Counterexample Search

batch: First Iteration/Batch 37/Wave 2

Exception Search

notes: Useful retrieval name for the same intervention when the target rule is challenged through violations or exceptional cases.

Negative Case Search

notes: Emphasizes disconfirming cases; should point here unless it becomes a research-method-specific mechanism.

Falsification Check

notes: A common mechanism under this archetype, not a standalone archetype by itself.

Proof by Counterexample

notes: A formal method for universal claims and a recognized variant/mechanism within this archetype.

slug: uncertainty

slug: robustness

components:
Proposed Rule
Claim Scope
Falsification Condition
Counterexample Search Space
Counterexample Record
Relevance Test
Scope Revision
Confidence Update
Negative Case Inventory
Adversarial Reviewer Role
Search Coverage Note

mechanisms:
Falsification Check
Exception Search
Edge-Case Testing
Red-Team Review
Adversarial Example Generation
Proof by Counterexample
Negative Case Analysis
Boundary Condition Matrix

essence_one_line: Actively search for cases that would break a proposed rule, pattern, or generalization before treating it as reliable.

compression_statement: When a rule or pattern seems plausible because supporting examples are visible, define what would count as a breaking case, search the spaces where such cases are likely to appear, test their relevance, and revise the rule scope or confidence accordingly.

core_intuition: Supporting examples are usually easier to notice than disconfirming ones. Counterexample Search turns the absence of visible exceptions into an active search problem: ask what would prove the rule too broad, look where that kind of case would be found, and use the result to make the rule more bounded, more robust, or less overconfident.

canonical_formula: proposed rule + stated scope + falsification condition + targeted breaking-case search + relevance test + counterexample record + scope revision + confidence update = bounded rule with tested limits

structural_problem: A generalization, rule, diagnosis, model pattern, or strategic claim is accepted because fitting cases are salient while breaking cases are absent, hidden, inconvenient, or never deliberately sought.

trigger_conditions: A claim is being generalized from visible examples, successful cases, or repeated observations.

trigger_conditions: The decision cost of being wrong is significant enough that supporting evidence alone is insufficient.

trigger_conditions: The rule contains broad language such as always, never, usually, all, safe, reliable, sufficient, or necessary.

trigger_conditions: Known edge conditions, subgroups, time shifts, adversarial conditions, or exceptional contexts might violate the rule.

trigger_conditions: A group has incentives, habits, or confirmation bias that make disconfirming cases socially or cognitively easy to ignore.

trigger_conditions: A pattern-detection, diagnostic, policy, or design process needs a deliberately negative test before adoption.

symptoms: A proposed rule accumulates confirming anecdotes but has no record of where it has been challenged.

symptoms: Exceptions are treated as noise without checking whether they fall inside the rule scope.

symptoms: The same claim is restated with hidden qualifications after a challenge, suggesting moving goalposts.

symptoms: A model, policy, or strategy works in ordinary cases but repeatedly fails at the boundary.

symptoms: Teams debate whether a case is an exception because falsification criteria were never stated.

symptoms: Confidence stays high even though no one can name where counterexamples would have been found if they existed.

root_tension: Rules and patterns are useful because they compress experience, but compression hides exceptions. A search that is too weak leaves overgeneralization intact, while a search that overweights irrelevant exceptions can destroy useful bounded rules.

anti_signatures: The issue is whether a conclusion follows from already accepted premises; use deductive_chain_validation instead.

anti_signatures: The issue is validating whether a detected pattern is real in the first place; use pattern_detection_with_validation unless breaking-case search is the main intervention.

anti_signatures: The issue is making uncertainty visible in claims, estimates, or communication; use uncertainty_explicitness or a related uncertainty archetype.

anti_signatures: The issue is designing a full experimental comparison against a null/default with error costs; use hypothesis_testing_frame.

anti_signatures: The issue is filling missing details in an incomplete story or signal; use cautious_pattern_completion.

anti_signatures: A single isolated objection is being used rhetorically without search-space design, relevance testing, or rule revision.

structural_intervention: Make negative evidence a designed search target: state the rule and its assumed scope, predefine what would count as a counterexample, search likely breaking spaces, evaluate candidate exceptions, and revise the rule boundary or confidence rather than leaving the claim either untouched or discarded wholesale.

State the proposed rule

logic: Convert the belief, pattern, diagnosis, policy rule, model claim, or design assumption into a statement clear enough to be challenged.

Bound the current scope

logic: Name the contexts, populations, time periods, thresholds, operating conditions, or cases where the rule is presumed to hold.

Define the falsification condition

logic: Specify what kind of in-scope case would break, narrow, or seriously weaken the rule before beginning the search.

Select counterexample search spaces

logic: Choose boundary cases, negative cases, subgroups, historical failures, adversarial inputs, or edge conditions where violations are most likely to appear.

name: Search and record candidate counterexamples

logic: Gather potential breaking cases without dismissing them prematurely or treating ordinary confirming cases as sufficient.

Test relevance and validity

logic: Check whether each candidate counterexample actually lies inside the claimed scope and contradicts the rule rather than reflecting measurement error, category error, or an unrelated condition.

Revise scope or confidence

logic: Update the rule boundary, add exception clauses, lower confidence, redesign the model, or preserve the rule with a documented search coverage note.

Preserve residual uncertainty

logic: Record unsearched spaces and conditions under which future counterexamples should trigger another revision.

rule: If the rule is universal and a valid in-scope counterexample is found, reject or qualify the universal form.

rule: If the rule is probabilistic or contextual, use counterexamples to revise confidence, scope, thresholds, or exception handling rather than treating one case as decisive.

rule: If a candidate counterexample lies outside the stated scope, record it as a possible scope-extension warning, not as a disproof.

rule: If no counterexamples are found, increase confidence only in proportion to the quality and coverage of the search space.

rule: If the team cannot say where counterexamples would be found, the search design is not yet adequate.

target_invariants: The proposed rule must remain explicit enough to challenge.

target_invariants: Falsification conditions must be stated before uncomfortable cases are evaluated.

target_invariants: Candidate counterexamples must be tested for relevance rather than automatically accepted or dismissed.

target_invariants: The output must include a scope or confidence update, not only a list of objections.

target_invariants: Useful bounded rules should be preserved when exceptions reveal limits rather than total failure.

target_invariants: Absence of found counterexamples must not be presented as universal proof when search coverage is narrow.

expected_outcomes: Overbroad rules become narrower, more qualified, and more reliable.

expected_outcomes: Hidden exceptions and boundary failures surface before decisions depend on the rule.

expected_outcomes: Teams become less vulnerable to confirmation bias and selective supporting examples.

expected_outcomes: Patterns, policies, diagnoses, and models carry clearer limits of applicability.

expected_outcomes: Confidence becomes tied to search coverage and exception quality rather than rhetorical certainty.

prose_essence:
Counterexample Search is the intervention pattern of deliberately looking for cases that would break a proposed rule, pattern, diagnosis, policy, model claim, or generalization. It is useful when a claim feels convincing because it has many supporting examples but has not been challenged where it is most likely to fail.

The core move is not simply being skeptical. The archetype requires a stated claim, a defined scope, a falsification condition, a targeted search space, a relevance test for candidate exceptions, and a scope or confidence update. The strongest outcome is often not rejection of the rule, but a better bounded rule that says where it works, where it fails, and how much confidence remains.

prose_when_to_use:
Use this archetype when a rule or generalization is about to guide action and the visible evidence is mostly positive. It is especially valuable when the claim uses broad language, when failure at the boundary would be costly, when a group is attached to the claim, or when rare cases matter more than average cases.

Typical triggers include a diagnostic explanation that fits most signs, a policy rule that seems fair in standard cases, a software rule that passes normal inputs, a strategic thesis based on successful examples, or a research generalization that has not been checked against negative cases.

prose_structural_problem:
The structural problem is one-sided visibility. Supporting examples are salient because they were noticed, collected, rewarded, or easy to explain. Breaking cases may be rare, embarrassing, hidden at edges, excluded by sampling, or dismissed as noise. As a result, a rule can become trusted before anyone knows where it stops applying.

Counterexample Search treats that gap as a design problem. Instead of asking whether the rule has support, it asks what would count as an in-scope violation and where such a violation would most likely be found.

prose_intervention_logic:
The intervention begins by converting a vague belief into a proposed rule. It then states the current claim scope and defines the falsification condition before searching. The search targets places where ordinary confirmation is least informative: edge cases, historical failures, subgroups, boundary conditions, adversarial inputs, exception records, and cases that have been filtered out of the usual evidence stream.

Candidate counterexamples are then tested for relevance. A real counterexample must fall within the claimed scope and contradict the claim as stated. If it does, the rule must be rejected, narrowed, qualified, or assigned lower confidence. If no relevant counterexamples are found, confidence may rise only in proportion to search coverage.

prose_key_components:
- **Proposed Rule** (`proposed_rule`): the rule, claim, pattern, diagnosis, or generalization being challenged. It must be explicit enough that a breaking case can be recognized.
- **Claim Scope** (`claim_scope`): the contexts, populations, thresholds, time periods, or operating conditions where the rule is assumed to hold. This prevents every exception from being either dismissed or overgeneralized.
- **Falsification Condition** (`falsification_condition`): the pre-stated criterion for what would count as a breaking case. It prevents moving the goalposts after a counterexample appears.
- **Counterexample Search Space** (`counterexample_search_space`): the targeted set of cases, histories, subgroups, edge conditions, or adversarial inputs where violations are most likely.
- **Counterexample Record** (`counterexample_record`): the captured candidate exception, including context and why it may challenge the rule.
- **Relevance Test** (`relevance_test`): the check that a candidate exception is actually in scope and contradictory rather than merely unusual or misclassified.
- **Scope Revision** (`scope_revision`): the updated boundary, exception clause, qualifier, or precondition after counterexamples are evaluated.
- **Confidence Update** (`confidence_update`): the revised certainty level based on the quality of counterexamples and the coverage of the search.

Optional components include a negative case inventory for recurring use, an adversarial reviewer role when social pressure suppresses exceptions, and a search coverage note to keep absence of found counterexamples from becoming false certainty.

prose_common_mechanisms:
- **Falsification checks** translate a claim into a form that can be challenged. They implement the first step but are not the whole archetype.
- **Exception searches** deliberately look for cases that violate a rule. They are useful in diagnosis, incident review, research, and policy design.
- **Edge-case testing** stresses thresholds and boundary conditions. It is common in software, operations, safety, and administrative rules.
- **Red-team reviews** assign challengers to seek disconfirming cases. They work best when the output is a specific scope or confidence revision.
- **Adversarial example generation** constructs hard cases designed to expose failure. It is powerful for models, security, and abuse testing, but cases must be relevant to the real operating scope.
- **Proof by counterexample** is decisive for universal claims, but narrower than the full archetype because many practical claims are probabilistic or contextual.
- **Negative case analysis** studies non-fitting cases so a theory, diagnosis, or explanation can be revised rather than merely defended.
- **Boundary condition matrices** organize where the rule has and has not been challenged; they are artifacts that support the archetype.

prose_failure_modes:
Common failures include narrow search disguised as rigor, irrelevant exception over-weighting, moving the goalposts after counterexamples appear, abandoning useful bounded rules too quickly, performative red-teaming with no scope update, overconfidence after a weak search finds nothing, and social suppression of inconvenient negative cases.

Each failure has a practical mitigation: require search-space coverage notes, apply relevance tests, pre-state falsification conditions, make scope revision the default, tie challenge sessions to decisions, scale confidence to search coverage, and assign protected roles for raising negative cases.

prose_neighbor_distinctions:
Counterexample Search is distinct from **Deductive Chain Validation** because it challenges whether the rule or generalization is too broad, not merely whether a conclusion follows from premises.

It is distinct from **Pattern Detection with Validation** because pattern validation asks whether a candidate pattern is real. Counterexample Search asks where a plausible rule or pattern breaks and how its scope should change.

It is distinct from **Cautious Pattern Completion** because the target is not a missing whole inferred from partial evidence. The target is a proposed rule whose limits need to be tested.

It is distinct from **Hypothesis Testing Frame** because hypothesis testing structures a broader formal claim-evaluation frame, often around null/default comparisons and error costs. Counterexample Search is the narrower active search for breaking cases and rule-boundary revision.

It is related to the held `induction_boundary_setting` candidate. The boundary-setting archetype would define what can be generalized from observed cases; Counterexample Search supplies a central way to find the cases that narrow that boundary.

prose_cross_domain_examples: In software testing, a validation rule is challenged with malformed, empty, extreme, locale-specific, and adversarial inputs. In policy design, an eligibility rule is checked against transitional, mixed-status, or edge-case households before rollout. In diagnosis, a team looks for signs that would rule out its favored explanation. In strategy, a market thesis is challenged by studying similar failures, not only similar successes. In mathematics, a universal claim can be defeated by one valid in-scope counterexample. In safety engineering, a procedure is tested against abnormal modes rather than only normal operation.

prose_non_examples: A generic critique session is not Counterexample Search if it does not define the rule, falsification condition, search space, and revision criteria. A dashboard anomaly alert is not this archetype unless anomalies are used to challenge a proposed rule. A p-value report is not this archetype by itself. A critic naming an exotic out-of-scope exception is not a valid counterexample unless the scope is revised to include that case.

prose_tradeoffs: Counterexample Search improves reliability but can slow decisions. It encourages healthy skepticism but can become cynicism if objection-making is rewarded without revision. It makes rules more accurate but sometimes harder to communicate. Adversarial mechanisms can reveal hidden failures but may damage psychological safety if they become personal attacks. In low-stakes contexts, the cost of searching for rare exceptions can exceed the value gained.

prose_invariants_to_preserve: The proposed rule must remain explicit. Falsification conditions must be stated before the search. Candidate counterexamples must be tested for relevance. The search must produce a scope or confidence update rather than a pile of objections. Useful bounded rules should be preserved when exceptions reveal limits rather than total failure. Finally, no found counterexample should not be presented as proof of universality unless search coverage justifies that confidence.

prose_target_outcomes: The target outcome is a rule whose limits are known better than before. The intervention should surface hidden exceptions, prevent overgeneralization, reduce confirmation bias, improve robustness at boundaries, and tie confidence to search coverage. A successful use of the archetype often produces a narrower but more dependable rule.

prose_parameter_dimensions:
Important tuning dimensions include claim breadth, search intensity, tolerance for rare exceptions, severity of failure, degree of adversarial pressure, evidence quality, search-space coverage, and the threshold for revising versus abandoning the rule.

A universal claim needs stricter counterexample handling than a probabilistic claim. A safety-critical rule should weight rare severe exceptions more heavily than a low-stakes heuristic. A strategic or policy claim may need broad historical and contextual search, while a software rule may need focused edge-case and adversarial input testing.

prose_variants_and_near_names:
Recognized variants include edge-case counterexample search, universal-claim counterexample, diagnostic exception search, and adversarial counterexample generation. Near names include exception search, negative case search, falsification check, and proof by counterexample. These names should route to this archetype unless the context clearly points to a neighboring hypothesis-testing, diagnostic, or formal proof mechanism.

Collapsed candidates include minimal counterexample search, counterexample sets, and red-team prompts when they function only as mechanisms or artifacts. `disconfirming_evidence_protocol` remains a promotion candidate for global review if future batches show a distinct bias-focused archetype rather than a variant of this one.

raw_bundle_path: solution_archetypes/_Batches/First Iteration/Batch 37/Wave 2/counterexample_search_solution_archetype_output_bundle.zip

Edge-Case Counterexample Search

short_definition: Searches boundary conditions where a rule is most likely to fail rather than sampling ordinary cases.

distinctive_feature: The search deliberately concentrates on boundary conditions instead of the average case.

distinct_from_parent: The parent can search any plausible breaking space; this variant focuses on edges and thresholds.

still_same_parent_because: The core intervention remains counterexample discovery followed by scope or confidence revision.

use_when: A rule works in routine cases but may break at thresholds, extremes, rare combinations, or unusual operating conditions.

use_when: Failure at the boundary would be more costly than ordinary misclassification.

failure_modes: Treating unrealistic extremes as decisive against a rule meant for ordinary cases.

failure_modes: Missing central failure cases because only edges are examined.

Universal Claim Counterexample

short_definition: Uses a valid single breaking case to invalidate or qualify a universal rule.

distinctive_feature: One valid counterexample can be decisive because the target claim is universal.

distinct_from_parent: The parent also covers probabilistic and contextual claims where counterexamples update confidence rather than logically disprove the rule.

still_same_parent_because: The intervention still searches for breaking cases and revises the rule boundary.

use_when: A claim is phrased as always, never, all, none, necessary, or sufficient.

use_when: A single in-scope violation would logically defeat the claim as stated.

failure_modes: Mistaking a case outside the claim scope for a disproof.

Diagnostic Exception Search

short_definition: Looks for cases, symptoms, or observations that would rule out or narrow a favored diagnosis or pattern match.

distinctive_feature: The breaking cases are diagnostic exceptions rather than general rule violations.

distinct_from_parent: The parent is domain-general; this variant is tuned to diagnosis and pattern-match discipline.

still_same_parent_because: The central move is still active search for evidence that would break the current rule or explanation.

use_when: An initial explanation fits visible facts but may hide disconfirming signs.

use_when: A diagnostic pattern is plausible enough to create premature closure.

failure_modes: Collecting exceptions without updating the favored diagnosis.

Adversarial Counterexample Generation

short_definition: Generates deliberately difficult cases to expose where a rule, classifier, plan, or system fails.

distinctive_feature: The search creates stress cases rather than only finding naturally occurring exceptions.

distinct_from_parent: The parent covers both discovered and generated counterexamples; this variant emphasizes constructed challenge cases.

still_same_parent_because: Constructed examples still function as candidate breaking cases that drive scope and confidence revision.

use_when: The rule is likely to face strategic, unusual, or distribution-shifted inputs.

use_when: Passive observation is unlikely to reveal failure cases before they matter.

failure_modes: Generating clever but irrelevant cases that do not apply to the real operating scope.

near_names:
Falsification Check
Exception Search
Negative Case Analysis
Proof by Counterexample
Counterexample


Counterfactual Comparison

type: archetype

source: generated

batch: First Iteration/Batch 42

source_primes: counterfactuals causality

slug: counterfactuals

slug: causality

slug: confounding

components:
Actual Path
Counterfactual Condition
Reference Baseline
Plausibility Check
Outcome Comparison
Uncertainty Note
Causal or Value Claim Revision
Comparison Unit Matching
Time Horizon
Sensitivity Probe
Decision Use Case

mechanisms:
What-If Analysis
Control Group Comparison
Synthetic Control Method
A/B Test
Baseline Comparison
Scenario Contrast
Matched Case Comparison
Counterfactual History Review

essence_one_line: Compare what happened with a plausible alternative to isolate causal effect or decision value.

compression_statement:
When an outcome is interpreted as caused, valuable, harmful, avoidable, or inevitable, Counterfactual Comparison
defines the actual path, constructs a plausible alternate condition, chooses a defensible reference baseline,
compares outcomes, records uncertainty, and revises the causal or value claim so judgment is based on the difference
between paths rather than the observed outcome alone.

core_intuition:
Outcomes do not interpret themselves. A good result after an action does not prove the action helped, and a bad result
after a decision does not prove the decision was wrong. The practical question is always comparative: what would likely
have happened otherwise? Counterfactual Comparison makes that missing comparison explicit and bounded.

canonical_formula:
actual_path + counterfactual_condition + reference_baseline + plausibility_check -> outcome_comparison + uncertainty_note
-> revised_causal_or_value_claim

structural_problem:
Actors evaluate an action, policy, intervention, exposure, or decision using only the observed outcome. They infer
success, failure, causation, harm, inevitability, or responsibility without comparing the actual path to a plausible
alternative path that did not occur. This makes interpretation vulnerable to hindsight bias, selection effects,
baseline manipulation, regression to the mean, and stories that sound causal but do not identify the difference
made by the focal condition.

condition: An outcome is being attributed to a decision or intervention.

example: A city says a new safety program reduced injuries because injuries fell after launch, but it has not compared the decline to similar cities, prior trends, or expected regression.

condition: A decision is judged by the final result alone.

example: A product team calls a launch decision bad because revenue fell, ignoring the plausible counterfactual that revenue would have fallen faster without the launch.

condition: A baseline is implicit or contested.

example: A vendor claims savings against an inflated expected-spend baseline, while the buyer believes the correct comparison is last year's cost trajectory.

condition: The relevant alternative path cannot be directly observed.

example: A hospital wants to know whether a triage change prevented harm, but there is no exact replay of the same patient flow without the change.

condition: An outcome is treated as inevitable or impossible to avoid.

example: A strategy review says market share loss was unavoidable, but no one tests plausible choices that could have changed timing, magnitude, or distribution of losses.

condition: The decision use is high-stakes enough that casual what-if reasoning is inadequate.

example: A policy board must decide whether to continue, expand, or end a program and needs a defensible comparison rather than anecdotes about participants.

symptom: Outcome-only evaluation

diagnostic_meaning: People treat the observed result as the full evidence of success or failure without asking what would likely have happened otherwise.

symptom: Hindsight blame or credit

diagnostic_meaning: Actors assign responsibility after knowing the outcome rather than comparing the actual decision with feasible alternatives available at the time.

symptom: Unstated baseline

diagnostic_meaning: The comparison standard is doing major argumentative work but has not been named, defended, or tested.

symptom: Cherry-picked alternative

diagnostic_meaning: The chosen counterfactual flatters a preferred conclusion by comparing the actual path to an unrealistic best case, worst case, or straw path.

symptom: Confusion between plausible and imaginable

diagnostic_meaning: The alternative path is possible to imagine but not grounded in constraints, evidence, or actual choice sets.

symptom: Disagreement about value despite agreement on facts

diagnostic_meaning: Stakeholders may accept the observed outcome but disagree about the relevant alternative, opportunity cost, avoided harm, or decision standard.

root_tension:
The most important comparison is often unobservable: the path that did not happen. The archetype resolves this tension
by making the missing alternative explicit, checking its plausibility, and limiting conclusions to what the comparison
can support.

condition: The main task is to explain how a cause produces an effect.

why_not: Use Causal Mechanism Mapping; Counterfactual Comparison may supply a contrast, but the center here is actual-versus-alternative outcome judgment.

condition: The main task is to test a predicted hypothesis under a research design.

why_not: Use Hypothesis Testing Frame unless the missing piece is the counterfactual baseline for interpreting the observed outcome.

condition: The task is broad exploration of multiple possible futures.

why_not: Use Scenario Portfolio Planning; this archetype requires a focused comparison to a relevant alternative path.

condition: The issue is specifically regression toward average after an extreme observation.

why_not: Use Regression-to-Mean Guardrail as the sharper intervention, possibly as a component of counterfactual comparison.

condition: The alternative is purely speculative and cannot be plausibility-constrained.

why_not: Do not use this archetype to convert imaginative storytelling into evidence. Use narrative exploration or scenario imagination with clear non-evidentiary labeling.

condition: There is no decision, attribution, evaluation, or learning use for the comparison.

why_not: A casual thought experiment may be interesting, but it is not a solution archetype application without a consequential claim to revise.

structural_intervention:
Replace outcome-only judgment with a bounded actual-versus-counterfactual comparison. The intervention names the
actual path, defines the plausible alternate condition, selects and justifies a reference baseline, compares relevant
outcomes over an explicit time horizon, records uncertainty and sensitivity to assumptions, and updates the causal,
value, responsibility, or decision claim accordingly.

State the focal claim

logic: Name the claim being judged: this action caused the outcome, this decision was good, this harm was avoided, this outcome was inevitable, or this path created value.

Describe the actual path

logic: Record what happened, to whom or what, when, under which context, and with which observed outcomes.

Construct the counterfactual condition

logic: Define the alternative path that is relevant to the claim: no action, delayed action, different intervention, different decision, or feasible baseline trajectory.

Defend plausibility

logic: Check whether the alternative was possible, comparable, temporally coherent, contextually grounded, and aligned with what was knowable or feasible.

Select or build the reference baseline

logic: Use a control group, matched case, historical trend, synthetic control, no-action projection, expert model, or scenario contrast with explicit limitations.

Compare outcomes

logic: Compare actual and counterfactual outcomes on the dimensions that matter, including magnitude, distribution, timing, side effects, and opportunity cost.

Bound uncertainty

logic: Record confidence, assumptions, sensitivity, rival explanations, missing evidence, and conditions under which the conclusion would change.

name: Revise the claim and decision implication

logic: Update causal attribution, value judgment, responsibility assignment, continuation decision, design choice, or learning rule according to the comparison's strength.

rule: Use the narrowest counterfactual that answers the decision question.

reason: A broad alternate world invites speculation; a focused alternate condition supports judgment.

rule: Prefer feasible alternatives over idealized alternatives.

reason: A comparison is actionable only if the alternative could plausibly have occurred or been chosen.

rule: Defend the baseline before interpreting the effect.

reason: The same outcome can look like success, failure, or neutrality depending on the reference path.

rule: Separate decision quality from outcome luck.

reason: A good decision can have a bad outcome and a bad decision can have a good outcome relative to what was knowable at the time.

rule: Treat unobservable alternatives as uncertainty-bearing estimates, not facts.

reason: The counterfactual path disciplines reasoning but is still constructed or inferred.

rule: Match evidentiary rigor to stakes.

reason: A casual learning review may use a lighter comparison than a safety, medical, legal, or major resource-allocation judgment.

invariant: Plausibility over imagination

preserve: The alternate path must be grounded enough to constrain inference.

invariant: Explicit baseline

preserve: The reference condition must be named and defensible.

invariant: Comparability

preserve: Actual and counterfactual paths should be comparable on the dimensions relevant to the claim.

invariant: Outcome relevance

preserve: The comparison must focus on outcomes that matter for the causal, value, or decision claim.

invariant: Uncertainty visibility

preserve: Confidence, assumptions, missing evidence, and sensitivity should remain attached to the conclusion.

invariant: Decision linkage

preserve: The comparison should end in a revised claim or decision use, not merely in an interesting alternate story.

outcome: Better causal attribution

description: Teams become less likely to mistake temporal sequence, correlation, or outcome salience for the difference made by an action.

outcome: Fairer decision evaluation

description: Decisions are judged against feasible alternatives and information available at the time, reducing hindsight blame or undeserved credit.

outcome: More credible program and policy evaluation

description: Observed outcomes are interpreted relative to a defensible no-action or alternative-action baseline.

outcome: Clearer opportunity cost

description: Teams see not only what happened, but what was likely forgone by choosing one path over another.

outcome: Improved learning under uncertainty

description: Uncertainty does not disappear, but assumptions and sensitivity become visible enough to guide future tests and decisions.

outcome: Less speculative counterfactual reasoning

description: What-if thinking becomes bounded by plausibility, comparability, and decision use.

prose_essence:
Counterfactual Comparison is the archetype for situations where the observed outcome is not enough to judge what an action, decision, policy, treatment, design, or event actually did. It asks: compared with what? The missing comparison might be a no-action path, a different intervention, a prior baseline, a matched control, a synthetic control, a feasible decision alternative, or a historically plausible path that did not occur.

The archetype turns counterfactual thinking into disciplined comparison. It does not simply ask people to imagine a different world. It requires an actual path, a counterfactual condition, a defensible baseline, a plausibility check, an outcome comparison, an uncertainty note, and a revised causal or value claim.

prose_when_to_use:
Use this archetype when someone is about to claim that an action worked, failed, caused harm, prevented harm, created value, wasted resources, changed history, or was inevitable. The pattern is especially important when a decision is being judged by its result alone, when a program is being evaluated by pre/post change, when a product or policy change is credited for a metric movement, or when a retrospective review is assigning blame or praise.

It is also useful when the relevant alternative is contested. A program sponsor may compare results to a world with no program. A critic may compare them to a better-designed program. A finance team may compare savings to last year's spend. A vendor may compare savings to projected future spend. Counterfactual Comparison makes those hidden baselines visible so the dispute can be examined rather than smuggled into the conclusion.

prose_structural_problem:
The structural problem is outcome-only interpretation. People see what happened and treat it as the full evidence of what a decision was worth or what an intervention caused. This is attractive because observed outcomes are concrete, while the unchosen path is invisible. But the invisible path is often the path that matters.

A good outcome after an action does not prove the action helped. A bad outcome after a decision does not prove the decision was poor. A decline after a policy does not prove the policy caused decline; the no-policy path might have been worse. An improvement after a program does not prove the program caused improvement; the same trend might have happened anyway. Without a counterfactual comparison, actors can mistake luck, trend, selection, regression to the mean, or baseline choice for evidence.

prose_intervention_logic:
The intervention begins by naming the claim being made. The claim might be causal: “the program reduced harm.” It might be evaluative: “this was a good decision.” It might be strategic: “we should have chosen the other path.” It might be historical: “this outcome was inevitable.” Once the claim is named, the actual path is described with enough context to compare: action, timing, affected unit, relevant conditions, and observed outcomes.

Next, the counterfactual condition is defined. This is the alternate path that makes the claim meaningful. Sometimes it is no action. Sometimes it is a different action. Sometimes it is what would have happened if the same trend had continued. Sometimes it is what comparable groups experienced. The key is that the alternative must be plausible and relevant, not merely imaginable.

Then the comparison baseline is selected or built. Strong mechanisms include randomized controls, matched comparisons, synthetic controls, and well-defended historical baselines. Lighter mechanisms include scenario contrasts or expert estimates, but those require more explicit uncertainty. The outcome comparison then estimates the difference between actual and counterfactual paths, and the final claim is revised to fit the strength of the comparison.

prose_key_components:
### Actual Path

The actual path is the real sequence being interpreted. It includes what happened, who or what was affected, when it happened, and what outcome was observed. This component prevents the comparison from becoming detached from the case.

### Counterfactual Condition

The counterfactual condition is the alternate path. It might be no treatment, a delayed decision, a prior design, a different policy, or another feasible option. A good counterfactual condition is specific enough to compare and plausible enough to constrain speculation.

### Reference Baseline

The reference baseline is the comparison anchor. It may be a control group, matched case, pre-intervention trend, synthetic control, benchmark, or no-action projection. The baseline is often where the argument lives: change the baseline, and the conclusion may change.

### Plausibility Check

The plausibility check asks whether the counterfactual could reasonably have occurred and whether the comparison is fair. It examines feasibility, actor knowledge, timing, constraints, comparability, selection effects, and omitted context.

### Outcome Comparison

The outcome comparison estimates or reasons about the difference between the actual path and the counterfactual path. It should specify which outcomes matter, how they are weighted, whether timing matters, and whether distributional effects matter.

### Uncertainty Note

The uncertainty note keeps the final claim honest. It records confidence, assumptions, missing evidence, rival explanations, sensitivity to baseline choice, and the conditions under which the conclusion would change.

### Causal or Value Claim Revision

The claim revision is the action-facing output. The original claim may be strengthened, weakened, bounded, reversed, or split by context. The revised claim should be strong enough for its decision use and no stronger.

prose_common_mechanisms:
### What-If Analysis

What-if analysis is a mechanism for eliciting an alternate condition. It implements the archetype only when the what-if path is made specific, checked for plausibility, compared against actual outcomes, and tied to an uncertainty-bounded claim.

### Control Group Comparison

A control group comparison approximates what would have happened without the focal action or exposure. It is a strong mechanism when groups are comparable and assignment or selection issues are handled.

### Synthetic Control Method

A synthetic control method constructs a comparison baseline from weighted combinations of other units. It is useful when there is no single natural control, especially in policy and regional interventions.

### A/B Test

An A/B test implements counterfactual comparison by assigning comparable units to different versions. It is powerful when ethical and feasible, but it still requires interpretation of construct validity, time horizon, and decision relevance.

### Baseline Comparison

Baseline comparison uses a previous trend, expected trajectory, benchmark, or no-action forecast. It is common and practical, but it can mislead when trends, shocks, or regression to the mean are ignored.

### Scenario Contrast

Scenario contrast compares the actual path with a described alternative. It is useful in strategy and design, where controlled tests may be impossible, but it needs explicit plausibility discipline.

### Matched Case Comparison

Matched case comparison pairs similar cases, groups, time periods, or contexts. It improves fairness of comparison but does not eliminate all hidden differences.

### Counterfactual History Review

Counterfactual history review uses historically plausible alternatives to test contingency. It should be constrained by evidence about what actors knew, could do, and were likely to do, rather than by narrative convenience.

prose_failure_modes:
The most common failure mode is the fantasy counterfactual: an alternative path that is easy to imagine but not actually plausible. This produces confident claims from fictional premises.

A second failure mode is the cherry-picked baseline. The comparison standard is selected because it makes the actual path look better or worse. Sensitivity probes and explicit baseline selection criteria help mitigate this.

A third failure mode is false precision. A modeled, inferred, or narrative counterfactual is presented as though it were directly observed. Uncertainty notes, assumption logs, and rival explanation checks reduce this risk.

A fourth failure mode is confounded comparison. Actual and comparison paths differ in ways that independently affect outcomes. Matching, controls, covariate checks, and weaker claims can help.

A fifth failure mode is moral erasure. Someone uses “it could have been worse” to minimize actual harm, or “it could have been better” to assign blame without feasibility checks. Counterfactual comparison should not erase harm recognition or normative judgment.

prose_neighbor_distinctions:
Counterfactual Comparison is distinct from Causal Mechanism Mapping. Causal Mechanism Mapping asks how a cause produces an effect through a pathway. Counterfactual Comparison asks what difference the actual path made compared with a plausible alternative.

It is distinct from Hypothesis Testing Frame. Hypothesis testing structures predictions and evidence tests. Counterfactual Comparison structures the missing baseline needed to interpret an outcome or decision value.

It is distinct from Scenario Portfolio Planning. Scenario planning explores many possible futures to improve preparedness. Counterfactual Comparison uses a focused alternative to evaluate a claim about what happened or what choice was valuable.

It is distinct from Regression-to-Mean Guardrail. Regression-to-mean reasoning is a specific statistical warning that can improve counterfactual comparisons, especially pre/post comparisons after extreme observations.

It is distinct from What-If Prompt. A prompt is a mechanism. The archetype is the full disciplined comparison process.

prose_cross_domain_examples:
In policy evaluation, a housing subsidy should be judged against a credible no-subsidy or alternative-subsidy baseline, not only against what happened after launch.

In product experimentation, an onboarding change is compared with the prior flow through an A/B test so the team can estimate the difference made by the new design.

In healthcare operations, an appointment reminder is evaluated by comparing attendance with comparable patients, historical trends, and uncertainty about patient selection.

In strategy, an acquisition is evaluated against feasible partnership and no-acquisition paths so leaders can reason about opportunity cost rather than absolute outcome alone.

In incident response, a team compares actual recovery with a feasible earlier rollback path to learn whether response timing changed downtime.

In historical analysis, a source-grounded alternate coalition or decision path can help test whether an institutional outcome was contingent, while preserving uncertainty about the unchosen path.

prose_non_examples:
A vague statement such as “things would have been worse without us” is not Counterfactual Comparison unless the alternative path, baseline, plausibility, and uncertainty are specified.

A brainstorming exercise that imagines many possible futures is not this archetype unless it evaluates a focal actual path against a relevant alternative.

An A/B test by itself is not the archetype. It is a mechanism that can instantiate the archetype when tied to a decision claim and interpreted with appropriate boundaries.

An alternate-history story written for entertainment is not the archetype, even though it uses counterfactual imagination.

A causal diagram is not this archetype. It may support mechanism reasoning, but Counterfactual Comparison centers the difference between actual and plausible alternative outcomes.

prose_tradeoffs:
The main tradeoff is rigor versus practicality. Randomized or highly controlled comparisons are stronger but may be expensive, slow, unethical, or impossible. Lightweight comparisons are faster but more vulnerable to speculation and baseline manipulation.

Another tradeoff is fairness versus accountability. It is fair to judge decisions against what was knowable and feasible at the time, but that fairness must not become a way to excuse ignored risks or foreseeable harms. The archetype should separate decision quality from outcome luck while still preserving responsibility for what should reasonably have been known.

A third tradeoff is quantitative precision versus contextual fidelity. Quantitative counterfactuals can discipline claims, but qualitative constraints may better capture institutional context, actor knowledge, or lived consequences. The strongest applications often combine both.

prose_invariants_to_preserve:
Preserve plausibility. The alternate path must be more than imaginable; it must be grounded in constraints, evidence, comparable cases, feasible choices, or defensible models.

Preserve explicit baselines. Every comparison has a “compared with what.” If the baseline remains implicit, the conclusion remains underexamined.

Preserve comparability. Actual and counterfactual paths should be similar enough on relevant dimensions, or the difference should be treated cautiously.

Preserve uncertainty. The unchosen path is inferred, modeled, observed indirectly, or reconstructed. The final claim should carry that uncertainty forward.

Preserve decision linkage. The comparison should improve attribution, evaluation, accountability, learning, or future action. It should not end as a decorative thought experiment.

prose_target_outcomes:
A successful Counterfactual Comparison produces better causal attribution because the team no longer treats observed change as proof of effect. It produces fairer decision evaluation because decisions are judged against feasible alternatives and information available at the time. It produces better program and policy learning because continuation, expansion, or redesign is tied to the difference the action likely made.

It also clarifies opportunity cost. Choosing one path means not choosing another. By comparing actual outcomes with plausible alternatives, teams can see whether a decision created value, avoided harm, delayed loss, or merely looked good against a weak baseline.

prose_parameter_dimensions:
The first tuning dimension is baseline strictness. A highly strict baseline uses comparable controls or strong modeling; a looser baseline uses expert judgment or scenario contrast. Higher stakes require stricter baselines.

The second dimension is counterfactual distance. A near counterfactual changes one condition while preserving much of the actual context. A far counterfactual changes more of the world. Near counterfactuals usually support stronger inference; far counterfactuals may support broader strategic imagination but weaker claims.

The third dimension is time horizon. Short horizons may show immediate effect while missing delayed cost or benefit. Long horizons may better capture downstream value but introduce more confounding context.

The fourth dimension is outcome scope. Some comparisons focus on one metric. Others compare multiple outcomes, distributional effects, side effects, and opportunity costs. Narrow outcomes are easier to compare; broad outcomes are often more faithful.

The fifth dimension is uncertainty tolerance. Exploratory learning can tolerate rough comparison. Accountability, medical, legal, safety, or major funding decisions require more evidence, stronger review, and clearer uncertainty notes.

prose_variants_and_near_names:
Causal Effect Counterfactual Comparison focuses on estimating what difference an intervention or exposure made. It often uses control groups, A/B tests, synthetic controls, matched cases, or baseline projections.

Decision-Value Counterfactual Comparison focuses on whether a choice was valuable relative to feasible alternatives. It is useful in strategy, product, operations, and after-action reviews where outcome luck must be separated from decision quality.

Historical Counterfactual Comparison uses plausible alternatives to test contingency or turning points. It requires stronger guardrails against speculative storytelling because the unchosen path cannot be experimentally tested.

Baseline Selection Counterfactual Comparison focuses on choosing and defending the reference path. It is usually a subtype or component emphasis rather than a standalone archetype.

Counterfactual Contingency Testing is preserved as a merge-review variant. It should not be separately drafted here unless future reconciliation shows distinct components and boundaries from Counterfactual Comparison and history/narrative archetypes.

Near names include actual-vs-counterfactual comparison, alternative path comparison, counterfactual baseline comparison, what-would-have-happened-otherwise comparison, and counterfactual reasoning for evaluation. What-if prompts, A/B tests, control groups, and synthetic controls are mechanisms, not the parent archetype.

Causal Effect Counterfactual Comparison

variant_type: subtype

short_definition:
Compares observed outcome under an action or exposure with the outcome expected under no action or a different
exposure in order to estimate causal effect.

distinctive_feature:
The comparison is explicitly effect-centered: the actual path is evaluated against a counterfactual baseline
to isolate the difference made by a cause or intervention.

distinct_from_parent:
The parent covers causal, evaluative, strategic, and historical counterfactual uses; this variant narrows the
purpose to effect estimation.

still_same_parent_because:
It still uses the parent structure of actual path, counterfactual condition, plausibility check, outcome comparison,
and uncertainty-bounded claim revision.

use_when: An observed outcome is being attributed to an intervention, exposure, design, treatment, or policy.

use_when: The central question is what difference the action made compared with non-action or an alternate action.

use_when: The comparison can use a control, baseline, natural experiment, synthetic control, or carefully bounded judgment.

failure_modes: Treating a non-comparable baseline as a valid counterfactual.

failure_modes: Ignoring selection effects that explain the observed difference.

failure_modes: Overstating precision when the comparison rests on judgment or weak data.

short_definition:
Compares the actual decision with a feasible alternative to judge value, regret, opportunity cost, avoided harm,
or future decision policy.

distinctive_feature: It centers decision quality and opportunity cost rather than pure causal attribution.

distinct_from_parent:
The parent is any actual-versus-alternative comparison; this variant focuses on whether a choice was valuable
relative to available options and information.

still_same_parent_because: It still requires actual path, alternative condition, plausibility, outcome comparison, uncertainty, and claim revision.

use_when: A team is evaluating whether a decision was good given what was knowable at the time.

use_when: The observed outcome alone is an unfair basis for judging decision quality.

use_when: The practical question is what should be done differently next time, not only what caused the result.

failure_modes: Judging a decision only by hindsight outcomes rather than what was knowable at decision time.

failure_modes: Inventing an unrealistically perfect alternative path.

Historical Counterfactual Comparison

short_definition:
Uses historically plausible alternatives to test contingency, agency, structural force, or decision significance
in events that cannot be experimentally replayed.

distinctive_feature: Plausibility depends on historical constraints and source-grounded reconstruction rather than randomization or matched controls.

distinct_from_parent: The parent applies across domains; this variant adds special caution around hindsight, narrative temptation, and evidence-bounded imagination.

still_same_parent_because: The same actual-versus-plausible-alternative comparison structure remains central.

source_primes: counterfactuals historicism causality

use_when: The question is whether an outcome was inevitable, contingent, or dependent on a specific decision or condition.

use_when: The alternate path can be constrained by evidence about what actors knew, could do, and were likely to do.

use_when: The work is interpretive or strategic rather than experimentally testable.

failure_modes: Speculative storytelling unconstrained by evidence.

failure_modes: Retrofitting an alternative path to support a preferred moral or political conclusion.

failure_modes: Ignoring structural conditions that made the alternative infeasible.

name: Baseline Selection Counterfactual Comparison

short_definition: Focuses the counterfactual work on choosing and defending the baseline that stands in for the unobserved alternative.

distinctive_feature: The main intervention effort is baseline justification rather than outcome measurement or scenario exploration.

distinct_from_parent: It narrows the parent to the baseline-design problem that often determines the credibility of the whole comparison.

still_same_parent_because: Baseline selection matters only because it supports the parent comparison between actual and counterfactual outcomes.

use_when: Several possible baselines would produce different judgments about the same action.

use_when: The biggest risk is not the comparison formula but the legitimacy of the reference case.

use_when: Stakeholders dispute whether the no-action or alternative-action baseline is fair.

failure_modes: Choosing a baseline because it flatters the preferred conclusion.

failure_modes: Using pre/post comparison when a trend or shock changed independently of the action.

Counterfactual Contingency Testing

short_definition:
Uses plausible what-if alternatives to test whether an outcome depended on a specific condition, choice, timing,
or contingent event.

distinctive_feature: It foregrounds contingency testing: whether removing or changing one condition would likely alter the outcome.

distinct_from_parent:
The parent compares actual and counterfactual paths broadly; this variant emphasizes dependence on a specific
condition or turning point.

still_same_parent_because: It still works by comparing the actual path with a plausible alternative and revising a causal or value claim.

use_when: The question is whether an outcome was contingent on a specific condition rather than inevitable.

use_when: The variant appears in history, strategy, institutional learning, or scenario review.

use_when: The alternate path can be constrained by evidence rather than free speculation.

failure_modes: Speculative counterfactual branching without a bounded comparison question.

near_names:
Actual-vs-Counterfactual Comparison
Counterfactual Reasoning
What-If Analysis
Baseline Comparison
Control Group Comparison
Synthetic Control Method
A/B Test
Scenario Contrast


name: Counterfactual Proximity Signal Calibration

batch: Batch 02

slug: regret

slug: probability

slug: calibration

slug: comparison

slug: feedback

slug: reinforcement

slug: salience

components:
Factual Outcome Anchor
Valued Counterfactual Contrast
Proximity Metric or Ordering
Value Delta Frame
Signal Transformation Rule
Eligibility and Plausibility Gate
Threshold Band Partition
Learning Update Boundary
Salience Distortion Guard
Calibration Feedback Loop

mechanisms:
Near-Miss Distance Scorecard
Counterfactual Value-Delta Table
Threshold Band Map
Close-Call Review Protocol
Regret-Weighted Decision Log
Almost-Reward Annotation
Counterfactual Plausibility Filter
Salience Overweighting Check
Proximity Signal Backtest
Near-Miss Response Tier

essence_one_line: Calibrate how much an almost-happened better or worse outcome should teach, motivate, warn, or matter.

compression_statement: Counterfactual proximity signal calibration anchors on what actually happened, identifies a plausible nearby outcome of different value, measures how close the actual episode came to that outcome, weights the value gap by proximity, and constrains the resulting signal so near misses inform learning without being counted as wins, losses, harms, or proof.

core_intuition: A miss by one millimeter and a miss by one kilometer can share the same factual label while carrying very different information. The archetype preserves that difference without letting vivid almost-events override the actual outcome record.

canonical_formula: signal_weight = bounded_transform(counterfactual_value_delta × proximity_to_value_changing_counterfactual × plausibility_confidence)

structural_problem: Outcome-only evaluation erases distance to value-changing alternatives, while vivid counterfactual imagination can overstate it. A failed attempt that was nearly successful, a safe operation that nearly harmed someone, or a decision that almost entered a better path may contain information that binary success/failure labels miss. But if proximity is not explicitly bounded, the system confuses almost-success with success, almost-harm with harm, or imagined regret with valid evidence.

trigger_conditions: A case lands just short of, just beyond, or just beside a meaningful threshold.

trigger_conditions: A near miss, close call, almost-win, almost-loss, or narrowly avoided harm changes attention or motivation.

trigger_conditions: The actual outcome alone is too coarse to guide learning, training, safety response, or decision review.

trigger_conditions: People are reacting emotionally to what almost happened rather than to what happened.

trigger_conditions: A plausible nearby counterfactual of different value can be specified without altering fixed facts arbitrarily.

trigger_conditions: Outcome data are sparse but near-miss data are abundant enough to serve as early evidence.

trigger_conditions: A system is vulnerable to either normalizing near misses or overreacting to vivid anecdotal almost-events.

symptoms: Near misses are either ignored because no harm occurred or treated as if harm fully occurred.

symptoms: Almost-success receives full reward, no reward, or arbitrary reward depending on the evaluator’s mood.

symptoms: Post-hoc stories create regret signals around alternatives that were not actually available at the time.

symptoms: Teams learn the wrong lesson from close calls because proximity dimensions are unstated.

symptoms: A vivid close case receives more attention than a frequent but less dramatic pattern.

symptoms: Safety, product, education, or decision-review systems cannot explain why one failed case is more informative than another.

symptoms: People confuse prediction error, counterfactual comparison, and near-miss proximity into one undifferentiated update.

root_tension: Binary outcome records preserve factual integrity, but learning and motivation often depend on graded distance to nearby alternatives that the binary record omits.

anti_signatures: The main task is estimating causal effect by comparing actual and absent-intervention baselines; use counterfactual_comparison or counterfactual_subtraction instead.

anti_signatures: The main task is updating value from expected-versus-received reward; use prediction_error_learning_calibration unless proximity to an unrealized alternative is the key signal.

anti_signatures: The main task is probability conditioning; use conditioned_probability_frame_specification.

anti_signatures: The case has no plausible nearby better-or-worse counterfactual, only vague disappointment or relief.

anti_signatures: The counterfactual requires changing fixed facts, unavailable knowledge, or impossible actions.

anti_signatures: The system should record an actual harm, actual reward, or actual failure directly rather than treating it as a near miss.

anti_signatures: The problem is merely collecting near-miss reports without using proximity to calibrate response or learning.

structural_intervention: Add an explicit proximity-weighted counterfactual signal layer beside the factual outcome record: anchor the actual outcome, define the nearby value-changing counterfactual, measure distance and plausibility, transform distance and value delta into a bounded signal, assign a proportional response, and recalibrate against later evidence.

action: Record the factual result, timing, decision, constraints, and observed evidence before counterfactual interpretation begins.

reason: The actual record prevents near-miss analysis from replacing facts with imagined alternatives.

action: Specify the nearby outcome of different value, such as injury, reward, threshold crossing, detection, failure, or success.

reason: Proximity is meaningful only relative to a named better-or-worse alternative.

action: Check whether the alternative was accessible under the known timing, information, capabilities, and constraints.

reason: Unavailable alternatives create uncalibrated hindsight regret or fantasy reward.

action: Choose the relevant distance dimension: time, space, score, threshold margin, causal step, procedural barrier, probability mass, or resource gap.

reason: Different distances imply different learning and response weights.

action: State how much better or worse the nearby alternative would have been and for whom.

reason: Close proximity to a trivial outcome should not dominate farther proximity to severe harm or large value.

action: Convert proximity and value delta into a signal with limits, uncertainty notes, and no automatic replacement of the factual outcome.

reason: Bounded transformation keeps near-miss signals informative without letting them become false outcomes.

action: Map signal bands to review, reward, retraining, redesign, monitoring, escalation, or no-action tiers.

reason: The point is not simply to feel the near miss but to decide what calibrated response it warrants.

action: Compare the near-miss signal with base frequency, similar cases, actual outcomes, and possible storytelling bias.

reason: Vivid almost-events can crowd out less memorable but more diagnostic evidence.

action: Backtest whether proximity-weighted signals predict later outcomes, learning gains, risk reduction, or decision improvement.

reason: The proximity metric should get better over time rather than accumulate superstition.

decision_rules: Never count a near miss as the outcome itself; record it in a separate proximity-signal field.

decision_rules: Do not weight a counterfactual unless it is plausible under facts known at the time.

decision_rules: Weight by both proximity and value delta; a close trivial alternative should not dominate a less close severe alternative without justification.

decision_rules: When multiple counterfactuals are nearby, select the most decision-relevant one or report a small set with separate weights.

decision_rules: Use response tiers so near misses trigger proportionate action rather than binary blame, reward, or panic.

decision_rules: Treat repeated near misses around the same threshold as evidence of system fragility even if no adverse outcome has occurred yet.

decision_rules: Downweight emotionally vivid near misses if base rates, comparable cases, or later outcomes show weak diagnostic value.

decision_rules: If the proximity signal changes incentives, audit for gaming around thresholds and almost-reward claims.

target_invariants: Actual outcomes remain factually distinct from counterfactual signals.

target_invariants: Every weighted counterfactual has a stated value polarity, distance measure, and plausibility basis.

target_invariants: Signal strength is bounded and proportional to both proximity and value delta.

target_invariants: Near misses can inform learning, attention, and safety response without being treated as completed harm or reward.

target_invariants: Hindsight-only alternatives are excluded or flagged as low-plausibility.

target_invariants: Vividness, blame, or narrative salience does not substitute for distance and plausibility.

target_invariants: Responses are calibrated against later evidence and comparable cases.

expected_outcomes: Better use of close-call evidence before full failures occur.

expected_outcomes: More nuanced feedback than binary win/loss, harm/no-harm, or reward/no-reward scoring.

expected_outcomes: Reduced normalization of near misses in safety-critical systems.

expected_outcomes: Less overreaction to vivid but weakly diagnostic almost-events.

expected_outcomes: Clearer separation between reward prediction error, counterfactual comparison, and proximity-weighted counterfactual signal.

expected_outcomes: More proportional coaching, design, review, escalation, and learning responses.

Near-Miss Signal Calibration

short_definition: A safety or reliability variant that treats almost-harm as graded evidence without counting it as harm itself.

distinctive_feature: It assigns attention and response weight to how close the system came to harm.

distinct_from_parent: The parent covers any proximity-weighted counterfactual signal; this variant specializes in avoided-loss and safety-learning cases.

still_same_parent_because: The core operation remains distance-to-counterfactual weighting, not a separate safety-reporting archetype.

use_when: A process narrowly avoided failure, injury, breach, outage, or loss.

use_when: Actual incident counts are too sparse to reveal danger early enough.

use_when: The organization needs to learn from proximity without creating panic or blame inflation.

example: A medication error caught seconds before administration is weighted by how close it came to patient harm.

domain: cybersecurity

example: A blocked intrusion attempt is scored by its distance from privilege escalation rather than treated as merely a nonincident.

Almost-Reward Feedback

short_definition: A learning variant in which coming close to a reward changes attention, motivation, or policy despite the reward not being received.

distinctive_feature: It separates actual reward from the motivational or instructional signal produced by a close better outcome.

distinct_from_parent: The parent includes both near-gain and near-loss signals; this variant emphasizes almost-success and learning motivation.

still_same_parent_because: The signal is still produced by distance to a counterfactual outcome of different value.

use_when: A learner, agent, team, or product nearly reached a valued target.

use_when: Outcome-only scoring would treat the episode as equivalent to a distant failure.

use_when: The system needs to preserve the informational value of almost-success without awarding full credit.

domain: education

example: A student answer that is one inference away from the correct proof receives targeted feedback rather than the same treatment as a blank answer.

example: A feature that narrowly misses an adoption threshold is reviewed for proximity clues before being discarded.

Regret Proximity Weighting

short_definition: A decision-review variant that gives stronger learning weight to choices close to a better forgone outcome.

distinctive_feature: It grades regret by plausible distance to a better path, not merely by ex post desire.

distinct_from_parent: The parent covers internal signal calibration generally; this variant emphasizes decision review and regret governance.

still_same_parent_because: The operational form remains proximity-weighted counterfactual value difference.

use_when: A decision’s actual result is being compared with a plausible nearby alternative.

use_when: Decision-makers may overlearn from distant fantasies or underlearn from close misses.

use_when: The review needs proportional regret rather than binary success/failure judgment.

example: A missed trade is reviewed only if the alternative was actionable at the time, not merely visible after the fact.

near_names:
Counterfactual Closeness Weighting
Near-Reward Signal Calibration
Close-Call Weighting


Counterflow Gradient Preservation

batch: Batch 15

aliases:
Counter-Current Exchange Design
Countercurrent Exchange
Opposed-Flow Exchange
Gradient-Preserving Counterflow
Counterflow Exchange Optimization

source_primes: counter_current_exchange flow gradient coupling

slug: flow

slug: gradient

slug: coupling

slug: interface

slug: boundary

slug: permeability

slug: exchange

slug: equilibrium

slug: diffusion

slug: optimization

slug: observability

slug: containment

slug: resilience

slug: scale

slug: balance

slug: asymmetry

slug: time

components: Interface Conductance and Selectivity Model

components: Axial Mixing and Backdiffusion Guardrail

components: Containment and Cross-Contamination Guardrail

mechanisms: Countercurrent Washing or Leaching Train

essence_one_line: Arrange two coupled streams to move in opposite directions along a shared interface so a useful local difference persists across the whole contact and cumulative exchange can approach its feasible maximum.

compression_statement: When two streams must exchange a quantity and co-directional contact causes their states to converge too early, define the transferred quantity and terminal targets, model both stream capacities, route them in opposite directions along a selective interface, preserve a same-sign local driving gradient, tune contact length and capacity balance, prevent bypass and axial mixing, and govern pressure drop, contamination, fouling, startup, and distributed control. The geometry improves effectiveness by ensuring that each local segment meets a counterpart that is still sufficiently different, but performance remains bounded by conservation, capacity asymmetry, interface resistance, safety, and cost.

core_intuition: A transfer surface is useful only where the two sides are different enough to drive exchange. In co-current contact, the streams become similar as they move together and the remaining surface contributes less. In counterflow, each stream continually meets a fresher portion of the other, so the difference can remain useful from end to end. The gain comes from ordering the encounters—not from creating energy or violating equilibrium—and disappears when mixing, bypass, poor coupling, or capacity mismatch destroys the profile.

canonical_formula: For position x along contact length L, preserve sign[Δ(x)] and useful magnitude |Δ(x)| where Δ(x) = state_A(x) − state_B(x); cumulative transfer Q = ∫₀ᴸ K(x)·a(x)·Δ(x) dx, bounded by conservation, the smaller stream capacity, interface selectivity, and operating constraints. Choose opposed progression when it increases the integral of useful local driving force relative to co-current or unmapped contact.

structural_problem: Two streams or ordered sequences can exchange a useful quantity through a shared interface, but their contact arrangement rapidly erodes the driving difference. Under co-current or poorly staged flow, the streams approach one another near the inlet, local transfer stalls, downstream interface area is underused, and terminal targets require excessive area, extreme inlet conditions, repeated utilities, or unacceptable loss. The system may appear well coupled in aggregate while maldistribution, backmixing, capacity imbalance, or fouling collapses the local profile that actually determines exchange.

trigger_conditions: A target quantity can cross between two distinguishable streams, lanes, phases, populations, or ordered sequences while bulk separation remains meaningful.

trigger_conditions: The transfer rate depends on a local state difference such as temperature, concentration, partial pressure, potential, quality, freshness, or another defensible gradient.

trigger_conditions: Contact can be extended spatially or staged sequentially so the relative order of encounters is a design choice.

trigger_conditions: Co-current, batch, or unordered contact produces early equilibration, weak downstream driving force, excessive utility demand, or low recovery.

trigger_conditions: The two streams have measurable inlet states, transfer capacities, and safe outlet ranges.

trigger_conditions: A selective interface can permit the intended transfer while controlling leakage, contamination, or unwanted mixing.

trigger_conditions: The gain from greater exchange effectiveness is material relative to routing, pressure-drop, control, area, maintenance, and safety costs.

symptoms: Most transfer occurs near one end while the rest of the interface contributes little.

symptoms: Outlet targets are missed even after adding surface area or contact time.

symptoms: One stream exits close to the other stream’s outlet state rather than approaching its inlet state.

symptoms: Utilities, solvent, wash medium, cooling, heating, or repeated review cycles remain high despite substantial contact equipment or process length.

symptoms: Endpoint averages look acceptable while local probes show pinch, reversal, dead zones, bypass, or uneven lane loading.

symptoms: Small changes in one stream’s rate or inlet condition cause disproportionate loss of effectiveness.

symptoms: The nominal counterflow design degrades over time because fouling, leakage, backmixing, or channel imbalance erases the expected gradient profile.

symptoms: Optimization focuses on gross throughput or exchange percentage without accounting for pressure drop, latency, contamination, product quality, or lifecycle cost.

root_tension: High exchange effectiveness favors long, intimate, well-ordered contact and small terminal approach differences. Safe, economical operation favors lower resistance, shorter paths, simpler control, robust separation, cleanability, and margin from reversal or contamination. The design must preserve enough local difference to use the interface fully without making the coupling so tight, long, or fragile that operating costs and failure risks exceed the recovered value.

anti_signatures: Only one stream circulates or the task is to prevent stagnation rather than exchange between paired flows; use Circulation Loop Design.

anti_signatures: The core problem is deciding what may cross a boundary, not preserving a longitudinal driving profile; use Boundary Permeability Control.

anti_signatures: The task is a whole-cycle loss or exergy audit with no specific opposed-flow redesign; use Cycle Efficiency and Reversibility Assessment.

anti_signatures: The intervention should reduce a harmful disparity rather than use a difference to transfer something; use Gradient Flattening.

anti_signatures: The gradient only guides where resources should be allocated; use Gradient-Guided Intervention.

anti_signatures: The problem is routing through a capacity-constrained network without an extended shared interface; use Network Flow Optimization or Flow Channelization.

anti_signatures: The two streams must fully mix to react, blend, deliberate, or pool; counterflow separation may be irrelevant or harmful.

anti_signatures: The transfer is not driven by a local difference, or stream order cannot affect the sequence of local encounters.

anti_signatures: Hazard, infection, privacy, security, or contamination risk makes an extended shared interface unacceptable even with safeguards.

anti_signatures: A cross-domain analogy cannot specify two streams, exchanged quantity, interface, state profile, and measurable local gradient; treat it as metaphor rather than this archetype.

structural_intervention: Convert an exchange process from co-directional, unordered, or endpoint-driven contact into a paired opposed-flow system. Specify the exchange quantity, streams, capacities, and terminal targets; create a selective extended interface; orient progression in opposite directions; measure the local gradient and flux profile; balance capacity rates and contact length; suppress bypass, backmixing, and leakage; manage resistance, pressure drop, latency, fouling, and safe transients; and control the coupled system from distributed measurements rather than terminal output alone.

action_logic: Define the transferred quantity, net direction, quality constraints, conservation boundary, and maximum feasible transfer.

action_logic: Characterize both streams: inlet states, flow or progression rates, effective capacities, variability, hazards, and allowable terminal states.

action_logic: Map the current encounter order and measure where the driving difference collapses, reverses, or remains unused.

action_logic: Test whether opposite progression can maintain a same-sign useful gradient along a continuous interface or an ordered stage cascade.

action_logic: Design the selective interface for adequate area, conductance, compatibility, integrity, and maintainability without unacceptable bulk mixing.

action_logic: Tune capacity-rate ratio, contact length, residence distribution, stage count, and terminal approach targets within conservation and safety limits.

action_logic: Add distributors, baffles, lane balancing, stage discipline, or routing controls to prevent bypass, short-circuiting, dead zones, and axial backmixing.

action_logic: Budget pressure drop, latency, pumping or coordination energy, capital, maintenance, cleaning, and control complexity against recovered value.

action_logic: Instrument local gradients, fluxes, interface health, containment, and terminal states; coordinate both streams through one control model.

action_logic: Design startup, shutdown, one-stream loss, cleaning, isolation, and safe-bypass modes before steady-state operation.

action_logic: Validate under variable and degraded conditions, not only nominal balanced steady state.

action_logic: Retain counterflow only where net effectiveness, safety, robustness, and lifecycle value outperform co-current, crossflow, staged batch, or simpler alternatives.

decision_rules: Use counterflow when transfer is difference-driven and co-current contact allows the driving force to collapse before the desired terminal state is reached.

decision_rules: Require a measurable local profile; do not infer counterflow performance solely from inlet and outlet averages.

decision_rules: Treat the lower effective capacity stream as a primary bound on maximum transferable quantity.

decision_rules: Prefer matched or deliberately chosen capacity rates when the gain in effectiveness exceeds control and upstream/downstream disruption costs.

decision_rules: Stop adding area or stages when marginal transfer value falls below added pressure drop, delay, fouling, capital, or maintenance burden.

decision_rules: Reject operating points that permit gradient reversal, unsafe terminal approach, phase change outside the design envelope, or product-quality violation.

decision_rules: Choose co-current, crossflow, batch, or indirect recovery when counterflow routing creates unacceptable containment, cleanability, footprint, startup, or reliability risk.

decision_rules: In social or informational analogies, require explicit stream ordering and state-transfer evidence; opposition alone is not counter-current exchange.

target_invariants: The intended quantity is conserved or explicitly accounted for across the system boundary.

target_invariants: The local driving gradient keeps the intended sign across the active contact region unless a designed neutral point is explicitly governed.

target_invariants: Bulk streams remain separated except for permitted transfer.

target_invariants: No bypass or dead zone carries a material share of flow outside effective contact.

target_invariants: Pressure, temperature, composition, latency, load, and interface stress stay inside safe operating limits.

target_invariants: Product quality, biological viability, data integrity, privacy, rights, or other domain-specific non-transfer properties are preserved.

target_invariants: Startup, shutdown, cleaning, and failure modes do not create uncontrolled reversal, contamination, or overload.

expected_outcomes: Greater exchange effectiveness for the same inlet conditions and interface area.

expected_outcomes: Closer feasible terminal approach and better recovery of heat, solute, gas, or other transferable value.

expected_outcomes: More uniform use of the contact region and fewer low-gradient dead sections.

expected_outcomes: Lower utility, solvent, wash medium, or repeated-processing demand for a given outcome.

expected_outcomes: Reduced avoidable dissipation relative to poorly ordered contact, subject to pressure-drop and lifecycle accounting.

expected_outcomes: Earlier detection of pinch, reversal, fouling, bypass, and containment failure.

expected_outcomes: A clearer decision boundary for when counterflow is superior to simpler exchange geometries.

prose_tradeoffs:
Counterflow typically trades greater exchange effectiveness for more routing, surface, pressure drop, control coupling, and maintenance. Close terminal approaches may require larger area and tighter control. High conductance may reduce selectivity or durability. Turbulence can improve cross-interface transfer while axial mixing destroys stage order. A long shared interface improves opportunity but also enlarges leakage, fouling, contamination, and common-mode failure exposure.

The right objective is not maximum extraction at any cost. It is maximum *net* value under conservation, safety, quality, robustness, and lifecycle constraints.

Balanced-Capacity Counterflow

short_definition: Tune the two stream capacity rates toward parity so both state profiles change across the contact length and the interface is used more uniformly.

distinctive_feature: Capacity matching is treated as a first-class design variable rather than a fixed input.

distinct_from_parent: The parent requires a valid opposed-flow geometry but can operate with substantial capacity asymmetry; this variant specifically optimizes the capacity-rate ratio.

still_same_parent_because: It retains the same paired streams, shared interface, local gradient, and counterflow intervention logic.

use_when: Both stream rates or effective capacities are adjustable.

use_when: Terminal pinch or unused interface is caused primarily by capacity imbalance.

use_when: The exchange objective values high effectiveness more than unilateral outlet stabilization.

example: Modulate hot- and cold-side flow so the exchanger avoids a severe terminal pinch while meeting outlet targets.

example: Balance phase throughput across a countercurrent contactor so neither phase saturates the available stages.

failure_modes: Chasing a ratio target destabilizes upstream or downstream processes.

failure_modes: Averages conceal lane-level maldistribution.

Staged Countercurrent Contact

short_definition: Approximate continuous counterflow with discrete stages in which each stream advances in opposite stage order and exchanges locally before the next handoff.

distinctive_feature: The spatial gradient is represented by an ordered cascade of local contacts rather than one continuous interface.

distinct_from_parent: The parent is geometry-general across continuous and discrete systems; this variant depends on stage discipline and interstage handoffs.

still_same_parent_because: Opposed ordering keeps each incoming stage paired with a counterpart that preserves useful difference.

source_primes: counter_current_exchange pipeline flow

use_when: Continuous contact is impractical, unsafe, or difficult to control.

use_when: Each stage can equilibrate partially and preserve ordering between stages.

use_when: Stage count, mixing, and handoff losses can be measured and governed.

example: Fresh wash contacts nearly clean solids while earlier, dirtier wash contacts incoming solids in reverse stage order.

example: A tightly specified two-stream review pipeline sends increasingly refined artifacts one way and increasingly contextual feedback the other, with exchange at each stage.

failure_modes: Stage backmixing erases the ordered profile.

failure_modes: Handoff delay or rework dominates the gain from staged contact.

Biological Countercurrent Exchange

short_definition: Use opposed biological flows across a thin selective interface to sustain heat, gas, salt, or solute transfer while preserving organism viability.

distinctive_feature: Interface maintenance and control are performed by living tissue or biomimetic structures with strong viability constraints.

distinct_from_parent: The parent is substrate-neutral; this variant adds physiology, adaptation, and biological safety boundaries.

still_same_parent_because: The local-gradient-preserving opposed-flow geometry remains the causal exchange structure.

use_when: The interface is living, adaptive, vulnerable to damage, and constrained by physiology.

use_when: Exchange must be integrated with perfusion, ventilation, metabolism, or osmoregulation.

use_when: Safety requires explicit limits on shear, pressure, toxicity, and tissue exposure.

example: Blood flows opposite water across gill lamellae, preserving oxygen partial-pressure difference along the exchange surface.

domain: physiology

example: Arterial and venous vessels exchange heat in opposite directions to retain or shed heat while keeping blood streams separate.

failure_modes: Tissue damage or perfusion failure reduces exchange despite preserved nominal geometry.

failure_modes: Optimization for extraction compromises organism-level viability.

near_names:
Countercurrent Exchange
Counter-Current Exchange Design
Opposed-Flow Exchange
Counterflow Exchange Optimization
Reverse-Flow Exchanger Design