Ballast-Water Transfer

one_liner: The marine mechanism by which a ship entrains a port's pelagic community in ballast water taken on for stability and discharges it at a destination — an accidental, infrastructure-coupled vector whose commercial route, not ecology, sets which ecosystems become biologically linked.

core_idea:
Ballast-water transfer is the marine-ecology mechanism by which a ship takes on seawater at one port for stability and trim, entraining the pelagic community present at that location — plankton, larvae, dinoflagellates, eggs, microbes, and small invertebrates — and then discharges that water at a destination port, releasing a viable propagule pool into a recipient ecosystem that the organisms could not have reached unaided. The mechanism is structurally defined by three features acting together: it is *accidental* (the biological transport is incidental to the ship's operational purpose, not intended); it is *vector-mediated* (the ship's physical infrastructure is the carrier, not a biological pathway); and it is *infrastructure-coupled* (the route the carrier travels, determined by commerce not ecology, determines which pairs of ecosystems become connected). The volume of ballast water discharged globally is estimated at 3–5 billion tonnes per year, making the shipping network effectively a pump redistributing coastal-marine communities across biogeographic barriers that evolved over millions of years.

The biological consequence depends on whether the discharged organisms find a recipient environment with suitable temperature, salinity, and prey, and without effective native predators or competitors — conditions that vary by route, season, and recipient ecosystem. When establishment succeeds the outcomes can be severe and irreversible: the zebra mussel (*Dreissena polymorpha*) entered the North American Great Lakes via ballast from European ports in the late 1980s and restructured benthic communities across the basin; the comb jelly *Mnemiopsis leidyi* entered the Black Sea via ballast and drove the collapse of anchovy fisheries in the 1980s–90s; toxic dinoflagellate species have been introduced to ports where their harmful algal blooms were previously absent. Regulatory response has moved through ballast-water exchange (replacing coastal ballast with open-ocean water mid-voyage, where the entrained community is less likely to survive in coastal recipients) toward onboard treatment systems (UV irradiation, deoxygenation, biocides) under the 2004 IMO Ballast Water Management Convention.

scope_of_application:
Ballast-water transfer lives within the invasive-species ecology of marine science and the marine-policy that regulates it; its reach is that ship-and-port substrate, since the water-and-ballast mechanics are what the frame depends on. The general "operational pathway as unintended vector" (container pests, software sub-dependencies, M&A debt) travels under `vector` plus `invasive_species`, not this label.

- **Marine invasion ecology** — the home subfield, attributing arrivals to the ballast vector (against hull fouling and the live trade) and modeling propagule supply into recipient ports.
- **Port-pair invasion-risk assessment** — overlaying shipping-network traffic, routes, and ballast volumes on biogeographic barriers and donor-recipient environmental match to rank high-probability transfer routes.
- **Ballast-water management regulation** — the 2004 IMO Ballast Water Management Convention regime of mid-voyage exchange and onboard treatment (UV, deoxygenation, biocides) as the chokepoint levers.
- **Harmful-algal-bloom introduction** — the pathway by which toxic dinoflagellate species reach ports where their blooms were previously absent.
- **Fisheries-impact and benthic ecology** — assessing establishment consequences such as the *Mnemiopsis*-driven Black Sea anchovy collapse and zebra-mussel restructuring of Great Lakes benthos.

structural_signature:
**Sig role-phrases:**

- **the ship-as-vector** — operational infrastructure whose primary purpose (commerce) is non-biological, carrying incidentally
- **the uptake event** — seawater taken on at the donor port for stability and trim, entraining the local pelagic community (plankton, larvae, dinoflagellates, eggs, microbes)
- **the entrained propagule pool** — the viable organisms riding in the ballast tanks, the cargo no one intended
- **the route coupling** — the connection set by commerce not ecology: the carrier's commercial route determines which ecosystem pairs become linked across biogeographic barriers
- **the discharge event** — release of the propagule pool into the recipient port
- **the donor-recipient match** — the establishment filter: temperature, salinity, prey, and absence of effective native predators/competitors, distinct from the bare fact of discharge
- **the establishment outcome** — when the match clears, a breeding population takes hold, with severe and often irreversible effects (benthic restructuring, fishery collapse, novel harmful algal blooms)
- **the chokepoint** — the identifiable point where the link can be broken: open-ocean exchange mid-voyage, onboard treatment (UV, deoxygenation, biocides), or port screening

what_it_is_not:
- **Not just any introduction pathway.** Ballast-water transfer is one specific vector, distinct from hull fouling, intentional stocking, and the aquarium and live-trade releases. It entrains the *pelagic* community — plankton, larvae, dinoflagellates, resting eggs riding in the tanks — whereas fouling carries sessile encrusters on the hull and the live trade moves deliberate single species. Each entrains a different slice of the community and submits to a different control, so the life stages that arrive identify the vector.
- **Not intentional.** The biological transport is *incidental* to the ship's operational purpose — water is taken on for stability and trim, not to move organisms. The vector is accidental and infrastructure-coupled, which is exactly why the route is set by commerce; treating it as deliberate stocking misreads both the cause and the lever.
- **Not the same as an invasion having occurred.** Discharge releases a viable propagule pool, but *establishment* requires the recipient to supply suitable temperature, salinity, and prey, and to lack effective native predators and competitors. A release into a mismatched recipient does not take. The bare occurrence of discharge is necessary but not sufficient — the donor-recipient environmental match, not the discharge event, predicts establishment.
- **Not a connection set by ecological affinity.** Which ecosystems become biologically joined is determined by the *shipping network* — port-pair traffic, routes, ballast volumes — not by any biological similarity between donor and recipient. The risk map is commerce overlaid on the biogeographic barriers the routes cross, so two ports being ecologically alike means nothing unless a high-traffic route actually links them.
- **Not the cross-domain metaphor.** Stretched to shipping containers carrying pests, software packages carrying a vulnerable sub-dependency, or an acquisition carrying along unwanted technical debt, "ballast-water transfer" borrows the shape while dropping the water-and-ship mechanics — the ballast volumes, the entrained life stages, ports as chokepoints. What genuinely recurs there is bare `vector` plus `invasive_species`, not this marine instance.

clarity:
Naming ballast-water transfer isolates one introduction pathway from the others an invasion biologist must keep apart — hull fouling, intentional stocking, the aquarium and live-trade releases, and natural range expansion — each of which entrains a different part of the community (sessile encrusting organisms on a hull versus the pelagic plankton, larvae, and resting eggs that ride in ballast tanks) and submits to a different intervention. Lumping a new arrival under generic "invasion" leaves the manager with no lever; attributing it to ballast specifies the carrier, the entrained life stages, and the chokepoint, and tells you which control — open-ocean exchange, onboard treatment, port screening — could actually break the link.

Its sharper analytical payoff is the recognition that the pattern of *which ecosystems get connected* is set by commerce, not biology. Because the vector is accidental and infrastructure-coupled, the relevant map of invasion risk is the shipping network — port-pair traffic, voyage routes, ballast volumes — overlaid on the environmental match (temperature, salinity, prey, predator-free space) between donor and recipient. That reframing lets a practitioner ask the productive questions: which high-traffic port pairs cross a biogeographic barrier into an environmentally similar recipient, and are therefore the high-probability transfer routes; and at which point in the voyage is the entrained community most vulnerable to disruption. It separates the bare fact of two ecosystems being biologically linked from the contingent commercial reason they are linked, and makes the donor-recipient environmental match — not the mere occurrence of discharge — the predictor of whether a release becomes an establishment.

manages_complexity:
The question an invasion biologist faces — out of all the world's coastal ecosystems, which pairs are at risk of being biologically joined, and which arrivals will turn into established invaders — is, stated naively, intractable: the donor-recipient combinations are astronomically many, each arrival event has its own organisms, life stages, and timing, and the bare fact that two ports both discharge ballast says nothing about whether a transfer will take. Ballast-water transfer compresses that sprawl by fixing the carrier and the entrained material — the ship as an accidental, infrastructure-coupled vector entraining the pelagic community, not the sessile foulers of a hull or the deliberate releases of the aquarium trade — and thereby converting an open-ended "which ecosystems might connect" into two trackable coordinates. The first is set by commerce, not biology: the shipping network — port-pair traffic, voyage routes, ballast volumes — determines which ecosystems are physically linked, so the relevant map of connection is the commercial route structure overlaid on the biogeographic barriers it crosses. The second is the donor-recipient environmental match: temperature, salinity, prey availability, and the presence or absence of effective native predators and competitors at the recipient. The analyst stops enumerating arrivals and tracks these two: does a high-traffic route cross a biogeographic barrier into an environmentally similar recipient?

From that pairing the qualitative outcome reads off through a clear branch structure. High traffic across a barrier into an environmentally matched, predator-free recipient is the high-probability establishment route — the regime that produced the zebra mussel in the Great Lakes, *Mnemiopsis* in the Black Sea, toxic dinoflagellates in previously bloom-free ports. Discharge into an environmentally mismatched recipient, or along a route that crosses no barrier, drops to low risk regardless of how much water moves. And because the vector is a known physical pathway with an identifiable chokepoint, the control options read off the same compression directly: the intervention is to break the link where the entrained community is most vulnerable — open-ocean exchange mid-voyage, onboard treatment, port screening — rather than to manage each recipient ecosystem after the fact. A combinatorial "which of countless arrivals becomes an invasion" problem collapses to a two-coordinate read — route-crosses-barrier and donor-recipient match — with establishment as the predicted outcome and a single named chokepoint as the lever.

abstract_reasoning: Ballast-water transfer licenses a set of moves on marine-invasion problems, all routed through the two coordinates the mechanism fixes — commerce-set connection and donor-recipient environmental match — and the named chokepoint the vector affords. *Diagnostic (the signature move) — attribute an arrival to its pathway by its life stages*: confronted with a new species in a recipient port, the move is to read the *entrained life stages* back to the carrier — pelagic plankton, larvae, dinoflagellates, and resting eggs implicate ballast water, whereas sessile encrusting organisms implicate hull fouling, and deliberate single-species presence implicates the aquarium or live trade. So the analyst reasons from "what part of the community arrived" to "which vector carried it," because each pathway entrains a different slice of the community and submits to a different control. Mis-attributing the pathway means reaching for a lever that cannot break the actual link. *Predictive — map risk onto commerce, not biology*: the characteristic and counterintuitive move is to predict *which* ecosystems will be joined from the shipping network rather than from any ecological affinity — port-pair traffic, voyage routes, and ballast volumes set the connections, because the vector is accidental and infrastructure-coupled. The analyst overlays that commercial route map on the biogeographic barriers it crosses and the environmental match at each recipient, and predicts the high-probability transfer routes as the high-traffic port pairs that cross a barrier into an environmentally similar recipient. Reason from "this route carries large ballast volumes across a barrier into a temperature- and salinity-matched, predator-poor recipient" to "this is where establishment is likely," and from "no barrier crossed, or recipient environmentally mismatched" to "low risk regardless of volume." *Boundary-drawing — separate discharge from establishment*: the decisive move is to refuse to equate a release with an invasion. A viable propagule pool discharged into a recipient that lacks suitable temperature, salinity, or prey, or that holds effective native predators and competitors, does not establish — so the analyst predicts establishment only where the donor-recipient environmental match clears those conditions, and treats the bare occurrence of discharge as a necessary but not sufficient signal. The reasoning runs from the *match*, not the *event*, to the outcome — which is why two ports both discharging ballast tells you nothing until you check the environmental compatibility between them. *Interventionist — break the link at the chokepoint, before the recipient*: because the vector is a known physical pathway with an identifiable chokepoint, the move is to intervene where the entrained community is most vulnerable rather than to manage each recipient ecosystem after invasion. The analyst predicts the effect of each control from where it acts on the community: open-ocean exchange mid-voyage works by replacing the coastal community with oceanic organisms unlikely to survive in a coastal recipient; onboard treatment (UV, deoxygenation, biocides) works by killing the entrained pool before discharge; port screening works at the point of release. Reason from "the community is least likely to survive if swapped for open-ocean water" to "exchange mid-voyage is a control point," choosing the intervention by the life stage and location at which it severs the donor-recipient link, and recognizing that post-establishment management of a recipient is the move of last resort because the outcomes — benthic restructuring, fishery collapse, novel harmful algal blooms — are severe and often irreversible.

knowledge_transfer:
Within marine invasion ecology ballast-water transfer transfers *as mechanism*: the two-coordinate risk map (commerce-set connection overlaid on donor-recipient environmental match), the life-stage attribution that distinguishes it from hull fouling and the aquarium trade, the discharge-versus-establishment boundary, and the break-the-link-at-the-chokepoint intervention all apply wherever ships move ballast across biogeographic barriers. They carry intact across the canonical cases — the zebra mussel in the Great Lakes, *Mnemiopsis* in the Black Sea, toxic dinoflagellates in previously bloom-free ports — and across recipient ecosystems, seasons, and routes, because the carrier (the ship's ballast volume) and the entrained material (the pelagic community) are the same each time. The regulatory toolkit ports the same way *within* marine policy: ballast-water exchange mid-voyage and onboard treatment (UV, deoxygenation, biocides) under the 2004 IMO Ballast Water Management Convention are levers on the same chokepoint regardless of which port pair is involved. Adjacent biological-introduction pathways stay distinct under the same frame — hull fouling entrains sessile encrusters, the live trade entrains deliberate single species — which is exactly what makes the within-domain attribution sharp.

Beyond marine ecology the honest split is two-layered. The general *shape* — *operational infrastructure incidentally transports entities across barriers they could not cross unaided, with the route set by the carrier's commercial purpose, not the cargo's function* — really does recur: shipping containers carrying insect pests, air freight carrying pathogens, a widely-installed software package transitively carrying a vulnerable sub-dependency no consumer intended, a viral social-media post carrying an embedded misattributed claim, an acquired company carrying along employees and technical debt the acquirer never contracted for. But invoking "ballast-water transfer" for these renames the components and borrows the shape while dropping the water-and-ship mechanics, so the cross-domain use is (A) **analogy** — and the underlying recurrence is a (B) **shared abstract mechanism** already named by the catalog: `vector` (the carrier-mediated transfer), `contagion`/diffusion (the spread), `contamination` (unintended cargo), and `invasive_species` (the establishment consequence). Ballast-water transfer's own structural addition over bare `vector` is thin — "the carrier's infrastructure is operationally separate from the cargo's function" — which is a useful *frame* on vector (the operational-pathway-is-also-a-vector subcase) rather than a new mechanism. The home-bound cargo is everything that makes the marine policy bite: the volumetric mechanics of ballast (3–5 billion tonnes/year), the specific entrained life stages, ports as chokepoints, exchange-versus-treatment economics — none of which translates to software dependencies or M&A. So the genuinely portable lesson, "every operational pathway is also a potential biological or informational pathway; break the vector and screen at chokepoints," belongs to `vector` and `invasive_species`, and "ballast-water transfer," as named, should stay the marine instance of vector whose ship-and-port machinery actually bites (see *Structural Core vs. Domain Accent*).


Bank Run

one_liner: A self-reinforcing mass withdrawal or refusal to renew short-term claims on a financial institution, where a finite liquid pool rewards early exit and each withdrawal worsens later claimants' prospects, so even a solvent institution can be drained by the race.

status: draft

core_idea:
A bank run is a self-reinforcing mass withdrawal, redemption, or refusal to renew short-term claims on a financial institution or runnable funding vehicle. Claimants can exit faster than the institution's assets can be converted into par liquidity. Because the immediately available pool is finite, early exit improves an individual claimant's chance of full repayment while each withdrawal worsens the position of those who remain. That first-mover advantage can shift the institution from continued funding into a run equilibrium.

The identity does not require initial insolvency. A solvent institution may own assets whose long-run value exceeds its liabilities while still being unable to meet simultaneous demands without selling at discounts. The run can then produce the very losses and liquidity shortage that claimants feared. Retail deposit queues, wholesale creditors refusing overnight rollover, money-market fund redemptions, and digital withdrawals are different operational forms of the same financial event.

scope_of_application:
- **Retail banking:** depositors transfer or withdraw demand deposits.
- **Wholesale banking:** professional creditors refuse to roll repo, commercial paper, or other short-tenor funding.
- **Shadow banking:** runnable vehicles promise liquid or stable-value claims backed by less-liquid assets.
- **Money-market funds:** investors redeem shares whose stable value depends on liquid reserves or sponsor support.
- **Digital banking:** mobile transfers compress the event from visible queues into rapid remote outflows.
- **Currency boards or reserve arrangements:** claimants race against a finite pool of liquid backing, subject to the exact institutional claim structure.

structural_signature:
- **The runnable institution or vehicle** — an intermediary financed by short, redeemable, or renewable claims.
- **The illiquid asset side** — commitments cannot be converted to par liquidity as fast as claims can leave.
- **The finite liquid pool or backstop** — cash and immediately available support cannot satisfy universal simultaneous exit.
- **The first-mover advantage** — earlier claimants expect better repayment than later claimants.
- **The withdrawal or non-rollover action** — claims are redeemed, transferred, or allowed to mature without renewal.
- **The endogenous deterioration** — each exit reduces what remains or forces costly liquidation.
- **The equilibrium transition** — expectations and action move the institution from continued funding to a run.

what_it_is_not:
A bank run is not identical to insolvency. Insolvency concerns the value of assets relative to liabilities; a run concerns the timing and convertibility of claims. It is not Funding Fragility, which is the structural exposure present before anyone runs. It is not a Fire Sale, although forced liquidation often transmits the run into market prices and balance-sheet losses.

Nor is every simultaneous financial exit a bank run. A broad credit crunch can involve lenders refusing new credit without racing to redeem claims on one runnable intermediary. Generic users leaving a platform lack the par-claim and maturity-transformation machinery.

clarity: The abstraction separates condition, event, and consequence. Funding Fragility is the maturity- and confidence-sensitive balance sheet. Bank Run is the coordinated withdrawal event. Fire Sale is one possible liquidation consequence. Insolvency may be an initial fundamental, a result of the run, or absent. This decomposition prevents every bank failure from being explained as the same thing.

manages_complexity: The event can be represented by six roles: runnable claim, asset-conversion delay, liquid pool, first-mover advantage, withdrawal feedback, and equilibrium transition. Interventions map onto roles. Deposit insurance weakens the incentive to exit; lender-of-last-resort lending enlarges credible liquidity; liquidity requirements change the pool; longer funding maturities reduce runnability; gates slow the timing but can also intensify pre-gate exit.

abstract_reasoning:
The run is an equilibrium-selection problem with financial differentia. Continued funding may be sustainable if claimants expect one another to remain. If they expect exit, leaving early becomes individually rational because later claimants face a depleted pool and more forced liquidation. Their action then validates the expectation. Fundamentals can select or strengthen the bad equilibrium, but pure coordination can also matter.

The abstraction also separates stocks from flows. The institution may have enough total asset value but insufficient liquid flow capacity at the speed claims are leaving. Converting assets faster can reduce their realized value, making liquidity stress feed solvency stress.

knowledge_transfer: Within finance the mechanism transfers literally across claimant classes and technologies. A wholesale run has no retail queue, yet short claims, finite liquidity, rollover refusal, and first-mover advantage remain. Digital access changes speed but not identity. Transfer outside finance should be conservative: an exit cascade is only analogous unless it preserves redeemable claims, par-liquidity expectations, and maturity transformation.

notes: Created from `workspace/mixed_dag_2026/missing_node_adjudications/bank_run.yaml`. Identity and hierarchy are adjudicated; final voice and citations remain editorial tasks.


Barnum Effect

one_liner: The tendency to accept vague, high-base-rate personality descriptions as strikingly accurate accounts of oneself when framed as personally intended — because the reader privately narrows a statement that actually fits almost everyone.

core_idea:
The Barnum effect (also the Forer effect, after Bertram Forer's 1949 demonstration) is the tendency for people to accept vague, high-base-rate personality descriptions as strikingly accurate and personally revealing accounts of themselves, especially when the descriptions are framed as products of individual assessment. Forer gave every student in a class the same 13-statement personality profile assembled from a newspaper astrology column — items true of nearly everyone, such as "At times you have serious doubts as to whether you have made the right decision" — and obtained a mean self-rated accuracy of 4.3 out of 5.

The mechanism has three interlocking components. First, the statements themselves are constructed to have high base-rate applicability: they describe properties that most people share most of the time, so the probability that any given reader genuinely fits is high regardless of any individual information. Second, the framing of personal intent activates a confirmatory search: told that this description was generated specifically for them, readers search autobiographical memory for instances where the statement was true rather than asking whether it distinguishes them from anyone else. Third, confirmation bias completes the circuit — matching memories register as hits and are weighted heavily; the many contexts where the statement did not apply leave no comparable trace. The result is a felt specificity that is entirely subjective: the description has not narrowed the space of people it could describe, but the reader has narrowed it by substituting private exemplars that the vague wording accommodates.

scope_of_application:
The Barnum effect lives in one domain — personality and social cognition — and the contexts below are application settings of that single substrate (a human reader judging whether a description fits them), not structurally distinct systems. The discriminant-validity-versus-endorsement lesson generalizes through its parents (base-rate neglect, subjective validation); cross-domain invocations are metaphor and stay out of this map.

- **Pseudoscientific personality systems** — astrology, palmistry, MBTI feedback, graphology, and Tarot draw their felt accuracy heavily from high-base-rate statements read as personal.
- **Cold reading** — psychics, mediums, and confidence tricksters issue vague, near-universal statements the mark fills in with private specifics.
- **Marketing personalization** — "this profile is uniquely yours" framing makes generic content feel tailored, the commercial exploitation of the personal-intent cue.
- **Self-help and motivational profiles** — "the procrastinator," "the perfectionist," "the empath" feel diagnostic because almost anyone can supply a matching memory.
- **Clinical and personality-test assessment** — feedback delivered without contrasts is over-credited by clinician and client alike, the legitimate-use hazard the discriminant-testing fix targets.

structural_signature:
**Sig role-phrases:**

- **the high-base-rate statement** — a personality description constructed of properties most people share most of the time, so it picks out almost no one in particular
- **the personal-intent framing** — the description presented as generated specifically for this reader, the cue that licenses self-application
- **the reader** — a person judging whether the description fits them, the substrate of the effect
- **the confirmatory autobiographical search** — the personal framing prompting the reader to hunt memory for instances where the statement was true rather than asking whether it distinguishes them from others
- **the confirmation-biased registration** — matching exemplars weighted as hits while the many non-applying contexts leave no trace
- **the private narrowing** — the reader (not the wording) shrinking the candidate population by substituting fitting specifics the vague language accommodates
- **the felt specificity** — a subjective sense of striking, personal accuracy disproportionate to the statement's actual discriminant power
- **the cross-population corrective** — the discriminant test the bias resists: rate the statement as if written for someone else, or ask what fraction of the population could endorse it, collapsing the apparent accuracy

what_it_is_not:
- **Not evidence that the description is accurate.** High endorsement measures *felt* accuracy, not discriminant specificity — how narrow a population the statement actually picks out. A statement that fits almost everyone draws "that's so me" precisely because the narrowing was done privately by the reader, so endorsement under vague, personally-framed conditions is evidence about the reader's substitution, not about the description's power.
- **Not a validity signal.** "That's so me" is exactly the sensation the effect predicts a *non-diagnostic* statement will produce, so recognition cannot certify that feedback distinguishes the reader from anyone else. The check is discriminant: would I rate this accurate if told it was written for someone else, and what fraction of the population could endorse it just as readily?
- **Not the description narrowing to fit you.** The felt precision is supplied by the *reader*, who substitutes fitting autobiographical exemplars the vague wording accommodates; the statement itself has not shrunk the candidate population at all. Attributing the fit to the description inverts where the narrowing actually happened.
- **Not a quirk of gullible or unintelligent people.** The substitution-plus-confirmation process is a near-universal feature of how people read self-relevant material — Forer obtained a mean accuracy of 4.3/5 from an entire class given identical profiles. Reading it as a failing of credulous individuals misses that it predicts the response of typical readers under the right framing.
- **Not dependent on deception or pseudoscience.** Astrology and cold reading exploit it, but the effect is structural and appears wherever vague, high-base-rate content meets personal framing — including legitimate clinical and personality-test feedback delivered without contrasts, which clinician and client alike over-credit. The trap is the specificity-versus-endorsement gap, not the presence of a trickster.

clarity:
Naming the effect pries apart two things the experience of recognition fuses: the *actual specificity* of a description — how narrow a population it genuinely picks out — and the *felt personal accuracy* of that description, how strongly the reader sees themselves in it. The trap is that these feel like the same quantity; a statement that fits almost everyone reads as if it had been narrowed to fit you, because the narrowing was done privately, by the reader's own substituted memories, rather than by the wording. With the label in hand, a clinician, evaluator, or reader can stop treating "that's so me" as a validity signal and recognize it instead as exactly the sensation the effect predicts a non-diagnostic statement will produce.

That reframing converts a fuzzy "does this describe me?" into a structured, checkable pair of questions: would I rate this description as accurate if I were told it had been written for someone else, and what fraction of the population could endorse it just as readily? The sharper question underneath is causal — when a description feels like it hit precisely, did the *description* narrow the candidate population, or did *I* narrow it by silently filling in specifics the vague language left open? Asking it is what separates genuine discriminant accuracy (the statement distinguishes me from others) from the Barnum payoff (the statement accommodates anyone), and it names the corresponding fix in assessment practice: test feedback against base-rate prevalence and contrast, never against endorsement alone.

manages_complexity: Astrology readings, cold-reading scripts, personality-test feedback, "the procrastinator" self-help profiles, and "uniquely yours" marketing copy form a long list of contexts in which generic material reads as personal insight, and validating each one through its own study of why it persuades would be open-ended. The effect compresses the list by reducing felt accuracy to a single ratio the reader never computes: a statement's actual discriminant specificity (how narrow a population it picks out) against the private narrowing the reader supplies by substituting fitting autobiographical exemplars under a confirmatory, personally-framed search. Holding that one contrast, the analyst predicts the endorsement payoff directly from two readable parameters — the base-rate prevalence of the statement and whether the framing invites self-substitution — without re-deriving each pseudoscience or sales technique. It also fixes the corrective measurement to a single design across all of them: test feedback against base-rate prevalence and against ratings of descriptions written for someone else, never against bare endorsement. So a sprawling family of "that's so me" phenomena collapses to one specificity-versus-felt-accuracy gap with a uniform diagnostic, and the qualitative verdict — genuine discriminant accuracy versus the Barnum payoff — reads off that gap rather than from the particulars of any one system.

abstract_reasoning:
The effect underwrites a set of inferences about when a "that's so me" reaction is informative about the description versus about the reader, and how to tell which.

*Diagnostic — from felt accuracy back to private narrowing.* The core move runs from a surface signature (a reader reports striking, personal accuracy) to a hidden cause (the reader, not the wording, narrowed the candidate population by substituting fitting autobiographical exemplars under a confirmatory, personally-framed search), against the naive reading that high endorsement signals a description that genuinely picks the reader out. The tell is *vagueness plus personal framing*: a high-base-rate statement delivered as "written for you" is precisely the configuration the effect predicts will read as specific, so felt accuracy under those conditions is evidence about the reader's substitution, not about the statement's discriminant power. The model also predicts the introspective trap that makes the misattribution automatic — the narrowing happens privately, so the reader experiences a description that fits everyone as one tailored to them.

*Boundary-drawing via two fixed counterfactuals.* The effect hands the evaluator the tests that separate genuine discriminant accuracy from the Barnum payoff. First: *would I rate this description as accurate if I were told it had been written for someone else?* If yes, the felt accuracy is non-diagnostic. Second: *what fraction of the population could endorse it just as readily?* A high fraction marks a statement that has not narrowed its candidate population at all. Together these draw the line between a statement that distinguishes the reader from others (discriminant accuracy, which survives the counterfactuals) and one that merely accommodates anyone (the Barnum payoff, which collapses under them), and they specify the conditions under which the effect operates — vague, high-base-rate content under personally-targeted framing.

*Interventionist — what to change to recover true specificity.* Because the felt accuracy is driven by a private specificity-versus-substitution gap the reader never computes, the corrective lever is to supply the missing contrast: test feedback against base-rate prevalence and against descriptions written for others, never against bare endorsement, and predict that apparent accuracy collapses when the same statement is shown to discriminate no one. The model fixes concrete measurement moves — give a client several profiles (genuine and randomized) and have them pick their own; contrast statements against population prevalence in cold-reading exposure — each a prediction that perceived hit rate falls once cross-population comparison is forced. What the model says will *not* work is collecting more endorsement or more vivid recognition, since both feed the confirmatory substitution that produced the illusion; only discriminant testing moves the estimate.

*Predictive — where the payoff appears and how large.* Holding the two readable parameters — base-rate prevalence of the statement and whether the framing invites self-substitution — the model predicts the endorsement payoff directly, and ranks contexts without studying each: astrology, cold reading, generic personality-test feedback, and "uniquely yours" marketing maximize the effect because they pair near-universal content with personal framing, while a description that genuinely constrains its population, or one delivered without the personal-intent cue, is predicted to draw weaker recognition. The same parameters predict the failure mode in assessment practice — feedback delivered without contrasts will be over-credited by clinician and client alike — and locate the fix at exactly the point where discriminant validity, not endorsement, is the thing measured.

knowledge_transfer:
Within personality and social cognition the diagnosis transfers as mechanism, intact, though its reach is a span of application contexts of one substrate — a human reader judging whether a description fits them — rather than structurally distinct systems. With that understood, the specificity-versus-felt-accuracy diagnosis and its two fixed counterfactuals (would I rate this accurate if told it was written for someone else? what fraction of the population could endorse it?) carry without translation across **pseudoscientific personality systems** (astrology, palmistry, MBTI feedback, graphology, Tarot), **cold reading** (psychics and confidence tricksters issuing high-base-rate statements the mark fills in), **marketing personalization** ("this profile is uniquely yours"), **self-help profiles** ("the procrastinator," "the empath"), and **clinical assessment**, where feedback delivered without contrasts is over-credited by clinician and client alike. The vocabulary (discriminant specificity versus felt accuracy, self-substitution, confirmatory personally-framed search), the diagnostic (vagueness-plus-personal-framing is the tell; "that's so me" is not a validity signal), and the intervention (test against base-rate prevalence and against descriptions written for others, never against bare endorsement; have the client pick their own profile from randomized alternatives) all move freely, because the same substitution-and-confirmation machinery operates in each.

Beyond human social cognition the named effect does **not** travel as mechanism; elsewhere it is only **metaphor**, and what genuinely carries is the set of more-general parents it bundles. The illusion decomposes into *subjective validation* (treating felt personal relevance as evidence of accuracy), *confirmation bias* (matches register as hits, the many misses leave no trace), *the availability heuristic* (which supplies the fitting autobiographical exemplars), and *base-rate neglect* (the reader never computes the high prevalence of the statement). Each of those travels on its own terms — base-rate neglect, in particular, is the substrate-general statistical structure of which the Barnum payoff is the social-cognition expression, and it recurs wherever a felt fit is mistaken for a discriminating one. There is also a thin structural skeleton worth naming above the substrate — *felt specificity is not actual specificity; a signal that fits everyone can feel as though it were narrowed to one* — which recurs in any matching or scoring context (an overfit pattern that "explains" any case, a horoscope-grade metric that flatters every reading). When the lesson is needed outside personality feedback, that skeleton and the base-rate / subjective-validation parents are what carry; the Barnum effect's own cargo (the Forer profile, the astrology-column wording, the personal-intent framing, the clinical-feedback playbook) stays bound to human readers judging descriptions of themselves. So the honest cross-domain account is: as mechanism it stays inside social cognition; the discriminant-validity-versus-endorsement lesson generalizes through the parents; and the right move elsewhere is to carry those parents, with "Barnum effect" kept as the memorable cognitive label (see *Structural Core vs. Domain Accent*).


Barrier to Entry

one_liner: Read market power off the durable, asymmetric cost of joining a market rather than the current count of firms — sorting each barrier into structural, strategic, or legal to select the remedy that can actually remove it.

core_idea:
A barrier to entry is a structural feature of a market that imposes on potential entrants a cost or disadvantage not borne by established incumbents, and that persists into long-run equilibrium rather than dissipating as new firms enter. The defining structural commitment is *asymmetric cost of access*: the incumbent already occupies a position whose acquisition — in sunk capital, brand recognition, accumulated learning, regulatory license, control of essential inputs, or customer switching costs — the entrant must pay again, or cannot pay at all. Where this asymmetry is large and durable, incumbents can sustain prices above long-run average cost (earn rents) without triggering the entry that would compete them away; where the asymmetry is absent, pricing above cost invites entry and markets are contestable even with few current competitors.

The analytical taxonomy standard since Bain (1956) and refined by Stigler (1968) distinguishes three types: *structural barriers*, which arise from cost or demand conditions in the industry — economies of scale, capital intensity, product differentiation, network effects — and require no deliberate action by the incumbent; *strategic barriers*, which incumbents erect deliberately to deter or disadvantage entrants — predatory pricing, excess capacity as a commitment to post-entry price war, exclusive contracts with input suppliers or distributors; and *legal barriers*, which governments install — patents, copyrights, occupational licensing, regulatory certification requirements. Each type has a characteristic policy response: structural barriers warrant open-access or divestiture remedies; strategic barriers warrant conduct prohibitions; legal barriers warrant deregulation or licensing reform. The barrier concept is the central diagnostic in competition-policy analysis — the load-bearing input in merger review, predation cases, and assessments of market power — because a finding of high entry barriers converts a current monopoly price into a durable one, while low barriers make incumbent market power self-correcting.

scope_of_application:
The barrier-to-entry concept lives across the market-analysis subfields of economics; its reach is bounded to markets and quasi-market settings where the firm, the rent, the incumbent, and the entrant are load-bearing, and the thinner portable core (durable, asymmetric access friction) travels under its parents `asymmetry` plus sunk-cost-of-access — uses of the phrase outside markets (a profession, a political contest) are the economic concept borrowed by analogy, not the mechanism travelling.

- **Industrial organization** — the home turf: the central determinant of market structure alongside firm count and product differentiation, sorted into the structural / strategic / legal typology since Bain and Stigler.
- **Antitrust and competition policy** — the standard test for contestability and the load-bearing input to merger review, predation cases, and assessments of market power.
- **Strategy** — the "threat of new entrants" force in Porter's Five Forces, with barriers as the load-bearing variable.
- **Platform economics** — network effects and switching costs analysed as endogenous barriers, with a lock-in sub-literature.
- **Regulatory economics** — occupational licensing, professional credentialing, and zoning studied as legally-erected barriers, with empirical work on welfare effects.

structural_signature:
**Sig role-phrases:**

- **the incumbent's occupied position** — an advantage (sunk capital, brand, learning, license, input control, switching costs) the incumbent has already paid for or never needed to pay
- **the entrant's asymmetric cost of access** — the same position the entrant must pay for again, or cannot pay at all
- **the persistence condition** — the access asymmetry survives into long-run equilibrium rather than dissipating as entry erodes it
- **the durable rent** — the price-above-long-run-average-cost margin the barrier sustains for the incumbent
- **the contestability test** — whether the mere threat of entry disciplines incumbent pricing, so market power is read off barrier height rather than firm count
- **the structural barrier branch** — cost or demand conditions (scale, network effects, capital intensity) requiring no incumbent action; remedy is open-access or divestiture
- **the strategic barrier branch** — deliberately erected by the incumbent (predatory pricing, capacity preemption, exclusive dealing); remedy is conduct prohibition
- **the legal barrier branch** — installed by government (patents, licensing, certification); remedy is deregulation or licensing reform
- **the type-selects-remedy mapping** — the diagnosis of which barrier type applies determines which intervention can actually move the market

what_it_is_not:
- **Not simply "anything that makes entry hard."** The load-bearing commitment is an *asymmetric* cost — one the entrant must pay that the incumbent already paid or never faced — that *persists into long-run equilibrium*. A cost borne equally by everyone, or one that dissipates as firms enter, is not a barrier in the strict (Stiglerian) sense, even if entry is difficult; the asymmetry and the durability are what matter.
- **Not the same as a market having few firms.** A one-incumbent market need not be a durable monopoly, and a many-firm market need not be competitive. Market power is read off the *height of the barriers*, not the current count of competitors — because the mere credible *threat* of entry disciplines incumbent pricing wherever barriers are low (contestability).
- **Not a temporary advantage from innovation or short-run friction.** Rents thrown off by a fresh innovation or transient frictions are *erodible* — entry will compete them away. A barrier protects *sustained* rents. Reading a short-lived lead as a durable barrier (or vice versa) confuses the two rent regimes the concept is built to separate.
- **Not a single undifferentiated obstacle.** The three types — *structural* (scale, network effects, capital intensity), *strategic* (predatory pricing, capacity preemption, exclusive dealing), and *legal* (patents, licensing, certification) — are not interchangeable. Each implies a different remedy (open-access/divestiture, conduct prohibition, deregulation), so misclassifying the barrier mislabels the cure — prosecuting conduct that was never the source, or deregulating where the advantage was structural.
- **Not a substrate-portable mechanism under its own name.** Used outside markets — a profession, a political contest, a social institution — "barrier to entry" is the *economic concept borrowed by analogy*, carrying its market interpretation onto a new setting; the antitrust apparatus does not deploy there. What genuinely generalises is the thinner core — `asymmetry` plus durable access friction — not the firms-in-markets machinery.

clarity:
The barrier-to-entry concept makes legible *why* a price above long-run average cost can persist instead of being competed away, and it does so by relocating the question from the number of firms currently in a market to the cost of joining it. A market with one incumbent need not be a durable monopoly, and a market with many incumbents need not be competitive; what decides the matter is whether an entrant attracted by above-cost prices can actually come in. By naming the cost asymmetry between insiders and outsiders as the load-bearing variable, the concept separates two things a snapshot of market shares conflates: *temporary* rents, which short-run frictions or a fresh innovation throw off and which entry will erode, from *sustained* rents, which a durable barrier protects. This is the distinction on which contestability theory turns — the recognition that the mere threat of entry disciplines incumbent pricing, so that market power must be read off the height of the barriers, not off the current count of competitors.

The concept also sharpens the practitioner's question from the diffuse "does this firm have market power?" to the structural "is this advantage one an entrant must pay for again, and will the asymmetry survive into long-run equilibrium?" — and, because the standard typology sorts barriers into structural, strategic, and legal, it converts that diagnosis directly into a remedy choice. A finding that the barrier is *structural* (scale, network effects, capital intensity) points toward open-access or divestiture; a *strategic* barrier (predatory pricing, capacity preemption, exclusive dealing) points toward conduct prohibition; a *legal* one (patent, licensing, certification) points toward deregulation. Misclassifying the barrier mislabels the cure — prosecuting conduct that was never the source of the protection, or deregulating where the advantage was structural all along. Naming the barrier type is therefore not bookkeeping but the step that tells merger review, predation analysis, and licensing reform which lever can actually move the market.

manages_complexity:
The complexity the barrier concept compresses is the unruly list of particular reasons a market resists competition — sunk capital requirements, economies of scale, brand recognition, learning curves, network effects, customer switching costs, control of essential inputs, predatory pricing, capacity preemption, exclusive dealing, patents, occupational licensing, certification regimes — a catalogue that grows with every industry and that, taken item by item, would force the analyst to reason from first principles about each market's idiosyncratic facts. The concept collapses all of it onto a single load-bearing variable: the *asymmetric cost of access* between insider and outsider, qualified by one durability test — does the asymmetry survive into long-run equilibrium, or dissipate as entry erodes it? Every item on the list is reduced to a contribution to that one quantity. The analyst no longer tracks the dozen mechanisms separately; he tracks the height of the barrier and whether it persists, and reads the central outcome — durable rents versus self-correcting market power — directly off them. This is precisely why a snapshot of market shares can be set aside: the count of current firms drops out of the calculation, replaced by the cost of joining.

The compression has a second axis that converts the same diagnosis into action. The standard typology bins every barrier into one of three kinds — structural (cost or demand conditions requiring no incumbent action), strategic (deliberately erected by the incumbent), legal (installed by government) — and this binning is not descriptive bookkeeping but a branch that selects the remedy. Read off the branch and the lever follows: a structural barrier points to open-access or divestiture, a strategic one to conduct prohibition, a legal one to deregulation or licensing reform. So the practitioner facing any market-power question — a merger, a predation complaint, a licensing challenge — does not re-derive the competitive analysis from the industry's particulars. He measures one scalar (the durable access asymmetry), assigns one of three types, and arrives at both the market-power finding and the appropriate intervention. A sprawling, case-by-case judgment about why firms keep their rents reduces to a height-plus-durability reading on a three-way branch, which is the whole working apparatus of competition-policy diagnosis.

abstract_reasoning:
The barrier-to-entry concept licenses inferences that relocate the analysis of market power from the *current count of firms* to the *cost of joining*, and then convert that diagnosis into a remedy.

*Diagnostic — infer durable rents from access asymmetry, not from market share.* The signature move is to read above-long-run-average-cost pricing as either temporary or sustained according to the height and durability of the barrier, deliberately setting aside the snapshot of market shares. A one-incumbent market is *not* inferred to be a durable monopoly, and a many-firm market is *not* inferred to be competitive; instead the analyst asks whether an entrant attracted by above-cost prices can actually come in, and infers durable rents only where the access asymmetry is large and survives into long-run equilibrium. The reasoning runs from the structural fact (an advantage the entrant must pay for again, or cannot pay at all) to the prediction (incumbents can hold prices above cost without triggering corrective entry) — and conversely, where the asymmetry is absent, the analyst predicts that any above-cost pricing is self-correcting and the market is contestable regardless of how few firms it currently contains.

*Boundary-drawing — the durability test separates the two rent regimes.* A central move is to apply the persistence condition as the line between regimes: does the cost asymmetry dissipate as entry erodes it, or survive into long-run equilibrium? Temporary rents thrown off by short-run frictions or a fresh innovation are bracketed as erodible and outside the market-power finding; sustained rents protected by a durable barrier are inside it. This is also the inference that grounds contestability reasoning — the analyst predicts that the mere *threat* of entry disciplines incumbent pricing, so market power must be read off the barrier height rather than off realized entry, and a credible entry threat is treated as competitively equivalent to actual competition.

*Classification-driven intervention — barrier type selects the remedy.* The most actionable move is to sort the barrier into one of three kinds and read the lever off the type. A *structural* barrier (economies of scale, network effects, capital intensity, product differentiation, arising from industry conditions and requiring no incumbent action) points the analyst toward open-access or divestiture remedies. A *strategic* barrier (predatory pricing, excess-capacity preemption, exclusive contracts, deliberately erected by the incumbent) points toward conduct prohibition. A *legal* barrier (patents, licensing, certification, installed by government) points toward deregulation or licensing reform. The reasoning is that the source of the protection determines which intervention can actually remove it — so the analyst predicts that misclassifying the barrier mislabels the cure, prosecuting conduct that was never the source of the rent or deregulating where the advantage was structural all along.

*Counterfactual / interventionist prediction.* Because the barrier is the load-bearing variable, the analyst reasons counterfactually about market structure under changes to it: lowering a specific barrier (mandating interoperability to neutralize a network effect, compelling input access, shortening a patent, relaxing a license) is predicted to invite the entry that competes the rents away, while raising one (a merger that consolidates control of an essential input, an exclusive-dealing arrangement) is predicted to convert a contestable market into a protected one. Merger review, predation analysis, and licensing challenges are all run as predictions about how a proposed change moves the durable access asymmetry, and therefore the durability of incumbent market power.

knowledge_transfer:
Within economics the barrier-to-entry concept transfers *as mechanism*: the diagnostic (relocate market-power analysis from the current count of firms to the durable cost of joining, and infer sustained rents only where the access asymmetry survives into long-run equilibrium), the structural-strategic-legal typology, and the type-selects-remedy mapping (open-access or divestiture for structural, conduct prohibition for strategic, deregulation for legal) all carry intact across the home domain's subfields. So the same apparatus, and the same contestability reasoning (the threat of entry disciplines pricing, so market power is read off barrier height rather than firm count), applies across **industrial organization** (the central determinant of market structure), **antitrust and competition policy** (the standard test for contestability and the load-bearing input to merger and predation review), **strategy** (the threat-of-new-entrants force in Porter's Five Forces, with barriers as the load-bearing variable), **platform economics** (network effects and switching costs analysed as endogenous barriers), and **regulatory economics** (occupational licensing, credentialing, and zoning studied as legally-erected barriers). These are all markets or quasi-market settings — the firm, the rent, the incumbent, and the entrant are load-bearing throughout — so this is depth within one domain, not transfer across substrates.

Beyond markets the honest characterisation has two parts. First, the most visible "cross-domain uses" of the term — barriers to entry in a profession, a political contest, a social institution — are explicitly **analogy**: they import the economic phrase *along with its economic interpretation* onto a new setting, rather than the economic concept being recognised as an instance of something more general. The structural content rides along with the metaphor but rarely earns independent analytic treatment in those domains; the antitrust machinery (contestability, the three-way typology, the remedy mapping) does not deploy in a political campaign, even when the words "barrier to entry" are used there. Second, what *does* genuinely generalise is not the barrier-to-entry concept but the thinner structural pattern it sits on, which should be carried by its **parents**: `asymmetry` (an outsider faces a cost an insider does not) combined with a sunk-cost-of-access and `transaction_costs` / `friction` (the cost is real and persists). Strip the jargon — firm, market, incumbent, rent — and what remains is "an outsider faces a cost an insider does not, sustained over time," which is a substrate-portable shape but one already housed under `asymmetry`, and which would merit its own entry only as a distinct general prime along the lines of `access_friction` or `entry_cost_asymmetry` (surfaced separately as an emergent candidate). The barrier-to-entry concept adds, on top of that portable core, the institutional vocabulary of firms-in-markets and the antitrust analytic apparatus — and it is exactly that added apparatus, which makes the concept productive at home, that makes it ill-fitting elsewhere. So when the cross-domain lesson is needed, the honest move is to carry the parent pattern (asymmetric, durable access friction), and to recognise that an invocation of "barrier to entry" outside markets is the economic concept being *borrowed by analogy*, not the economic mechanism travelling under its own power (see *Structural Core vs. Domain Accent*).


Base Rate Fallacy

core_idea:
The base rate fallacy is the systematic tendency for human reasoners to underweight or ignore prior probability — the base rate — when a vivid, specific piece of evidence is available, producing posterior probability estimates that track the specific evidence far more than Bayes' rule warrants. The canonical form: a test for a disease that affects 1 in 1000 people has a 99% sensitivity and a 5% false-positive rate; a positive result still yields a true positive probability below 2%, but most people, including trained clinicians, report intuitions near 95%. The specific evidence (this test is highly accurate; the result was positive) crowds out the prior (the disease is rare) almost entirely.

The proximate mechanism is representativeness (Kahneman and Tversky): reasoners judge the probability of a hypothesis by how well the specific evidence resembles or fits the hypothesis category, treating match as a proxy for probability. Because similarity to the hypothesis does not encode the hypothesis's prior probability, the base rate drops out. This is compounded by the relative vividness of the two information types: a positive test result or a detailed case description is concrete and imageable; a population prevalence statistic is abstract and easily set aside. Gigerenzer demonstrated that presenting the same information as natural frequencies — "10 out of 1000 people have the disease; of those 10, 9 test positive; of the 990 without it, 49 test positive" — dramatically reduces the fallacy, because the frequency format makes the prior population structure explicit and visible in a way that abstract percentages do not.

scope_of_application:
The base rate fallacy lives in one domain — human judgment and decision-making — and the contexts below are application settings of that single substrate (a human reasoner forming a posterior), not structurally distinct systems. The Bayesian rule it violates is fully substrate-general and travels everywhere as the *parent*; but where the prior is integrated by construction there is no fallacy, so non-agent uses are analogy and stay out of this map.

- **Medical screening** — the canonical mammography / HIV-test problem: a positive result on a high-sensitivity test for a rare condition is over-read by patients and clinicians alike.
- **Criminal forensics** — DNA and other random-match probabilities presented without population context produce wrongful confidence in a courtroom.
- **Hiring and admissions** — a vivid interview signal is weighted without anchoring on the base rate of comparable candidates, driving over-confident predictions.
- **Security and counter-terrorism screening** — profiling and anti-terror systems where rare-event base rates dominate the math and false positives swamp true ones.
- **Intelligence analysis** — vivid specific intelligence overrides cool prior probabilities, a recurring structural pattern behind analytic failures.
- **Personal risk self-assessment** — "I know someone who had X happen" overrides population statistics in everyday probability judgments.

structural_signature:
**Sig role-phrases:**

- **the reasoner forming a posterior** — a person estimating how probable a hypothesis is given evidence, the substrate of the fallacy
- **the vivid specific evidence** — a concrete, imageable signal (a positive test result, a detailed case) with a high likelihood under the hypothesis
- **the dull rare prior** — the base rate, an abstract population-prevalence statistic easily set aside, often low
- **the representativeness substitution** — judging probability by how well the evidence resembles the hypothesis category, using match as a proxy for probability
- **the dropped base rate** — the prior silently omitted, because resemblance does not encode prior probability
- **the likelihood-tracking posterior** — the resulting estimate collapsing toward the likelihood (e.g. ~95% from a 99%-accurate test) rather than the true Bayesian value (<2%)
- **the danger-zone trigger** — the failure acute precisely when a rare base rate meets a vivid high-likelihood signal; common priors or dull evidence recede the danger
- **the natural-frequency repair** — recasting the figures as frequencies that expose the prior population structure, the representational (not exhortatory) fix that the fallacy resists and that exhortation alone cannot achieve

what_it_is_not:
- **Not the claim that a high-accuracy test warrants a high posterior.** A 99%-accurate positive test for a 1-in-1000 disease yields a true-positive probability below 2%, because the rare prior dominates. Confusing the *likelihood* (how characteristic the evidence is if the hypothesis holds) with the *posterior* (how probable the hypothesis is given the evidence and its prior) is the core move the fallacy names.
- **Not evidence that the test is inaccurate.** The low posterior is not a flaw in the instrument; a perfectly good 99%-sensitivity test still produces mostly false positives when the condition is rare, because the prior is what pins the answer low. Blaming the test misreads a correct Bayesian consequence as a measurement failure.
- **Not an error in Bayes' rule.** The mathematics was always right and fully general; the fallacy is the *human* failure to apply it — representativeness substituting resemblance-to-the-hypothesis for probability, so the prior silently drops out. In non-human systems that integrate the prior by construction there is no fallacy at all, only the rule correctly used.
- **Not mere innumeracy or "ignoring statistics."** The failure is specific: underweighting the *prior* while integrating it with a vivid likelihood, not a general inability with numbers. People who can compute fluently still commit it when the evidence is concrete and the base rate abstract, because the mechanism is representativeness, not arithmetic incompetence.
- **Not curable by warning people to be careful.** Because the error is structural rather than a lapse of effort, telling reasoners the answer is counterintuitive leaves their estimate near the likelihood; only a representational fix — recasting the figures as natural frequencies that expose the prior population structure — moves it toward the true posterior. Exhortation does not restore the dropped prior.
- **Not the same as its sibling representativeness errors.** Underweighting the prior when integrating it with a likelihood is distinct from confusing the likelihood with the posterior outright (the inverse-probability fallacy) and from judging a conjunction more probable than its conjunct (the conjunction fallacy). Same heuristic family, different specific errors — a repair tuned to one is not automatically licensed for the others.

clarity:
Naming the fallacy separates two quantities that intuition routinely collapses into one: the *likelihood* — how characteristic the evidence would be if the hypothesis were true (a positive result from a 99%-accurate test) — and the *posterior* — how probable the hypothesis is given that evidence together with its prior. The felt answer to "the test is positive, how worried should I be?" reports the likelihood while seeming to report the posterior, and without the label the gap between them is invisible; the rare-disease case simply feels like a near-certainty. With it, a reasoner can recognize that a high likelihood says nothing about how common the hypothesis was to begin with, and that omitting the prior is precisely the move the fallacy names.

That distinction converts a fuzzy worry into a structured triple — prior probability, likelihood ratio, posterior — whose arithmetic forces the usually-missing term into view. The sharp question it licenses is diagnostic: am I integrating the base rate, or is the vividness of this specific result overriding it; what would my estimate be if I were made to write the prior down first? And it points at the corresponding repair, which is representational rather than exhortatory: recasting the same numbers as natural frequencies makes the prior population structure explicit and visible, so the base rate can no longer quietly drop out. The contribution is not new mathematics — Bayes' rule was always there — but a name for the specific place human inference fails to apply it, and a check that localizes the failure to the neglected prior.

manages_complexity: Over-confident inferences recur across medical screening, DNA forensics, hiring, security profiling, and intelligence analysis, and each arena seems to need its own account of why the experts there get the numbers wrong. The fallacy compresses all of them to one diagnosis with a fixed shape: whenever a posterior is being formed, vivid specific evidence crowds out the prior because representativeness substitutes resemblance for probability, and the base rate silently drops out. An analyst then needs to track only the same three quantities everywhere — prior probability, likelihood, posterior — to predict in advance where intuition will fail (rare base rate plus a vivid, high-likelihood signal is the danger zone) and by roughly how much, rather than re-litigating each domain's confidence error from scratch. The compression extends to the repair: the failure localizes to a single neglected term, and one representational move — recasting the figures as natural frequencies that expose the prior population structure — addresses it identically across screening, courtroom, and forensic settings. A wide catalogue of "everyone here is over-confident" puzzles thus reduces to one missing-prior structure with a known trigger condition and a uniform fix.

abstract_reasoning:
The fallacy licenses a tight set of inferences about when a confident posterior should be distrusted, what to change to repair it, and which problems are dangerous in advance.

*Diagnostic — from an over-confident estimate back to the dropped prior.* The governing move runs from a surface signature (a reasoner reports a posterior near the likelihood — "the test is 99% accurate and positive, so I'm about 95% likely to have it") to a hidden error (the prior was never integrated; representativeness substituted resemblance-to-the-hypothesis for probability, so the base rate silently dropped out), against the naive reading that a high-accuracy test warrants a high posterior. The tell is *a posterior that tracks the specific evidence while the prior is rare and abstract*: when the felt answer to "how worried should I be?" equals the test's accuracy, the prior has been omitted by construction. The discriminating self-probe is built in — would my estimate change if I were made to write the base rate down first? — and a posterior that does not move when the prior is forced into view is the fingerprint of the fallacy rather than of a correctly computed near-certainty.

*Interventionist — what to change, and the predicted effect on the estimate.* Because the failure localizes to a single neglected term, the corrective lever is representational, not exhortatory: recast the same figures as natural frequencies that expose the prior population structure ("10 of 1000 have it; of those 10, 9 test positive; of the 990 without it, 49 test positive"), and predict that the estimate drops sharply toward the true posterior because the prior can no longer quietly disappear. The model is specific about direction and about what will *not* work — telling reasoners the answer is counterintuitive, or urging them to be careful, leaves the estimate near the likelihood, because the error is structural rather than a lapse of effort; only making the prior visible moves it. It also predicts the magnitude of the correction scales with how rare the base rate is: the rarer the prior, the larger the gap the reframing closes.

*Boundary-drawing — the danger zone, and where the inference does not extend.* The fallacy marks its own trigger condition, turning "people get probabilities wrong" into a regime prediction: the failure is acute precisely when a *rare base rate* meets a *vivid, high-likelihood signal* (a positive test for a 1-in-1000 disease, a DNA match presented without population context, a detailed case that fits a category). Where the prior is common, or the evidence dull and abstract, intuition tracks Bayes more closely and the danger recedes. The effect also fixes an internal boundary against its neighbors that licenses or blocks inference: it names underweighting the prior when integrating it with a likelihood — distinct from confusing the likelihood with the posterior outright (the inverse-probability fallacy) and from judging a conjunction more probable than its conjunct (the conjunction fallacy) — same representativeness family, different specific errors, so a repair aimed at one is not automatically licensed for the others.

*Predictive — anticipating the error before it is made.* Holding the three quantities — prior, likelihood, posterior — the model predicts *in advance* who will be over-confident and roughly by how much, without studying each arena separately: clinicians reading rare-disease screens, jurors hearing random-match statistics, analysts weighing vivid intelligence against cool priors all land in the same danger zone, and all over-state the posterior by approximately the amount the omitted prior would have discounted it. The same structure predicts the form of the residual error after a partial fix — an estimate still biased toward the evidence-only value whenever the prior is present but underweighted rather than fully restored — and so tells a designer to build the prior into the representation at the point of judgment rather than trusting it to be recruited on demand.

knowledge_transfer:
Within judgment and decision-making the diagnosis transfers as mechanism, intact, with the standing caveat that its "domains" are application contexts of one substrate — a human reasoner forming a posterior — not structurally distinct systems. With that understood, the prior-likelihood-posterior triple, the danger-zone trigger (rare base rate plus vivid high-likelihood signal), and the natural-frequency repair carry without translation across **medical screening** (the mammography/HIV-test problem), **criminal forensics** (DNA random-match probabilities read without population context), **hiring and admissions** (interview signal weighted without candidate base rates), **security and counter-terror screening** (rare-event base rates dominating the math), and **intelligence analysis** (vivid specific intelligence overriding cool priors). The vocabulary (base rate, likelihood, posterior, representativeness), the diagnostic (a posterior that tracks the evidence while the prior is rare and abstract; the self-probe "would my estimate change if I wrote the prior down first?"), and the intervention (recast as natural frequencies that expose the prior population structure; predict the estimate drops sharply, and that exhortation alone will not move it) all move freely — because the same representativeness-substitutes-for-probability machinery is at work in every case.

Beyond the human reasoner the *fallacy* does **not** travel — and the reason is sharper here than for most cognitive entries. The underlying mathematics (Bayes' rule, the prior-likelihood-posterior integration) is fully substrate-general and genuinely recurs across biology, physics, statistics, and computation — but in those settings there is *no fallacy*, only the rule correctly applied; a Bayesian filter or a population-genetics calculation integrates the prior by construction. So what travels cross-domain is the **parent — *Bayesian updating*, the normative structure the fallacy violates** — not "the base rate fallacy," which names specifically the *human* deviation from that structure. Invoking "base rate neglect" for a system that fails to weight a prior is therefore an **analogy** unless that system is itself a reasoning agent prone to substituting resemblance for probability; absent a representativeness-style mechanism, the right description is just "it underweighted the prior," and the load-bearing content is Bayesian updating plus the general lesson *a posterior requires integrating a prior with a likelihood, and absent that integration the posterior collapses to the likelihood*. The fallacy's own cargo — the representativeness mechanism, the vividness asymmetry, the natural-frequency debiasing, the clinician-and-juror failure cases — stays bound to human cognition, where it sits as one named violation under *bayesian_updating*, *representativeness*, and the broader heuristics-and-biases family. The honest cross-domain account: as mechanism the fallacy stays inside human judgment; the Bayesian rule it violates travels everywhere as the parent; and any use of the *fallacy* outside a reasoning agent is borrowing the name. Carry Bayes, not the fallacy (see *Structural Core vs. Domain Accent*).


Basis-Risk Failure

one_liner: Diagnose why a hedge collapses at the worst moment: the proxy instrument, chosen for its calm-market correlation with the exposure, decouples under stress, so protection that passed every ex-ante metric evaporates exactly when it is needed.

core_idea:
A basis-risk failure is the realized breakdown of a hedge when the hedging instrument's price movement does not co-move closely enough with the underlying exposure to offset it, producing a net loss that the hedge was designed to prevent. The *basis* is the ongoing difference between the price of the hedging instrument and the price of the exposure; *basis risk* is the variance of that difference; a *basis-risk failure* is the event in which the basis widens materially at exactly the moment the hedge is being depended on.

The structural mechanism runs as follows. A hedger holds an exposure — a commodity position, a currency obligation, a fixed-income portfolio — whose exact instrument is unavailable, illiquid, or prohibitively expensive to hedge directly. The hedger instead takes an offsetting position in a *proxy* instrument chosen because it has historically moved closely with the exposure: WTI crude futures as a proxy for jet-fuel prices, a major-currency forward as a proxy for an illiquid emerging-market currency, an ETF as a proxy for an underlying corporate-bond portfolio. The justification is a historical correlation, typically estimated over normal market conditions. The failure occurs when the regime shifts — a supply shock widens the crude-to-jet-fuel crack, a credit stress event creates a flight-to-quality gap between the EM currency and its proxy, a liquidity dislocation causes the ETF to trade at a discount to its NAV — and the correlation that held in calm markets collapses in the stressed market. The hedge produces near-zero offset while the underlying exposure posts a large loss. What makes basis-risk failure structurally distinctive, rather than merely bad luck, is the *false-sense-of-security* component: the books looked hedged by every ex-ante metric — daily VaR, stress-test output, coverage ratio — because those metrics relied on the pre-stress correlation. The protection was not absent; it was present in name and past-data, and it evaporated precisely when it was needed. Historical episodes include the WTI-Midland location-basis blowout during Permian Basin pipeline bottlenecks, Libor-OIS basis widening in 2008 that caught interest-rate hedgers, and the March 2020 corporate-bond ETF discount that inverted hedgers' positions.

scope_of_application:
Basis-risk failure lives across the hedging contexts of finance and risk management; its reach is bounded to settings with a priced basis, a derivatives hedge, and the lulling ex-ante metrics (VaR, coverage ratios, stress tests) computed off a calm-market correlation, while the genuinely travelling mechanism — a proxy used to act on a hard-to-reach target stops tracking it under stress — is carried across substrates by the parent candidate `proxy_target_divergence`, not by this finance idiom.

- **Commodity-producer hedging** — location, quality, and time basis: an oil producer hedging with WTI futures caught when a Permian pipeline bottleneck blows out the Cushing-to-Midland location spread.
- **Cross-currency proxy hedging** — an emerging-market exposure hedged with a major-currency proxy that decouples under a credit-stress flight-to-quality.
- **ETF hedging of illiquid bonds** — the March 2020 corporate-bond ETF discount-to-NAV that inverted hedgers' positions while underlying credit risk rose.
- **Cross-asset insurance** — an airline's jet-fuel exposure hedged on crude, the offset vanishing when a refining shock widens the crack.
- **Interest-rate basis** — Libor-OIS basis widening in 2008 and the cross-tenor dislocations of the Libor transition catching rate hedgers.
- **Parametric catastrophe reinsurance** — a wind-speed-trigger cat bond that fails to pay though indemnity losses are large, a basis mismatch between parametric trigger and realised loss.

structural_signature:
**Sig role-phrases:**

- **the exposure** — the position whose loss the hedger wishes to neutralise
- **the unavailable match-hedge** — the perfectly-matched instrument, which is illiquid, expensive, or unavailable for direct hedging
- **the proxy instrument** — an offsetting position chosen because it has historically co-moved with the exposure
- **the basis** — the ongoing residual difference between proxy price and exposure price, quietly assumed to be zero on the books
- **the historical-correlation justification** — a co-movement estimated over calm-market conditions that under-states stress-period decoupling
- **the lulling ex-ante metrics** — VaR, coverage ratios, and stress tests, all computed off that same calm-market correlation, so the books look safe
- **the regime-shift decoupling event** — a supply shock, credit stress, or liquidity dislocation that widens the basis exactly when the hedge is depended on
- **the false-security failure** — the protection present in name and past-data evaporates, producing the loss the hedge was meant to prevent
- **the direction-of-decoupling hazard** — when the proxy moves the same way as the exposure loss, the hedge inverts into a second source of loss
- **the three-way remedy branch** — tighten the basis (direct/custom hedging), reserve against it (basis-risk capital), or diversify it across proxies

what_it_is_not:
- **Not a missing or forgotten hedge.** The hedger *did* hedge; the protection was present in name and in past data. The failure is hedging with the *wrong instrument* — a proxy whose co-movement with the exposure collapses under stress. The correct diagnosis is "hedged with the wrong instrument," not "failed to hedge."
- **Not generic hedge underperformance or bad luck.** What makes it structurally distinctive is the *false-sense-of-security* component: the books passed every ex-ante test — VaR, coverage ratio, stress output — because all of those were computed off the same calm-market correlation that justified the proxy. The loss is a structural property of a proxy-hedge that was never going to hold through a correlation breakdown, not a one-off dislocation.
- **Not merely a hedge that produces no offset.** The sharpest hazard is *inversion*: when the proxy decouples in the *same direction* as the exposure loss, the hedge becomes a *second source of loss* rather than simply failing to protect. That is worse than zero offset, and the average-correlation view cannot see the difference.
- **Not adequately captured by average correlation.** The load-bearing quantity is the variance of the basis *under stress* — tail correlation — not mean co-movement over calm conditions. A 0.95 historical correlation says nothing about how far the proxy and exposure decouple in the regime where the offset is actually needed.
- **Not a substrate-portable mechanism under its own name.** "Basis-risk failure" is finance idiom. The genuinely travelling pattern is one level up — `proxy_target_divergence`: a proxy used to act on a hard-to-reach target stops tracking it at the moment of need. A manager's KPI decoupling from the goal or a forecaster's leading indicator ceasing to lead are co-instances of that parent, not metaphorical "basis-risk failures."

clarity:
Naming basis-risk failure separates two protections that look identical on the books but behave oppositely under stress: a *match-hedge*, whose instrument is the exposure itself or a cash-settlement of it, and a *proxy-hedge*, whose instrument is merely something expected to move with the exposure. Both reduce daily VaR, both report a comfortable coverage ratio, both pass the stress test — because every one of those metrics is computed off the historical, calm-market correlation that justified the proxy in the first place. The basis-risk vocabulary makes the hidden difference legible: the residual between proxy and exposure is not zero but a variance the books have quietly assumed away, and it is precisely this assumed-zero that blows out when the regime shifts. Without the concept, the resulting loss reads as bad luck or a one-off dislocation; with it, the loss is correctly diagnosed as a structural property of a proxy-hedge that was never going to hold through a correlation breakdown.

The concept's sharper contribution is reframing the failure from "the hedger forgot to hedge" to "the hedger hedged with the wrong instrument," which relocates the entire intervention space. Once the basis is named as a live quantity rather than an assumed identity, the practitioner can ask the operative questions: how wide is the basis under stress, not just on average; does the proxy decouple in the *same direction* as the exposure loss, turning a hedge into a second source of loss; and is the historical correlation load-bearing or merely incidental? This forces the residual to be managed explicitly — tightened by direct or custom hedging, reserved against with basis-risk capital, or diversified across proxies — instead of being read as protection that is simply present. The crucial legibility is that basis-risk failure is most dangerous exactly where the books look safest, so the concept directs scrutiny toward the hedges that pass every ex-ante test rather than away from them.

manages_complexity:
Risk management accumulates a long bestiary of hedge failures that, told individually, share nothing on the surface: an oil producer's WTI hedge fails when a pipeline bottleneck blows out the Cushing-to-Midland location spread; an interest-rate hedger is caught when the Libor-OIS cross-tenor basis widens in 2008; an airline's crude hedge produces no offset when a refining shock widens the jet-fuel crack; a bond desk's ETF hedge inverts when the fund trades to a deep discount to NAV in March 2020; a parametric cat-bond fails to pay though losses are large because the wind-speed trigger was not breached. Different instruments, different markets, different stress events. The basis-risk concept collapses every one of them onto a single quantity — the *basis*, the residual difference between proxy and exposure — and a single statistic about it, the variance of that residual under stress. The analyst stops cataloguing failure stories and instead tracks one number that the books had quietly set to zero, reading off it the thing that actually matters: whether the protection survives a regime shift or evaporates inside it.

That collapse is what makes the problem low-dimensional and the intervention space finite. Because every location, quality, time, cross-asset, and cross-tenor failure is the same residual blowing out, the remedies do not multiply with the cases either — they reduce to one short branch on the basis itself: tighten it (direct or custom-OTC hedging), reserve against it (basis-risk capital), or diversify it across proxies. To choose the branch the analyst reads three diagnostics off the basis-as-live-variable: how wide it runs under stress rather than on average, whether it decouples in the *same* direction as the exposure loss (which converts the hedge into a second loss), and whether the historical correlation justifying the proxy is load-bearing or incidental. The general regularity — protection is most fragile exactly where the ex-ante metrics look safest, because VaR, coverage ratios, and stress tests are all computed off the same calm-market correlation — then tells the analyst where to point that scrutiny: at the hedges that pass every test, not the ones that fail visibly. A sprawling, instrument-by-instrument failure literature thereby compresses to one residual, one stress-variance, three diagnostics, and a three-way remedy branch.

abstract_reasoning:
The basis-risk-failure concept licenses inferences that treat the hedge not as protection-present-or-absent but as a *joint position* whose total payoff depends on the co-movement of two instruments under stress.

*Reframing — reason about the pair, not either leg.* The foundational move is to model a hedge as a single joint position — long the exposure, short the proxy — whose payoff distribution depends on the *joint* distribution of exposure and proxy, not on either alone. The analyst therefore infers that the relevant risk is the variance of the residual (the basis), a quantity the books have quietly set to zero, and reasons about the hedge's reliability by asking how that residual behaves rather than by confirming that a hedge exists. This converts "are we hedged?" into "what is the basis, and what is its variance when we will need the offset?"

*Diagnostic — scrutinize where the metrics look safest.* The signature, counterintuitive inference is that basis-risk failure is *most dangerous exactly where the books look safest*. Because daily VaR, coverage ratios, and stress-test output are all computed off the same historical calm-market correlation that justified the proxy, the analyst infers that a hedge passing every ex-ante metric carries an unmeasured residual, and directs scrutiny *toward* the hedges that pass all tests rather than away from them. A realized loss on such a hedge is then diagnosed not as bad luck or a one-off dislocation but as a structural property of a proxy-hedge that was never going to hold through a correlation breakdown — relocating the failure from "the hedger forgot to hedge" to "the hedger hedged with the wrong instrument."

*Boundary-drawing — match-hedge versus proxy-hedge, and stress versus average.* A central classification move is to separate a *match-hedge* (instrument is the exposure itself or its cash-settlement) from a *proxy-hedge* (instrument merely expected to co-move), because the two behave oppositely under stress despite looking identical on the books. For any proxy-hedge the analyst then refuses the average correlation as the relevant statistic and asks instead how wide the basis runs *under stress* — inferring that a correlation estimated over normal conditions understates stress-period decoupling, and that the load-bearing question is tail-correlation, not mean co-movement.

*Direction-of-decoupling reasoning — detect the hedge that becomes a second loss.* A sharp predictive move is to ask whether the proxy decouples in the *same direction* as the exposure loss. If it does, the analyst infers that the hedge has inverted into a second source of loss rather than merely failing to offset — the worst case — and flags such proxies as actively dangerous, not merely ineffective. This distinguishes a hedge that produces near-zero offset from one that compounds the loss, which is a difference the average-correlation view cannot see.

*Interventionist — a three-way remedy branch keyed to the basis.* Having made the basis a live, separately-managed variable, the analyst selects among a finite remedy set by reading three diagnostics (stress width, direction of decoupling, whether the historical correlation is load-bearing or incidental): *tighten* the basis (direct or custom-OTC hedging that brings the instrument closer to the exposure), *reserve* against it (hold basis-risk capital sized to the stress residual), or *diversify* it (spread across multiple proxies whose joint behavior better approximates the exposure). The reasoning is that because every location, quality, time, cross-asset, and cross-tenor failure is the same residual blowing out, the remedies do not multiply with the cases — the analyst predicts the right lever from the basis's stress behavior rather than re-deriving a fix per instrument.

knowledge_transfer:
Within finance and risk management basis-risk failure transfers *as mechanism*: the reframing (model the hedge as a joint long-exposure/short-proxy position whose payoff depends on the variance of the residual, not on either leg), the counterintuitive diagnostic (scrutinise the hedges that pass every ex-ante metric, because VaR, coverage ratios, and stress tests are all computed off the same calm-market correlation), the match-hedge-versus-proxy-hedge classification, the direction-of-decoupling test (does the proxy invert into a *second* loss?), and the three-way remedy branch (tighten, reserve, or diversify the basis) all carry intact across the home domain's hedging contexts. So the same apparatus applies to commodity-producer hedging (the WTI Cushing-to-Midland location-basis blowout), cross-currency proxy hedging (an EM exposure hedged with a major-currency proxy that decouples under stress), ETF hedging of illiquid bonds (the March 2020 discount-to-NAV inversion), cross-asset insurance (an airline's jet-fuel-on-crude hedge widening at the crack), interest-rate basis (Libor-OIS in 2008 and the Libor transition), and parametric catastrophe reinsurance (a wind-speed trigger that fails to pay though indemnity losses are large). Location, quality, time, cross-asset, and cross-tenor failures are all the same residual blowing out, drawing additionally on tail-correlation and copula analysis from statistical correlation theory — so the vocabulary and remedies transfer freely *between* finance sub-domains, which is reach within one domain.

Beyond finance the honest characterisation is **a shared abstract mechanism that genuinely travels — but as a more general parent pattern, not under the name "basis-risk failure," which is finance idiom**. The structural pattern one level up recurs across substrates as true co-instances, with the *mechanism* intact rather than merely the shape: *an actor uses a proxy of a hard-to-act-on target because the target itself is unavailable, illiquid, or unmeasurable; the action is calibrated to the historical proxy-target relationship; under stress or regime change the proxy and target decouple, so the instrument calibrated to their co-movement becomes the wrong instrument at the worst moment.* That pattern — call it proxy-target divergence, or proxy decoupling under stress — is the same causal structure whether it shows up as a basis-risk failure in derivatives, as Goodhart's-law breakdown when a measure becomes a target and stops tracking the goal, as leading-indicator failure in macro forecasting, as parametric-insurance trigger mismatch, as KPI-goal divergence in management, or as a validity threat in social-science measurement when a behavioural proxy stops tracking the construct. Because the mechanism (not just the silhouette) recurs, this is a case where the cross-domain lesson should carry the *parent* — and the project files exactly that broader pattern as a stronger prime candidate (`proxy_target_divergence`). What stays home-bound is the finance machinery: the basis as a priced quantity, the hedge as a derivatives position, VaR and coverage ratios as the lulling metrics, the OTC-and-capital remedy menu, and the case-bank of market dislocations. So the honest move is to recognise that when a manager's KPI decouples from the goal or a forecaster's leading indicator stops leading, they are not metaphorically experiencing a "basis-risk failure" — they are co-instances of the same proxy-target-divergence mechanism, which is what travels; "basis-risk failure" is its finance-specific name, and the strip-the-jargon residue ("the proxy used to act on a target stopped tracking it at the moment the action was needed") simply *is* that parent pattern (see *Structural Core vs. Domain Accent*).