit

short_definition: Checks whether administrative rules, data-system cutoffs, transfers, eligibility changes, or record linkage failures remove units unevenly.