Incident Response

one_liner: A system facing acute, time-critical disruption engages a temporary command regime that prioritises stabilisation over diagnosis, accepts reversible degradation, defers root-cause analysis, and compresses decision authority for the duration of the acute phase only.

core_idea:
Incident response is the structural pattern by which a system facing an *acute, time-critical disruption* engages a temporary command regime that *prioritises stabilisation over diagnosis*, accepts *reversible degradation* to limit the disruption's blast radius, *defers root-cause investigation* until the acute phase ends, and *compresses decision authority* into a designated commander operating under explicit time pressure and partial information. The defining commitment is the inversion of normal-operations priorities: during the acute phase, the objective is *not* to optimise performance, *not* to identify causes, and *not* to follow standard deliberative process — it is to *get to a safe-but-degraded state quickly and preserve the ability to recover later*. The pattern names the acute phase as its own regime with its own optimisation target, distinct from the post-mortem phase that follows.

The structural commitments are six. A *trigger event* — breach, code, mass casualty, outage, hull breach, market dislocation — displaces the system from normal operation. A *detection step* registers the trigger after a non-trivial delay that itself matters operationally. A *containment step* limits the disruption's spread before any fix is attempted. A *stabilisation step* brings the system to a safe, often degraded, state. A *deferred root-cause analysis* does not begin until the acute phase closes, because root-cause analysis under time pressure is unreliable and competes with stabilisation for the same attention. And a *compressed command structure* — incident commander, attending physician, on-call SRE — absorbs decision authority normally distributed across the organisation, for the duration of the acute phase only. The prime's distinctive content is that without it, practitioners run normal-operations playbooks during incidents — full information-gathering, distributed decision-making, deliberative consultation, optimisation for outcome quality — and the playbook actively degrades acute-phase outcomes. The prime licenses the otherwise-counterintuitive moves: act on partial information, accept rework, override normal authority chains, truncate diagnosis, and preserve forensic state for later rather than acting on it now. The pattern is heavily institutional and human-organisational — it presupposes roles, command structures, and a practice of hazard management — which is why its vocabulary (commander, containment, post-mortem) travels as a recognisable institutional form.

structural_signature:
*the trigger event displacing the system from normal operation* — *the detection step with operationally consequential delay* — *the containment step limiting spread before any fix* — *the stabilisation step reaching a safe degraded state* — *the deferred root-cause analysis held until the acute phase closes* — *the compressed command structure absorbing distributed authority for the duration only*

A configuration exhibits incident response when each of the following holds:

- **A trigger event.** An acute, time-critical disruption displaces the system from normal operation: a breach, a code, a mass casualty, an outage, a hull breach, a market dislocation.
- **A detection step.** The trigger is registered after a non-trivial delay that itself matters operationally — the gap between onset and recognition is part of the structure, not incidental.
- **A containment step.** The disruption's spread is limited before any fix is attempted — isolate not eradicate, tourniquet not amputate — the load-bearing inversion of normal optimisation.
- **A stabilisation step.** The system is brought to a safe, often degraded, state; the acute-phase objective is a safe-but-degraded state quickly, not optimal performance.
- **A deferred root-cause analysis.** Diagnosis does not begin until the acute phase closes, because root-cause work under time pressure is unreliable and competes with stabilisation for the same attention; forensic state is preserved for it rather than acted on now.
- **A compressed command structure.** A designated commander — incident commander, attending, on-call SRE — absorbs decision authority normally distributed across the organisation, for the duration of the acute phase only, then relinquishes it in the post-mortem phase.

The components compose around the phase inversion: the acute phase is its own regime with its own optimisation target (safe degraded state, forensic state preserved, cause unknown), distinct from the post-mortem phase (cause identified, capacity improved) — and acute-phase capability is bounded by the playbook staged in the calm phase, not by acute-phase talent.

what_it_is_not:
- **Not `controlled_reentry`.** Controlled reentry is the
  *governed return* of a system to normal operation across a
  boundary; incident response is the *acute-phase regime
  itself* — stabilise, contain, defer diagnosis — that
  precedes any reentry. Reentry is what follows recovery, not
  this.
- **Not `event_lifecycle_phases`.** That spans the whole
  pre/event/post trichotomy and allocates effort across all
  three; incident response is the *acute phase specifically*,
  with its own inverted optimisation target.
- **Not `failure_mode_and_effects_analysis_fmea`.** FMEA is
  *pre-event* hazard cataloguing; incident response is the
  *time-critical acute handling* once a hazard fires, where
  root-cause analysis is deliberately deferred.
- **Not `local_autonomy_tiered_escalation`.** That is a
  standing governance structure for routing decisions;
  incident response *compresses* distributed authority into
  one commander for the acute phase only, then relinquishes
  it.
- **Not `containment` alone.** Containment is one step within
  incident response (limit spread before fixing); the prime
  is the whole six-element regime — trigger, detection,
  containment, stabilisation, deferred root-cause, compressed
  command.
- **Common misclassification.** Running normal-operations
  priorities during the acute phase — full information,
  distributed authority, root-cause focus — so the system
  bleeds while the team diagnoses. Catch it by asking "are we
  still in the acute phase?"; if yes, stabilise first and
  defer diagnosis.

broad_use:
- **Cybersecurity incident response**: the NIST IR cycle (Detection & Analysis → Containment, Eradication, Recovery → Post-Incident Activity) and the SANS PICERL model carry the skeleton, with containment-before-eradication as the load-bearing inversion — a compromised host is isolated before the attacker is removed.<!-- FACT-D64-851 -->[^nist-800-61]
- **Emergency medicine**: the ABC primary survey stabilises before the secondary survey diagnoses, and ATLS codifies treat-as-you-go — intervene on a collapsing airway before any imaging completes; diagnosis is deferred to the post-resuscitation workup.<!-- FACT-D64-852 -->[^atls-2018]
- **Disaster management**: the FEMA/ICS framework distinguishes the command-driven Response phase from the distributed-authority Recovery phase, and the Incident Command System was developed precisely because distributed authority failed during acute wildfire response.<!-- FACT-D64-853 -->[^fema-ics-2018]
- **Site reliability engineering**: mitigate before resolve; MTTA and MTTR are the acute-phase metrics, and root-cause analysis lives in the post-mortem after the page is closed.<!-- FACT-D64-854 -->[^beyer-etal-2016]
- **Military operations**: immediate-action and react-to-contact drills and "consolidate before exploit" doctrine compress authority into the small-unit leader for the duration of contact, with full intelligence assessment coming after consolidation.<!-- FACT-D64-855 -->[^us-army-fm-3-21-8-2007]
- **Aviation, public health, and financial response**: aviate-navigate-communicate ordering and engine-fire memory items, WHO outbreak-response authority compression, and central-bank weekend liquidity operations all run the acute-phase regime — act now on partial information, accept reversible footprint, preserve the ability to unwind.<!-- FACT-D64-856 -->[^faa-amt-handbook-2018]

clarity:
Naming the pattern clarifies a distinction operators chronically blur: the difference between the *acute phase* and the *post-mortem phase*. Each has its own optimisation target, information regime, authority structure, and metrics, and running post-mortem priorities during the acute phase — full information, distributed authority, root-cause focus — produces predictable failure: patients code while the team awaits imaging, outages extend while engineers debate cause, intruders pivot while the team negotiates whether to isolate. The clarifying force is to make the phase boundary an explicit object, so that at any moment the question "are we still in the acute phase?" has a definite answer and determines which playbook applies.

The prime also clarifies *what counts as success in each phase*. The acute phase succeeds when the system reaches a safe degraded state with forensic state preserved, *regardless of whether the cause is known*; the post-mortem phase succeeds when the cause is identified and response capacity improved, *regardless of how quickly it produces its answer*. Conflating the two metrics produces the characteristic anti-patterns — heroic debugging during an outage, trauma-bay analysis paralysis, security incidents that tip off the attacker while investigators discuss attribution. By separating the success criteria, the prime makes legible why moves that feel wasteful in the acute phase (snapshotting a host, preserving evidence, reverting rather than fixing forward) are correct: the regret for skipping them is paid later, in the post-mortem phase, when the unpreserved state would have made the difference.

manages_complexity:
The pattern manages complexity by compressing a large family of substrate-local frameworks — NIST IR, SANS PICERL, ATLS/ABCDE, ICS, SRE incident management, react-to-contact drills, WHO outbreak response, aviation emergency procedures, fire-service incident command — into a single six-element skeleton. Each substrate independently developed the same regime under local terminology, and the prime shows them as one pattern, absorbing the false impression that each domain's emergency practice is sui generis.

A second compression is a shared intervention catalogue: *pre-staged playbooks* (memory items, checklists, runbooks); *named command roles* with explicit authority (incident commander, attending, scene commander); *time-pressure rituals* (the standup, the bridge call, the trauma-bay call-outs); *reversibility-preserving moves* (isolate not eradicate, tourniquet not amputate, rollback before fix-forward); and *forensic-preservation discipline* (don't reboot the compromised host, bag the patient's clothes, capture the crash dump). These transfer wholesale across substrates, and the prime makes their shared structure explicit so that a team in one substrate can borrow from another. The catalogue also encodes portable trade-offs: the *reversibility-versus-throughput* trade (reversible moves are cheap when wrong, because the diagnosis is partial), the *authority-compression cost* (the commander makes calls the organisation would not have endorsed in normal times, paid willingly because distributed authority during acute response is worse), and *pre-staging arbitrage* (acute-phase capability is bounded by the playbook staged in the calm phase, not by acute-phase talent). Each trade-off is the same shape in every substrate, which is what lets the prime guide design before an incident rather than only explain it after.

abstract_reasoning:
The prime trains a reasoner to ask, at any moment of disruption, "are we still in the acute phase?" — a question with a definite answer that determines which playbook applies — and to recognise cross-phase mistakes as the characteristic failure mode. It licenses several substrate-neutral inferences. The first is the *reversibility-versus-throughput trade-off*: because the diagnosis is partial, acute-phase moves should be reversible when possible, since a reversible move that turns out wrong is cheap while an irreversible one is expensive — an ordering that inverts the normal-operations preference for one-shot optimal action. The second is the *authority-compression cost*: compressed authority during the acute phase has a real cost the organisation must accept, paid willingly because distributed authority during acute response is worse, and reasserted in the post-mortem phase.

The deeper inferences concern what feels wasteful in the moment but is not. *Forensic-preservation regret* recognises that most preserve-state moves feel wasteful during the acute phase ("we could have moved faster if we had not snapshotted the host") but that the regret is paid later, in the post-mortem, when the unpreserved state would have made the difference — so the prime licenses paying the small acute-phase cost to avoid the large post-mortem cost. *Pre-staging arbitrage* recognises that the playbook a team can execute in the acute phase is the one they staged in the calm phase, so acute-phase capability is bounded by pre-staging investment rather than by acute-phase intelligence or talent — a conclusion that redirects effort toward preparation rather than heroics. The reasoner is thereby led to evaluate each move by its phase (acute or post-mortem), its reversibility, and its forensic consequence, and to judge an incident's eventual handling by what was staged before it began.

knowledge_transfer:
The transferable content is the *trigger / detection / containment / stabilisation / deferred-root-cause / compressed-authority* diagnostic together with the intervention catalogue (pre-staged playbooks, named command roles, time-pressure rituals, reversibility preservation, forensic preservation). The role mappings are regular: the *trigger* maps to a breach, a code, an outage, a hull breach; the *commander* maps to an incident commander, an attending physician, a scene commander, a pilot-in-command; the *containment move* maps to host isolation, tourniquet application, traffic-shift, hasty defence; the *post-mortem* maps to the after-action review, the morbidity-and-mortality conference, the blameless retro.

The transfers are documented institutional migrations, not loose analogies. The Incident Command System moved from fire service into emergency management, then into hospital incident command, then into private-sector business continuity.<!-- FACT-D64-857 -->[^fema-ics-2018] Crew Resource Management moved from aviation into surgical teams and trauma resuscitation.<!-- FACT-D64-858 -->[^gaba-2000] The SRE incident-command model is explicitly modelled on NIMS/ICS, and tabletop-exercise methodology moved from military and fire-service roots into cybersecurity, public health, and business continuity.<!-- FACT-D64-859 -->[^beyer-etal-2016] Each migration carries the same six-element skeleton and the same intervention catalogue, retuned with substrate-specific tooling. The load-bearing recognition that transfers is the phase inversion itself: when something is going wrong fast, get the system to a safe degraded state first, give one person authority to call the shots, and defer figuring out why until after the bleeding stops. That sentence does work in cybersecurity, emergency medicine, disaster management, SRE, military operations, aviation, maritime emergencies, public-health outbreaks, and financial-market crises. Because the pattern is bound to human-organisational hazard practice — roles, command structures, and institutional playbooks are constitutive of it — the transfer is between organisations' ways of handling acute disruption, and the institutional vocabulary travels as a recognisable form even though the specific hazards and tools differ by substrate.

formal_abstract: Emergency-medicine trauma resuscitation under the ATLS/ABC protocol is the prime's most rigorously codified instance, where the phase inversion is doctrine. The *trigger event* is a major-trauma patient arriving in the bay. The *detection step* is the primary survey, whose speed is operationally consequential. The *containment step* is the load-bearing inversion: the ABC ordering — Airway, Breathing, Circulation — stabilises before it diagnoses, so a collapsing airway is secured *before* any imaging completes, the medical analogue of "isolate, don't eradicate."<!-- FACT-D64-860 -->[^atls-2018] The *stabilisation step* brings the patient to a safe-but-degraded state (perfusing, ventilating) rather than to optimal health. The *deferred root-cause analysis* is the secondary survey and definitive workup, held until after the acute phase, because diagnosis under time pressure is unreliable and competes with stabilisation for the same attention. The *compressed command structure* is the trauma-team leader who absorbs decision authority normally distributed across the team, for the duration of the resuscitation only. The prime's phase inversion is the core insight: running post-mortem priorities (full information, distributed authority, root-cause focus) during the acute phase produces the characteristic failure — the patient codes while the team awaits a CT. The success criterion is phase-specific — the acute phase succeeds when the patient is stabilised with diagnostic options preserved, *regardless of whether the cause is yet known*. And acute-phase capability is bounded by pre-staging: the team executes the playbook it drilled in the calm phase, so simulation is investment, not training. **Mapped back:** the trauma patient is the trigger, the primary survey is detection, ABC stabilisation-before-diagnosis is the containment inversion, the secondary survey is the deferred root-cause, and the team leader is the compressed command — with cross-phase priority confusion (analysis paralysis in the bay) the predicted failure mode.

applied_industry: Two applied instances run the identical six-element skeleton, and the cross-substrate transfer is a documented institutional migration. First, cybersecurity incident response under the NIST/SANS models: the *trigger* is a breach; *detection* registers it after a consequential delay; *containment-before-eradication* is the inversion — a compromised host is *isolated* before the attacker is removed, preserving forensic state rather than rebooting it away;<!-- FACT-D64-861 -->[^nist-800-61] *stabilisation* reaches a safe degraded posture; *deferred root-cause* lives in the post-incident review; and a *compressed command structure* (the incident commander) absorbs authority for the duration. The reversibility-versus-throughput trade is explicit — reversible moves (traffic-shift, isolate) are cheap when the partial diagnosis turns out wrong — and forensic-preservation regret is real: snapshotting the host feels wasteful in the moment but pays off in the post-mortem when the unpreserved state would have been decisive. Second, site reliability engineering: the *trigger* is an outage, *containment* is mitigate-before-resolve (roll back before fix-forward), the acute metrics are mean-time-to-acknowledge and mean-time-to-recover, and root-cause analysis is deferred to the blameless post-mortem.<!-- FACT-D64-862 -->[^beyer-etal-2016] The transfer is not analogy but lineage: the SRE incident-command model is explicitly modelled on the fire service's Incident Command System, which itself migrated into emergency management, hospital incident command, and business continuity.<!-- FACT-D64-863 -->[^beyer-etal-2016] The load-bearing recognition that transfers is the phase inversion sentence — when something is going wrong fast, get to a safe degraded state first, give one person authority, and defer diagnosis until after the bleeding stops — and it does work in security, SRE, medicine, and disaster response alike. **Mapped back:** breaches and outages are triggers; the incident commander and on-call SRE are the compressed commands; host isolation and rollback are the containment inversions; the post-incident review and blameless post-mortem are the deferred root-cause phases; and pre-staged playbooks bound acute-phase capability in every substrate, exactly as the prime predicts.


Incomplete Contract

type: prime

one_liner: A rule-set that deliberately leaves gaps and designates a handler to fill them.

status: draft

core_idea:
An incomplete contract is the structural pattern of *a rule-set that
deliberately leaves some contingencies unspecified*, in the knowledge that
exhaustive enumeration is impossible, prohibitively costly, or
unenforceable, and that *some other mechanism* — residual control rights,
renegotiation, default doctrines, trusted arbiters, norms, or good faith —
will fill the gap when the unspecified contingency arises.<!-- FACT-D64-876 -->[^hart-moore-1988] The defining
commitment is the *acknowledged gap* plus a *gap-filling regime*:
incomplete contracts are not bad contracts but honest ones about the
limits of foresight, language, verifiability, or enforcement.

Three features distinguish the pattern from "a contract with mistakes."
*Bounded foresight*: the parties recognise that the space of future states
is too large or uncertain to specify exhaustively. *Verifiability gap*:
even when a contingency can be described, some variables — effort, intent,
quality, future value — are hard for a third party to observe, so writing
them in is pointless without an oracle. *Designed residual authority*: the
contract specifies *who decides* when the unspecified case arises, even if
it cannot specify *what* they will decide. The structural signature is
therefore *gap + gap-handler + governance of the handler*: the gap is the
unwritten zone, the handler is the rule, person, doctrine, or norm that
activates when the zone is entered, and the governance is whatever
constrains the handler from abusing the discretion. Reading any rule-set
as "this part is specified, this part is left to a handler, this is how the
handler is held to account" is the structural move the prime supplies.
That move is abstractly general, but the prime's vocabulary and theory are
deeply law-and-economics-bound, and its non-human instances arrive largely
as analogies — which is why it sits at the framed end of the spectrum.

structural_signature:
*a rule-set covering some contingencies* — *an acknowledged gap of
unspecified contingencies* — *the bounded-foresight and verifiability
limits that force the gap* — *a designated gap-handler* — *a governance
regime constraining the handler* — *the discretion-abuse failure modes*

A rule-set is an incomplete contract when the following hold:

- **A partially specified rule-set.** An agreement, code, or specification
  that fixes outcomes for some contingencies explicitly.
- **An acknowledged gap.** A zone of contingencies deliberately left
  unspecified, recognised as such rather than overlooked — the unwritten
  part is a design choice, not a mistake.
- **The forces that make the gap necessary.** Bounded foresight (the state
  space is too large to enumerate), a verifiability gap (some variables —
  effort, intent, quality — cannot be observed by a third party), and
  enforcement cost together guarantee that residual gaps cannot be closed.
- **A designated gap-handler.** A named rule, person, doctrine, or process
  — residual control rights, a court, a regulator, a runtime, a learned
  prior — that activates when the unspecified contingency arises and
  decides *what* happens, even though the contract could not.
- **A governance regime.** Whatever constrains the handler from abusing
  its discretion: fiduciary duty, precedent, appeal, audit, reciprocity,
  market discipline.
- **The discretion-abuse failure modes.** Because the handler holds
  residual authority, the characteristic risks are hold-up, opportunism,
  handler overreach, capture, and drift.

These compose into one move: replace the impossible task of specifying
every contingency with the bounded task of locating the gaps, designating
a handler for them, and governing that handler.

what_it_is_not:
- **Not a contract with mistakes.** An incomplete contract leaves gaps
  *deliberately and knowingly*, having decided exhaustive specification is
  impossible or not worth it; a defective contract has gaps by *error*. The
  presence of a designated gap-handler is what marks the difference.
- **Not an `interface`.** An interface specifies a *complete* contract over
  the cases it covers — every legal call has a defined behaviour; an
  incomplete contract explicitly admits cases it does *not* cover and
  routes them to a handler with discretion.
- **Not `optionality`.** Optionality is a held *right to act later*; an
  incomplete contract is an *acknowledged gap in specification* plus a
  regime for filling it. The gap is not anyone's option — it is an
  unspecified zone someone is empowered to resolve when it arises.
- **Not `authority_delegation_under_uncertainty`.** Delegation hands a
  *decision right* to an agent within a defined scope; an incomplete
  contract is the broader structure — gap, plus handler, plus *governance of
  the handler* — of which a delegated decision right is one possible
  gap-handling mechanism.
- **Not `discretion` alone.** Discretion is the *latitude* a handler enjoys
  in the gap; the incomplete contract is the full apparatus that *creates*
  the gap, *assigns* the handler, and *constrains* that discretion so it is
  not abused.
- **Common misclassification.** Reading any unspecified term as a drafting
  failure. Catch it by asking whether the gap is *acknowledged* and a
  *handler with governance* is designated; if so, the incompleteness is a
  rational design choice, not an oversight.

broad_use:
The skeleton recurs across substrates, anchored in human institutions. In
economics and contract theory it is the transaction-cost origin of
incomplete contracts, the property-rights view of ownership as residual
control, and relational contracting in long-term supply relationships.<!-- FACT-D64-877 -->[^williamson-1985] In
law and governance it is constitutional vagueness ("due process,"
"reasonable") delegating gap-filling to courts, common-law evolution as
ongoing gap-filling, and statutory gaps filled by regulatory agencies.<!-- FACT-D64-878 -->[^fallon-vagueness] In
diplomacy it is constructive ambiguity — deliberate vagueness that secures
agreement despite irreconcilable underlying preferences — and arbitration
clauses naming a handler without resolving the dispute.<!-- FACT-D64-879 -->[^pehar-2001] In organisations
it is open-ended employment where the employer holds residual decision
rights over unnamed tasks. In software engineering it is specifications
that intentionally underspecify (ordering of side effects, behaviour under
malformed input, timing) and rely on conventions, defaults, or designated
handlers such as the compiler or runtime. In biology it is genomes that
underspecify developmental outcomes, with environmental cues and
epigenetic state filling the gap. In AI alignment it is reward functions
and prompts as incomplete specifications whose gap-handlers are learned
priors and fallback rules — the specification problem being fundamentally
contract incompleteness.<!-- FACT-D64-880 -->[^russell-norvig-2021] These instances share the three structural
features and the same family of failure modes — hold-up, opportunism,
overreach by the handler, drift — though the non-human cases are read as
analogies to the legal-economic original.

clarity:
The prime makes visible that *the unwritten parts of any rule-set are not
omissions but design choices*. Once a practitioner sees this, the question
shifts from "did we write down enough?" — a losing race — to "who handles
the gap, and how is that handler constrained?" This reframes contract
drafting, statutory interpretation, API design, and treaty negotiation
around the same three knobs. The pattern also exposes the *hidden
completeness premise* in naive specification work: the assumption that a
thorough enough specification can close all gaps. The prime explains why
that is structurally impossible — bounded foresight, verifiability limits,
and enforcement costs guarantee residual gaps — and where to put one's
energy instead, namely on the handler and its governance. The clarifying
force is to convert an open-ended drafting ambition into a bounded design
problem: locate the gaps, designate a handler, and constrain it.

manages_complexity:
The prime replaces the impossible task of *specifying every contingency*
with the tractable task of *designing the gap-handler*. The combinatorial
blow-up of contingency enumeration is absorbed by delegating to a process
or a person who handles the open set at runtime — the same
complexity-management move that virtual memory makes for address spaces,
that exception handling makes for control flow, and that constitutional
vagueness makes for legislation: trade exhaustive enumeration for
principled delegation.<!-- FACT-D64-881 -->[^tanenbaum-bos-2015] The cost is a *new* question — who gets the residual
authority and how is it constrained — but that is one design problem
instead of infinitely many. The management payoff is that an unbounded
specification burden collapses into a single, bounded governance design:
rather than anticipating every future state, the rule-set names an
authority to decide the unanticipated and a regime to keep that authority
honest.

abstract_reasoning:
The pattern enables a set of substrate-independent questions. *Where are
the gaps?* — which contingencies are unspecified, unverifiable, or
unenforceable. *Who is the gap-handler?* — residual control rights, default
doctrines, courts, regulators, the runtime, learned priors. *What
constrains the handler?* — fiduciary duty, precedent, reciprocity, market
discipline, audit, a principal-agent contract. *What is the failure mode of
unconstrained discretion?* — hold-up, regulatory capture, scope creep,
undefined-behaviour bugs, reward hacking. *Can the gap be partially
closed?* — more specific clauses, better monitoring, machine-verifiable
specs — and at what drafting or verification cost? These questions transfer
across contract law, constitutional design, software specification,
alignment, and organisational charter design. The reasoner asks, of any
rule-set: where is it deliberately silent, who decides in that silence, and
what holds that decider to account?

knowledge_transfer:
The intervention catalog carries portable design knobs. *Default-rule
design*: choose defaults the majority of parties would have negotiated to,
reducing the handler's load. *Residual-control assignment*: give residual
rights to the party whose ex-post investment matters most, which applies to
ownership, custody, project authority, and model-output control.
*Constraining-process design*: bind the handler with precedent, appeal,
reviewability, audit, or arbitration, applying to judges, regulators,
project managers, and AI policy heads. *Renegotiation-cost reduction*:
cheaper renegotiation tolerates more incompleteness, more expensive
renegotiation pushes toward more upfront specification. And *constructive
ambiguity*: when full specification would block agreement, deliberate
incompleteness can be rational, applying to treaties, coalition agreements,
and product roadmaps. The role mappings are direct: gap ↔ unspecified
contingency / undefined behaviour / unfilled developmental outcome,
handler ↔ residual-rights holder / court / runtime / learned prior,
handler constraint ↔ fiduciary duty / precedent / oversight board /
appeal, failure mode ↔ hold-up / capture / undefined-behaviour bug /
reward hacking. A municipality contracting a 30-year wastewater plant
makes three incomplete-contract choices — gap location (rates specified,
future regulatory compliance left open), handler design (a regulatory
adjustment commission plus reversion of residual control to the city), and
handler constraint (state-board review, arbitration, a seven-year
renegotiation right) — and the identical three-knob design recurs in API
contracts (versioning policy plus a breaking-change committee plus an RFC
process) and AI deployment (reward function plus fine-tuning plus an
oversight board). Because the vocabulary and theory are inherently
law-and-economics-bound and the non-human instances are analogies, the
transfer is the deliberate carrying of a legal-economic design discipline —
gap, handler, governance — into other substrates, rather than the
recognition of a substrate-free structure already present in them.

formal_abstract:
Take the property-rights theory of the firm (the Grossman–Hart–Moore
framework) as the rigorous instance, since it is where the prime received
its sharpest formal treatment.<!-- FACT-D64-882 -->[^grossman-hart-1986] Two parties — a buyer and a supplier — must
make relationship-specific investments whose value depends on a future
transaction whose terms cannot be fully written down. The *partially
specified rule-set* fixes price and quantity for foreseeable states; the
*acknowledged gap* is the open set of contingencies (a novel design change,
an unanticipated cost shock) that *bounded foresight* and a *verifiability
gap* — effort and quality are observable to the parties but not provable to
a court — make impossible to enumerate. The theory's central move is the
prime's *designated gap-handler*: ownership is defined as *residual control
rights*, the authority to decide whatever the contract left open.<!-- FACT-D64-883 -->[^hart-1995] Whoever
owns the critical asset decides in the gap, and the *governance regime* is
the surrounding law of property and the threat of inefficient renegotiation.
The *discretion-abuse failure mode* the prime names appears precisely as
*hold-up*: the residual-rights holder can, ex post, threaten to withhold
cooperation to extract the other party's specific investment, which
distorts ex-ante investment incentives.<!-- FACT-D64-884 -->[^klein-etal-1978] The intervention the framework
derives is exactly the prime's: assign residual control to the party whose
non-contractible investment matters most, so the unavoidable discretion
lands where it does least damage<!-- FACT-D64-885 -->[^grossman-hart-1986] — a result about *who should own what* that
follows directly from reading the relationship as gap-plus-handler.

**Mapped back:** The property-rights model instantiates every role —
specified terms, an acknowledged unverifiable gap, ownership as the
residual-control handler, property law as governance, hold-up as the
discretion-abuse mode — and shows the prime converting an impossible
complete-contracting problem into a tractable question of authority
allocation.

applied_industry:
Consider a 30-year municipal wastewater-treatment concession and a
software API's versioning policy as two applied instances. The wastewater
contract *specifies* current rates and service levels but cannot specify
*future regulatory compliance* over three decades — the *acknowledged gap*
forced by bounded foresight. The city designs a *gap-handler*: a regulatory
adjustment commission empowered to reset terms when rules change, with
*residual control* reverting to the city.<!-- FACT-D64-886 -->[^guasch-2004] The *governance regime*
constraining that handler is layered — state-board review, mandatory
arbitration, and a seven-year renegotiation right — directly answering the
prime's "what holds the decider to account?"<!-- FACT-D64-887 -->[^guasch-2004] The forecast *failure modes*
are hold-up (the private operator threatening service degradation to extract
rate increases) and handler capture (the commission co-opted by the
operator), both anticipated at design time by the constraining process. An
API contract runs the identical three-knob design: the spec deliberately
underspecifies behaviour under future feature requests (the gap), names a
breaking-change committee plus an RFC process as the *handler*, and binds it
with a published versioning policy and deprecation timeline as *governance* —
guarding against the analog of overreach, an unannounced breaking change
that strands downstream consumers.<!-- FACT-D64-888 -->[^semver-2013] The transferable intervention: stop
racing to specify every future state, and instead locate the gap, name the
handler, and constrain it.

**Mapped back:** The concession and the API policy both run the prime
end-to-end — specified core, acknowledged gap, designated handler, governing
constraints, forecast discretion-abuse modes — confirming that the same
gap/handler/governance discipline carries from a public-works contract to a
software interface, even as the vocabulary remains law-and-economics in
origin.


Inconsistent Shared Model

one_liner: Subsystems hold mutually incompatible models of the same external state, undetected until a forcing event requires joint action.

core_idea:
An inconsistent shared model is the pattern in which two or more subsystems persist with mutually incompatible models of a shared external state. Each subsystem's model is internally consistent and was derived from a defensible inference process over the data that subsystem had access to. The inconsistency is detected only when a downstream consumer tries to act on both models at once, at which point one or both models must be revised under time pressure, or the action proceeds in a fragmented way that exposes the inconsistency to the world. It is structurally distinct from *information asymmetry* (one party simply knows more), from *disagreement* (parties hold different evaluative opinions), and from *belief revision* (a single agent updating): it is the specific case where two or more subsystems represent the *same external state* differently, each believes its representation is ground truth, neither has the information that would force a reconciliation, and a downstream layer relies on their joint output.

Three commitments fix the shape. First, a *shared referent*: there must be a single external state both subsystems model, not two related-but-distinct states. Second, *persistent inconsistent representations*: each subsystem's model survives unchallenged because the subsystems do not directly compare notes — their channels of inference do not cross. Third, a *forcing event*: a downstream consumer (a coordinated action, a joint output, a customer touch-point, a regulator request) requires both models to ground its decision, and the inconsistency becomes load-bearing at that moment. Without the forcing event the inconsistency would remain invisible; with it, the cost lands all at once and under time pressure. The decisive feature is *structural invisibility before the forcing event*: the inconsistency exists but cannot be observed without explicit reconciliation machinery, so a system can be exposed even when every subsystem is working correctly.

structural_signature:
*the single shared referent* — *the two-or-more internally-consistent subsystem models* — *the uncoupled inference channels* — *the structural invisibility before comparison* — *the forcing event requiring joint action* — *the symmetry invariant (neither subsystem more informed overall)*

A configuration is an inconsistent shared model when each of the following holds:

- **A single shared referent.** There is one external state that two or more subsystems each model — not two related-but-distinct states. The referent's singularity is what makes the divergence a genuine inconsistency rather than mere difference.
- **Multiple internally-consistent subsystem models.** Each subsystem holds a model that is internally coherent and was produced by a defensible inference over the data that subsystem had. No subsystem is malfunctioning.
- **Uncoupled inference channels.** The subsystems do not directly compare notes; their channels of inference do not cross, so each model survives unchallenged.
- **Structural invisibility before comparison.** The inconsistency exists but cannot be observed without explicit reconciliation machinery. A system can be exposed even when every subsystem inspected individually is working correctly.
- **A forcing event.** A downstream consumer requires both models to ground a single joint action, output, or decision, at which point the inconsistency becomes load-bearing — typically all at once and under time pressure.
- **A symmetry invariant.** Neither subsystem is more informed overall; both have defensible models from their own data. This distinguishes the pattern from information asymmetry and rules out "give one party the missing data" as the fix.

These components reduce to three system-level rates — each model's update frequency, the coupling rate between models, and the forcing-event distribution — so inconsistency exposure can be reasoned about and intervened on without enumerating individual disagreements.

what_it_is_not:
- **Not `information_asymmetry`.** Asymmetry is the case where one party knows *more*; this prime is the *symmetric* case where neither subsystem is more informed overall, each holds a defensible model from its own data, and "give one party the missing data" is not the fix.
- **Not `coordination`.** Coordination is the achievement of aligned joint action; this prime is the pre-design or design-failure state in which the subsystems *cannot* align because no channel surfaces the inconsistency before the forcing event. Consistency protocols and consensus are the engineered responses, not this.
- **Not a `consensus_problem`.** A consensus problem assumes the parties are actively trying to agree on a value; here the subsystems are not even comparing notes — the inconsistency is structurally invisible until joint action forces it.
- **Not `confounding`.** Confounding is a spurious association from an unmeasured common cause in a single inference. This is multiple subsystems each running a *defensible* inference over different data, with no shared inference at all.
- **Not value `disagreement` or conflict.** The subsystems are not pursuing different objectives (a value clash); they model the *same external state* differently, each believing its representation is ground truth. Reconciling values will not fix a model inconsistency.
- **Common misclassification.** Diagnosing the pathology as a knowledge gap and pouring data into one side. The remedy is a *comparison channel* between symmetric models, not a data transfer; "just inform them" is aimed at the wrong structure.

broad_use: The same shared-referent, uncoupled-channels, forcing-event skeleton recurs across substrates that share no mechanism. In **distributed computing**, split-brain failures (two leaders each authoritative after a partition) and eventual-consistency anomalies (two replicas reading and writing divergent values until reconciliation) are exactly this pattern, and the CAP-theorem trade-off characterises how a system handles it when a partition heals.<!-- FACT-D64-901 -->[^brewer-2000] In **sensor fusion**, a vehicle's lidar and camera disagree about an object's presence, position, or class, and the planner must act under the disagreement; aviation experiences the same on disagreeing airspeed or attitude sensors, with several catastrophic accidents traced to an inconsistent shared model plus a crew unable to resolve it under time pressure.<!-- FACT-D64-902 -->[^bea-2012] In **multi-database systems**, a data warehouse and an operational store report different numbers for the same business question until a regulator or board forces reconciliation. In **organisations**, sales-and-operations disconnect (incompatible demand forecasts discovered only at a quarterly shortfall) and pre-crisis intelligence-sharing failures (each agency holding a defensible threat model from its own collection, the joint model never existing until the forcing event) instantiate it. The pattern extends to **cognitive science** (bilateral hemispheric disconnect, each hemisphere internally consistent until a motor command needs both), **public health** (epidemiology and clinical-care models with measurably different transmission parameters, surfacing only at policy formulation), **multi-team software engineering** (backend and frontend holding divergent models of a shared API contract until deployment forces joint execution, a Conway's-law inevitability<!-- FACT-D64-903 -->[^conway-1968]), **diplomacy** (two allied nations modelling a third country's intentions), and **regulation** (two regulators with differing models of the same activity until a cross-agency action forces both to ground the same case). In every case the vocabulary travels unchanged.

clarity:
The prime clarifies by separating three failure modes that collapse together when one has only the language of "they disagreed." *One subsystem is wrong* is an epistemic failure that the right data would fix. *The subsystems have different objectives* is a value conflict that data will not fix. *The subsystems hold inconsistent models of the same state under no pressure to compare* is a structural pathology that only forced comparison reveals — and it is its own thing, distinct from the other two. Naming it makes the upstream condition inspectable: do the subsystems share a channel that would surface inconsistency before the forcing event? If not, the system is structurally exposed even when every subsystem is working correctly, and that exposure is invisible to any inspection that examines the subsystems one at a time.

The clarifying force is sharpest at the boundaries with neighbouring patterns, which the prime distinguishes precisely. It is not information asymmetry, where one party knows more; it is the *symmetric* case, in which both parties have defensible models from their own data and neither is more informed overall. It is not the underdetermination-from-below of multiple equally good models of the *same* data; it is persistent inconsistency from above — different subsystems with different data, each producing a defensible model, with no channel to compare. It is not paradigm incommensurability, where vocabularies cannot align; the prime presupposes the subsystems share enough vocabulary to be comparable in principle, and lack only the channel to perform the comparison. And it is the pathology that consistency models, eventual consistency, and consensus protocols are the engineered responses to — the pre-design or design-failure state, not the contract or protocol that prevents or resolves it. Holding these apart keeps the prime from being mistaken for a knowledge gap, a value clash, or a solved coordination problem.

manages_complexity:
In a system with $N$ subsystems each modelling a shared state, the combinatorial space of possible inconsistencies is the $N(N-1)/2$ pairwise disagreements. The prime collapses this to three operational quantities: the *update frequency* of each subsystem's model, the *coupling rate* between subsystems (how often their models touch), and the *forcing-event distribution* (how often joint action is required). Inconsistency risk is a function of update frequency divided by coupling rate, evaluated at the forcing-event scale, and a designer can intervene on any of the three — slow updates, increase coupling, batch forcing events — without enumerating individual inconsistencies.<!-- FACT-D64-904 -->[^unverified-904] The intractable inspection of every pairwise model comparison becomes a tractable reasoning over a handful of system-level rates.

The compression is operational because it lets the analyst reason about the *structural exposure* of a system independent of the content of any particular disagreement. Two systems can be compared by their inconsistency-exposure profile — how many subsystems hold models of how many shared referents with what coupling — without examining the contents of any model, and a proposed architecture can be evaluated by predicting its exposure before it is built. The same exposure calculation applies to a distributed database, an aviation cockpit, a multinational coalition, and a multi-team software organisation, because the variables are substrate-neutral. The prime thereby converts a problem that appears to require modelling every subsystem's beliefs into one that requires only counting subsystems, referents, and coupling, and reasoning about three rates — a decisive reduction in what the designer must hold in mind.

abstract_reasoning:
The prime supports reasoning about a system's structural exposure independent of the content of any disagreement. The reasoner asks: how many subsystems model how many shared referents? How often does each subsystem's model update, and how often do the models touch? How often does a forcing event require joint action grounded in multiple models? Because these questions reference only the abstract roles — shared referent, internally consistent subsystem models, uncoupled inference channels, forcing event — they apply to a distributed database, a cockpit, a coalition, or a software organisation without translation, and a proposed architecture can be evaluated by predicting its inconsistency-exposure before it is built.

Several reusable moves follow. The exposure-profile move treats inconsistency risk as a function of update frequency over coupling rate at the forcing-event scale, so the reasoner can rank architectures by exposure without inspecting any model's content. The forcing-event move recognises that the danger is not the inconsistency itself but the moment joint action requires reconciliation under time pressure, which directs design attention to making comparison happen *before* that moment rather than at it. The symmetry move distinguishes this pattern from information asymmetry by noting that neither subsystem is more informed overall, which rules out "give one party the missing data" as a fix and points instead to building a comparison channel. And the fail-safe move recognises that some forcing events will arrive before reconciliation, so the system needs a pre-declared behaviour for that case — defer, escalate, pick the conservative model, or refuse and log — rather than an improvised one. The same reasoning that tells a distributed-systems engineer to add anti-entropy between replicas tells an intelligence officer to schedule deconfliction calls between agencies, because both are reasoning about coupling rate against forcing-event scale.

knowledge_transfer:
The transferable content is a family of constant-shape interventions that practitioners in unrelated domains recognise as instances of one another. Establishing a single source of truth for the shared referent — master-data management, primary-source designation, leader election, authoritative-system designation — removes the symmetric ambiguity by fiat.<!-- FACT-D64-905 -->[^loshin-2009] Scheduling explicit reconciliation cadences ensures the forcing event is not the first comparison: sales-and-operations meetings, per-frame sensor-fusion checks, daily standups, and anti-entropy in distributed databases are the same move. Introducing a reconciliation protocol the subsystems run voluntarily and recurrently — two-phase commit, vector clocks, consensus protocols, fact-check meetings, sensor-fusion arbiters with explicit uncertainty, CRDTs — is the same move again.<!-- FACT-D64-906 -->[^lamport-1978] Making inconsistency observable before the forcing event — dashboards showing model divergence, alarms on cross-system metric drift, cross-team review of shared assumptions, intelligence-community deconfliction — converts a hidden exposure into a watched signal. And designing fail-safe behaviour for the forcing event itself gives the system a pre-declared response rather than an improvisation.

The transfer is deep because these interventions are not analogies but instances of one structural toolkit applied to one structural problem: a vector-clock reconciliation and a quarterly sales-and-operations meeting are doing the same work. A regional hospital network running three coexisting electronic-health-record systems makes the mapping concrete. Each system maintains its own model of every patient's allergy list, populated by the clinicians who use it; a patient has an allergy recorded at one hospital, presents at another whose record shows none, and is prescribed the contraindicated drug. The three models were each internally consistent, each derived from a defensible inference (the clinician recorded what they were told), none was challenged because the systems did not exchange notes, and the inconsistency was structurally invisible until the forcing event — a prescription requiring the joint allergy model — made it load-bearing. The matched interventions are the substrate-independent toolkit: designate a master allergy list (single source of truth), reconcile at each admission (cadence), run a bridge service that pushes updates across systems (protocol), show a divergence warning when the local list differs from the network reference (observability), and require explicit override when the systems disagree on prescription (fail-safe). Because the toolkit is substrate-neutral, a clinician designing this safeguard and a distributed-systems engineer designing anti-entropy are applying the same five moves to the same structural pathology, and a practitioner who has internalised it in one domain can specify the safeguard for another on first contact.

formal_abstract:
A distributed key-value store under network partition is the pattern in its cleanest engineered form. The *single shared referent* is the current value of one key — say an account balance. Two replicas, A and B, each hold a model of that value. Before the partition both agree; then the network splits them into two sides that can still each take client writes. The *uncoupled inference channel* is now literal: the replication link between A and B is severed, so each continues to apply writes and update its own *internally consistent* model with no way to compare against the other.<!-- FACT-D64-907 -->[^gilbert-lynch-2002] A accepts a deposit and reads balance 150; B accepts a withdrawal and reads balance 70; neither is malfunctioning, and the *symmetry invariant* holds exactly<!-- FACT-D64-908 -->[^gilbert-lynch-2002] — neither replica is more informed, each has a defensible model from the writes it saw. The inconsistency is *structurally invisible* during the partition: any inspection of A alone, or B alone, shows a coherent system. The *forcing event* is the partition healing, at which point a client read or a reconciliation pass requires a single authoritative value and the two models must be merged under time pressure. The CAP-theorem trade-off is precisely the menu of pre-declared responses: refuse writes during partition to preserve consistency (CP), or accept divergence and reconcile afterward via vector clocks or a CRDT merge function (AP).<!-- FACT-D64-909 -->[^gilbert-lynch-2002] The engineered toolkit maps one-to-one onto the prime's interventions — leader election (single source of truth), anti-entropy gossip (reconciliation cadence), vector clocks (reconciliation protocol), divergence metrics (observability), and a declared conflict-resolution rule (fail-safe).<!-- FACT-D64-910 -->[^shapiro-etal-2011]

**Mapped back:** The key's value is the shared referent, the two replicas are the internally-consistent subsystem models, the severed link is the uncoupled channel, partition-heal is the forcing event, and the CAP choice plus vector-clock merge are the reconciliation protocol and fail-safe — inconsistent shared model with the machinery made explicit.

applied_industry:
A regional hospital network running three coexisting electronic-health-record systems instantiates the identical structure in a substrate with no replication link at all. The *shared referent* is a single patient's allergy list. Each EHR maintains its own *internally consistent* model of it, populated by the clinicians who use that system; the *uncoupled channels* are the three vendors' databases that do not exchange notes. A patient has a penicillin allergy recorded at Hospital 1, presents at Hospital 2 whose record shows none, and is prescribed the contraindicated drug.<!-- FACT-D64-911 -->[^shaikh-etal-2025] Each model was defensibly derived — the clinician recorded what they were told — and the *symmetry invariant* holds: no system is "more right," each simply saw different encounters. The inconsistency is *structurally invisible* until the *forcing event*, a prescription decision that requires the joint allergy model and makes the divergence load-bearing all at once, under time pressure, with a patient in front of the prescriber. A second instance is intelligence-sharing before a coordinated threat: two agencies each hold a defensible threat model from their own collection, the channels never cross, and the joint model does not exist until an operational decision forces it.<!-- FACT-D64-912 -->[^kean-hamilton-2004] In both the matched five-move toolkit applies identically: designate a master list (single source of truth), reconcile at each admission or via scheduled deconfliction (cadence), run a bridge service or liaison that pushes updates across systems (protocol), surface a divergence warning when the local record differs from the network reference (observability), and require explicit override when systems disagree (fail-safe). A clinician designing the allergy safeguard and an intelligence officer designing deconfliction are running one playbook.

**Mapped back:** The allergy list and the threat picture are shared referents; the three EHRs and the two agencies are internally-consistent subsystems on uncoupled channels; the prescription and the operation are forcing events that make the hidden inconsistency load-bearing — the same prime in clinical informatics and intelligence coordination.


Increasing Returns

one_liner: Marginal benefit of each additional unit rises rather than falls as the cumulative state grows, compounding advantage.

review_flags: ACCEPTED as a prime from project-06 (Kurt, 2026-05-29). Promoted from candidate; review_flag history below.

review_flags: CANDIDATE from project-06. CONVERGENT signal: Claude flagged this gap in R11 when `network_effect -> economies_of_scale` was REJECTED by both models (demand-side vs supply-side mismatch, both being siblings under a missing umbrella). ChatGPT Pro independently surfaced the same gap and proposed the same slug, with an extended would-parent list including bubble dynamics and learning-curve effects.

review_flags: Would parent: network_effect (demand-side), economies_of_scale (supply-side), speculative_bubble, learning_curve_effects. The network_effect/economies_of_ scale pair is the canonical use case.

review_flags: Distinct from `feedback` (feedback is the dynamic loop; increasing_returns is the PAYOFF STRUCTURE that often makes the loop positive) and from `economies_of_scale` (which is supply-side cost decline, one specific kind).

review_flags: substrate_independence scores are PROPOSER estimates, pending triangulation.

review_flags: 2026-05-28 schema reformat: original project-06 v1 used the 5-section template (Core Idea / Broad Use / Structural Signature / Distinction from Neighbors / Notes); reformatted to the canonical 8-section runbook v1 schema. Structural Signature content folded into Manages Complexity + Abstract Reasoning; project-06 load-bearing rationale preserved in Notes.

core_idea:
Increasing returns is the structural pattern in which the **marginal benefit of additional inputs, users, scale, experience, or commitment rises rather than falls** as the cumulative state variable grows. Each successive unit of accumulation makes the option *more* attractive than the prior unit did, so output rises faster than input and advantage compounds against alternatives. The classical-economics default — diminishing marginal returns, where each extra unit yields less — is locally overturned: the curve bends up instead of down, often producing self-reinforcing dynamics, multiple equilibria, and lock-in, an observation Young (1928) had already pressed against the standard textbook framing.<!-- FACT-D56-196 -->[^young-1928] The pattern was named most sharply by W. Brian Arthur, whose 1994 essays argued that whole sectors of modern industry — software, networks, knowledge work — operate under rising-marginal payoffs rather than the textbook diminishing ones.<!-- FACT-D56-197 -->[^arthur-1994]

The core commitment is **cumulative advantage with rising marginal attractiveness**: a function over a cumulative state variable whose slope is positive. It is not supply cost per se, not demand value per se, not the dynamic feedback loop, and not the endpoint state of lock-in — it is the *payoff gradient itself*. Once that gradient is named, an analyst can predict where small early differences amplify and where the system will tip into a winner-take-most regime. The pattern recurs from autocatalytic chemistry — Eigen's (1971) hypercycle and Kauffman's (1986) autocatalytic-set models both turn on the product-catalyzes-production gradient — through the Allee effect in ecology, where per-capita reproductive success rises with density up to a point, to the cumulative-citation Matthew effect in science.<!-- FACT-D56-198 -->[^eigen-1971]<!-- FACT-D56-199 -->[^kauffman-1986]<!-- FACT-D56-200 -->[^allee-1931]

structural_signature:
Increasing returns encodes a structural pattern: **cumulative state variable → positive-slope payoff (or negative-slope cost) function → self-reinforcing advantage → multi-equilibrium / lock-in regime**. The signature is a four-role decomposition: an accumulating quantity, a payoff gradient over it whose slope is positive, a specific reinforcement channel by which more accumulation raises attractiveness, and a threshold beyond which alternatives can no longer compete even on intrinsic merit. The decomposition is what makes the pattern portable across substrates that share no surface vocabulary — what economists following Arthur (1989) call network effects, biologists following Stephens and Sutherland (1999) call the Allee effect, sociologists following Merton (1968) call the Matthew effect, and chemists following Kauffman (1986) call autocatalysis all instantiate the same four-role topology.<!-- FACT-D56-201 -->[^arthur-1989]<!-- FACT-D56-202 -->[^stephens-sutherland-1999]<!-- FACT-D56-203 -->[^merton-1968]

**Recurring features:**

- Marginal benefit rises with cumulative adoption, scale, or experience
- Output grows faster than proportional to input
- Positive-slope payoff function over an accumulating state variable
- Self-reinforcing advantage relative to alternatives
- Multiple equilibria selected by early conditions
- Winner-take-most regime with lock-in past a threshold
- Rising-marginal topology where textbooks assume falling-marginal

The structural insight is that a chip foundry, a paradigm-selecting research field, a forest with founder-species dominance, a viral cascade, and an autocatalytic reaction all share the same gradient. What looks domain-specific — Metcalfe's-law scaling, learning-by-doing, cumulative citations — collapses to a single payoff topology once the four roles are named.

what_it_is_not:
Increasing returns is **not** the same as positive feedback. Positive feedback is the broader category — the routing of output back to input with same-sign amplification — and applies to thermostats hunting, audio howl, runaway greenhouse effects, and many systems with no economic content at all. Increasing returns is the specific *payoff-structure* case: a payoff or cost function whose marginal value gets better as a cumulative variable grows. Most economic instances of increasing returns produce positive feedback in adoption dynamics, but the prime names the gradient, not the loop, a distinction Arthur (1994) draws explicitly when separating the rising-marginal payoff function from the dynamic adoption process it powers.<!-- FACT-D56-204 -->[^arthur-1994]

It is also **not** simply "more is better." A linear payoff (each unit equally valuable) is not increasing returns; the marginal value must rise. The second derivative of the payoff with respect to accumulation must be positive. Many domains casually describe any compounding advantage as "increasing returns" when the underlying gradient is actually flat — the rich-get-richer rhetoric does not always rest on a rising-marginal substrate.

It is **not** lock-in. Lock-in is the endpoint state in which switching costs exceed the value gap between the incumbent and an alternative. Increasing returns is the gradient that *produces* lock-in over time when it compounds long enough. A system can have increasing returns without yet being locked in (early-stage adoption, the gradient still steep but switching still cheap); it can be locked in without ongoing increasing returns (legacy infrastructure that no longer accumulates new advantage but is too expensive to replace).

It is **not** path dependence. Path dependence is the broader pattern that present states are constrained by historical trajectories. Increasing returns is one canonical *mechanism* producing path dependence — cumulative advantage routes the path — but path dependence also arises from sunk costs, institutional inertia, regulatory ratchets, and habit, none of which require a rising payoff gradient.

Finally, increasing returns says **nothing about welfare or efficiency**. The locked-in technology may be the inferior one (QWERTY-style stories); the dominant platform may concentrate rents rather than create them; the runaway research paradigm may crowd out better ones. The prime describes the structural mechanism; whether the resulting equilibrium is good is a separate question for the analyst. Practitioners sometimes assume increasing returns is a story of efficient winners; it is just as easily a story of frozen suboptimality, as David's (1985) QWERTY case study famously argued for keyboard layout standards.<!-- FACT-D56-205 -->[^david-1985]

broad_use:
**Economics — supply side:** Economies of scale (unit cost falls with cumulative output), learning-by-doing (productivity rises with cumulative production), fixed-cost spreading across larger volumes, scope economies across related product lines.

**Economics — demand side:** Network effects (value to each user rises with installed base), two-sided market dynamics (more users attract more developers attract more users), positive network externalities, platform ecosystems.

**Technology and industry:** Lock-in (early choices become harder to reverse as complementary investment accumulates — Arthur's canonical case studies of QWERTY, VHS/Betamax, and electrical-network standardization), path dependence in standards selection, dominant-design emergence in industrial categories.

**Finance and markets:** Speculative bubbles (rising prices attract more buyers, raising prices further) — the destabilizing case of the same payoff structure; momentum trading regimes; herding under information cascades; Shiller's (2000) narrative-driven price dynamics, in which rising prices generate stories that attract further capital that lifts prices further.<!-- FACT-D56-206 -->[^shiller-2000]

**Science and culture:** The Matthew effect — cumulative advantage in citations, attention, awards, and resources, where early success compounds and late entrants find marginal returns on equivalent quality lower than early entrants got, a sociological pattern Merton (1968) formalized in his analysis of the reward system of science.<!-- FACT-D56-207 -->[^merton-1968]

**Biology and ecology:** Autocatalytic chemical reactions (the reaction product catalyzes its own production — RNA-world models of the origin of life lean heavily on this gradient); the Allee effect in ecology, where per-capita reproductive or survival rates rise with population density up to a point, the opposite of the standard density-dependent decline; founder effects and competitive exclusion in ecosystems where early-arriving species accumulate niche and reproductive advantage.

**Cities and geography:** Agglomeration economies (denser cities are more productive per worker), Jacobs-style urban knowledge spillovers, the cumulative thickness of labor markets in dominant metropolises.

**Organizations and skills:** Compounding returns to specialization, deliberate-practice gradients in expert performance, cumulative advantage of incumbents in attracting top talent who then make them more attractive.

clarity:
A core function of "increasing returns" is to distinguish a pattern that microeconomics textbooks routinely blur. Standard models assume constant or decreasing returns — each additional unit yields the same or less than the prior one — which makes equilibrium analysis tractable but mischaracterizes the regime where the opposite holds. The prime names the rising-marginal regime explicitly, separating it from three things it is conflated with. It is not the *loop* through which output feeds back to input — that is feedback. It is not the *endpoint state* where switching costs dominate — that is lock-in. And it is not the broader *history-shapes-options* pattern — that is path dependence. What increasing returns adds is the payoff gradient itself.

Naming that gradient is what lets the analyst predict where positive feedback, lock-in, and path dependence will emerge — and where they will not. It also clarifies why so much of modern economic life looks unlike textbook competitive markets: knowledge industries, network platforms, and learning-intensive manufacturing all operate in the rising-marginal regime, while textbooks still center the diminishing-marginal one. Diminishing returns is not a universal law but one regime among several.

The prime also clarifies a misreading of "winner-take-all" outcomes. The winner is not necessarily the best alternative; it is the alternative that crossed the threshold first while the gradient was still building. Recognizing the gradient is what lets an analyst say "this is not a story of inherent superiority — it is cumulative advantage compounding from a small early lead."

manages_complexity:
Increasing returns decomposes a self-reinforcing situation into four named roles. First, a **cumulative state variable** — adoption count, output volume, experience hours, prior commitment, citations, installed base, population density. Second, a **payoff (or cost) function whose marginal value improves with that variable** — the gradient itself. Third, a **reinforcement channel** — the specific mechanism by which more accumulation raises attractiveness (learning, fixed-cost spreading, complementary-good ecosystem, prestige, network density, autocatalysis). Fourth, a **threshold or lock-in risk** — the point at which alternatives become prohibitively costly to switch to, even if intrinsically superior.

Once those four roles are named, an opaque "rich-get-richer" intuition becomes a structured object the analyst can interrogate: which variable is accumulating? What is the channel? Where is the threshold? Naming the roles also clarifies which sibling case is in play — network_effect foregrounds the demand-side channel where Katz and Shapiro (1985) modeled value to each user as rising with installed base, economies_of_scale foregrounds the supply-side channel where Smith (1776) located rising productivity in the division of labor, and learning_curve_effects foregrounds the experience channel that Wright (1936) first quantified in airplane manufacturing — all three are the same four-role pattern under different reinforcement channels.<!-- FACT-D56-208 -->[^katz-shapiro-1985]<!-- FACT-D56-209 -->[^smith-1776]<!-- FACT-D56-210 -->[^wright-1936]

The decomposition also gives a clear menu of *de-locking* interventions. If the cumulative variable can be reset or partitioned (interoperability standards that let competing networks share users), the gradient is broken at the variable. If the channel can be neutralized (modular standards that decouple a complementary-good ecosystem from any single incumbent), the gradient is broken at the channel. If the threshold can be raised (subsidies for laggards, public infrastructure for the smaller network), late entrants can keep climbing. Each de-locking lever targets a specific role in the four-role decomposition, so the analyst can match intervention to mechanism rather than groping at the surface.

abstract_reasoning:
Increasing returns supports a sharp counterfactual: *if returns were constant or decreasing, this advantage would not compound.* That move lets the analyst predict regime-defining behavior across substrates. In any domain where you can identify the cumulative state variable plus a positive-slope payoff function, you can predict (a) self-reinforcing advantage relative to alternatives, (b) possible multiple equilibria depending on which option accumulates first, (c) path-sensitivity such that small early differences amplify into large outcome differences, and (d) lock-in risk once switching cost exceeds the alternative's payoff improvement.

The reasoning also enables *de-locking* analysis: what would flatten the gradient — modular standards, interoperability, subsidies for laggards, antitrust intervention — so that alternatives can catch up before the threshold closes. The signature is structural: rising-marginal is not unique to economics; it is a topology over any state variable that accumulates and any payoff function defined over it. Wherever that topology appears, the same predictions follow.

The counterfactual structure also disciplines diagnosis. When an analyst sees a dominant incumbent, the question is not "is this firm the best?" but "what is accumulating, through what channel, against what threshold, and how would the dynamics change if that accumulation slowed?" That sequence transfers cleanly from a platform to a citation network to an autocatalytic loop because the four-role decomposition is substrate-neutral. The same diagnostic also forces a level check: a firm in rising-marginal territory can sit inside an industry whose own gradient has flattened or inverted as the dominant winner extracts rents and slows innovation, and the four-role question has to be re-asked at each level rather than read off the firm-level gradient alone, a Marshallian (1890) point that external economies and internal economies operate at different scales and need separate accounting.[^marshall-1890]

knowledge_transfer:
The pattern recurs across substrates with no metaphorical stretch. A biologist studying founder effects (early-arriving species accumulate niche advantage that excludes later competitors), a sociologist studying Merton's (1968) Matthew effect (cited papers attract more citations), a software architect studying platform ecosystems where Rohlfs's (1974) original network-effect analysis applies (more users attract more developers attract more users), and an industrial economist studying Yelle's (1979) learning-curve regularity in chip fabrication can recognize each other's problems as variants of the same payoff structure.[^rohlfs-1974][^yelle-1979]

The biological case is especially clean for breadth: autocatalytic reactions involve no markets, no prices, no agents — just a species whose marginal reproductive advantage rises with prior concentration. That rules out the suspicion that increasing returns is an economics specialty. The Allee effect shows the pattern with no production function, just per-capita success rising with density. The Matthew effect shows it with attention flowing toward already-attended objects.

All four cases — autocatalysis, Allee, Matthew, and platform network effects — instantiate the same four-role structure. A researcher who has internalized the prime in one domain can move to another and ask the diagnostic questions immediately, without re-learning vocabulary. That is the transfer payoff: not a metaphor, but a shared topology the local language was hiding.

formal_abstract:
**Autocatalytic chemistry.** Consider a reaction A + X → 2X, where X is both reactant and product — the species X catalyzes its own production. As X accumulates, the rate of production rises (more catalyst available), which produces more X, which raises the rate further. The cumulative state variable is the concentration of X. The payoff function (in the sense of reaction rate per unit precursor) rises with that concentration. The reinforcement channel is catalysis itself. The threshold is the point at which precursor A is depleted or a competing reaction becomes rate-limiting. This system has been studied closely as a candidate model for the origin of life: RNA-world hypotheses posit autocatalytic sets of RNA molecules whose mutual production constitutes a rising-marginal payoff regime that is the chemical signature of life-before-cells.

**Mapped back:** Strip away the chemistry — concentration, catalyst, rate — and what remains is the four-role pattern. A cumulative variable (X), a positive-slope payoff over it (reaction rate), a specific reinforcement channel (catalysis), a saturation threshold (precursor depletion). The same four roles describe a city's agglomeration economies, a research paradigm's cumulative-citation advantage, and a social platform's network-effect lock-in. Increasing returns is the structural prime; autocatalysis is one of its cleanest natural-substrate instances.

**Allee effect in ecology.** Standard population ecology assumes density-dependent decline: per-capita reproductive success falls as population density rises (more competition for resources). The Allee effect names a lower-density regime where the opposite holds: per-capita success *rises* with density up to a point, because mate-finding, cooperative defense, group foraging, and predator dilution all work better in larger groups. A small isolated population is at a worse per-capita rate than a moderately denser one; advantage accumulates with density. Past a threshold, standard density-dependent decline reasserts, so the full per-capita curve is hump-shaped — increasing returns at low density, diminishing returns at high.

**Mapped back:** The cumulative variable is population density; the payoff is per-capita reproductive success; the reinforcement channel is whichever cooperative mechanism is in play (mate-finding, group defense, etc.); the threshold is the density at which competition for resources overtakes cooperation gains. Same four roles, biological substrate. The Allee effect is significant for conservation precisely because the rising-marginal regime means that small populations can crash below a critical threshold and fail to recover even when habitat is restored — a structural prediction Stephens and Sutherland (1999) trace directly to the rising-marginal payoff at low density.[^stephens-sutherland-1999]

applied_industry:
**Two-sided platform with network effects.** Consider an early-stage developer-and-user platform. The cumulative state variable is two: number of users on one side and number of complementary-product developers on the other. The payoff function is value-per-user rising with developer count (more apps to use) and value-per-developer rising with user count (larger addressable market). The reinforcement channel is cross-side network effects: more users attract more developers attract more users. The threshold is the point at which a competing platform's joint value, even if its product is technically superior, falls below the incumbent's joint installed-base advantage — switching would mean losing both the apps and the audience. The same four-role structure governs operating-system platforms, payment networks, ride-share marketplaces, and language-model-plus-tool ecosystems. The reason platform competition tends toward winner-take-most outcomes is structural: cross-side network effects produce rising-marginal payoffs on both sides simultaneously, so the gradient compounds twice, an outcome Katz and Shapiro (1985) derived formally from compatibility and installed-base dynamics.[^katz-shapiro-1985]

**Mapped back:** This is the demand-side specialization of increasing returns under cross-side reinforcement. Strip away the "platform" framing and what remains is two interlocking cumulative variables with positive-slope payoffs over each other. The same logic — minus the "two sides" — applies to single-side network effects (telephones, social networks) and to supply-side learning curves in chip foundries. The umbrella is the prime; the specializations are the children — and each child (network_effect, economies_of_scale, learning_curve_effects, speculative_bubble) retains a domain-specific reinforcement mechanism that the umbrella deliberately abstracts away, so the cross-substrate topology lives at the umbrella while the mechanism lives at the child, a hierarchical separation Krugman (1991) used when porting increasing-returns logic across trade and geography.[^krugman-1991]

**Speculative bubble.** A momentum-driven asset market exhibits increasing returns in price-attractiveness rather than in underlying value. The cumulative variable is past price appreciation; the payoff function (in the sense of expected near-term return, as perceived by trend-following buyers) rises with that appreciation — the higher the price has gone, the more buyers expect it to keep going. The reinforcement channel is the narrative-and-flows feedback: rising prices generate stories, stories attract capital, capital lifts prices. The threshold is the point at which marginal buyers run out or fundamental anchors reassert. Unlike the platform case, the bubble case is *destabilizing* — the rising-marginal regime detaches price from value rather than concentrating real economic advantage, the welfare cost Shiller (2000) ascribes to narrative-and-flows feedback in asset markets.[^shiller-2000]

**Mapped back:** Same four roles, payoff inverted in welfare terms. The bubble shows that increasing returns is not a synonym for healthy compounding — it is a payoff topology that can produce either productive concentration or speculative detachment, depending on what the cumulative variable tracks. This is part of why the prime needed to be elevated above its supply-side and demand-side specializations: the same gradient powers both Silicon Valley's platform economies and the tulip mania, and that is the structural unification the umbrella provides.

title: The rising-marginal regime is sharply defined in mathematics but contested empirically

body: In a model, the second derivative of the payoff function with respect to the cumulative variable is positive or it is not. In a real industry, the same data can support increasing-returns and diminishing-returns interpretations depending on which time window, which input bundle, and which competitor set the analyst chooses. Arthur's claim that whole sectors of the modern economy run on increasing returns was empirically contested for years precisely because the gradient is hard to estimate cleanly — the curve flattens, steepens, and re-flattens as technology shifts. The structural pattern is crisp; field measurement is not.

title: Naming a regime as increasing-returns can be self-fulfilling or self-defeating

body: Once investors, regulators, or competitors come to believe a market runs on increasing returns, they act accordingly — pouring capital into the leading firm, accepting concentration as inevitable, or intervening to break the gradient. Each of these actions changes the underlying dynamics. A market that *was* in a mild rising-marginal regime can be pushed deeper into winner-take-most by belief in increasing returns, or pulled back toward competition by regulatory intervention motivated by the same belief. The analyst's frame shapes the system being analyzed.

title: Increasing returns at one level can produce diminishing returns at another

body: A firm in a network-effect market faces increasing returns in its own scale, but the *industry* may face diminishing returns as the dominant winner extracts rents and stops innovating. A research paradigm under cumulative-citation advantage faces increasing returns at the paradigm level, but the *field* may face diminishing returns as the dominant paradigm crowds out diversifying inquiry. Welfare conclusions cannot be read off the firm-level or paradigm-level gradient alone; the level matters and the gradients can point opposite directions.

title: Lock-in is both the danger and the value of the regime

body: Increasing returns produces lock-in past a threshold — and lock-in is what makes infrastructure investments, platform ecosystems, and standards-setting valuable in the first place. Society wants electrical grids and payment networks to lock in (the alternative is fragmentation and waste); it does not want operating systems and social platforms to lock in (the alternative is competition and choice). The same structural prime underwrites both the value and the harm. Distinguishing them is a normative call the gradient cannot make on its own.

title: The de-locking levers can flatten healthy gradients along with pathological ones

body: Modular standards, interoperability mandates, and subsidies for laggards all flatten the rising-marginal regime — but the flattening hits good gradients and bad ones indiscriminately. A regulator imposing interoperability on a dominant platform may be breaking pathological lock-in or may be destroying the very network-effect value the platform was producing. The intervention targets the *role* in the four-role decomposition, not the welfare consequences. Analysts who reason structurally about de-locking must reason separately about whether the gradient should be flattened.

title: The umbrella subsumes its children but does not replace them

body: Network effects, economies of scale, learning curves, and speculative bubbles are all special cases of increasing returns — but each retains domain-specific structure that the umbrella does not capture. Network effects involve a specific cross-user externality structure; economies of scale involve a specific fixed-cost-and-output relationship; speculative bubbles involve a specific narrative-and-flows feedback that has no analogue in chip fabrication. The prime gives the cross-substrate topology; the children give the domain mechanics. Working only at the umbrella level loses the mechanism; working only at the child level loses the cross-domain transfer. The catalog wants both, and the analyst has to move between them.

prose:
1. **Identify the four roles, then intervene at one.** Diagnose any rising-marginal regime by naming the cumulative variable, the payoff gradient, the reinforcement channel, and the threshold. Each role is a candidate intervention site. To flatten a network effect, partition the cumulative variable through interoperability. To break a scale-economy moat, neutralize the channel through modular standards. To prevent lock-in, raise the threshold through subsidies or public-option alternatives. Matching intervention to role beats grasping at surface features.

2. **De-lock by gradient-flattening.** When the regime is producing pathological concentration, the structural intervention is to flatten the payoff gradient with respect to the cumulative variable. Modular standards do this in technology (a complementary good works on any platform, breaking the platform-specific reinforcement). Interoperability mandates do this in networks (users on competing networks can interact, breaking the cross-user externality). Antitrust unbundling does this in firms (cumulative scale advantage is broken into separable competing units). Each intervention targets the gradient itself, not the incumbent.

3. **Seed the alternative before the threshold closes.** When the analyst predicts a winner-take-most regime, the window for fostering competition is before lock-in. Investments in laggards, subsidies for new entrants, and public infrastructure for smaller networks all keep alternatives climbing while the incumbent's gradient is still building. Once the threshold closes, the same investments will not recover competition — they have to be timed against the gradient.

4. **Use the regime deliberately when concentration is desired.** Standards-setting, infrastructure investment, and platform-building are all cases where society *wants* lock-in. The structural prime gives the playbook: design the cumulative variable so that adoption is observable and irreversible (durable installations, sunk training, embedded interfaces), strengthen the reinforcement channel (complementary-good ecosystems, regulatory anchoring), and let the threshold close. Increasing returns is a tool as much as a danger.

5. **Distinguish gradient at firm level from gradient at industry level.** Many policy errors come from reading one level off the other. A firm in increasing-returns territory will lobby as if its dominance benefits the industry; the industry may be in diminishing-returns territory because the dominant firm has stopped innovating. The structural diagnosis requires checking the gradient at each level and resisting the rhetorical move that conflates them.

6. **Watch for the destabilizing case.** Increasing returns underwrites speculative bubbles as well as productive platform growth. When the cumulative variable being tracked is decoupled from underlying value — past price appreciation rather than fundamental performance — the rising-marginal regime is destabilizing rather than productive. The structural diagnostic is the same; the welfare implication is opposite. Practitioners who treat all rising-marginal regimes as healthy concentration miss this.

notes:
Increasing returns was elevated to the umbrella status in this catalog after a corpus-level inconsistency surfaced in project-06: the proposed parent edge from network_effect to economies_of_scale was rejected by multiple reviewers as a demand-side/supply-side mismatch, but both children clearly belonged under a common parent. Increasing returns was the missing umbrella. ChatGPT Pro independently identified the same gap and the same slug, extending the would-parent list to include speculative_bubble and learning_curve_effects. The structural reading — a single rising-marginal payoff topology that instantiates differently on the demand side, the supply side, the experience axis, and the speculative-flows axis — is the load-bearing reason for the elevation.

The relation to feedback is the open relational question. Increasing returns most likely *presupposes* feedback rather than decomposing to it: the loop is the dynamic substrate over which the rising-marginal gradient compounds, and a static rising-marginal payoff without a feedback loop is structurally rare. But the alternative reading — that increasing returns *decomposes to* feedback with a positive-slope coupling — is defensible. The catalog records the presupposes lean while flagging the question for future review.

The substrate-furthest cases anchor the breadth claim. Autocatalytic reactions in chemistry have no markets, no prices, no rational agents — just a chemical species whose marginal production rate rises with prior concentration. The Allee effect in ecology has no production function, no consumers — just a population whose per-capita success rises with density up to a saturation point. These are not metaphors but structural instances; an analyst who has internalized the prime in either domain recognizes the same four-role pattern in a platform-economics paper or a citation-network study without needing to translate. That recognition is the transfer payoff the catalog is built to enable.

Practitioners should resist the conflation of increasing returns with healthy compounding. The speculative-bubble case demonstrates that the same payoff topology can produce productive concentration (platforms, networks, agglomeration economies) or destabilizing detachment from value (bubbles, manias, runaway narratives). The structural prime is welfare-neutral; whether to encourage or break a given rising-marginal regime depends on what the cumulative variable tracks and what the resulting equilibrium concentrates.