Stability

slug: stability

one_liner: A system's tendency to return toward an operating point after perturbation.

origin_domain: mathematics

core_idea:
Stability is the structural pattern of a system's tendency, after a perturbation away from an operating point, to return toward that operating point under its own dynamics, within a basin around the point and on a characteristic timescale. The defining commitment is a three-part split: an *operating point* (or set, manifold, regime) at which the system currently sits, a *perturbation* that displaces the system away from it, and *restoring dynamics* that act under the system's own rules to return the state toward the operating point rather than amplify away from it.

What makes stability structural rather than a vague synonym for "stays put" is the *return under the system's own rules*. A rock in a hole is not stable in a trivial sense; it is stable in the structural sense because if pushed, gravity *under its own rules* returns it. The pattern requires a discoverable mechanism that does the restoring work, and it is broken when that mechanism is absent, saturated, or overrun. Stability is a claim about dynamics, not about momentary quiet.

Stability has internal structure. It is *local*, holding within a basin of attraction; it is *parameterized*, with the rate of return and the basin size set by the system's tuning; and it is *brittle at the edges*, since near the basin boundary the system may flip to another attractor — a regime change. The pattern travels because the same triple — operating point, perturbation, restoring dynamics under intrinsic rules — recurs across control engineering, ecosystem dynamics, financial markets, political regimes, neural circuits, body temperature, and metabolic networks, with the Lyapunov framework as its canonical mathematical form.<!-- FACT-D72-226 -->[^lyapunov-1892]

structural_signature:
*the operating point* — *the displacing perturbation* — *the restoring dynamics under intrinsic rules* — *the basin of attraction* — *the return rate* — *the breakdown threshold*

A system exhibits stability when each of the following holds:

- **An operating point.** There is a distinguished state, set, manifold, or regime at which the system currently sits and toward which return is measured. Stability is always *stability about* something; absent a named reference point the predicate is empty.
- **A displacing perturbation.** Some disturbance moves the state away from the operating point. The pattern is defined by the system's *response* to this displacement, not by the quiet that precedes it.
- **Restoring dynamics intrinsic to the system.** A discoverable mechanism, operating under the system's own rules, acts to pull the state back toward the operating point rather than amplify away from it. This intrinsic-return condition is the load-bearing invariant that separates stability from mere momentary rest.
- **A basin of attraction.** Return holds only locally: perturbations within a bounded region return, those beyond it do not. Stability is parameterized by basin size, not unconditional.
- **A return rate.** The restoring dynamics act on a characteristic timescale, setting how fast the system recovers. Damping alone drifts and restoring alone oscillates; their combination yields bounded return.
- **A breakdown threshold.** At the basin boundary the system tips to another attractor — a regime change — and a finite stability budget is consumed in maintaining the restoring force.

Composed: a reference point, a class of displacements it can absorb, and an intrinsic mechanism that returns the state — bounded by a basin, paced by a return rate, and bounded above by the perturbation magnitude past which the operating point loses its hold.

what_it_is_not:
- **Not mere quiet.** A system sitting undisturbed at an operating point displays no stability; stability is a claim about the *response to perturbation*, not the absence of one. A motionless system at rest may be poised at an unstable fixed point that any nudge sends running away.
- **Not `equilibrium`.** Equilibrium is a *state* property — a balance of forces with no net change — whereas stability is a *dynamical* claim about whether the system returns to that state when displaced. An equilibrium can be stable or unstable; the pencil balanced on its tip is in equilibrium but not stable.
- **Not `homeostasis`.** Homeostasis is one biological *instance* of stability — a regulated variable held near a setpoint by negative feedback. Stability is the general structural pattern; it also covers structural buckling, monetary regimes, and software recovery where no "homeostat" language applies.
- **Not `resilience`.** Resilience emphasizes basin *size* — how large a shock can be absorbed before regime change — while stability in the engineering sense emphasizes return *rate*. The two are distinct dimensions that trade off, and conflating them hides the fact that a fast-returning system may have a tiny basin.
- **Not `inertia`.** Inertia is resistance to *initiating* change; stability is the tendency to *return* after change has been forced. An inert system that, once moved, stays moved is the opposite of stable.
- **Not `instability` flipped.** The negation of stability is not simply "unstable everywhere"; many systems are stable in one coordinate and unstable in another, or stable to small shocks and unstable to large ones. Stability is always local and conditional.
- **Common misclassification.** Certifying a system "stable" from observed calm without naming the operating point, the basin, or the restoring mechanism. Catch it by asking "stable about *what*, and what pulls it back?" — if no restoring mechanism can be named, the calm is mere quiet, not stability.

broad_use:
- **Dynamical systems and control engineering** (canonical): Lyapunov stability of equilibria; stable versus unstable fixed points; closed-loop stability via gain and phase margins; the PID controller exists to *induce* stability where it is absent.<!-- FACT-D72-227 -->[^astrom-murray-2008]
- **Ecosystem ecology**: a system's return to a characteristic species composition after disturbance; Holling's engineering resilience is essentially return rate after perturbation.<!-- FACT-D72-228 -->[^holling-1973]
- **Macroeconomics**: a central bank targets a stable inflation regime, the question being whether the policy rule makes the operating point dynamically stable against shocks.
- **Political regimes**: a polity's tendency to return to a characteristic regime after shocks, the comparative-politics literature being structurally identical to dynamical-system stability analysis.
- **Physiological homeostasis**: pH, temperature, and glucose are operating points held by restoring dynamics (buffering, sweating, insulin) against perturbations.<!-- FACT-D72-229 -->[^cannon-1932]
- **Structural engineering**: a building's return to its design configuration under wind, seismic, or live load; buckling analysis is the search for loss of stability under increasing load.
- **Neural circuits and software**: firing rates settling at characteristic patterns via excitation-inhibition balance; runtime recovery from transient errors via retries, circuit breakers, and autoscaling, with chaos engineering as deliberate perturbation to test stability.<!-- FACT-D72-230 -->[^basiri-etal-2016]
- **Currency pegs**: a fixed rate is an operating point, central-bank intervention the restoring dynamics, and stability fails when reserves are exhausted.

clarity:
Naming stability clarifies a load-bearing distinction routinely muddled in system description: between a system that is *unperturbed and sitting at an operating point* and a system that has the *intrinsic capacity to return* if perturbed. The two look identical at rest but behave fundamentally differently under stress, and many failures of system understanding stem from confusing "currently quiet" with "would return if disturbed."

The clarification surfaces a battery of diagnostic questions: what is the operating point, what is the basin of attraction around it, what are the restoring dynamics (explicit mechanism or implicit damping), what is the characteristic return rate, and at what perturbation magnitude does the system tip out of the basin into another regime? These questions sharpen design and forecasting across every substrate, because they convert the vague predicate "stable" into a set of measurable properties.

A second clarification is that stability is *cheap to enjoy and expensive to lose*. Once a system sits in a stable regime it appears free, but losing stability — regime change, peg break, seizure, ecosystem collapse — typically requires costly intervention to restore. Recognizing the *stability budget*, the perturbation magnitude the system can absorb before regime change, sets prudent operating margins and replaces the comfortable illusion of permanence with an explicit reserve to be managed.

manages_complexity:
The pattern compresses a wide family of return-to-operating-point phenomena — control equilibria, ecosystem composition, monetary and political regimes, homeostatic variables, structural safety, neural firing rates, software recovery, currency pegs — into one diagnostic family: operating point, basin, restoring dynamics, return rate, and breakdown threshold. Cross-cutting design problems that look unrelated — controller tuning, ecosystem management, monetary-policy rules, constitutional design, structural safety factors, chaos-engineering targets — become legible as one problem family.

The intervention space then sorts cleanly. One can *increase restoring-dynamics gain* for faster return, *enlarge the basin* for greater shock absorption, *reduce perturbation magnitudes* by calming the environment, *add explicit negative feedback* to engineer stability where it was not intrinsic, or *migrate to a more stable operating point*. Each is recognizable across substrates: adding forward guidance to anchor inflation expectations and adding spinning reserves to a power grid are the same structural move of strengthening the restoring force. The complexity stability manages is the complexity of a system's response to disturbance; it manages it by reducing that response to a small set of named handles plus a fixed menu of moves for tuning them.

abstract_reasoning:
Recognizing stability enables several portable inferences. *Operating-point identification* comes first: stability is relative to a point, and many disputes ("is this stable?") evaporate once the question becomes "stable about what?". The *basin-of-attraction concept* says stability is local — perturbations within the basin return, those beyond it do not — making basin-sizing a key forecasting question (how big a recession can monetary policy absorb? how big a fire can the ecosystem absorb?).

The *Lyapunov-function intuition* says stability can often be proved by exhibiting a quantity that monotonically decreases under the dynamics, an intuition transferring from control engineering to ecology (entropy, free energy), economics (welfare functions), and any substrate with an energy-like quantity. The *damping-versus-restoring distinction* separates removing energy from pulling toward an operating point: damping alone drifts, restoring alone oscillates, the combination returns. The *bifurcation boundary* marks where the basin collapses or the operating point loses stability, flagging dangerous-tuning regions in control, policy, and ecosystem management. And the *cost-of-stability* names the finite stability budget — heating to hold temperature, reserves to hold a peg — whose rationing is a recurring substrate-independent problem.

knowledge_transfer:
The transfers are mature and well-documented. Lyapunov's 1892 framework moved from differential equations into all modern controller design — PID, LQR, MPC, robust and adaptive control — with the linear-matrix-inequality formulation as its operational form.<!-- FACT-D72-231 -->[^boyd-etal-1994] Control stability moved into economic policy through the Taylor rule, which is structurally a feedback controller using interest-rate adjustment to stabilize inflation around a target, and the literature explicitly imports control-system stability concepts.<!-- FACT-D72-232 -->[^taylor-1993] Cannon's physiological homeostasis moved via Wiener's cybernetics into engineering practice — thermostats, autopilots, process control — and back into systems biology as whole-cell stability analysis.<!-- FACT-D72-233 -->[^wiener-1948] Holling's distinction between engineering resilience (return rate) and ecological resilience (basin size) moved from forest ecology into climate adaptation and critical-infrastructure protection.<!-- FACT-D72-234 -->[^holling-1996] Structural buckling analysis moved into comprehensive finite-element stability analysis. And the stability analysis of gradient-descent fixed points moved from dynamical systems into the analysis of neural-network training dynamics.

What makes these transfers genuine is the interchangeability of structural roles. *The operating point* about which stability is claimed, *the basin of attraction* from which the system returns, *the perturbation* whose response defines stability, *the restoring dynamics* that pull toward the operating point, *the return rate* setting the recovery timescale, *the breakdown threshold* beyond which the system tips out of the basin, and *the stability budget* of resources spent maintaining the restoring dynamics — these map one-to-one across control, ecology, economics, politics, physiology, structural engineering, neuroscience, and software. Stripped of mathematical vocabulary, stability is "if you push the system away from its usual state, the system's own behavior pulls it back — up to some perturbation size beyond which it does not return." A practitioner carrying that sentence into any of these domains inherits both the diagnostic framework — operating point, basin, return rate, breakdown threshold — and the intervention set: raise gain, enlarge basin, lower perturbations, switch operating points.

formal_abstract:
Consider the damped pendulum, governed by $\ddot{\theta} + b\dot{\theta} + \frac{g}{L}\sin\theta = 0$.<!-- FACT-D72-235 -->[^strogatz-1994] The *operating point* is the downward equilibrium $\theta = 0, \dot{\theta} = 0$. A nudge — the *perturbation* — displaces the bob. The *restoring dynamics* are gravity (the $\sin\theta$ term, pulling back toward vertical) combined with damping $b\dot\theta$ (bleeding off energy). Linearizing about $\theta = 0$ gives eigenvalues with negative real parts whenever $b > 0$, so the *return rate* is set by $b$ and $g/L$: more damping, faster settling; more damping still, an overdamped crawl rather than ringing. The *basin of attraction* is the bowl up to roughly $\theta = \pi$; the *breakdown threshold* sits at the inverted equilibrium $\theta = \pi$, an unstable fixed point past which the bob falls toward the other well rather than back. A Lyapunov function — total mechanical energy $E = \frac{1}{2}\dot\theta^2 + \frac{g}{L}(1-\cos\theta)$ — proves stability without solving the equation: $\dot E = -b\dot\theta^2 \le 0$, so energy monotonically decreases inside the basin.<!-- FACT-D72-236 -->[^khalil-2002] The intervention this licenses is direct: to make the system settle faster, increase $b$; to widen the basin, deepen the well ($g/L$); to detect impending loss of stability, watch the eigenvalues approach the imaginary axis as a parameter is tuned (a bifurcation).

**Mapped back:** The pendulum instantiates the full signature — named operating point, restoring dynamics under intrinsic rules, basin, return rate, and a breakdown threshold at the inverted equilibrium — with the Lyapunov energy serving as the decreasing quantity that certifies return.

applied_industry:
A central bank running an inflation target instantiates the same structure in macroeconomics. The *operating point* is the target inflation regime (say 2% annual). A demand shock or commodity spike is the *perturbation*, displacing realized inflation. The *restoring dynamics* are the policy rule: a Taylor-rule central bank raises the interest rate more than one-for-one with inflation, which cools demand and pulls inflation back toward target — structurally a negative-feedback controller.<!-- FACT-D72-237 -->[^taylor-1993] The *return rate* is how quickly inflation converges, set by the rule's aggressiveness (the coefficient on the inflation gap) and the lags in monetary transmission. The *basin of attraction* is the set of shocks the rule can absorb while keeping expectations anchored; the *breakdown threshold* is the de-anchoring point, where a large enough shock or a too-timid rule lets expectations drift, the operating point loses its hold, and the economy flips to a high-inflation regime that costly intervention is then needed to escape. The same diagnosis runs in an analogous applied case: a managed currency peg, where the operating point is the fixed rate, the restoring dynamics are reserve-funded intervention, and the breakdown threshold is reserve exhaustion — past which the peg breaks and the rate jumps to a new attractor. In both, the intervention menu is identical to the engineering case: raise the gain (a more aggressive rule, larger intervention), enlarge the basin (build credibility or reserves), reduce perturbations (smooth fiscal shocks), or migrate to a more defensible operating point (a band rather than a hard peg).

**Mapped back:** Monetary stabilization and currency-peg defense are the restoring-dynamics-under-intrinsic-rules pattern in economic substrate; reading inflation drift or reserve depletion as approach to the basin boundary turns "is the regime stable?" into the measurable question "how large a shock before the operating point loses its hold?"


Stability-Induced Fragility

type: prime

one_liner: Prolonged calm endogenously erodes the response capacity that stressors keep exercised.

status: draft

core_idea:
Stability-induced fragility is the structural pattern in which extended
periods of stability — calm performance, absence of bad news,
predictable operation — *endogenously build* the fragility that the
stability appeared to certify as safe. The pattern is a positive
feedback running through the agents and components *inside* a system:
low observed volatility lowers the perceived cost of risk-taking, raises
exposure, relaxes vigilance, atrophies the response machinery, and
erodes the slack that once absorbed shocks. Together these moves
manufacture the fragility the calm disguises, so that when the
inevitable shock arrives the system fails harder than it would have if
shocks had been arriving regularly throughout.

Five commitments are load-bearing. There is a *quiet observation window*
in which adverse outcomes are rare or absent. There is an *endogenous
response function* by which agents inside the system update on the
absence of bad news — lowering buffers, deferring maintenance, taking
more risk, skipping drills, relaxing constraints. There is a *latent
fragility variable* — leverage, fuel load, exposure, deviance from
procedure, missed patches, depleted reserves — that *increases* under
that response. There is a *shock-magnitude relationship* in which the
eventual shock's impact scales with the accumulated fragility rather than
with the shock's intrinsic size. And there is a *misreading* in which
observers of the quiet window infer safety from the calm, supplying the
budget and policy conditions that license further accumulation.

The pattern is the *dynamic process* by which a system becomes fragile —
distinct from fragility as a state property and distinct from
antifragility as a response property. It names the *production
mechanism* of fragility through the absence of the very stressors that
would have kept the system exercised, and that production-through-absence
structure is what makes the failure counterintuitive: nothing visibly
goes wrong while the fragility is being built.

structural_signature:
*the quiet observation window* — *the endogenous response function updating on absence-of-bad-news* — *the latent fragility variable that increases under that response* — *the shock-magnitude relationship scaling with accumulated fragility* — *the safety-misreading by outside observers* — *the production-through-absence invariant*

The pattern is present whenever these components are configured together:

- **The quiet window (role).** An interval in which adverse outcomes are
  rare or absent — calm performance, predictable operation, no bad news.
- **The endogenous response function (relation).** Agents inside the
  system update on the absence of bad news — lowering buffers, deferring
  maintenance, taking more risk, skipping drills, relaxing constraints.
- **The latent fragility variable (role).** A quantity — leverage, fuel
  load, exposure, deviance, missed patches, depleted reserves — that
  *increases* under that response while nothing visibly goes wrong.
- **The shock-coupling (relation).** The eventual shock's impact scales
  with accumulated fragility rather than with the shock's intrinsic size,
  so the discharge magnitude is set by the build-up, not the trigger.
- **The misreading (relation).** Outside observers infer safety from the
  calm, supplying the budget and policy conditions that license further
  accumulation.
- **The production-through-absence invariant.** The defining condition:
  fragility is manufactured by the *absence* of the very stressors that
  would have kept the response machinery exercised — distinguishing this
  dynamic process from fragility-as-state and antifragility-as-response.

The components compose into the signature: a positive feedback in which
prolonged calm endogenously erodes the slack and capacity that occasional
stressors would have maintained, so the system fails hardest after the
longest quiet.

what_it_is_not:
- **Not `antifragility`.** Antifragility is a *response property* — a
  system that *gains* from stressors; stability-induced fragility is the
  *dynamic process* by which the *absence* of stressors builds fragility.
  They are near-inverses: the former wants volatility, the latter is
  harmed by its suppression.
- **Not `stressor_induced_adaptation`.** That prime names stressors
  *strengthening* a system; this one names the loss of capacity when
  stressors are *removed*. The repair (reintroduce controlled stressors)
  works precisely because it restores stressor-induced adaptation.
- **Not fragility-as-state.** A system can be *born* fragile; this prime
  is specifically fragility *manufactured endogenously during calm*. The
  fix differs — redesign for a born-fragile system, reintroduce stressors
  for a calm-built one.
- **Not `black_swan_high_impact_low_probability_events`.** A black swan
  is a rare high-impact *trigger*; here the trigger may be modest, and the
  discharge magnitude is set by *accumulated fragility*, not the trigger's
  intrinsic size.
- **Not `temporal_decay_and_degradation`.** Ordinary decay is gradual
  loss from use or time; stability-induced fragility is loss from *disuse*
  driven by an endogenous response to the absence of bad news, with a
  discharge that scales with the build-up.
- **Not `maintenance` neglect alone.** Deferred maintenance is one
  channel of the response function, but the prime is the broader
  positive-feedback loop (relaxed vigilance, raised exposure, atrophied
  response) plus the safety-misreading that funds further accumulation.
- **Common misclassification.** Blaming a collapse on its proximate
  shock ("an unprecedented event"). If a similar-sized shock earlier in
  the calm would have been absorbed, the fragility — not the shock — set
  the outcome.

broad_use:
- **Finance.** Stable returns lower perceived default risk and raise
  leverage and exposure to common shocks; the discharge is the Minsky
  moment, and a long calm of moderate growth can literally license the
  leverage that breaks the system.<!-- FACT-D72-251 -->[^minsky-1986]
- **Forest ecology.** Fire suppression removes the small frequent fires
  that consume fuel; fuel load accumulates until the system shifts from
  absorbing many small fires to discharging in catastrophic
  conflagrations.<!-- FACT-D72-252 -->[^holling-meffe-1996]
- **Reliability engineering.** Long accident-free periods erode
  adherence to procedure as shortcuts that work once become routine —
  the normalisation of deviance behind major industrial disasters.<!-- FACT-D72-253 -->[^vaughan-1996]
- **Public health and aviation.** Long disease absence erodes
  vaccination urgency until herd immunity fails, and long incident-free
  periods erode crew-resource-management discipline until a high-profile
  failure.<!-- FACT-D72-254 -->[^macdonald-2015]
- **Cybersecurity and software.** Long breach-free periods erode patch
  and drill discipline, and systems that never experience controlled
  failure accumulate untested failure paths that surface together in the
  first real incident.
- **Physiology and polity.** Prolonged comfort atrophies physiological
  reserves, and long-stable regimes erode political-skill reserves, so
  the first stress event finds the reserves depleted.

clarity:
Naming the pattern reroutes diagnostic attention from the *triggering
event* to the *accumulated fragility* — from "what caused the crash?" to
"what state had the system been driven into during the calm?" The
trigger may be small or large; the discharge magnitude is set by the
fragility, not the trigger. This reframing is what separates a useful
post-mortem from a misleading one, because attributing a collapse to its
proximate shock systematically points the analyst at the wrong cause and
licenses the wrong fix.

The framing also clarifies a recurring misreading: stability is not a
safety signal. A long calm window can be evidence of genuine robustness
*or* evidence of fragility accumulation, and the two are
indistinguishable from outside without examining the internal
latent-fragility variable. This inverts ordinary signal-reading — the
systems that *look* safest by their track record may be the most fragile
— and the inversion is precisely why the pattern is dangerous: the very
evidence that reassures observers is the evidence that should worry them.

manages_complexity:
The pattern compresses a wide family of slow-burning catastrophes —
financial crises, conflagrations, industrial accidents, disease
resurgences, breach cascades, ecosystem collapses, software incidents —
into one diagnostic procedure: identify the fragility variable specific
to the substrate, measure its trajectory under the current operating
regime, diagnose the response function that converts absence-of-shocks
into accumulation, and intervene by re-introducing controlled stressors
or counter-cyclical discipline. The procedure is the same regardless of
whether the fragility variable is leverage, fuel, deviance, or untested
code paths.

The intervention catalogue is portable. *Induce small perturbations
deliberately* — controlled burns, war games, fire drills, stress tests,
chaos engineering. *Install counter-cyclical discipline* —
countercyclical capital buffers, mandatory recurring drills, sunset
clauses on stable programmes, rotation of long-stable teams to expose
hidden fragilities. *Make fragility observable* — telemetry, audit, and
"if a bad day hit today, what fails?" exercises that read the latent
variable directly rather than the outcome record. *Reward vigilance
during quiet periods* rather than treating it as overhead. Each move
attacks the response function or the misreading, which are the two
commitment points an outsider can actually influence.

abstract_reasoning:
Several abstractions sharpen the pattern. The *absence-of-evidence
fallacy*: the calm record does not evidence safety, it evidences the
absence of triggering shocks; risk is fragility times shock frequency,
and calm windows lower the observed frequency while raising the
unobserved fragility multiplier. The *realised-versus-structural
volatility gap*: the diagnostic question is whether realised volatility
has been suppressed below structural volatility, because if so the
suppressed variance is being stored somewhere — usually as accumulated
fragility — and will discharge eventually. The *exercise-atrophy
duality*: response capacity that is not used decays, whether the
substrate is muscle, immune system, fire infrastructure, crisis
institution, or an auto-scaling code path; unused capacity atrophies in
every substrate.

A further abstraction is the calibration trap, sometimes named after
Lucretius: agents calibrate to the worst observed shock and prepare
against that, but the worst observed shock is necessarily smaller than
the worst possible, so calibrating to history under-prepares against the
next event.<!-- FACT-D72-255 -->[^taleb-2012] Combined with a baseline-shifting update — each successful
calm period makes the next risk-taking step seem reasonable — the
cumulative drift, not any single step, is what produces the fragility.
These abstractions together explain why the pattern is invisible from
inside: every local step is defensible, and only the integral over the
calm window reveals the accumulated exposure.

knowledge_transfer:
Because the failure-mode signature — long quiet, eroded reserves,
outsized discharge — is structurally identical across substrates,
practitioners trained in one recognise it in another without
translation, and the interventions port with them. The Minsky-cycle
insight from finance ports directly into countercyclical-capital policy,
where calm-period accumulation is taxed to fund the inevitable
discharge.<!-- FACT-D72-256 -->[^minsky-1986] Holling's pathology-of-regulation insight from ecology
transferred into let-burn controlled-fire doctrine, and the underlying
recipe — re-introduce small frequent shocks — is the same in finance,
ecology, and software.<!-- FACT-D72-257 -->[^holling-meffe-1996] The normalisation-of-deviance insight from a
single spaceflight disaster transferred into safety auditing across
aviation, chemicals, nuclear, and healthcare, carrying its recipe —
audit for drift from procedure during calm periods — intact.<!-- FACT-D72-258 -->[^vaughan-1996]

The cleanest modern transfer runs from finance and ecology into software
operations: chaos engineering is the stability-induced-fragility
counter-intervention applied to distributed systems, deliberately
re-introducing the controlled failure that the calm would otherwise let
the system forget how to survive.<!-- FACT-D72-259 -->[^basiri-etal-2016] Across all substrates the same two
moves transfer as portable practice: re-introduce controlled stressors
to keep the response machinery exercised, and monitor the latent
fragility variable directly rather than the outcome record, since the
outcome record is exactly the calm that conceals the build-up. A
practitioner who has watched one system accumulate fragility under calm
arrives in any other already asking the diagnostic question — "what is
this system's fragility variable, and what is its trajectory while
nothing is going wrong?" — and already holding the intervention
catalogue that answers it.

formal_abstract:
The Minsky financial-instability cycle is the formal worked instance, and it
makes the positive feedback explicit.<!-- FACT-D72-260 -->[^minsky-1986] The *quiet window* is a prolonged period
of stable returns and low realised volatility. The *endogenous response
function* is the way agents inside the system update on that calm: lenders,
seeing few defaults, lower risk premia and relax covenants; borrowers,
financing comfortably, take on more debt. The *latent fragility variable* is
leverage — the ratio of debt to equity across the system — which rises steadily
under that response while nothing visibly goes wrong. The *shock-coupling* is
the defining nonlinearity: when a shock eventually arrives, its impact scales
with accumulated leverage, not with the shock's intrinsic size, so a modest
trigger forces fire-sales, margin calls, and a cascade whose magnitude was set
during the calm. The *misreading* is outside observers (and regulators) reading
the long calm as evidence of a safer system, supplying the policy permission
for still more leverage. The *production-through-absence invariant* is exact:
the fragility is manufactured by the *absence* of the small losses that would
otherwise have kept lenders cautious. Two sharpening abstractions apply
directly: the *realised-versus-structural volatility gap* — suppressed realised
volatility means variance is being stored as leverage and will discharge — and
the *Lucretian calibration trap*, agents preparing only against the worst shock
they have observed, which is necessarily smaller than the worst possible.<!-- FACT-D72-261 -->[^taleb-2012] The
counter-intervention is countercyclical capital: tax calm-period accumulation
to fund the inevitable discharge. **Mapped back:** the stable-returns window is
the quiet window, rising leverage is the latent fragility variable, the
crisis-cascade magnitude scaling with leverage is the shock-coupling, and "long
calm proves safety" is the misreading the production-through-absence invariant
inverts.

applied_industry:
Forest fire-suppression policy is the applied worked case, exercising an
ecological domain where the response function is mechanical rather than agentic.
The *quiet window* is decades during which every ignition is promptly
extinguished, so the landscape experiences no fires. The *response function* is
ecological: suppressing the small, frequent fires that historically consumed
undergrowth means dead wood, leaf litter, and dense understory are never
cleared. The *latent fragility variable* is the accumulated fuel load, which
climbs year over year while the forest looks healthy and the fire record looks
reassuringly empty. The *shock-coupling* is the discharge: when a fire finally
escapes suppression, its severity scales with the accumulated fuel, not with
the ignition source, so the system shifts from absorbing many small burns to
discharging a single catastrophic, canopy-destroying conflagration. The
*misreading* is reading the long fire-free record as forest health and as
vindication of the suppression policy, which funds still more suppression. The
repair is the pattern's signature counter-intervention — re-introduce small,
frequent, controlled stressors — instantiated as prescribed burns and let-burn
doctrine, plus *making the fragility observable* by measuring fuel load
directly rather than reading the (empty) fire-outcome record.<!-- FACT-D72-262 -->[^holling-meffe-1996] The structural
recipe — re-introduce controlled stress to keep the system exercised — is
identically the one finance uses (stress tests) and the one software operations
uses, giving a third genuine domain: chaos engineering deliberately injects
controlled failures into distributed systems so that untested failure paths do
not accumulate silently and surface together in the first real incident.
**Mapped back:** the fire-free decades are the quiet window, fuel load is the
latent fragility variable, the conflagration's severity scaling with fuel is
the shock-coupling, and prescribed burns are the controlled-stressor repair the
catalogue prescribes.


Stack

slug: stack

one_liner: A strictly nested discipline where what was opened last must close first.

core_idea:
A stack is the structural pattern of a strictly nested sequence of obligations or activations such that what was opened last must be closed first. The defining commitment is the *last-in-first-out discipline*: items are pushed onto the top in some opening order, items must be popped from the top in the strict reverse of that order, and no item may be retrieved from beneath the top while items remain above it.

What makes the stack a structural pattern rather than mere "list" is the *strict nesting*. The discipline enforces that any pair of items maintains an inviolable relationship: if A was pushed before B, then B must be popped before A. This enforced nesting is what distinguishes the stack from other ordering structures — queue, priority queue, set — and it is what makes stacks the natural representation of nested obligations, call activations, scope contexts, parenthesized structures, and step-by-step backtrackable explorations. The order of closing is not free; it is fully determined by the order of opening, reversed.

The pattern travels because the same nested-obligation structure appears in many substrates. A function call cannot return until functions it called have returned; an interpretation cannot pop a parenthesis level until inner levels are closed; a search cannot retract a decision until decisions made downstream of it are unwound; a person multitasking cannot resume an outer task until inner tasks complete; a legal argument depending on a precedent cannot be finalized until the precedent's status is settled.<!-- FACT-D72-276 -->[^knuth-1997] In each case the operational rule is identical: the topmost obligation must complete first. The geological case — sedimentary strata, oldest at the bottom — shows the structure arising in nature, with no human discipline imposing it.<!-- FACT-D72-277 -->[^steno-1669]

structural_signature:
*the ordered collection of open obligations* — *the top (current must-discharge item)* — *the push (open) operation* — *the pop (close) operation* — *the last-in-first-out invariant* — *the bracket-matching (balanced push/pop) closure*

A structure is a stack when each of the following holds:

- **A collection of open obligations.** There is a set of items — activations, scopes, brackets, decisions, layers — that have been opened and not yet discharged. The collection has a definite size, the *depth*.
- **A distinguished top.** Exactly one item is accessible: the most recently opened. The interior is sealed; no item beneath the top may be retrieved while items remain above it.
- **A push operation.** New obligations are added only at the top, extending the open sequence by one and becoming the new accessible item.
- **A pop operation.** Obligations are removed only from the top, discharging the most recent and re-exposing whatever lay immediately beneath it.
- **The last-in-first-out invariant.** For any pair, if A was pushed before B then B must be popped before A. Closing order is not free — it is fully determined as the strict reverse of opening order. This enforced nesting is the load-bearing condition distinguishing a stack from a queue, set, or flat list.
- **Bracket-matching closure.** Every push must eventually be matched by a pop: unmatched pushes leak open obligations (overflow), unmatched pops corrupt state (underflow). On abnormal termination the structure must be unwound from the top down, discharging each pending obligation in turn.

Composed: an opening sequence forces a reverse closing sequence through a single accessible top, so the entire nesting discipline reduces to one rule — reverse of opening — plus the balance and unwinding invariants that keep it consistent.

what_it_is_not:
- **Not a queue.** A queue closes in *arrival* order (first-in-first-out); the stack closes in strict *reverse-of-arrival* order. The closing order is the discriminator: nested obligations need a stack, fairness-ordered ones need a queue.
- **Not `interleaving`.** Interleaving alternates between two or more independent strands at runtime without requiring strict nesting; a stack forbids touching any item beneath the top. Interleaved tasks may resume in any order, stacked ones may not.
- **Not `hierarchy`.** A hierarchy is a static containment tree of *which* item contains which; a stack is a dynamic discipline of *when* obligations open and close in time. A hierarchy can be traversed in any order; the stack fixes the order of discharge.
- **Not `recursion`.** Recursion is self-reference in a *definition* — a thing defined in terms of itself. A stack is the runtime *mechanism* that often implements recursion (the call stack), but stacks arise without any self-reference, and recursion can be implemented without an explicit stack.
- **Not `sequencing`.** Sequencing imposes a linear order on independent steps; a stack imposes a *paired* open/close discipline where each close is bound to its matching open. Steps in a sequence have no nesting obligation.
- **Not `layering`.** Layering is a fixed architectural stratification (a network stack's layers) that persists; the data-structure stack is a transient store whose contents change with each push and pop. The shared word "stack" hides that one is static structure, the other a runtime discipline.
- **Common misclassification.** Forcing LIFO discipline onto obligations that are actually parallel — serializing concurrent work, or making an outer task wait on an inner one that does not depend on it. Catch it by testing each pair: does discharging one *require* the other to still be open, or are they merely both open at once?

broad_use:
- **Function call stacks** (canonical): activation records pushed on call and popped on return; recursion is the discipline applied to self-similar sub-problems.<!-- FACT-D72-278 -->[^knuth-1997]
- **Expression parsing and bracket matching**: parser stacks track opened brackets and ensure each closes in reverse-opening order, as in the shunting-yard algorithm.<!-- FACT-D72-279 -->[^dijkstra-1961]
- **Undo histories**: text, image, and CAD editors implement undo as a stack — the most recent action undone first; multi-level undo *is* the stack made user-visible.
- **Backtracking search**: depth-first search, SAT solvers, and game-tree exploration use a stack to record decisions so they can be unwound on dead-end discovery.<!-- FACT-D72-280 -->[^russell-norvig-2010]
- **Nested interruption handling**: an interrupted process pushes its state; the handler may itself be interrupted; the stack preserves strict resumption order.
- **Cognitive task nesting**: a digression must complete before the original conversation resumes; "now back to the original problem" is the cognitive pop.
- **Legal argument and precedent**: a court ruling on a top-level issue may require first resolving an issue it depends on, the dependency being nested rather than parallel.
- **Geological stratigraphy**: the principle of superposition deposits layers in a stack — oldest at the bottom, newest on top — a LIFO temporal discipline reflected spatially.<!-- FACT-D72-281 -->[^steno-1669]

clarity:
Naming the stack clarifies a load-bearing distinction routinely muddled in system description: between *nested* obligations and *parallel* or *ordered* obligations. Nested obligations require last-in-first-out discipline; trying to resume an outer obligation without completing the inner one leaves the system in an inconsistent state. Many cognitive and organizational failures stem from ignoring this — returning to an outer task without properly closing the inner one, or popping a precedent without resolving its dependents.

The clarification surfaces several diagnostic questions: is the structure here last-in-first-out, what is the top (the current outstanding obligation), what is the depth (how many obligations are open), is the stack at risk of overflow (too many open at once), and is there an underflow (popping more than was pushed)? These questions sharpen design across substrates because they convert a vague sense of "things are tangled" into specific, checkable properties of a nesting discipline.

Crucially, the prime distinguishes the stack from superficially similar structures whose closing order is *not* determined by opening order. A queue closes in arrival order; a set imposes no order; a flat list permits any closing order. Only the stack ties every close to its open in strict reverse, and recognizing that tie is what lets a designer notice when a problem genuinely demands stack discipline rather than merely resembling one.

manages_complexity:
The pattern compresses a wide family of nested-obligation phenomena — call activations, expression parsing, undo systems, backtracking search, interrupt handling, cognitive task nesting, legal-argument structure, change management, geological strata, override hierarchies — into one diagnostic family: a strictly nested opening sequence implies a strictly reversed closing sequence. Cross-cutting design problems that look unrelated — stack overflow, leaked obligations, ill-matched brackets, broken nesting, incomplete unwinding — become legible as one problem family.

The intervention space then sorts cleanly. One can *limit stack depth* to reduce nesting, *convert recursion to iteration* to flatten the stack, install *explicit unwind handlers* to ensure cleanup on pop, *detect mismatched push/pop pairs* through bracket matching, or *restructure the problem* to use a different discipline entirely such as a queue or set. Each is recognizable across substrates: flattening a deeply recursive computation and de-nesting an over-complicated chain of conditional obligations in a procedure are the same move. The complexity the stack manages is the complexity of dependencies that must be discharged in a forced order; it manages it by reducing that order to one rule — reverse of opening — and a small menu of moves for when the nesting grows unwieldy.

abstract_reasoning:
Recognizing the stack enables reasoning about the *nested-versus-parallel distinction*: many obligations are parallel and close in any order, some are nested and require strict reverse order, and the diagnostic is to check whether each pair is dependent or independent. It names the *stack-depth-versus-resource tradeoff*: every push consumes memory, attention, or time-to-resume, so deep stacks risk overflow, and recognizing the depth bound in advance permits preventive redesign through iteration, accumulator passing, or explicit work-lists.

It names the *bracket-matching invariant*: every push must eventually be matched by a pop, unmatched pushes leak resources and unmatched pops corrupt state — an invariant generalizing from parser theory to resource-management discipline (RAII, try-finally, with-blocks) and to the organizational discipline of "closing the loop." It names the *unwinding-on-failure discipline*: when an obligation cannot be discharged, the stack must be unwound with each pending obligation handled explicitly, as in exception handling, distributed-transaction sagas, and evacuation protocols. And it names the *continuation-versus-stack equivalence*: any stack-based program can be rewritten with explicit continuations carrying the reified rest-of-the-stack, an insight transferring from programming-language theory to asynchronous programming, coroutines, and the cognitive distinction between procedural and event-driven task management.

knowledge_transfer:
The transfers are concrete and well-attested. The function-call-stack discipline transferred into structured exception handling in C++, Java, and Python, where exceptions unwind the stack discharging cleanup handlers.<!-- FACT-D72-282 -->[^stroustrup-1994] The stack-machine architecture — Forth, PostScript, then the JVM and CLR — reifies stack discipline as an entire computational model.<!-- FACT-D72-283 -->[^koopman-1989] The backtracking-search stack transferred from the DPLL algorithm into modern SAT and SMT solvers, where CDCL manipulates the stack with clause learning.<!-- FACT-D72-284 -->[^marques-silva-1999] The editor undo stack transferred into a near-universal UX pattern across word processing, image editing, CAD, and spreadsheets, and into operating-system snapshot rollback. The stratigraphic stack transferred from geology into archaeology, paleontology, and ice-core analysis as the dominant relative-dating discipline.<!-- FACT-D72-285 -->[^harris-1989] And stack discipline transferred into resource management through RAII and its descendants — Python's `with`, Java's try-with-resources, Rust's drop.<!-- FACT-D72-286 -->[^stroustrup-1994]

What makes these transfers genuine is the interchangeability of structural roles. *The top* as the most recently opened, must-discharge-first obligation; *the push* opening a new obligation; *the pop* closing the topmost and exposing what was beneath; *the LIFO invariant* forcing reverse-of-opening order; *the depth* counting open obligations; *the bracket-matching property* requiring every push eventually matched; and *the unwinding discipline* handling abnormal termination by popping through pending obligations with cleanup — these map one-to-one whether the substrate is a call stack, a parser, an undo history, a backtracking search, a courtroom, or a sediment column. Stripped of computer-science vocabulary, a stack is "the discipline of strictly nested obligations: what was opened most recently must be closed first." A practitioner carrying that sentence into undo design, interrupt handling, conversational digression, court reasoning, exception handling, constraint-search backtracking, or geological dating inherits the same transferable kit — the LIFO invariant, the bracket-matching invariant, and the unwinding-on-failure discipline.

formal_abstract:
Recursive-descent evaluation of a nested arithmetic expression makes the stack explicit. Evaluate `( 3 + ( 4 * 2 ) )`. The *collection of open obligations* is the set of partially-evaluated subexpressions; the *top* is the innermost one currently being worked. Scanning left to right, the outer `(` is a *push* — an obligation to compute one sum. Reaching the inner `(` *pushes* a second obligation, the product, which becomes the new top, sealing the outer sum beneath it. The *LIFO invariant* now forces the order: `4 * 2` must be discharged before the outer sum can resume — there is no legal way to finish the addition while the multiplication remains open. Computing `4 * 2 = 8` is a *pop*, re-exposing the addition obligation, which now reads `3 + 8`; *popping* that yields 11 and empties the stack. *Bracket-matching closure* is checkable directly: two `(` pushed, two `)` popped, balanced — an unmatched `(` would leave an open obligation (the parser reports "unexpected end of input"), an extra `)` would underflow (a pop with nothing to close). The *depth* peaked at two, the nesting level. The intervention this enables is precise: a malformed expression is diagnosed not as "syntax is wrong" but as a specific stack fault — overflow (runaway nesting), leaked open obligation (missing close), or underflow (excess close) — and recovery means unwinding the stack from the top down, reporting each unclosed level.

**Mapped back:** Parenthesized evaluation is the LIFO discipline made literal — the innermost-open subexpression is the top, opening order forces reverse closing order, and balance/underflow checks are exactly the bracket-matching invariant.

applied_industry:
Multi-level undo in a document editor reifies the stack as a user-facing feature, and geological stratigraphy reveals the same structure arising without any designer. In the editor, each edit (*push*) records a reversible action onto an undo stack; the *top* is the most recent change. Ctrl-Z is a *pop*: the LIFO invariant guarantees the *last* action is reversed *first*, so undoing walks history in strict reverse-of-opening order — you cannot un-apply a paragraph-format from three edits ago without first undoing the two edits stacked above it. The *depth* is how far back you can go; bounding it (a fixed history limit) is the *limit-stack-depth* intervention, trading memory for reach. A crash mid-edit demands the *unwinding discipline*: pending operations are rolled back top-down to a consistent state, exactly the exception-unwind pattern. Stratigraphy shows the identical LIFO structure with no human discipline imposing it: the principle of superposition deposits sediment layers so the oldest sits at the bottom and each new layer is *pushed* on top.<!-- FACT-D72-287 -->[^steno-1669] To read the geological record an archaeologist must *pop* from the top — excavate the youngest layer first — because the closing (excavation) order is forced to be the strict reverse of the opening (deposition) order. A layer found out of sequence (an intrusion, a fault) is read precisely as a violated LIFO invariant, flagging disturbance. The shared intervention is the same nesting check: in both the editor and the dig, "what is the top, and is the close-order the strict reverse of the open-order?" is the diagnostic that separates a consistent history from a corrupted one.

**Mapped back:** Undo histories and sedimentary strata are the same strictly-nested LIFO discipline — most-recently-opened must close first — one engineered into software, one emergent in nature, both diagnosable by checking whether closing order is the forced reverse of opening order.


Stage Gate Process

one_liner: Partition a long commitment into evidence-gated stages with escalating commitment and a funnel of kills.

core_idea:
A stage-gate process is a designed sequence in which a long-horizon
commitment is partitioned into stages separated by go/no-go decision gates,
with each gate requiring evidence accumulated in the prior stage and
licensing escalated resource commitment for the next. The structural
commitment is a triple: a *sequence of stages*, each producing a body of
evidence about whether the underlying bet is working; *gates* at the stage
boundaries at which an evidence-conditional go/no-go decision is taken; and
*escalating commitment*, where each subsequent stage costs more, locks in
more, and demands more decisive evidence to justify. The pattern *expects
most candidates to die at gates*; the funnel is a feature, not a bug.

What stage-gate makes visible is the *separation of evidence generation from
commitment escalation*. The actor never has to commit the full project
budget on initial-stage evidence, because the gate structure permits
incremental commitment proportional to accumulated evidence. The total
budget is decomposed into a sum of stage-conditional commitments rather than
a single up-front bet, and the project decision is decomposed into a sequence
of gate decisions. The intervention space is correspondingly rich: gate
criteria, kill criteria, per-stage evidence requirements, advance-commitment
levels, gate-keeper composition, and the option to revert a candidate to an
earlier stage rather than killing it outright. The pattern's force depends on
two structural facts that distinguish a real gate from a check-in: each gate
has explicit *kill* criteria, not just *go* criteria, and the gate-keepers
are *independent of the stage executors*. An instance lacking either is
gate-shaped but missing the structural force.

structural_signature:
*the long-horizon commitment to be partitioned* — *the ordered sequence of evidence-producing stages* — *the gate as evidence-conditional go/no-go boundary* — *the escalating-commitment ladder* — *the kill-criterion alongside the go-criterion* — *the executor/adjudicator independence invariant* — *the attrition funnel as expected output*

A configuration exhibits the stage-gate pattern when each of the following holds:

- **A partitionable commitment.** There is a single high-uncertainty,
  long-horizon bet whose total cost can be decomposed into a sequence of
  smaller, conditionally-incurred commitments rather than a lump up-front
  outlay.
- **An ordered sequence of stages.** Each stage is a bounded segment of work
  whose product is a defined body of *evidence* about whether the underlying
  bet is working. Stages are ordered, and evidence accumulates across them.
- **Gates at the stage boundaries.** At each boundary an explicit decision is
  taken, conditioned on the evidence produced so far, that resolves to advance,
  terminate, or (optionally) revert. A gate is a decision moment, not a
  check-in.
- **Escalating commitment.** Each successive stage costs more, locks in more,
  and demands stronger evidence to justify; the commitment authorised by a
  gate-pass must be matched to the strength of the evidence behind it.
- **Paired go and kill criteria.** A genuine gate carries explicit thresholds
  for termination, not only for advancement; "no-go" is a designed, normal
  outcome. Absent kill criteria, the gate degenerates into a rubber-stamp.
- **Adjudicator–executor independence.** The party deciding whether to advance
  is structurally distinct from the party whose work is being judged; without
  this separation the go/no-go decision loses its honesty.
- **An attrition funnel.** Most candidates are *expected* to die at gates; high
  attrition is the signature of a well-tuned selection mechanism, not a string
  of failures.

Composed, these turn one irreducible project decision into a chain of
evidence-conditional gate decisions, and a single bet into a sum of staged,
revocable commitments whose funnel of kills is the intended product.

what_it_is_not:
- **Not a `decision` taken at one moment.** A `decision` resolves an
  alternative at a point; stage-gate is a *sequence* of evidence-conditional
  decisions with escalating commitment between them, so the unit of analysis
  is the chain, not the gate.
- **Not `escalation_of_commitment`.** That pathology is the *failure* of a
  stage-gate — sunk cost biasing the gate toward "go"; the prime's kill
  criteria and adjudicator independence exist precisely to resist it, so the
  two are antagonists, not synonyms.
- **Not `precedent_stare_decisis`.** Precedent binds a present decision to a
  past one by analogy; a gate conditions advancement on *freshly produced
  evidence about this bet*, with no claim that prior cases govern.
- **Not `prioritization`.** Prioritisation ranks candidates by value at one
  time; stage-gate sequences and kills them across time as evidence
  accumulates, and high attrition is its intended output, not a ranking
  artefact.
- **Not `local_autonomy_tiered_escalation`.** That routes a decision *upward*
  by severity; a gate is a *forward* checkpoint that the same candidate must
  clear to proceed, independent of any authority hierarchy.
- **Not mere `monitoring`.** Monitoring observes without a designed branch
  point; a gate is a decision moment with paired go/kill criteria — a
  check-in that cannot terminate the candidate is gate-shaped but lacks the
  structural force.
- **Common misclassification.** Calling any phased plan with review meetings
  "stage-gate." If the reviews lack explicit kill criteria and the reviewers
  are the executors themselves, the gates are rubber-stamps; the diagnostic
  is whether any candidate has actually been killed at a late gate.

broad_use:
- **New product development** (canonical): staged sequences of discovery,
  scoping, business case, development, testing, and launch, separated by idea
  screens and go-to-development, go-to-testing, and go-to-launch gates.<!-- FACT-D72-301 -->[^cooper-1990]
- **Pharmaceutical development**: Phase 0 through Phase IV, with evidence
  packages required between phases and heavy attrition between early phases
  and approval.<!-- FACT-D72-302 -->[^fda-ind]
- **Military acquisition**: milestone decision points (technology
  development, engineering and manufacturing, production) gated by review
  boards with evidence packages.<!-- FACT-D72-303 -->[^dod-5000-02]
- **Venture capital**: pre-seed through later rounds, each committing more
  capital, requiring more evidence, and applying explicit gate criteria.
- **Software releases**: alpha, beta, release candidate, and general
  availability, each with explicit exit criteria and an escalation in user
  exposure.<!-- FACT-D72-304 -->[^humble-farley-2010]
- **Academic credentialing**: coursework, qualifying exam, proposal defence,
  dissertation defence, and publication, each gate licensing escalated
  commitment of advisor and committee resources.
- **Real estate development**: site identification, feasibility,
  entitlement, design, construction, and lease-up, each escalating capital
  and requiring evidence (zoning, permits, pre-leasing).<!-- FACT-D72-305 -->[^miles-etal-2007]
- **Aerospace systems engineering**: the design-review sequence, each gate
  requiring evidence packages and licensing escalated commitment.

In each substrate the same triple recurs — sequenced stages producing
evidence, gates resolving evidence into go/no-go, and escalating commitment —
independently rediscovered because the underlying decision-economics recur.

clarity:
Naming a process as stage-gate exposes three things non-staged commitment
hides. The advance commitment is *not all-or-nothing* at project start; it is
sliced into incremental commitments, each licensed by accumulated evidence.
The *option to kill* is built into the structure — gates are decision
moments, not check-ins, and "no-go" is a normal outcome rather than a failure
of the stage. And the evidence requirements are *stage-specific*: an early
stage needs feasibility or safety evidence, a late stage needs efficacy
evidence at scale, and mismatching evidence to stage — demanding scale-level
proof at the first gate — is a common pathology.

The pattern also makes *attrition* explicit and acceptable. A funnel in which
nine of ten candidates die at an intermediate gate is not broken; it is
working as designed. This reframing is itself clarifying: it converts what
looks, under a non-staged frame, like a string of failures into the expected
operation of a well-designed selection mechanism, and it redirects attention
from "why do so many die?" to "are the gates killing the right candidates at
the right stages?"

manages_complexity:
Stage-gate compresses an open-ended, high-uncertainty commitment problem
into a tractable sequence of design choices: choose the stage scope, define
the stage exit criteria, define the gate evidence package, and define the
commitment escalation per gate-pass. The decision problem becomes a sequence
of gate decisions rather than a single irreducible project decision, and the
budget becomes a sum of conditional commitments rather than a lump bet.

It also organises *role specialisation*. Stage executors and gate
adjudicators are different roles — and should be, since gate-keepers who also
execute stages carry a conflict of interest — and evidence assemblers differ
from evaluators. The gate structure thereby provides natural handoff points
and review rituals, distributing a complex commitment across clearly
separated responsibilities. This separation is not incidental decoration: it
is what keeps the go/no-go decision honest, because the person deciding
whether to advance is not the person whose work is being judged.

abstract_reasoning:
The frame supports reasoning about commitment under uncertainty, attrition,
and option value. *Information-commitment matching*: at each gate the
commitment for the next stage should be matched to the strength of evidence
from the prior stage, and over-committing on weak evidence — pouring
late-stage budget into a candidate with a weak earlier signal — is a classic
failure. *Kill-criterion design*: gates need explicit thresholds below which
a candidate is killed; without them, gates degenerate into rubber-stamps.
*Option value*: the staged structure is equivalent to a sequence of real
options, and the value of preserving optionality grows with downstream
uncertainty. *Stage-skipping and reverting*: strong early evidence can
support skipping a stage, and a late-stage failure can support reverting
rather than killing, so the graph is not strictly linear. And a
characteristic catalogue of *failure modes* falls out of the structure:
bureaucratised gates that become compliance checks, evidence-quality drift
where stages produce evidence shaped to pass rather than to inform,
kill-avoidance where no candidate ever fails, gate-explosion that produces
paralysis, and the urgency-override that skips a gate without evidence.

knowledge_transfer:
The roles map across substrates: the *stage* is a bounded segment of work
producing a defined evidence package; the *gate* is the evidence-conditional
go/no-go at the boundary; the *escalation* is the increase in committed
capital, exposure, or lock-in that each gate-pass authorises; the *go* and
*kill* criteria are the conditions for advance and termination; the
*gate-keeper* is the decision-maker structurally independent of the executors;
and the *attrition profile* and *optionality value* are the portfolio-level
quantities the structure exposes. The intervention vocabulary ports
surgically, in part *because* the pattern has been independently codified in
multiple high-stakes domains, each refining a different aspect — one domain
best at evidence packages, another at round economics, another at gate-keeper
independence, another at gate-process design.

Concrete transfers are well attested. Gate-criteria design ports from
venture-capital term sheets to regulatory evidence packages to acquisition
milestone reviews, each substrate having learned conventions for adequate
per-stage evidence. Kill-criterion discipline ports as a single diagnostic
question — "what would kill this candidate?" — applicable to any staged
investment. Information-commitment matching ports as a portfolio rule: commit
proportional to evidence quality. Gate-keeper independence ports as an
organisational-design rule: the people who decide to advance should not be the
people whose work is judged. Attrition-as-design ports as a portfolio frame in
which high attrition is the signature of a well-designed funnel. And real-
option valuation ports from finance into staged R&D budgeting, connecting
formal valuation theory to organisational practice.<!-- FACT-D72-306 -->[^dixit-pindyck-1994] A single worked instance
shows the substance: an oncology drug advancing through microdosing, single-arm
safety, mid-stage efficacy, and large-scale efficacy gates, each producing
evidence that licenses the next-stage commitment, each with explicit go and
kill criteria, the total commitment sliced rather than committed up front, and
roughly one in ten Phase-I candidates reaching approval — by design, not by
failure.<!-- FACT-D72-307 -->[^dimasi-etal-2016] The same structure, with the same intervention vocabulary, governs a
venture portfolio, an aerospace design review, and a doctoral programme.

formal_abstract:
Sequential hypothesis testing as a multi-stage decision problem instantiates
the stage-gate structure formally. Consider a candidate treatment whose true
effect $\theta$ is unknown. The *partitionable commitment* is the full
trial-and-approval budget; the *ordered stages* are a sequence of experiments
of increasing size $n_1 < n_2 < n_3$, each producing an evidence statistic
$T_k$ (an estimated effect with shrinking confidence interval). At each *gate*,
a decision rule compares $T_k$ against two thresholds: a *go* boundary above
which the candidate advances and an explicit *kill* boundary below which it is
terminated — the classical Wald sequential probability ratio test structure,
where between the boundaries you keep sampling and outside them you stop.<!-- FACT-D72-308 -->[^wald-1947] The
*escalating commitment* is exact: each stage's sample size, and hence cost,
grows, so the evidence bar to license the next stage rises with it. The
*attrition funnel* is the prior-weighted fraction of candidates whose $T_k$
falls below the kill boundary at each gate — and the design optimises expected
total cost by killing weak candidates cheaply and early. The *adjudicator
independence* invariant maps to a pre-registered, blinded analysis: whoever
ran the experiment cannot move the boundary after seeing $T_k$. This shows the
intervention space directly — set kill thresholds to control false-advance
rate, set stage sizes to match commitment to evidence quality, and read the
attrition profile as a tuning diagnostic rather than a failure log.

**Mapped back:** The sequential-test gates, go/kill boundaries, escalating
sample-size commitment, and pre-registration independence are the prime's
stages, paired criteria, escalation ladder, and adjudicator-independence
invariant rendered as a formal decision rule.

applied_industry:
Venture-capital financing rounds run the stage-gate pattern at portfolio
scale. The *partitionable commitment* is the total capital a startup would
need to reach profitability; instead of writing one cheque, investors slice it
into pre-seed, seed, Series A, B, and C *stages*, each a bounded segment of
company-building that produces *evidence* — a working prototype, then early
users, then revenue retention, then unit economics, then scalable growth.<!-- FACT-D72-309 -->[^gompers-1995] Each
*gate* is a financing decision conditioned on the prior stage's metrics, and
the *escalation* is steep: a Series B round commits far more capital and
demands far stronger evidence than a seed round. The *kill criterion* is the
down-round-or-no-round outcome, a designed and normal event — most seed-funded
companies never raise a Series A, and that high *attrition funnel* is the
signature of a working selection mechanism, not a string of disasters.<!-- FACT-D72-310 -->[^gompers-1995] The
*adjudicator–executor independence* invariant appears as the investment
committee and new lead investors who are structurally distinct from the
founders being judged. The pattern diagnoses two classic pathologies: *over-
committing on weak evidence* (a hot seed market pouring growth-stage money into
companies with no retention signal) and *kill-avoidance* (insiders bridging a
failing company round after round rather than letting the gate do its work). A
parallel applied instance is software release management: alpha, beta, release
candidate, and general availability are stages with explicit exit criteria, the
escalation is user-exposure rather than capital, and a feature killed at beta
is the gate operating as designed.

**Mapped back:** Financing rounds and release tiers are stages; the funding
decision and the GA cutover are gates with paired go/kill criteria; the rising
cheque size and widening user exposure are the escalation ladder; and the high
fraction of startups and features that die between stages is the intended
attrition funnel.


Stakeholder Analysis

one_liner: Identify involved parties.

core_idea: Stakeholder Analysis is the systematic-mapping principle that before (or during) any project, policy, system, or decision with distributed consequences, an explicit enumeration and classification of the parties who have a legitimate interest in the outcome — who affect it, are affected by it, or can hold the decision-makers accountable for it — reduces blind spots, conflict, and failure by ensuring that stakeholder interests, power, and relationships are recognized and managed rather than discovered reactively. A stakeholder is any individual, group, organization, or entity with a stake (ownership, interest, right, claim, or exposure) in the matter, not merely those who are formally parties to the decision — formally, stakeholder analysis produces: (a) a stakeholder list generated through explicit identification procedures; (b) classifications of stakeholders along dimensions relevant to the decision (power, interest, legitimacy, urgency, proximity, affected population, veto potential); (c) a mapping of relationships among stakeholders (alliances, dependencies, rivalries); (d) an action implication — which stakeholders to engage, consult, inform, or monitor; and (e) an update cadence because stakeholder sets, positions, and power shift over project timelines. The concept has several overlapping origin streams: in strategic management, R. Edward Freeman's *Strategic Management: A Stakeholder Approach* (1984) established stakeholder theory as a counterweight to shareholder-primacy theory in corporate strategy; in project management, stakeholder identification and engagement is codified in PMI's PMBOK and PRINCE2; in policy analysis, stakeholder mapping emerged through participatory-development traditions (ODA's Logical Framework Approach, Chambers' participatory rural appraisal, 1970s–1990s); in technology ethics and AI governance, stakeholder analysis has been substantially extended (value-sensitive design, 1996+; algorithmic-impact-assessment practice); in critical systems thinking, Ulrich's boundary critique treats stakeholder identification itself as a contested act — who counts as a stakeholder is a question about system boundaries, power, and legitimacy. The deeper logic is that systems and decisions have distributed causal consequences affecting parties well beyond formally-authorized decision-makers, and that failing to account for distributed parties produces predictable failure modes: opposition mobilizes from unrecognized stakeholders; unexpected power centers block or redirect implementation; affected parties without voice bear disproportionate costs (ethical harm and downstream conflict); critical enabling relationships are missed; legitimacy deficits accumulate. Stakeholder analysis exists to convert these reactive failure modes into proactive management — making stakeholder awareness systematic rather than accidental<!-- FACT-D33-037 -->[^freeman-1984].

structural_signature:
- *The stakeholder-identification process using explicit generative procedures (brainstorming, snowball sampling, pre-existing taxonomies, boundary-critique probes, document review) to overcome identification blind spots* <!-- FACT-D33-031 -->[^freeman-1984]
- *The classification system mapping stakeholders on dimensions useful for action planning (power/interest matrix, salience model, primary/secondary, internal/external, aligned/opposed)* <!-- FACT-D33-032 -->[^mendelow-1991]
- *The relationship-mapping phase identifying alliances, coalitions, dependencies, rivalries among stakeholders producing network diagrams not just flat lists* <!-- FACT-D33-033 -->[^freeman-1984]
- *The interest-and-position analysis revealing what each stakeholder wants, their public stance, what they stand to gain or lose, their assumptions about the situation* <!-- FACT-D33-034 -->[^mitchell-agle-wood-1997]
- *The engagement-plan design specifying for each stakeholder category the type and frequency of engagement (manage closely, keep satisfied, keep informed, monitor) and success criteria* <!-- FACT-D33-035 -->[^mendelow-1991]
- *The reflexive-check component interrogating what assumptions about legitimate stakes, power, or system boundaries the analyst is making and whose voices are being excluded* <!-- FACT-D33-036 -->[^ulrich-1996]

what_it_is_not:
- **Not boundary critique alone.** Boundary critique is a specific critical-systems-thinking practice (Ulrich's CSH) interrogating value- and power-assumptions behind system boundary choices; stakeholder analysis is broader and more operational, including but not reducible to boundary critique. The two are tight companions: boundary critique sharpens the reflexive check in stakeholder analysis; stakeholder analysis operationalizes some of what boundary critique identifies.

- **Not user research or user-centered design.** User research focuses on primary users of a product or system; stakeholder analysis includes users but extends to operators, affected third parties, regulators, funders, and others. User research is a subset of stakeholder-relevant activity.

- **Not customer segmentation.** Customer segmentation classifies buyers by demographic, behavioral, or psychographic variables for marketing; stakeholder analysis addresses all parties with legitimate claims including non-buyers. The two overlap but cut differently.

- **Not influence mapping or power mapping alone.** These focus on power relations, which is one dimension of stakeholder analysis; stakeholder analysis also addresses interest, legitimacy, stake, and engagement design.

- **Not constituency management.** Political constituency management focuses on electoral and representational relationships; stakeholder analysis is broader and applies to contexts without formal representation.

- **Not public relations or external affairs.** PR manages communications with external audiences; stakeholder analysis informs but does not reduce to PR. Stakeholder analysis produces understanding and strategy; PR produces communications.

- **Not merely listing parties.** A stakeholder register without classification, relationship mapping, engagement plans, or reflexive check is a shell of stakeholder analysis; the value is in the structured use, not the list.

broad_use: Stakeholder Analysis appears in strategic management and corporate governance (Freeman's stakeholder theory versus shareholder-primacy, ESG reporting, triple-bottom-line accounting, benefit corporations, corporate-purpose debates), in project management (PMI and PRINCE2 codify stakeholder identification, analysis, and engagement as standard deliverables), in policy analysis and public administration (stakeholder consultation requirements in rulemaking, impact assessments, legislative hearings), in participatory development (Chambers' participatory rural appraisal, Logical Framework Approach, Theory of Change frameworks by DFID, USAID, World Bank), in technology and AI governance (value-sensitive design, algorithmic-impact-assessment frameworks, sociotechnical-AI literature), in critical systems thinking (Ulrich's Critical Systems Heuristics with its reflexive 12-questions structure), in environmental and sustainability analysis (non-human stakeholders, future generations, upstream/downstream communities), in healthcare (patient-centered care, shared decision-making, value-based healthcare), in urban planning and community development (community engagement in planning, equity-in-planning frameworks), in conflict resolution and mediation (mapping parties to a conflict, their interests, BATNAs), in research and science policy (research funders, researchers, research participants, affected publics).

clarity: Names the systematic practice of identifying and managing the distributed parties to a decision, preventing the otherwise-chronic failure modes of discovering stakeholders reactively (after opposition or harm has surfaced), treating stakeholder management as PR (communications without genuine engagement), or treating only formally-authorized parties as stakeholders (ignoring legitimately-affected parties without voice). Without the frame, stakeholder consideration is either absent or informal; with the frame, it becomes a structured analytic activity with methods, tools, and criteria. The frame also makes the reflexive question visible: whose interests, voices, and stakes is the current analysis missing? — a question that opens into boundary critique and that in contemporary AI-governance, environmental-justice, and participatory-development contexts is increasingly central. Clarity is further sharpened by distinguishing stakeholder analysis from related but narrower activities (user research, customer segmentation, PR, constituency management) that share some features but miss the systematic and reflexive scope.

manages_complexity: Decomposes a potentially-overwhelming relational field (the universe of parties with stakes in a major project or decision) into a structured and prioritized set, with engagement plans tailored to stakeholder categories rather than one-size-fits-all. This decomposition prevents the two main failure modes of stakeholder work: over-engagement (consulting everyone equally, exhausting time and attention without producing insight) and under-engagement (consulting only the most visible or powerful stakeholders, producing blind spots and legitimacy deficits). The structured approach allows delegation — different team members can own different stakeholder categories — and update — the stakeholder register can be maintained incrementally rather than re-built from scratch. Complex multi-stakeholder contexts (large infrastructure projects, international climate negotiations, AI-governance processes) are sometimes tractable only with stakeholder-analysis methods; without them, the relational complexity overwhelms decision processes.

abstract_reasoning: The analyst asks: who has stakes in this decision or system — directly, indirectly, through affected communities, through regulatory authority, through future consequences, through non-human-stakeholder representation? How are they classified by power, interest, legitimacy, urgency? What relationships connect them — alliances, dependencies, rivalries? What are their interests (what they want), positions (what they say publicly), stakes (what they stand to gain or lose), assumptions? How should each be engaged — managed closely, kept satisfied, kept informed, monitored? Who is missing from this analysis whose legitimate stakes would be overlooked without explicit probing? How will this analysis be refreshed as positions and power evolve? Mature stakeholder-analysis practice is systematic, reflexive (asking what the analysis itself is missing), relationship-mapped (not just flat list), action-oriented (produces engagement plans), updated through project lifecycle, and paired with boundary-critique inquiry about legitimate stakes and framing. Mature practice also recognizes that stakeholder analysis is itself a political act — decisions about who counts as a stakeholder, what interests are legitimate, and what engagement is appropriate are value-laden and contestable. Immature practice treats stakeholder analysis as a checkbox, produces a flat list of most visible parties, engages them via PR rather than substantive consultation, and updates rarely if at all — producing the predictable stakeholder failures that mature analysis exists to prevent.

knowledge_transfer:
| Domain | Stakeholder categories emphasized | Classification scheme | Distinctive concern |
| --- | --- | --- | --- |
| Project management | Sponsor, user, team, vendor, regulator | Mendelow matrix | Delivery risk |
| Corporate governance | Shareholders, employees, customers, community | Stakeholder theory | Corporate purpose |
| Public policy | Citizens, interest groups, regulated entities | Impact-assessment categories | Legitimacy |
| Participatory development | Beneficiaries, local government, donor | LogFrame / ToC | Empowerment |
| AI governance | Users, affected communities, operators | Direct/indirect | Algorithmic harm |
| Critical systems (CSH) | Involved, affected, excluded | Ulrich's 12 questions | Reflexive inclusion |
| Environmental | Communities, species, future generations | Ecosystem-services / rights-of-nature | Non-human voice |
| Healthcare | Patients, families, clinicians, payers | Clinical pathway | Shared decision-making |
| Urban planning | Residents, businesses, environmental groups | Equity-in-planning | Community voice |
| Conflict resolution | Parties, allies, spoilers | BATNA-interest | Negotiation design |

Across rows: the activity (identify, classify, map relationships, plan engagement) is invariant; classification schemes, stakeholder categories, and distinctive concerns are domain-adapted. The transfer move is to import methods across domains — CSH's reflexive-inclusion probes into AI governance; value-sensitive design's direct/indirect distinction into urban planning; ecosystem-services valuation's non-human-stakeholder methods into AI deployment; participatory-development's empowerment methods into corporate stakeholder engagement.

formal_abstract:
R. Edward Freeman's strategic-management stakeholder mapping for a mid-sized industrial firm — the canonical methodological case from stakeholder theory's origin text. Freeman's 1984 *Strategic Management: A Stakeholder Approach* develops stakeholder analysis as a strategic-management practice through worked examples including a hypothetical industrial corporation facing environmental regulation, community concerns, union negotiations, and activist pressure simultaneously. The analytic steps as Freeman presents them: (i) stakeholder identification — generate a list of parties with stakes, initially through broad brainstorming (shareholders, lenders, employees, customers, suppliers, communities, regulators, activist groups, media, competitors, trade associations, industry groups, political parties, educational institutions, environmental groups — the list is long by design); (ii) stake analysis — for each stakeholder group, identify what is at stake (financial returns, wages, working conditions, product quality, supply contracts, environmental quality, community effects, regulatory compliance, public image, competitive position, sectoral reputation, political influence, research partnership); (iii) stakeholder-power and stakeholder-relationship mapping — which stakeholders have power over which firm decisions (regulatory power, economic power, legitimacy/normative power, operational power); which stakeholders are allied or coalitional (community groups and environmental activists on pollution issues; shareholders and management on cost-containment; unions and community on plant-closure decisions); (iv) strategic response design — for each major stakeholder category, what strategic posture (accommodate, negotiate, resist, ignore) and what specific engagement activities; Freeman's argument is that strategic success requires simultaneous attention to multiple stakeholder categories rather than shareholder-value primacy alone; (v) integration with strategic planning — stakeholder analysis is not a side activity but integrated into firm's strategic planning cycle, with stakeholder-relations performance reported at board level; (vi) evolution over time — stakeholder maps are updated as composition and positions change (new regulations, new technologies, new competitors, activist-group emergence, community demographic changes). Freeman's worked examples show how stakeholder analysis reveals strategic options invisible from shareholder-primacy analysis: plant-closure decisions appearing value-destructive from pure shareholder perspective turn out to generate larger long-run value losses through community, regulatory, and employee-morale effects; new-product decisions appearing financially attractive trigger regulatory and community response undermining execution; mergers appearing financially attractive trigger union and regulatory resistance. Contemporary stakeholder-theory development (Donaldson-Preston typology of descriptive/instrumental/normative versions; Mitchell-Agle-Wood salience model with power-legitimacy-urgency dimensions; connected-stakeholder-relationship network-analytic tradition; empirical literature on stakeholder-management and firm performance) has substantially elaborated methodology while core practice (identify, classify, map, engage, update) remains recognizable from Freeman's 1984 framework. The framework has been foundational to ESG reporting, B-corporation certification, corporate-purpose redefinition, and integrated-reporting practice<!-- FACT-D33-044 -->[^freeman-1984].

**Mapped back:** This instantiates the structural signature — systematic identification, multi-dimensional classification, relationship mapping, interest-and-position analysis, engagement-plan design, and reflexive integration with strategic decision-making.

applied_industry:
A regional hospital system planning to close a money-losing rural clinic faces a decision with distributed consequences and engages a stakeholder-analysis process before announcing closure. The planning team conducts structured stakeholder identification: (a) brainstorms an initial list (patients, staff, hospital system, insurance payers, local employers, state health department); (b) applies snowball sampling asking early-identified stakeholders who else would be affected, revealing additional parties (county emergency services relying on clinic for urgent-care referrals; regional maternal-health network that clinic is part of; school nurses at nearby schools referring students there; town's only pharmacy dependent on clinic prescriptions; neighboring nursing home using clinic for minor medical needs; faith-based communities running support groups at clinic; regional ambulance services assuming clinic exists); (c) consults boundary-critique probes — who is affected but unrepresented? (uninsured low-income residents using clinic for charity-care; migrant farmworker communities served seasonally; homeless individuals for whom clinic is only healthcare touchpoint); who will be affected but doesn't exist yet? (future residents); who has legitimate stake but is non-human? (regional public-health surveillance capacity clinic reporting supports); (d) classifies identified stakeholders on power-interest matrix (high power, high interest: state health department, hospital board, county government, major employers; high interest, lower power: patients, clinic staff, nursing home, pharmacy; high power, lower interest: payers, competing health systems; lower power, lower interest: peripheral parties) and on legitimacy-urgency dimension (high legitimacy + high urgency: patients with active care plans disrupted; high legitimacy + lower urgency: future residents; contested legitimacy + high urgency: media attention risk); (e) maps stakeholder relationships and coalitions (likely alliances: patients + clinic staff + community leaders against closure; possible alliances: state health department + county government for alternative service arrangements; possible rivalries: hospital system vs competing health system); (f) conducts substantive engagement proportional to stakeholder salience — town-hall meetings with community, structured interviews with major employers, bilateral meetings with state health department and county government, mailings and phone outreach to current patients, targeted outreach to historically-excluded communities, formal notice to regulatory and payer parties; (g) incorporates stakeholder-surfaced information into closure decision — nearest replacement clinic is 35 minutes by car and inaccessible without vehicle; maternal-health network loss would measurably affect regional outcomes; school-nurse referral disruption affects thousands of students; pharmacy would likely close if clinic does; (h) develops modified plan based on stakeholder input — phased closure with transition period; partnership with state health department for smaller clinic focused on primary care and maternal health; transportation subsidy negotiated with county and state; coordination with nursing home and pharmacy to preserve viability; explicit transition-care management for current patients; targeted communication with historically-excluded communities; (i) maintains stakeholder register through implementation with quarterly updates; (j) publishes public stakeholder-engagement report documenting what was considered and adopted from stakeholder input. The hospital's chief community officer reports: "we almost made a purely financial decision and would have set off a firestorm — stakeholder analysis showed us stakes we didn't know existed and partners who could help us avoid harms we didn't know about." The case illustrates stakeholder analysis in a morally-freighted operational decision, combining classical project-management stakeholder mapping with boundary-critique-style reflexive inclusion, producing both better decisions and greater legitimacy<!-- FACT-D33-045 -->[^unep-impact-assessment-2002].

**Mapped back:** Shows systematic identification expanded through boundary-critique probes, multi-dimensional classification, relationship mapping revealing unexpected partnerships and coalitions, substantive engagement proportional to salience, and integration of stakeholder input into decision-making and ongoing relationship maintenance.

title: Comprehensive inclusion versus analytic tractability

body:
Stakeholder sets can expand indefinitely (every decision has nearly-unbounded indirect consequences; every stakeholder has further stakeholders behind them). Comprehensive inclusion maximizes sensitivity to affected parties but overwhelms analytic capacity and produces classification-without-engagement. Restricted inclusion maintains tractability but risks missing legitimate stakes. Mature practice uses iterative widening (start broad; triage aggressively; probe for missing stakeholders; widen again if necessary) and explicit scoping decisions with reasoning documented so they can be challenged<!-- FACT-D33-038 -->[^freeman-1984].

- **T2: Formal authority versus legitimate stake.** Formally-authorized parties (shareholders in a corporation, elected officials in a polity, contract signatories in a project) have decision rights; affected parties without formal authority (employees, communities, future generations, non-human stakeholders) have stakes but not decision rights. The tension is between respecting formal authority structures and including legitimate but formally-powerless stakes. Different domains resolve this differently — corporate law gives shareholders dominant formal authority while stakeholder theory argues for broader attention to stakes; environmental-impact-assessment frameworks give formal voice to affected communities even without decision rights; AI-governance frameworks are actively negotiating where to place this line. Mature practice requires deliberate governance rather than drift<!-- FACT-D33-039 -->[^donaldson-preston-1995].

- **T3: Analyst's framing versus stakeholder self-definition.** Stakeholder analysis is conducted by an analyst or team who classifies stakeholders according to the analyst's framing of interests, power, and legitimacy; but stakeholders have their own self-understandings that may not match the analyst's categorization. Imposed categorization is analytically efficient but can misrepresent or marginalize stakeholders; self-definition respects stakeholder agency but can be hard to operationalize. Mature practice combines structured analyst-driven analysis with stakeholder-consultation on the analysis itself — giving stakeholders opportunity to challenge and revise their categorization. Participatory-development traditions conduct analysis with stakeholders rather than about them<!-- FACT-D33-040 -->[^chambers-1994].

- **T4: Analysis as action versus analysis as communication.** Stakeholder analysis is sometimes conducted as genuine informational input to decision-making (changing what gets decided based on what analysis reveals); sometimes as communication activity (informing stakeholders of decisions already made; managing opposition rather than responding to it substantively). Both uses occur; but treating communication-focused stakeholder work as if it were substantive-analytic produces specific failure mode — stakeholders recognize that their input was not meaningfully integrated, and legitimacy gain is inverted into legitimacy loss. Ethical stakeholder-engagement frameworks (IAP2's spectrum of public participation) make this explicit by distinguishing engagement levels with clear implications for stakeholder influence<!-- FACT-D33-041 -->[^iap2-spectrum-2007].

- **T5: Stakeholder complexity versus decision speed.** Thorough stakeholder analysis is time-consuming; some decisions operate under tight timelines. Comprehensive stakeholder engagement slows decision-making; rapid decisions risk unrecognized stakeholder opposition. Mature practice scales analysis depth to decision horizon and stakes — routine low-stakes decisions require less analysis; transformational high-stakes decisions require more even if it delays decision-making. The tension also exists between scope-of-analysis and time-available; mature practice uses triage and iteration rather than attempting completeness upfront<!-- FACT-D33-042 -->[^freeman-1984].

- **T6: Power-aware analysis versus legitimacy deficit.** Stakeholder analysis that faithfully maps power dynamics sometimes reveals uncomfortable facts: some stakeholders have disproportionate influence despite weak legitimate claim; some stakeholders with legitimate claims have minimal power. Acknowledging power imbalances is analytically honest but can feel politically dangerous; sanitizing the analysis to avoid uncomfortable truths produces analysis that serves advocacy rather than understanding. Mature practice maps power honestly while treating power imbalances as legitimate subjects for design — deliberate mechanisms (affirmative engagement of under-powered stakeholders, formal voice requirements, veto points, consent procedures) can be designed to surface suppressed legitimate claims<!-- FACT-D33-043 -->[^mitchell-agle-wood-1997].

prose:
_(Pass B — deferred)_

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

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

**As related prime (3):** `completeness_audit`, `creative_destruction_management`, `relation_mapping`

notes: Organizational-management-science origin (Freeman 1984 stakeholder theory; PMBOK project-management codification) with substantial alternate origins in policy and governance (participatory-development traditions; regulatory impact assessment) and ethics of technology/AI governance (value-sensitive design; algorithmic impact assessment). The concept has genuinely co-developed in alternate domains; management-science remains most widely-cited origin point but the breadth of co-origin is substantial. Tight_pair_with_boundary_critique flag applies: boundary critique (#399) is the reflexive counterpart to stakeholder analysis, probing the framing-and-exclusion assumptions that stakeholder analysis operationalizes. The flag is reciprocal — to be added to #399's review_flags in cross-reference pass. Companion primes: #399 boundary_critique (reflexive-inclusion tight pair), #405 sociotechnical_systems (stakeholder analysis is part of sociotechnical design), #347 legitimacy (stakeholder engagement is legitimacy-production mechanism), #349 accountability (stakeholder analysis identifies accountability flows), #350 conflict_of_interest (stakeholder analysis surfaces conflicts structurally), #358 delegation_of_authority (authority in stakeholder relationships), #295 design_for_implementation (stakeholder engagement is critical to implementation), #286 user_centered_design (narrower stakeholder focus on users). Strong transfer targets: AI-governance and algorithmic-impact-assessment (active frontier where stakeholder analysis is substantively developed); climate and environmental policy (where non-human and future-generation stakeholders require methodological extension); corporate-purpose and ESG debates (where stakeholder theory is central); large infrastructure projects (where stakeholder failure is dominant failure mode); participatory research and science policy. Pass B should develop archetypes for *stakeholder-set generation*, *stakeholder classification and prioritization*, *reflexive-inclusion probing* (paired with #399), *engagement-mode calibration*, and *stakeholder-register maintenance through project lifecycle*.