Standardization

slug: standardization

one_liner: The act or process by which independent parties converge on a single shared specification, format, or interface — agreement on a common norm rather than the benefits that follow from it.

review_flags: Created 2026-06-13 post Phase-C emergent reconciliation (owner-approved); did NOT go through Phase-A triage. SI/SF/similarity grades are PROPOSER ESTIMATES pending triangulation.

review_flags: Phase-C action: re-similarity check vs the listed neighbors to confirm it does not collapse onto an existing prime or candidate; draw the proposed parent/child edges at incorporation.

review_flags: SCOPE NOTE: drafted as the agreement-on-a-norm *act/process* itself, deliberately distinct from its adjacent present primes `network_effect`, `interoperability`, and `lock_in` (which are, respectively, the value-with-adoption dynamic it enables, the resulting ability of standard-conformant parts to work together, and the switching-cost trap a entrenched standard can create). Points down to the candidates `convergent_independent_adoption` and `open_publication_for_interoperability` as mechanisms/sub-cases. Orchestrator to record the proposed sibling/parent edges.

core_idea:
Standardization is the act or process by which *independent parties converge on a single shared specification* — a common format, interface, protocol, unit, or norm — so that what they separately produce can interoperate, be mutually intelligible, or be substituted for one another. The governing fact is that independent production *without* a shared specification yields incompatibility: every producer's output works only with its own, parts do not fit, messages do not parse, measurements do not compare. Standardization is the convergence that ends this — the process, whether by deliberate agreement, market tipping, or imposed mandate, by which the many divergent ways of doing a thing collapse onto *one*. It is, at its core, an *agreement on a norm*: a commitment by separate parties to do a particular thing the *same* way.

The defining commitments are four. First, there are *multiple independent parties* — producers, speakers, manufacturers, nations, vendors — each of whom could do some thing in their own way. Second, there is a *space of possible specifications*: the many incompatible formats, interfaces, gauges, spellings, or units the thing *could* take. Third, there is a *convergence*: the parties come to do the thing the *same* way, settling on one specification out of the space — and crucially, *which* one is often less important than *that* they agree (a worse standard universally adopted beats a better one no one shares). Fourth, the convergence happens through a *coordination process* that can take several forms: deliberate agreement in a standards body (de jure), spontaneous market convergence as one option tips (de facto), or imposition by a regulator or dominant actor (mandated). The prime names this convergence-on-a-shared-norm *as the act itself* — distinct from the incompatibility it cures and from the benefits (interoperability, network effects) that follow once it is achieved.

The structural signature distinguishes standardization from its causes and its consequences. It is not the *incompatibility problem* that motivates it (that is the prior condition); it is not *interoperability* (which is the resulting *property* of standard-conformant parts working together); it is not the *network effect* (which is the *value dynamic* that makes a widely-adopted standard more valuable still); and it is not *lock-in* (which is the *trap* an entrenched standard can become). Standardization is the *act of agreeing on the norm* that produces interoperability, triggers the network effect, and can later harden into lock-in. The same act recurs across substrates under many names: standard-setting and protocol agreement in technology, the establishment of measurement units, spelling and grammar standardization in language, gauge standardization in railways, regulatory harmonization across jurisdictions. What standardization provides as a prime is the recognition that all of these are the same move — *independent parties converging on a single shared specification* — and that its central tensions are the coordination tensions of *getting* to a shared norm (which standard, by what process, at what cost in suppressed variety and entrenched incumbency) rather than the technical properties of any particular standard.

structural_signature:
*the multiple independent parties* — *the space of possible specifications* — *the convergence on a single one* — *the coordination process that achieves it (de jure, de facto, or mandated)* — *the primacy of agreement over optimality (that they agree often matters more than which they pick)* — *the suppression of variety the convergence entails*

Standardization is present when each of the following holds:

- **Multiple independent parties (the actors).** Two or more separate producers, speakers, vendors, nations, or makers, each capable of doing some thing in its own way, who would otherwise diverge.
- **A space of possible specifications (the options).** The many incompatible ways the thing could be done — formats, interfaces, protocols, gauges, spellings, units — among which the parties could each pick differently.
- **A convergence on one (the agreement).** The parties come to do the thing the *same* way, settling on a single specification out of the space — the defining event of standardization, by which divergence collapses to a shared norm.
- **A coordination process (the mechanism).** The convergence is achieved by some process: deliberate agreement in a standards body (de jure), spontaneous market tipping as adoption snowballs onto one option (de facto), or imposition by a regulator or dominant actor (mandated). The prime is agnostic about which; all three produce the shared norm.
- **The primacy of agreement (the coordination invariant).** Because the value is in the *sharing*, *which* specification is chosen is often less important than *that* the parties agree on the same one — a worse standard universally adopted is more useful than a better standard no one else uses. This is what makes standardization a coordination problem rather than an optimization one.
- **The suppression of variety (the cost invariant).** Convergence on one specification *eliminates* the others; the diversity of approaches is the price of the shared norm, and the foreclosed alternatives — possibly better ones — are the standing cost of having standardized.

The components compose into a single act — *independent parties converging, through some coordination process, on a single shared specification, where agreeing matters more than what is agreed and the suppressed alternatives are the price* — and it is the pairing of *independent parties* with *the need to do a thing the same way* that generates everything downstream: the interoperability that conformance enables, the network effects that reward adoption, the lock-in that entrenchment can become, and the coordination tensions of choosing and switching standards.

what_it_is_not:
- **Not interoperability (its consequence — see `interoperability`).** `interoperability` is the *resulting property* that independently-produced parts can work together — a state that *follows from* conformance to a shared standard. Standardization is the *act of agreeing on the standard* that makes interoperability possible. The two are cause and effect: parties standardize (converge on a norm), and interoperability is what they get. Conflating them mistakes the achievement (parts work together) for the act that produced it (parties agreed on how).
- **Not the network effect (the dynamic it triggers — see `network_effect`).** `network_effect` is the *value dynamic* in which a thing becomes more valuable as more parties adopt it — which is why a standard, once adopted, tends to attract still more adoption. Standardization is the *convergence on the shared norm* that the network effect then rewards and reinforces. The network effect explains *why* standardization tends to tip toward a single winner and entrench it; it is not the act of converging itself.
- **Not lock-in (the trap it can become — see `lock_in`).** `lock_in` is the *switching-cost trap* in which parties cannot easily abandon an entrenched standard even when a better one exists, because the cost of coordinated switching is prohibitive. Standardization is the *convergence* that can, once deeply entrenched by network effects and sunk investment, harden into lock-in. Lock-in is a *downstream pathology* of a successful standard, not the act of standardizing — a fresh standard is not yet a lock-in, and standardization can occur without ever becoming a trap.
- **Not convergent independent adoption alone (see `convergent_independent_adoption`).** `convergent_independent_adoption` names one *mechanism* — parties independently arriving at the same choice without coordinating (de facto standardization through market tipping). Standardization is the broader act, which also includes *deliberate* agreement (de jure, in a standards body) and *imposed* convergence (mandated by a regulator), so convergent independent adoption is a sub-case (the spontaneous-market path) of the genus.
- **Not the publication of a specification for interoperability (see `open_publication_for_interoperability`).** `open_publication_for_interoperability` names the *enabling move* of openly publishing a specification so others can conform to it — a mechanism that *promotes* standardization. Standardization is the convergence itself, of which open publication is a facilitating tactic, not the agreement.
- **Not uniformity or homogenization as such.** Standardization is convergence on a *specification for a purpose* (so parts interoperate or messages parse), not the elimination of all difference. Two products can conform to the same interface standard while differing in everything else; standardization targets the shared specification, leaving the rest free, whereas blanket uniformity flattens variety with no interoperability purpose. The prime is about agreeing on a *norm at an interface*, not making everything the same.
- **Common misclassification.** Reading the choice of standard as a *technical optimization* (pick the best specification) when it is fundamentally a *coordination problem* (get everyone on the same one). Catch it by asking whether the value comes from the specification's intrinsic merit or from the fact that others share it; if a worse standard everyone uses would beat a better one no one else does — the QWERTY situation — then the problem is coordination, not optimization, and arguing about which standard is "best" misses that *agreeing at all* is the harder and more valuable thing.

broad_use: Standardization, read as "independent parties converging on a single shared specification," recurs wherever separately-produced things must interoperate or be mutually intelligible. In **technology** it is the engine of the digital and physical infrastructure: TCP/IP standardized how independently-built machines exchange packets, USB standardized the connector so any device plugs into any port, HTTP and HTML standardized the web, and shipping containers standardized cargo dimensions so any container fits any ship, crane, and truck — in each, the value is overwhelmingly in the *sharing*, and the standards bodies (IETF, ISO, USB-IF) exist to manufacture the agreement.<!-- FACT-D72-326 -->[^david-greenstein-1990] In **measurement**, the establishment of the metric system and the SI units is standardization in its purest civic form: nations and trades converged on common units of length, mass, and time so that a measurement made by one party means the same to another — a convergence achieved partly de jure (treaty, the General Conference on Weights and Measures) and partly by the network effect of mutual intelligibility.<!-- FACT-D72-327 -->[^bipm-2019] In **language**, spelling and grammar standardization (dictionaries, academies, the spread of a prestige dialect) is the convergence of speakers and writers on shared orthographic and grammatical norms so that text written by one is legible to all — a centuries-long de facto and de jure process whose product is a standard language.<!-- FACT-D72-328 -->[^milroy-milroy-1999] In **infrastructure**, railway gauge standardization is the textbook case: railways built to incompatible gauges could not interchange rolling stock, and the convergence on a standard gauge (often by mandate or by the dominant network's de facto pull) was the act that let separate lines become one network — with the persistent minority of non-standard gauges a monument to the cost of failing to converge.<!-- FACT-D72-329 -->[^puffert-2009] In **governance and trade**, regulatory harmonization — common safety standards, accounting standards, trade specifications across jurisdictions — is standardization among nations and bodies, converging divergent rules onto shared ones so that goods and information cross borders. Across all of these, the recurring structure is identical: independent parties, a space of incompatible options, and a coordination process that collapses them onto one shared specification — with the recurring truth that *agreeing* is the hard part and *which* option wins is often secondary to the convergence itself.

clarity:
Naming standardization separates two questions that practitioners and policymakers chronically fuse: *which specification is best?* and *how do we get everyone onto the same one?* The first is an engineering or design question, answerable on the merits of the options; the second is a coordination question, answerable only by understanding the process — de jure, de facto, or mandated — that produces agreement among independent parties. The clarifying force of the prime is to convert "we should adopt the superior format" into "the value is in the *shared* format, so the real problem is convergence — and a good-enough standard that everyone adopts beats a superior one that fragments the field." This reframes standard wars, format battles, and adoption struggles as what they are: coordination contests where the prize is *being the shared norm*, not *being the best design* — which is why standards battles are won by tipping, bandwagons, and installed base as much as by technical merit, and why the loser of such a battle can have the better technology (Betamax, for the canonical example) and still lose.<!-- FACT-D72-330 -->[^katz-shapiro-1985]

The prime also clarifies a recurring confusion among standardization and the cluster of phenomena that surround it — interoperability, network effects, and lock-in — which are routinely run together as "the standards thing." Naming standardization as the *act of converging on a norm* sharply distinguishes it from the *property* it produces (interoperability: conformant parts work together), the *dynamic* that rewards it (network effect: value rises with adoption), and the *trap* it can become (lock-in: switching costs entrench it past its usefulness). This separation is clarifying because the four have different time-orders and different leverage points: one *achieves* a standard (the convergence), *gains* interoperability (the property), *rides* the network effect (the dynamic that tips and entrenches it), and *risks* lock-in (the later pathology). A policymaker or architect who conflates them cannot tell whether a problem is failure to converge (no standard yet), failure of conformance (a standard exists but parts still do not interoperate), excessive entrenchment (lock-in), or healthy network growth — each of which calls for a different response, and only the first is standardization proper.

manages_complexity:
Standardization is one of the most powerful complexity-management moves available, because convergence on a shared specification collapses a combinatorial interoperability problem into a constant one. Without a standard, $n$ independently-produced things that must interwork require, in the worst case, $O(n^2)$ pairwise adapters — every producer must accommodate every other's idiosyncratic format.<!-- FACT-D72-331 -->[^metcalfe-1995] A shared standard reduces this to $O(n)$: each party conforms to the *one* specification, and conformance to the standard guarantees interoperability with every other conformant party, with no pairwise negotiation.<!-- FACT-D72-332 -->[^metcalfe-1995] The complexity reduction is structural and large — it is why a single USB standard replaces a drawer of proprietary cables, why one TCP/IP lets any machine talk to any other without per-pair protocols, and why a common measurement system lets any measurement be compared to any other. Standardization is, in this sense, a *coordination compression*: it replaces many bilateral agreements with one multilateral norm.

Recognizing the move directs a consistent set of disciplines across substrates. *Standardize the interface, not the implementation*: the leverage comes from converging on the *shared specification at the boundary* (the connector, the protocol, the unit) while leaving everything behind it free to vary — over-standardizing into the implementation forecloses innovation and gains nothing for interoperability. *Get to "good enough and shared" rather than "best and fragmented"*: because the value is in the sharing, the management priority is convergence, so a workable standard adopted now usually beats a superior one that delays agreement and splinters the field. *Choose the convergence mechanism deliberately*: de jure standardization (a body, deliberate agreement) suits domains needing legitimacy and broad buy-in but is slow; de facto (market tipping) is fast but can entrench an inferior winner; mandated (regulatory) is decisive but blunt — the management move is to match the mechanism to the domain's tolerance for delay, fragmentation, and coercion. *Plan for versioning and exit*: because a successful standard can harden into lock-in, the foresighted move is to build in extensibility and migration paths so the standard can evolve without a wrenching coordinated switch. The unifying complexity move is to treat standardization as the deliberate manufacture of a shared norm that turns $O(n^2)$ incompatibility into $O(n)$ conformance — while remembering that the convergence suppresses variety and can entrench, so the act that tames complexity today can constrain adaptation tomorrow.

abstract_reasoning: The standardization pattern licenses several substrate-independent moves. *Treat interoperability problems as coordination problems*: when independently-produced things fail to interwork, recognize that the cure is convergence on a shared specification, and that the hard part is *getting agreement among independent parties*, not designing the specification — so reach for coordination mechanisms (bodies, bandwagons, mandates) rather than only better engineering. *Prize agreement over optimality where value is in sharing*: ask whether the benefit comes from the specification's intrinsic merit or from others sharing it; where it is the latter, a worse-but-shared standard dominates a better-but-fragmented one, and the reasoning move is to push for convergence even on an imperfect option. *Identify the convergence mechanism*: classify whether a standard is being set de jure (deliberate agreement), de facto (market tipping), or by mandate, because each has different speed, legitimacy, and entrenchment properties and different failure modes. *Standardize at the interface, leave the rest free*: locate the minimal shared specification that delivers interoperability and resist standardizing beyond it, preserving the variety and innovation that the implementation behind the interface can carry. *Anticipate entrenchment and lock-in*: recognize that a successful standard, reinforced by network effects and sunk investment, becomes progressively harder to change, so a standard chosen today is a long commitment, and the reasoning move is to weigh future inflexibility against present convergence. And *read the suppressed alternatives as a cost*: every standardization forecloses the options it did not pick — possibly better ones — so the disciplined stance counts the lost variety and the path-dependence (the QWERTY lesson: an early-locked standard can persist long past its optimality) as the standing price of the shared norm.

knowledge_transfer: Because standardization is the bare coordination act of independent parties converging on a shared specification, an insight built around it in one field transfers to any other by re-identifying the parties, the space of specifications, and the convergence mechanism. The technology-standards understanding that *standards battles are coordination contests, not merit contests* — that the winner is the option that tips the installed base, and that the better technology can lose (Betamax versus VHS, and the broader pattern of de facto standard wars) — transfers directly to any domain where independent parties must converge: a measurement system, a language norm, an accounting standard, or a rail gauge can entrench a worse option through early adoption and network effects, so the strategic lesson (seize the bandwagon, build the installed base, achieve critical mass) ports across substrates. The economics of *path dependence and lock-in* developed around the QWERTY keyboard and rail gauge — an early, possibly accidental convergence persists long past its optimality because the coordinated cost of switching exceeds the benefit — transfers as a general warning that standardization creates *durable commitments*: a standard chosen for contingent reasons can outlive its justification in technology, language, law, and infrastructure alike, and undoing it requires solving the same coordination problem in reverse, against entrenched network effects.<!-- FACT-D72-333 -->[^david-1985] The standards-body discipline of *de jure standardization* — convene the independent parties, negotiate a shared specification, and confer legitimacy on it so adoption follows — transfers to governance (treaty-based harmonization of regulations), to measurement (the metric convention), and to any field where a neutral coordinating body can manufacture agreement that the market alone would fragment.<!-- FACT-D72-334 -->[^david-greenstein-1990] And the foundational insight that *the value is in the sharing* — that a shared norm's worth comes from how many conform, not from its intrinsic design — transfers everywhere: it explains why a common language, a common currency, a common protocol, and a common unit all exhibit the same increasing-returns-to-adoption that rewards convergence and punishes fragmentation. In every transfer the practitioner runs the same diagnosis: identify the independent parties and the incompatible options, recognize that the value is in converging rather than in any one option's merit, choose or read the convergence mechanism (de jure, de facto, mandated), standardize at the interface while leaving implementations free, and weigh the suppressed alternatives and the future lock-in against the present interoperability — and the transfer is secure because none of these steps names the substrate: a protocol designer driving an internet standard, a metrologist establishing a unit, a linguist or academy codifying a spelling, and a regulator harmonizing rules are all manufacturing the same shared norm, distinguished only by what is being standardized and who the converging parties are.

formal_abstract:
Standardization as a coordination game is the prime in its native game-theoretic register. Model $n$ independent parties, each of whom must choose a specification from a set of incompatible options $\{A, B, \dots\}$ (the *space of specifications*), and let each party's payoff depend overwhelmingly on *how many other parties chose the same option* — a party that conforms to the option the majority shares can interoperate with all of them, while a party on a minority option is stranded (the *primacy-of-agreement invariant*, formalized as a payoff that is increasing in the number of co-conformers). This is a *coordination game* with multiple equilibria: "everyone chooses $A$" and "everyone chooses $B$" are both stable, and crucially *both are better for every party than any split*, because the value is in the matching, not the option — which is exactly why *which* standard wins can be less important than *that* one wins. The *convergence* is the game's selection of one equilibrium, and the prime's three mechanisms map onto three equilibrium-selection processes: *de facto* convergence is the dynamic tipping of an initially-mixed population onto one option as small early leads compound through increasing returns (the network effect doing the selecting); *de jure* convergence is a coordinating signal — a standards body announcing "$A$" — that lets parties select the same equilibrium without a costly tipping process; *mandated* convergence is an external actor fixing the equilibrium by fiat. The *suppression-of-variety invariant* is the foreclosed equilibrium: once the population tips to $A$, option $B$ is abandoned regardless of whether $B$ was the better design, and the *path-dependence* the prime names is the formal fact that the selected equilibrium can be locally stable and globally suboptimal — the QWERTY result, where an early lead locks in an inferior standard because no individual party can profitably deviate alone. The structural payoff is that standardization is *coordination-game equilibrium selection*, where agreement is the prize and the chosen specification is, within wide limits, secondary.

**Mapped back:** The coordination-game model instantiates every component — multiple independent parties (the players), a space of specifications (the option set), convergence (equilibrium selection onto one option), the three mechanisms (tipping, signaling, mandate as equilibrium-selection devices), the primacy of agreement (payoffs increasing in co-conformers, so any shared equilibrium beats any split), and the suppression of variety with path-dependence (the foreclosed, possibly-better equilibrium that no party can unilaterally restore) — and exhibits the prime's core pairing: independent parties plus a do-it-the-same-way payoff, making standardization a coordination problem in which agreeing matters more than what is agreed.

applied_industry:
The convergence of the personal-computer industry on the USB connector runs the identical structure in a hardware-industry substrate, with no game-theory vocabulary required. The *independent parties* are the many device makers, PC manufacturers, and peripheral vendors, each historically shipping its own incompatible connector (serial, parallel, PS/2, ADB, FireWire, a dozen proprietary plugs). The *space of specifications* is that thicket of incompatible interfaces, each requiring its own port, cable, and driver — the $O(n^2)$ incompatibility the prime's complexity argument describes, a drawer full of cables that fit only their own devices. The *convergence* is the industry's settling on USB as the single shared peripheral interface, achieved by a *coordination process* that blended all three of the prime's mechanisms: a *de jure* core (the USB Implementers Forum, a consortium of independent companies, deliberately specifying the standard and conferring legitimacy on it), a *de facto* tip (the network effect as device makers adopted USB because PCs had USB ports, and PCs standardized on USB ports because devices used them — increasing returns to adoption snowballing onto one option), and a degree of effective *mandate* (dominant platform vendors dropping legacy ports forced the convergence). The *primacy-of-agreement invariant* is vivid: USB's technical merits mattered far less than the fact that *everyone* adopted it — a peripheral with a USB plug works with every USB host on earth, which no technically-superior-but-unshared connector could offer.<!-- FACT-D72-335 -->[^shapiro-varian-1999] The *suppression-of-variety invariant* is equally clear — the convergence foreclosed the competing connectors (FireWire, technically superior for some uses, lost the coordination contest), and the cost is the standing one the prime names: alternatives possibly better for niche purposes were abandoned for the shared norm.<!-- FACT-D72-336 -->[^shapiro-varian-1999] And the *lock-in* the prime warns of duly followed: USB became so entrenched that even improving it required carefully versioned, backward-compatible revisions (USB 2.0, 3.0, USB-C) negotiated to avoid a wrenching coordinated switch — the successful standard hardening into a long commitment.<!-- FACT-D72-337 -->[^shapiro-varian-1999] The same structure governs the rail-gauge convergence, the metric system's spread, and the adoption of TCP/IP as the internet's shared protocol.

**Mapped back:** The USB case runs the prime end-to-end — independent parties (device and PC makers), a space of specifications (incompatible connectors), convergence (settling on USB), a blended coordination process (consortium plus market tipping plus platform mandate), the primacy of agreement (universal adoption mattering more than merit, beating a superior FireWire), and the suppression of variety with subsequent lock-in (foreclosed alternatives and carefully versioned evolution) — and demonstrates the transfer: a protocol consortium driving TCP/IP and a hardware forum driving USB are manufacturing the same shared norm, distinguished only by what is standardized and who the converging parties are.


Starvation

type: prime

slug: starvation

one_liner: A participant who is eligible and in the queue is perpetually denied service, not by exclusion but because a locally rational priority rule always picks someone else.

status: draft

core_idea:
Starvation is the structural pattern in which a participant in an allocation or scheduling system is *perpetually denied service* — not by an explicit exclusion rule, but as the cumulative effect of a priority or selection mechanism that always finds *someone else* more deserving. The defining structural fact is that the system as a whole can be *operating perfectly correctly*, with high throughput and full resource utilisation, while a particular participant or class of participants makes *no progress* — even when there is no fundamental scarcity blocking them, only the priority mechanism's cumulative effect.

The commitment is sharper than scarcity or exclusion. Scarcity means there is not enough; exclusion means the participant is ruled out. Starvation specifies a *third* failure mode: there is enough, no one is ruled out, but the selection rule persistently picks others. The starved participant is in the queue, eligible, possibly high-priority by some local measure — and yet receives nothing. The pattern is *dynamic*: a snapshot may show the participant "about to be served," while the trajectory shows them never quite reaching the front, because each time they near it a new higher-priority arrival displaces them. Starvation is visible only across time and only when the relevant comparison class is held in view. In its formal home, liveness properties capture it precisely: liveness says *every request eventually gets served*, and starvation is the violation of liveness while *safety* — nothing bad happens — remains intact. The system is correct in the safety sense and pathological in the liveness sense, and that combination is the structure.

structural_signature:
*the shared service allocated over time* — *the eligible participant in the queue* — *the locally rational priority rule selecting among contenders* — *the safety property that holds (nothing bad happens)* — *the liveness property that fails for one participant (nothing good eventually happens)* — *the per-participant trajectory that alone reveals the failure*

The pattern is present when each of the following holds:

1. **A shared service allocated over time.** A resource or service is granted repeatedly across cycles to participants competing for it, and there is no fundamental scarcity ruling the participant out.

2. **An eligible, queued participant.** The starved party is in the queue, admitted, possibly high-priority by some local measure — not excluded by any rule.

3. **A locally rational priority rule.** A selection mechanism picks among contenders by a criterion that is correct on each individual decision; each choice of someone-else-more-deserving is locally justified.

4. **Safety intact.** The system commits no error — throughput and utilisation are high, every served request is correctly served; the safety property holds.

5. **Liveness violated for a subclass.** The cumulative effect of the rule is that a particular participant or class makes no progress: the liveness guarantee that *every* request is eventually served fails for them.

6. **Trajectory-level visibility.** The failure is invisible to aggregate metrics and a snapshot (the participant always looks "about to be served"); it appears only when the individual participant's progress over time is held in view.

These compose into a third failure mode distinct from scarcity and exclusion — perpetual deferral under local rationality — whose remedy is the selection rule (aging, guaranteed minimum, lottery), never added capacity.

what_it_is_not:
- **Not `deadlock`.** Deadlock is a circular wait in which *no one* progresses; starvation is a system in which the system *as a whole* progresses (high throughput, full utilisation) while one participant never does. Deadlock blocks everyone; starvation singles out a subclass.
- **Not `livelock`.** Livelock is futile activity with no goal-progress for the coupled agents; starvation is genuine, correct progress for the *system* that perpetually defers one eligible participant. The starved party is idle-waiting, not busy-cycling.
- **Not a `bottleneck` or `scarcity`.** A bottleneck or scarcity means there is not enough to go round; starvation occurs when there *is* enough and no one is excluded — the priority rule simply never picks the participant. Adding capacity, the scarcity reflex, does nothing.
- **Not exclusion.** Exclusion rules a participant *out* by rule; the starved participant is admitted, eligible, often high-priority by some measure, and still receives nothing — denial by cumulative selection, not by exclusion.
- **Not `interference_and_contention` as such.** Contention is the friction of competing for a shared resource; starvation is the specific *liveness* outcome where contention plus a locally-rational priority rule leaves one party perpetually deferred — a failure mode of contention, not contention itself.
- **Common misclassification.** Diagnosing starvation as scarcity and buying more capacity. The tell: would the participant progress if more capacity existed, or would the rule still always pick someone else? If the latter, capacity is the wrong lever and the selection rule is the cause.

broad_use:
- **Concurrency and operating systems** — the origin: a low-priority thread that never gets the processor because higher-priority threads are always ready; a writer waiting on a lock that never sees zero readers.<!-- FACT-D72-351 -->[^dijkstra-1965]
- **Networks and databases** — low-priority packets perpetually deferred under unfair queuing; a long transaction repeatedly aborted by short ones in a contention loop.<!-- FACT-D72-352 -->[^tanenbaum-2014]
- **Legal and bureaucratic systems** — cases that languish for years because each cycle brings higher-priority items; applications perpetually deferred in triage loops.
- **Healthcare** — chronic, non-emergent conditions perpetually deferred by emergent ones; the deferred-care pattern in mental and dental health.<!-- FACT-D72-353 -->[^monzon-2005]
- **Education and credentialing** — applicants who meet every criterion but are never the highest-priority candidate; the "always next year" pattern.
- **Ecology and funding** — subordinate animals perpetually excluded from feeding or mating by dominants with no formal exclusion; research lines perpetually ranked just below the funding line.<!-- FACT-D72-354 -->[^miller-2017]

clarity:
Naming the pattern clarifies a *third failure mode* distinct from scarcity and exclusion: the failure of *fairness over time* under a priority mechanism that is locally rational. Many disputes about allocation become tractable once the question is reframed as "is this participant being denied because nothing is available, because they are ruled out, or because the priority mechanism never quite picks them?" These three diagnoses point at three different remedies, and conflating them — treating a starvation problem as a scarcity problem, for instance — leads to interventions that miss.

The clarification also exposes a load-bearing distinction in fairness reasoning: between *outcome fairness at a snapshot* — throughput, utilisation, average wait — and *liveness fairness across time* — every participant eventually progresses. Many systems optimise the former and unintentionally violate the latter, because the aggregate metrics they watch are blind to per-participant progress. A system can post excellent throughput while a subclass starves, and the throughput figure not only fails to reveal the starvation but actively reassures the operator that all is well. The frame makes this gap visible by insisting that fairness be evaluated at the level of the individual participant's trajectory, not at the level of system-wide averages, which is precisely where the failure hides.

manages_complexity:
The pattern compresses a wide family of "the system works but some participants never progress" phenomena — low-priority threads, deferred cases, chronic waitlists, perpetual non-promotions, subordinate exclusion — into one diagnostic family: *priority-induced perpetual deferral.* Cross-cutting failures across computing, law, medicine, education, and ecology become legible as one structure, which is the compression a prime supplies: a single object, parameterised by substrate, replaces a scatter of domain-specific complaints.

The intervention space compresses to a small, transferable set. Introduce *aging*, so a participant's priority rises with time spent waiting. Introduce a *guaranteed minimum service*, so every participant gets some nonzero allocation per cycle. Introduce a *lottery* or randomisation, giving a probabilistic guarantee against perpetual deferral. *Rate-limit* high-priority requests so they cannot indefinitely crowd the queue. Or *redesign the priority function* to bound the starvation interval directly. Each lever acts on the structural cause — a priority rule whose cumulative effect is unbounded deferral — and the menu is the same across substrates, so the introduction of case-age prioritisation in a docket and the introduction of aging in a scheduler are the same move in different vocabularies. The pattern also tells the analyst that adding capacity, the reflex for scarcity, does nothing here, because the bottleneck is the selection rule rather than the supply.

abstract_reasoning:
Recognising starvation as a structure enables reasoning about the *safety/liveness split*: safety says nothing bad happens, liveness says something good eventually happens, and starvation is the canonical liveness failure. The generalisation beyond its origin is that any well-functioning system can pass safety checks while failing liveness checks on a subclass of participants — a possibility invisible to anyone who treats "the system is working" as a single undifferentiated claim. It enables reasoning about the *local-rationality/global-pathology gap*: each individual scheduling decision is locally correct, since the higher-priority participant genuinely is more deserving by the rule, yet the cumulative pattern is pathological. This is the same shape as other gaps between locally rational moves and globally bad outcomes, but the specific form here is *cumulative deferral under local rationality*.

Two further inferences follow. The *intervention asymmetry*: prevention is cheap (aging, lottery, guaranteed minimum), detection after the fact is hard (the starved are often invisible to metrics that measure throughput rather than per-participant progress), and cure for entrenched starvation is expensive (overrides, affirmative scheduling). And the *visibility problem*: starvation is visible only when one holds the individual participant's progress trajectory in view, because aggregate metrics hide it, so designing the right per-participant observability is the first step toward intervention. These inferences are reached from the structure rather than from cases, and they apply wherever a locally rational priority rule governs access to a shared service over time.

knowledge_transfer:
The transfers carry concrete techniques, not just vocabulary, because the cumulative-deferral structure is the same wherever a priority rule allocates over time. *Concurrency theory into human scheduling*: starvation-avoidance techniques — aging, guaranteed quanta, lottery scheduling — transfer from processor scheduling into shift-scheduling, school assignment, and court-docket management, with the same liveness guarantee that no participant is perpetually deferred.<!-- FACT-D72-355 -->[^silberschatz-2018] *Scheduling into bureaucratic process design*: the explicit introduction of *aging* moves into case-scheduling (priority rising with case age), admissions (special handling for deferred applications), and waitlists (priority uplift after a wait threshold), each a direct port of the same lever.

The pattern ports further. *Network quality-of-service into representation*: traffic-shaping ideas such as a guaranteed minimum bandwidth per class transfer into discussions of guaranteed minimum representation for under-served groups, with the same liveness argument that some nonzero service per cycle prevents perpetual deferral.<!-- FACT-D72-356 -->[^demers-keshav-shenker-1989] *Behavioural ecology into organisational design*: the analysis of subordinate-animal starvation transfers into the design of promotion systems, addressing how to prevent perpetual non-promotion of competent-but-not-stellar members under up-or-out rules.<!-- FACT-D72-357 -->[^oflaherty-siow-1995] The transferable core, stripped of computing vocabulary, is one sentence: *a participant who is eligible, in the queue, and never quite served because the priority rule always picks someone else.* That sentence does real work in legal process, healthcare triage, ecology, funding allocation, and organisational promotion, and the same remedies — aging, guaranteed minimum, lottery, rate-limiting, priority redesign — apply in each. The pattern is cleanly structural at its core; its concurrency-origin vocabulary needs light translation, and human cases carry a mild normative residue (the starved are wronged in a way a deferred packet is not), which together place it just off the purely structural end of the spectrum.

formal_abstract:
A priority-scheduled processor running a low-priority thread is the origin case, and it cleanly separates the safety and liveness properties. The *shared service allocated over time* is the CPU, granted in scheduling quanta. The *eligible, queued participant* is a low-priority background thread — admitted to the ready queue, not blocked, fully able to run if selected. The *locally rational priority rule* is strict priority scheduling: at every dispatch the scheduler correctly picks the highest-priority ready thread, and each individual decision is justified.<!-- FACT-D72-358 -->[^silberschatz-2018] The *safety property holds*: throughput is high, the processor is fully utilised, every quantum is correctly awarded to a genuinely higher-priority thread — nothing bad happens. The *liveness property fails for the subclass*: because higher-priority threads are continuously ready, the low-priority thread is perpetually deferred and makes no progress, violating the guarantee that *every* ready thread eventually runs. The *trajectory-level visibility* is the diagnostic trap: aggregate metrics (utilisation, throughput) look excellent and a snapshot always shows the thread "next once the queue clears," so the failure is invisible except by tracking the individual thread's progress over time. The structure prescribes the remedy and rules out the wrong one: adding capacity (a faster CPU) does nothing, because the bottleneck is the selection rule, not the supply. The correct interventions act on the rule — *aging* (the thread's priority rises with time waited, eventually exceeding the contenders), a *guaranteed minimum* (every thread gets at least one quantum per cycle), or a *lottery* (probabilistic guarantee against perpetual deferral).<!-- FACT-D72-359 -->[^silberschatz-2018]

**Mapped back:** The CPU is the shared service, the background thread is the eligible queued participant, strict priority is the locally rational rule, high utilisation is the intact safety property, and the thread's flat progress trajectory is the liveness violation — starvation as a third failure mode distinct from scarcity and exclusion, fixed by the rule, not by capacity.

applied_industry:
A hospital emergency department triage queue instantiates the same structure with patients as the queued participants. The *shared service allocated over time* is clinician attention, granted to whoever is seen next. The *eligible, queued participant* is a patient with a chronic, non-emergent but real condition — checked in, in the waiting room, fully eligible for care, not turned away. The *locally rational priority rule* is acuity-based triage: at every decision the most medically urgent patient is correctly seen first, and each individual choice is defensible.<!-- FACT-D72-360 -->[^esi-2005] The *safety property holds*: the department posts excellent metrics — high patient throughput, no one with a life-threatening condition left untreated, every clinical decision sound. The *liveness property fails*: the non-emergent patient is perpetually displaced by each new higher-acuity arrival, waiting hours and sometimes leaving without being seen, making no progress through the queue — the deferred-care pattern. The *trajectory-level visibility* is exactly where the failure hides: average wait times and throughput look healthy and the patient always appears "about to be called," so only per-patient progress reveals the starvation, and the metrics that reassure the operator are blind to it. The intervention catalogue ports directly as a port of the same levers: introduce *aging* (a waiting patient's priority rises after a threshold, so a chronic case eventually outranks fresh low-acuity arrivals), a *guaranteed minimum* (reserve some capacity per cycle for long-waiters), or *rate-limit* the displacing stream. The identical structure governs a court docket where cases languish for years as each cycle brings higher-priority filings (fixed by case-age prioritisation, the same aging lever), and behavioural ecology where a subordinate animal is perpetually excluded from a food source by dominants with no formal exclusion rule.<!-- FACT-D72-361 -->[^miller-2017]

**Mapped back:** Clinician attention is the shared service, the chronic-condition patient is the eligible queued participant, acuity triage is the locally rational rule, healthy throughput is the intact safety property, and the patient's stalled progress is the liveness violation — starvation diagnosed only at the per-participant trajectory, remedied by aging rather than by adding capacity.


Stasis

slug: stasis

one_liner: Locate the kind of question at issue in a dispute—did it happen, what is it, was it justified, whose call is it—before contesting the substance, since disputants who seem to disagree about the answer are often disagreeing about the question.

origin_domain: rhetoric

core_idea:
Stasis is the structural move of locating the *kind* of question at issue in a dispute before contesting the substance. The classical rhetorical tradition named four canonical stases — conjecture (did it happen?), definition (what is it?), quality (was it justified?), and jurisdiction (is this the right forum?) — on the structural insight that disputants who appear to disagree about *the answer* are very often disagreeing about *the question*, and that the right intervention is to identify which level of question is genuinely at stake.<!-- FACT-D72-376 -->[^cicero-de-inventione] The structural commitment is that a dispute has a *typed* question underneath, and the type is itself contestable and prior. Productive argument requires that the parties first converge on the type — agree that the dispute is about facts, not definitions; about definitions, not values; about values, not jurisdiction — before evidence and argument can land.

Without that convergence, the parties produce *talking past*: each addresses a different stasis, and each is frustrated that the other "isn't engaging with the real issue." Identifying the stasis is neither persuasion nor concession; it is a structural prerequisite for the dispute to be a dispute about the same thing. Three features mark stasis as a distinct prime-level pattern. First, it is a *meta-level* move on argumentation — it operates on the structure of the disagreement rather than on its content. Second, the stases form a small, ordered *typology* of question-kinds, and the order matters: conjecture is logically prior to definition, which is prior to quality, which is prior to jurisdiction, and resolving an earlier stasis without identifying it produces only spurious agreement at the later ones. Third, mis-located disputes carry a characteristic signature — high heat, mutual sense of bad faith, repeated exchanges that fail to update either side — which makes the diagnostic itself diagnosable. The pattern is strongly framed: its vocabulary descends from a specific rhetorical and legal tradition and must be translated to travel, and its substrate is human argumentation throughout. But within that band the move and its small typology recur, discovered and re-discovered across dispute-resolution traditions, which is what gives it cross-domain reach despite its institutional origin.

structural_signature:
*a dispute with a typed question underneath* — *a small ordered typology of question-kinds (conjecture, definition, quality, jurisdiction)* — *a logical priority among the types* — *a meta-level move that types the question before contesting the answer* — *a convergence requirement that parties agree on the type for argument to land* — *a mismatch signature of high heat and non-updating exchange*

The pattern is present when each of the following holds:

- **A typed underlying question.** Beneath the surface content, the dispute turns on a question of a particular kind, and the kind is itself contestable and prior to the substance.
- **A small typology.** A bounded, complete-enough set of question-kinds — did it happen (conjecture), what is it (definition), was it justified (quality), whose call is it (jurisdiction) — that fits in working memory as a diagnostic checklist.
- **A logical ordering.** The types are sequenced: conjecture is prior to definition, definition to quality, quality to jurisdiction. Resolving a later type without settling an earlier one yields only spurious agreement.
- **A meta-level typing move.** The intervention operates on the structure of the disagreement, not its content: suspend the substance, identify the type at stake, route to the prior type first.
- **A convergence requirement.** Parties must agree on the question-type before evidence and argument can bite; absent convergence they produce "talking past," each addressing a different type.
- **A mismatch signature.** A non-converging, high-heat exchange in which neither side updates is itself the diagnostic of a stasis mismatch rather than of bad faith.

These compose into typed disagreement: the claim that question-kinds come in a small ordered set whose order is logical rather than conventional, which predicts which evidence and argument will bite on a given dispute and which will pass straight through. The pattern is strongly framed and human-argumentation-bound, but the move and its typology recur across dispute-resolution traditions.

what_it_is_not:
- **Not stasis-as-stillness.** Despite the name, this prime is not the physical or systemic sense of "stasis" as a motionless steady state. See `equilibrium` for that. Here, *stasis* is the classical-rhetoric term for the *point at which a dispute comes to rest on a typed question* — a diagnostic of disagreement-structure, not a state of non-change.
- **Not framing.** See `framing`. Framing is the broad claim that presentation shapes perception. Stasis is the *specific small, ordered typology* of question-kinds (conjecture, definition, quality, jurisdiction) with a logical priority among them — structurally load-bearing content that framing-in-general does not supply.
- **Not adjudication.** See `adjudication_dispute_resolution`. Adjudication is the *authoritative resolution* of a dispute by a deciding body. Stasis is the *prior diagnostic* of which question-type is at issue; identifying the stasis is neither deciding nor resolving, but locating what must be decided.
- **Not precedent.** See `precedent_stare_decisis`. Precedent governs *how a prior decision binds a later one*. Stasis governs *what kind of question a present dispute turns on*. One is about authority across time; the other about question-type within a dispute.
- **Not falsifiability.** See `falsifiability`. Falsifiability concerns whether a *claim* can be empirically refuted. Stasis concerns which *kind of question* a dispute occupies — and one of its payoffs is recognizing that some disputes (definitional, jurisdictional) resist evidence not because the claims are unfalsifiable but because the question is not conjectural.
- **Common misclassification.** Treating a non-converging, high-heat exchange as bad faith. The catch: the same signature is the diagnostic of a *stasis mismatch* — parties sincerely addressing different question-types and talking past each other. Name the divergent types before impugning motives.

broad_use:
- **Law.** The classical home: pre-trial motions are largely stasis moves — a motion to dismiss is jurisdictional stasis, summary judgment on the facts is conjectural stasis, characterization of the charge is definitional stasis, and affirmative defenses are quality stasis.<!-- FACT-D72-377 -->[^hohmann-1989] A skilled litigator's first move is to identify and contest the governing stasis rather than concede the framing.
- **Requirements engineering and product design.** "Are we disagreeing about what the user is doing, what the user *is*, what we should do for the user, or whose call this is?" Almost every protracted spec argument turns out to be at the wrong stasis, and skilled facilitation surfaces the actual stasis before debating remedies.
- **Conflict mediation.** Mediators are trained to ask what the dispute is actually about; the surface dispute (the driveway, the dishes) routinely masks a definitional dispute (what counts as fairness here?) or a jurisdictional one (whose decision is this?), and naming the stasis is often what unlocks resolution.<!-- FACT-D72-378 -->[^fisher-ury-2011]
- **Public policy.** Many disagreements appear to be about evidence but are at the quality stasis (is this intervention legitimate?) or the jurisdictional stasis (whose authority is this?); the sense that a debate "won't resolve no matter how much data we collect" is a stasis-mismatch signature.
- **Scientific controversy.** A replication dispute can be conjectural (did the effect occur?), definitional (does *this* count as the effect?), or quality (was the procedure appropriate?), and identifying which stasis it is at predicts what evidence would actually move it.<!-- FACT-D72-379 -->[^northcut-2007]
- **Engineering and code review.** "Is this a bug?" (conjecture) versus "is this what a bug *is*?" (definition) versus "should we fix it?" (quality) versus "is this our team's decision?" (jurisdiction) — the same four stases appear in technical review.<!-- FACT-D72-380 -->[^unverified-380]

clarity: Stasis sharpens the distinction between the *content* and the *type* of a dispute. Most ordinary argumentation conflates the two: parties experience disagreement as substantive and reach for more evidence, more rhetoric, or more emphasis, when in fact they are exchanging answers to different questions. Naming the stasis exposes a class of seemingly wicked disputes as actually well-structured but mis-typed — disagreements that look intractable because the parties are talking past each other, but that become tractable the moment the question-type is identified and aligned. This is the frame's central clarifying payoff: it relocates the difficulty from the substance, where it appears insoluble, to the typing, where it is often quickly resolved. The frame also clarifies *why* certain disputes resist evidence, which is otherwise mysterious and demoralizing. A jurisdictional disagreement cannot be resolved by adding facts about the matter, because the disagreement is about who decides, not about what is true. A definitional disagreement cannot be resolved by appealing to values, because it is about what a term means, not about what is good. A quality disagreement cannot be resolved by relitigating the facts, because both sides may agree on the facts and disagree about whether the act was justified. The typology thus predicts which kinds of evidence and argument will *bite* on a given dispute and which will pass straight through it — a prediction that lets a practitioner stop pouring the wrong kind of effort into a dispute and supply the kind that the operative stasis can actually absorb.

manages_complexity: The classical four stases — existence, definition, quality, jurisdiction — form a small, complete-enough partition of question-kinds that fits in working memory.<!-- FACT-D72-381 -->[^cicero-de-inventione] A practitioner can carry the four in mind as a diagnostic checklist and run a dispute through them quickly, asking which of these is the real question at issue. The compression is meaningful: a sprawling, heated argument with many apparent sub-points is reduced to one of four cells, and the cells are exhaustive enough to cover the most common mis-types. This turns an unbounded interpretive task — figuring out what a complex dispute is "really" about — into a bounded one, a choice among four typed questions, which is precisely why the typology has survived and been re-derived across traditions. The ordering itself does additional complexity-managing work. The stases are sequenced — one cannot sensibly argue quality before settling definition, nor settle definition before conjecture — and the sequence prevents the unproductive re-litigation of earlier stages once they have been agreed. By imposing an order, the typology gives a dispute a spine: it tells the parties which question must be resolved first, and lets resolved questions stay resolved rather than being reopened whenever the argument stalls at a later stage. The complexity of a tangled disagreement, in which factual, definitional, evaluative, and jurisdictional threads are all live at once and interfering with one another, is managed by separating the threads into a small ordered set and addressing them in sequence.

abstract_reasoning: Stasis exemplifies a deeper structural pattern: *typed disagreement*. Any dispute has not just a content but a logical type — a question being answered — and the type is itself prior, contestable, and small in cardinality. This more general skeleton lets the analyst recognize the same move in settings far from rhetoric: in formal-methods specification, where a dispute may be about the implementation, the specification, or the requirements; in machine-learning evaluation, where a dispute may be about the model, the benchmark, or the task definition; and in clinical disagreement, where the dispute may be about the diagnosis, the diagnostic criteria, or the standard of care.<!-- FACT-D72-382 -->[^stanley-campos-2013] In each, the productive first move is the stasis move — identify which typed question is genuinely at issue before arguing the answer. The pattern decomposes into smaller primes: distinction (separating one thing from another), framing (presentation shapes perception), levels-of-analysis (cross-level argument errors), and the broader meta-cognitive move of asking what kind of question is being asked.<!-- FACT-D72-383 -->[^fahnestock-secor-1988] What makes stasis a distinct entry rather than a mere instance of framing is the specific small typology — existence, definition, quality, jurisdiction — that has been discovered and re-discovered across dispute-resolution traditions. The typology is structurally load-bearing, not a vocabulary import: it is the claim that question-kinds come in a small, ordered set, and that the order is logical rather than conventional, which is what lets the frame predict which disputes must be resolved before which others.

knowledge_transfer:
A practitioner who has internalized stasis carries a portable diagnostic into any disputed terrain, and the move is identical across domains: suspend the substance, identify the question-type at stake, check whether the parties agree on it, and if not, route the dispute to the prior stasis first. Confronted with a contested issue — technical, legal, policy, interpersonal, philosophical — only the substrate-specific examples change; the move itself transfers unchanged, which is the practical content of treating stasis as a prime rather than as a fact about Roman rhetoric. The intervention catalogue transfers with it. Reframing moves ("are we arguing about whether it happened or about what to call what happened?"), forum-checking moves ("is this the right body to decide?"), and re-typing moves ("you're treating this as a values question; I'm treating it as a definitional one") are recognizably the same family across law, mediation, product design, and code review. A facilitator who has used the re-typing move to unstick a spec argument can use its analogue to unstick a policy debate, because the structural target — the parties' implicit, divergent answer to "what kind of question is this?" — is the same in both.

The role mappings that make these transfers reliable are direct. The conjectural stasis maps to the question of whether the effect occurred, whether the metric crossed the threshold, whether the event happened. The definitional stasis maps to what counts as the effect, what "Sev-1" means, what a bug is, what fairness is here. The quality stasis maps to whether the act was justified, whether the intervention is legitimate, whether the fix is worth making. The jurisdictional stasis maps to whose call it is, which forum decides, which team owns the decision. Because the typology and its ordering are shared, a concrete diagnosis transfers without modification: two engineering teams arguing for hours about whether an outage is "Sev-1" are, on inspection, one at the conjectural stasis (the metric crossed the threshold) and one at the definitional stasis (Sev-1 means user-facing, and this was internal-only), so the real disagreement is at the prior stasis — what Sev-1 means — and resolves quickly once the definition is revised, exactly as a mediator's recognition that a dispute over the dishes is really a definitional dispute over fairness resolves it once the type is named.<!-- FACT-D72-384 -->[^unverified-384] What transfers is the discipline of typing the question before contesting the answer, the small ordered typology that makes the typing tractable, and the recognition that a non-converging, high-heat exchange is itself the signature of a stasis mismatch rather than of bad faith.

formal_abstract:
Pre-trial litigation is the classical home of stasis, and a criminal case makes the typed-question structure and its logical ordering fully explicit. Suppose the defense and prosecution clash over a homicide charge. The *typed underlying question* is not single: the dispute can live at any of the four stases, and the parties' apparent disagreement about guilt dissolves into a disagreement about *which question* governs. At the *conjectural* stasis the question is "did the defendant cause the death?" — a factual question settled by evidence (alibi, forensics). At the *definitional* stasis the question is "granting the act, is it murder or self-defense?" — a question about what the established facts count *as*, contested by characterization rather than by more evidence. At the *quality* stasis the question is "granting it was a killing, was it justified?" — an evaluative question about legitimacy. At the *jurisdictional* stasis the question is "is this the right court, the right charge, the right body to decide?" — answered by a motion to dismiss. The *logical ordering* is load-bearing: arguing quality (was it justified?) before settling conjecture (did he do it?) yields only spurious agreement, which is why a skilled litigator's first move is to identify and contest the *governing* stasis rather than accept the opponent's framing. The *mismatch signature* — high heat, neither side updating — is itself the diagnostic: when the prosecution piles on forensic evidence and the defense keeps answering "but that was self-defense," they are at different stases (conjecture versus quality), talking past each other, and no quantity of evidence at the conjectural level will move a dispute that is really at the quality level. The *meta-level typing move* — suspend the substance, name the stasis, route to the prior one — is what converts an interminable exchange into a tractable sequence.

**Mapped back:** The homicide dispute is the typed underlying question, did-he-do-it / is-it-murder / was-it-justified / which-court are the four ordered stases, contesting the governing stasis is the meta-level typing move, and forensic evidence sliding past a self-defense argument is the mismatch signature of arguing at the wrong stasis.

applied_industry:
In an engineering incident review the same four stases appear, and naming them resolves a dispute that brute-force argument cannot. Two on-call teams argue for an hour over whether an outage was a "Sev-1." On the surface they disagree about severity; structurally they are at *different stases*. One team is at the *conjectural* stasis — "the error-rate metric crossed the Sev-1 threshold, therefore Sev-1" — treating it as a factual question about whether an event occurred.<!-- FACT-D72-385 -->[^unverified-385] The other is at the *definitional* stasis — "Sev-1 *means* customer-facing impact, and this outage was internal-only, therefore not Sev-1" — treating it as a question about what the term denotes. The *logical ordering* explains why the argument will not resolve on its own terms: the conjectural question (did the metric cross the line?) cannot be settled until the prior *definitional* question (what does Sev-1 mean — a metric threshold or a customer-impact criterion?) is agreed, and both teams are pouring evidence into the wrong level. The *mismatch signature* is textbook — high heat, mutual suspicion of bad faith, an hour of exchange with no movement — and the frame reads that signature not as obstinacy but as a stasis mismatch. The *meta-level typing move* resolves it fast: suspend the severity question, surface that the real disagreement is definitional, revise the Sev-1 definition explicitly, and the conjectural question then answers itself. The identical structure governs a requirements dispute ("are we arguing about what the user *does*, what the user *is*, what we should build, or whose call this is?"), a code-review disagreement ("is this a bug?" / "is this what a bug *is*?" / "should we fix it?" / "is this our team's decision?"), and a replication controversy in science (conjectural: did the effect occur? versus definitional: does *this* count as the effect?). A facilitator who has used the re-typing move to unstick one can unstick the others, because the structural target — the parties' divergent implicit answer to "what kind of question is this?" — is identical.

**Mapped back:** The Sev-1 argument is the typed dispute, threshold-crossing versus what-Sev-1-means are the conjectural and definitional stases, the unresolvable hour is the mismatch signature, and revising the definition before relitigating severity is the meta-level typing move routing to the logically prior stasis.


State and State Transition

one_liner: Captures system condition and evolution.

core_idea: A state is a complete specification of the relevant condition of a system at a moment in time; a state transition is a rule or event that takes the system from one such specification to another. The essential commitment is that the system's future behavior depends only on its current state (plus any incoming inputs), not on the full history by which it arrived — a principle known as the Markov property, where history is compressed into the state<!-- FACT-D31-106 -->[^lamperti-1977]. Every state-transition model specifies (1) the state space (the set of possible conditions), (2) the transition relation (what goes to what, under what trigger), (3) an initial state or distribution of initial states, and (4) the observable outputs, if any, associated with states or transitions. The essential commitment enables predictability, tractable analysis, and testability by reducing unbounded history to a finite sufficient summary.

structural_signature:
- *The finite or infinite state space defining all possible conditions the system may occupy* <!-- FACT-D31-107 -->[^hopcroft-1979]
- *The current-state sufficiency property: present state plus input determines future behavior, independent of history* <!-- FACT-D31-108 -->[^lamperti-1977]
- *The transition relation specifying which states follow which under conditions (deterministic, nondeterministic, or stochastic)* <!-- FACT-D31-109 -->[^hopcroft-1979]
- *The triggers or input symbols causing transitions, including time, events, thresholds, or spontaneous processes* <!-- FACT-D31-110 -->[^mealy-1955]
- *The initial state or probability distribution over initial states at system startup* <!-- FACT-D31-111 -->[^hopcroft-1979]
- *The observable outputs associated with states or transitions, distinguishing observable from hidden states* <!-- FACT-D31-112 -->[^moore-1956]

what_it_is_not:
- **Not any change over time.** A quantity that drifts continuously (temperature increasing linearly) without any discrete decomposition into states is describable by differential equations or process models, not by state-and-transition — unless the relevant behavior is naturally captured by regime or phase labels (e.g., "heating," "holding temperature," "cooling").

- **Not mere sequence.** A sequence of events is not a state-transition system unless the current state encapsulates everything needed to determine what comes next. A raw event log with no compressed summary lacks the sufficient-summary property and requires additional context to predict behavior.

- **Not equilibrium.** Equilibrium is a property of a state (balance of forces, no net change, a fixed point); state-and-transition is the structural framing within which equilibria, non-equilibria, and transients can all be described and analyzed.

- **Not the system itself.** A state-and-transition model is a representation of the system's behavior, not the system. A physical device has far more structure than any finite automaton that models it; the modeling choice decides what counts as a state and what is abstracted away.

- **Not the same as a process variable.** Temperature is a continuous variable; "heating / holding / cooling / off" is a set of discrete states. Both can describe the same device; the state framing commits to a discrete regime view, while a variable-based view treats properties as continuous.

- **Common misclassification:** Enumerating "states" that do not actually satisfy the sufficient-summary property — labels that omit information future transitions depend on, so that two systems in the "same state" behave differently. The label is not a true state if behavior diverges without further distinction.

broad_use: Finite state machines appear in **computing**: protocol implementation (TCP state machine), compiler lexical and syntax analysis, regular-expression engines, UI navigation, database transaction states, game logic. State-and-transition models are foundational in **physics and chemistry**: phases of matter (solid, liquid, gas), quantum states, reaction intermediates, thermodynamic macrostates. **Biology** employs state models for cell-cycle phases (G0, G1, S, G2, M), developmental stages, neuron firing states, ion channels (open/closed), and epidemiological compartmental models (susceptible, exposed, infectious, recovered). **Control theory** uses state-space representations extensively: observer design, feedback control, mode-switching controllers, and Kalman filtering. **Operations research and business** apply state models to order-fulfillment lifecycles, ticket/case statuses, loan lifecycles, Markov decision processes, and supply-chain visibility. **Psychology and cognition** model attention states, mood states, task-switching costs, sleep stages (REM, NREM 1-3), and learning-state transitions. **Linguistics** and **natural language processing** use state models in parsing (shift-reduce parsers), hidden Markov models for parts-of-speech tagging, and conversation-state tracking. **Distributed systems** employ state consensus, leader-election state machines, and Byzantine fault tolerance protocols.

clarity: State-and-transition clarifies by forcing a commitment to what counts as a "same situation" and when the system has crossed into a "different situation." A good state definition makes prediction and explanation local: to know what happens next, consult the current state plus any incoming inputs, nothing more. No need to revisit prior history or reason about how the system arrived at its present condition. The clarifying force is to collapse arbitrary histories into a compact sufficient summary and to name the distinguishing conditions under which that summary changes<!-- FACT-D31-113 -->[^harel-1987]. This discipline exposes hidden assumptions (e.g., "I assumed the system was stateless" or "I didn't realize this variable could diverge"). Clarity also enables unambiguous specification of behavior: "in state X, event E causes transition to state Y with output Z." Without this, specifications become vague ("the system responds to a request") and implementations diverge (different engineers interpret the spec differently). With state-and-transition, every scenario is explicitly named and every transition is explicitly defined, leaving no room for interpretation.

manages_complexity: The construct manages complexity by compressing history: instead of reasoning about every prior event, only the current state matters — a potentially unbounded log becomes a finite summary. This reduces working memory and reasoning cost dramatically. State-and-transition makes behavior enumerable: a finite state machine has a finite number of behaviors at each state-input pair, so reasoning, testing, and formal verification become tractable. Large systems can be modeled as products or compositions of smaller state machines, each with its own local state, enabling hierarchical or modular design<!-- FACT-D31-114 -->[^harel-1987]. The framework enables formal analysis: reachability (can state X be reached?), liveness (will progress always occur?), safety (are bad states unreachable?), and equivalence (do two machines behave the same?) are all well-defined questions with mature algorithmic tools (model checking, bisimulation). Testing becomes systematic: a test suite can enumerate all reachable states and all legal transitions, ensuring comprehensive coverage. Finally, state-and-transition licenses precise design: developers and protocol designers can ask "in what state should the system be when X happens?" and "what is the right transition from this state on event Y?" — questions that discipline interface design, protocol specification, and workflow modeling. These questions reveal gaps in specification that informal descriptions would hide.

abstract_reasoning: State-and-transition trains a reasoner to ask: What is a state here — what information must it carry for "current state + input ⇒ future behavior" to hold? Is the Markov property satisfied, or does behavior depend on hidden history? What is the transition relation — deterministic, nondeterministic, or probabilistic? What triggers transitions — time, events, inputs, thresholds, or spontaneous processes? What is the reachable subset of the state space from the initial conditions, and are there unreachable or absorbing states? Are there hidden states that an external observer cannot directly see, and how would one infer them from observations<!-- FACT-D31-115 -->[^rabiner-1989]? What happens at state explosion — where the naive state space is too large to enumerate, requiring abstraction, symbolic representation, or hierarchical decomposition? Can the state space be reduced by finding equivalent states or collapsing irrelevant distinctions? The diagnostic also includes questions about liveness: can the system get stuck in a state from which no transition is possible? Are there infinite-loop states that should not exist? The answers to these questions often reveal design flaws before implementation.

knowledge_transfer: The state-and-transition pattern transfers across domains by recognizing a common role structure: states represent distinct regimes or conditions; transitions represent boundary crossings; triggers initiate transitions. A protocol engineer modeling a TCP connection, a physician tracking a patient through treatment stages, an epidemiologist modeling disease progression, and an operations manager defining an order-fulfillment lifecycle are all doing the same structural work: name the states, specify which triggers move the system between them, and declare the behavior associated with each state. The same diagnostics apply across all three: Is the current label sufficient to predict what happens next? Are there events that should cause a transition but currently do not? Is the state space consistent with observed behavior? The universality of the pattern is profound: financial institutions model order states (pending, filled, cancelled), software projects model ticket states (open, assigned, in-review, merged, closed), and organisms progress through life stages (birth, juvenile, reproductive, senescent). In each case, the state determines what happens next, and transitions are triggered by external or internal events. Role mappings are universal: **state** ↔ configuration / mode / phase / status / regime / condition / situation; **state space** ↔ set of modes / phase space / ontology of statuses / repertoire of conditions; **transition** ↔ event / trigger-response / phase change / status update / movement / progression; **trigger** ↔ event / message / threshold crossing / stimulus / signal / occurrence; **deterministic transition** ↔ guaranteed next step / contractual progression / mechanical response; **nondeterministic transition** ↔ choice / branching point / possibility; **stochastic transition** ↔ probabilistic jump / Markov transition / random draw; **initial state** ↔ startup condition / birth / onboarding / genesis; **absorbing state** ↔ terminal condition / death / archive / irreversible outcome / sink state.

formal_abstract:
Harel's statechart formalism (1987) and the theoretical foundations in finite-state machine theory (Mealy 1955, Moore 1956) exemplify state-and-transition structure in formal automata<!-- FACT-D31-116 -->[^harel-1987]. A TCP connection's state machine (RFC 793) includes states `CLOSED`, `LISTEN`, `SYN-SENT`, `SYN-RECEIVED`, `ESTABLISHED`, `FIN-WAIT-1`, `FIN-WAIT-2`, `CLOSE-WAIT`, `CLOSING`, `TIME-WAIT`, and `LAST-ACK`. Transitions are triggered by sent or received TCP segments (SYN, ACK, FIN), timeouts (retransmission timers, 2MSL wait), or application calls (socket open, send data, close connection). The entire protocol's correctness — reliable in-order byte delivery despite packet loss and reordering — is specified in terms of this state-and-transition graph. Reasoning about concurrency (simultaneous open or close), retransmission and recovery from segment loss, and graceful connection teardown is reasoning over the graph. Each state has a well-defined set of allowed transitions; violating that discipline produces protocol failures (e.g., sending data in `CLOSED` state is invalid, sending FIN in `LISTEN` violates the protocol). The state machine is deterministic: given the current state and an incoming segment, the next state and the action (send ACK, send RST, send data) are always defined. This determinism is crucial for interoperability — thousands of TCP implementations can communicate correctly because they all follow the same state machine.

**Mapped back:** This instantiates the structural signature directly — a finite state space (12 states), current-state sufficiency (knowing the TCP state and the arriving segment determines the next state and action, independent of how the state was reached), deterministic transitions (no nondeterminism), events as triggers (segment arrivals, timeouts), and observable outputs (segments sent, state change notifications).

applied_industry:
A support ticket's lifecycle in a customer-service system: states include `NEW`, `IN-TRIAGE`, `IN-PROGRESS`, `WAITING-ON-CUSTOMER`, `RESOLVED`, `CLOSED`. Transitions are triggered by agent actions (assign ticket to engineer, begin work, mark resolved), customer replies (provide information, confirm resolution), SLA timers (escalate if waiting more than 24 hours), and business rules (auto-close after 30 days of resolution). The exact same structural questions apply: is `WAITING-ON-CUSTOMER` a sufficient summary to decide what the next allowed action is, or does it hide a distinction between "awaiting customer reply" (customer must send information) and "awaiting customer artifact" (customer must upload a file) that determines urgency, escalation rules, and allowed actions? If the latter, the label is underspecified, and behavior will diverge within a supposed single state — the same pathology that produces bugs in TCP state machines<!-- FACT-D31-117 -->[^rfc-793]. A real support system might discover through testing that agents behave differently in the two cases: they wait longer for artifacts, they send reminders differently, and they escalate differently. Introducing sub-states (`WAITING-ON-REPLY` vs `WAITING-ON-ARTIFACT`) refines the model and eliminates the ambiguity. The same discipline — ensuring that the state is sufficient and that transitions respect the state machine's contract — ensures both protocol correctness and business-process integrity. Both systems benefit from explicit state machines: they become predictable, testable (you can verify that every state has the correct transitions), and auditable (you can trace the exact path a ticket or connection took).

**Mapped back:** This shows how State-and-State-Transition applies in operational contexts: explicit states corresponding to business conditions, clear triggers for transitions, deterministic or stochastic transitions, and the discipline of ensuring current state is sufficient for all subsequent decisions and actions.

title: State Granularity — Coarseness vs Explosion

body:
Too-coarse states hide behaviorally-important distinctions (two systems in the "same state" behave differently, violating the sufficient-summary property); too-fine states explode the state space and overwhelm analysis, making the model harder to understand than the original system. The right granularity is driven by what the model must predict or decide. Common failure: adding states reactively every time a bug reveals hidden distinctions, until the state space becomes unmanageably large<!-- FACT-D31-118 -->[^clarke-1999].

- **T2: Current State vs Hidden History — The Markov Property's Fragility.**
  The Markov property — "the current state is a sufficient summary" — is a commitment that often fails in practice. Systems carry implicit history (caches, aging components, accumulated context, learned behavior) that changes behavior without changing the named state. Testing reveals divergent outcomes when the same state is reached via different paths. Remediation requires either expanding the state space to encode the hidden history, adding state variables, or recognizing non-Markovian dynamics.

- **T3: Deterministic vs Nondeterministic vs Stochastic Transitions.**
  Deterministic transitions simplify reasoning but often misrepresent systems with real concurrency, timing variability, or stochasticity. Nondeterministic and probabilistic models are more faithful but harder to analyze, verify, and predict. A common failure is modeling a concurrent or race-prone system with a deterministic state machine and missing the interleavings that cause production bugs.

- **T4: State Explosion and Compositional Complexity.**
  Composing several independent state machines multiplies the state space combinatorially; naive flat representations become intractable (the product of n machines with k states each has k^n states). Hierarchical state machines, orthogonal regions (Harel), symbolic representations, and abstraction layers contain the explosion at the cost of representational complexity. A common failure is attempting to enumerate a flat product state space and failing to make progress<!-- FACT-D31-119 -->[^harel-1987].

- **T5: Observability and Hidden State Inference.**
  Some states are observable (directly distinguishable by external measurement); others are hidden, distinguishable only through their effect on outputs. Partially Observable Markov Processes (POMDPs) and Hidden Markov Models (HMMs) address this, but inference under partial observability is computationally hard. A common failure is designing a system assuming states are fully observable when they are not, leading to ambiguous or non-deterministic behavior from the observer's perspective.

- **T6: State Machine Equivalence and Minimization.**
  Two state machines may exhibit identical input-output behavior despite different internal state structures. Minimization (finding the smallest equivalent machine) is non-trivial, especially for nondeterministic machines. Failure to minimize can result in over-specification, brittle models, and difficulty in understanding which state distinctions actually matter<!-- FACT-D31-120 -->[^hopcroft-1979].

prose:
_(Pass B — deferred)_

### Catalog cross-references (v1, batches 02–29)

Solution archetypes that reference this prime (per `solution_archetypes/_catalog/catalog_v1_batches_02_29.yaml`):

**As source prime (13):** `adaptive_response_recalibration`, `checkpoint_and_rollback`, `control_surface_creation`, `controlled_phase_transition`, `creative_destruction_management`, `deadlock_resolution`, `explicit_state_modeling`, `guarded_state_transition`, `hysteresis_management`, `idempotent_operation_design`, `phase_space_mapping`, `queue_draining`, `scale_transition_management`

**As related prime (21):** `adaptive_reconfiguration`, `bioaccumulation_prevention`, `concurrency_control`, `convergence_guidance`, `cycle_breaking`, `deadlock_prevention`, `deterioration_monitoring`, `discrete_continuous_model_selection`, `divergence_detection_and_correction`, `fail_safe_default`, `homeostatic_regulation`, `irreversible_commitment_management`, `layered_record_accumulation`, `nonlinear_threshold_response`, `observability_instrumentation`, `plateau_detection_and_switching`, `saturation_avoidance`, `tipping_point_prevention`, `transactional_atomicity`, `transition_boundary_monitoring`, `versioned_evolution`

notes: State-and-State-Transition is held at High confidence. It is foundational in computer science (automata theory, formal languages, compilers, protocol design) and appears across physics, biology, control engineering, and organizational modeling. The Markov property and the notion of state space are ancient in physics (Lagrangian mechanics, Hamiltonian mechanics, thermodynamics) and modern in computation (Turing machines, finite automata, software architecture). The discipline of state-and-transition modeling has produced the mature field of model checking (Clarke, Grumberg, Peled; SPIN, TLA+) and has shaped the design of safety-critical systems. Key hazards include underspecified states (violating sufficient-summary), state explosion in composition, and confusion between state machines and process flows or event sequences. Modern evolution includes hierarchical state machines, reactive extensions, and actor models for concurrent systems.


Stated Preference

one_liner: Infer an agent's valuation from a solicited report rather than an enacted choice, gaining access to hypothetical or unchosen alternatives at the cost of a cheap, channel-shaped signal.

core_idea:
Stated preference is the evidence rule that infers an agent's valuation from what the agent reports when asked, rather than from what the agent chooses when real alternatives and consequences are present. The datum can be a ranking, survey response, declared willingness to pay, vote intention, policy preference, or model-generated statement of a desired policy.

The method earns its place because it reaches values that behavior cannot yet reveal. Analysts can ask about a public good with no market, a product that does not exist, a contingency the respondent has never faced, or an option excluded from the current menu. Revealed behavior is silent in those cases; a report is available.

The cost is that the report is usually cheap and channel-mediated. Nothing may be forgone by naming a preference, and the recorded answer is produced through wording, ordering, response options, social setting, incentives, and a respondent's ability to predict future behavior. The report can be sincere yet still differ from what the same agent would choose. Stated Preference therefore names an access mode to valuation, not a claim that the spoken answer is the valuation itself.

structural_signature:
- **The latent preference** — an ordering over alternatives that the analyst seeks to recover.
- **The reporting agent** — an entity able to state, rank, or otherwise communicate a valuation.
- **The elicitation act** — a question, prompt, survey, interview, or stated-choice task.
- **The report** — the recorded answer treated as evidence about the latent ordering.
- **The low-cost channel** — reporting usually imposes less opportunity cost than acting.
- **The reach advantage** — hypothetical, unavailable, public, or future alternatives can be valued.
- **The distortion profile** — hypothetical bias, strategic response, social desirability, articulation limits, and channel contribution.
- **The validity test** — change the channel or compare later behavior and observe whether the inferred ordering persists.

what_it_is_not:
- **Not Preference itself.** Preference is the ordering; Stated Preference is one inference method for accessing it.
- **Not Revealed Preference.** Revealed Preference credits observed choice under a real menu; Stated Preference credits a solicited report.
- **Not automatically false or insincere.** A report can be sincere and still fail to predict action because the future choice context differs or the respondent cannot forecast it.
- **Not merely Cheap Talk.** Strategic costless speech is one failure mode, but channel effects and hypothetical error occur without deception.
- **Not a survey format.** Surveys are one implementation; interviews, stated-choice experiments, and model prompts instantiate the same report-as-evidence relation.

broad_use:
- **Environmental and public economics:** contingent valuation where no market purchase reveals value.
- **Transportation and product design:** stated-choice experiments over services or features not yet offered.
- **Polling and public policy:** declared priorities, vote intention, and support for hypothetical policies.
- **Psychology and health:** self-reported attitudes, intentions, treatment preferences, and risk tolerance.
- **User research and marketing:** claimed willingness to buy, switch, subscribe, or adopt.
- **Machine learning:** direct preference queries or stated policies compared with logged behavior.

clarity: The abstraction forces a clean distinction among three objects that are often collapsed: the agent's underlying valuation, the report produced by an instrument, and the analyst's inference from that report. Once separated, disagreement with later behavior is not automatically hypocrisy. It may identify a channel effect, a hypothetical-versus-situated context change, a constrained menu, or a genuinely unstable preference.

manages_complexity: Many instrument-specific disputes compress into one evidence-channel trade. The report channel has high reach and low behavioral cost; the choice channel has real stakes but limited menus and environmental confounds. An analyst can therefore choose the channel by what must be learned, then apply the appropriate validity checks instead of treating one source as universally superior.

abstract_reasoning:
Let \(P\) denote the latent preference, \(C_s\) the stated-preference elicitation channel, and \(R_s=f(P,C_s)\) the recorded report. Inference from \(R_s\) to \(P\) is an inverse problem. Identification improves when the analyst varies \(C_s\), introduces incentive-compatible consequences, checks internal consistency, or triangulates against choices produced under a documented menu and cost structure.

The decisive reasoning move is to ask what the report can reveal that behavior cannot, and what the channel can add that does not belong to the preference. That two-sided test prevents both reflexive dismissal of self-report and naive acceptance of it.

knowledge_transfer: The same structure runs through contingent valuation, polling, patient decision aids, user research, preference learning, and model evaluation. In every case, an agent is queried, the answer is treated as evidence of valuation, inaccessible alternatives become measurable, and validity depends on the response function of the elicitation channel. The application vocabulary changes; the report-versus-choice evidence rule does not.

notes:
Promoted from `prime_abstractions/v2_density_pilot/stated_preference.md`. The
density-pilot alias `say_do_gap` was deliberately removed and reserved for the
separate Stated–Revealed Preference Gap identity.