NASA Earth-observing missions overrun their committed budgets persistently across four decades of acquisition reform, and the coupling between a budget commitment and its overrun is almost always schedule: cost overruns and schedule delays are tightly linked, with 'directly related to' hiding several distinct root causes (unrealistic estimates, technical problems, supply-chain difficulty, scope change). This establishes that the problem the dissertation targets, undifferentiated schedule slip driving cost growth, is real.

Lieber & Donor, 'Schedule matters: Understanding the relationship between schedule delays and costs on overruns,' 2016 IEEE Aerospace Conference (candidate ref-1)


Low technology readiness is an established, measurable driver of spacecraft schedule slip: the TRL of a mission's least-mature technology at authorization maps to a measurable increase in expected schedule slip, with a nonlinear relationship at the low-maturity end. This grounds the instrument arm of the competing-risks structure as a real, documented hazard channel rather than a posited one.

Dubos, Saleh & Braun, 'Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design,' J. Spacecraft and Rockets (2008) (candidate ref-2)


The Fine-Gray proportional subdistribution-hazard model is a mature, validated apparatus for directly regressing the cumulative incidence of one competing event in the presence of others, which is precisely the predictive estimand a reserve-allocation decision consumes. Its existence and standing establish that the methodological machine the contribution imports is sound, so any weakness lives in measurement and identification, not the estimator.

Fine & Gray, 'A Proportional Hazards Model for the Subdistribution of a Competing Risk,' JASA 94(446), 1999 (candidate ref-4)


Instrument development is a primary, separable locus of cost and schedule risk distinct from the spacecraft bus and from launch: NASA instrument cost models estimate instrument cost from mass, power, data rate, and technology parameters, and the residuals load on technology maturity and novelty. This materiality of the instrument channel justifies treating instrument-driven slip as a competing event worth its own reserve lever.

Habib-agahi et al., 'NASA Instrument Cost Model for Explorer-Like Mission Instruments (NICM-E),' NASA Technical Reports Server, 2015 (candidate ref-9)


Cause-specific and subdistribution hazards answer different questions (etiologic instantaneous-rate versus predictive cumulative-probability), and a competing-risks analysis should report all cause-specific hazards and cumulative incidence functions together so that non-separation and measurement-driven fragility are visible. This standard makes the contribution's reserve-relevant predictive framing material and methodologically defensible.

Austin, Lee & Fine, 'Introduction to the Analysis of Survival Data in the Presence of Competing Risks,' Circulation 133, 2016 (candidate ref-5)


The structural premise of the question is valid and the demanded artifact is provably absent. The candidate's own prospectus states the work is design-stage, the cohort is 'on the order of 30 to 60 missions' from ~1990 to present, and 'all reported numerical results are explicitly labeled as illustrative and not yet executed on the full cohort'; it also lists 'calendar-period fixed effects to absorb era-specific acquisition policy' as a control. No archetype-by-era cross-tabulation therefore exists to inspect. The collinearity worry is real and external: the launch market is a documented categorical regime shift from an 'Old Space' state-organized core to a commercial 'NewSpace' core, so era and archetype can plausibly co-vary. Whether the diagonal cells actually co-occur is an unexecuted empirical fact the retrieval cannot settle.

One giant leap for capitalistkind: private enterprise in outer space (Humanities & Social Sciences Communications)


The measurement-instability concern is well grounded and not yet addressed by the design. The candidate's launch-side covariate set is exactly 'vehicle class, shared-manifest indicator, provider-in-development indicator', entered with a single (penalized) subdistribution coefficient; the prospectus describes no proportional-subdistribution-hazard diagnostic and no covariate-by-era interaction on the launch event. Independent literature confirms the launch market underwent a structural transition (Old Space to NewSpace; a recognized commercial-launch transition with its own speculative-bubble dynamics), which is the precise condition under which a covariate's hazard meaning need not be time-invariant. The candidate's only temporal handling is additive calendar-period fixed effects, which shift baseline hazard but do NOT test or relax proportionality of the launch covariates across the transition.

The Potential Speculative Bubble in the U.S. Commercial Space Launch Industry (New Space)

Space Business (2024), surfaced via braudel brain world-economy mapping


The rival is coherent and maps directly onto Braudel's three-tier stratification, and the design's existing defenses do not neutralize it. The candidate controls for general complexity (Bearden index) and estimating optimism (reference-class proxy) and treats archetype as an effect modifier, but those controls separate archetype from complexity and from optimism, NOT from launch epoch / program-maturation position. The prospectus itself concedes the cohort is observational with no randomization of archetype, and that the design relies on naturally occurring group comparison; it offers no within-lineage (within-program) heritage-progression identification holding launch infrastructure fixed. Braudel's apparatus supplies the form of the rival: an active first-of-kind is the frontier-capitalism layer and a passive-continuity rebuild is the late material-civilization layer of the same lineage, and entanglement of archetype with epoch is exactly the longue-duree confound the candidate's additive period dummies cannot break.

Cutting the Gordian Knot of World History: Giovanni Arrighi's Model (Journal of World-Systems Research)


Braudel's conjoncture reading is conceptually valid: the launch market is a medium-term structural trend, not a one-off shock, and a longue-duree/new-space governance transition over the cohort window is real and documented. The candidate's design itself concedes the launch and acquisition environment changed substantially across the era and motivates calendar-period controls on that basis (dissertation 3.6, citing the legacy-to-new-space governance transition). However, NO retrieved source supplies a measured monotonic trend in a launch-driven subdistribution hazard for NASA Earth-observing missions, and the candidate's own design does NOT construct a market-tightness index (active providers, manifest-backlog months) at KDP-B; its launch covariate is a qualitative manifest/shared-vs-dedicated descriptor. The affirmative claim Braudel demands (that the launch subdistribution hazard trends monotonically across the cohort and that FE plus a single launch dummy is therefore misspecified) is unverified by retrieval and is not demonstrated in the candidate's record.

Braudel dossier, Hall of Shoulders (conjoncture / longue-duree three-temporal-layers; debris regime as a conjoncture study)


Braudel's three-tier mechanism (a transparent competitive market economy versus an opaque concentrated-capitalism top layer where privileged providers entangled with the state make concentrated gains) is documented and maps onto the space/launch economy with precision, including the finding that new-space firms remain enmeshed in state funding, infrastructure, and regulatory frameworks. This makes the cascade hypothesis (one dominant provider's development slip propagating to every mission it manifests, violating per-mission independence) a coherent structural conjecture. However, the candidate's competing-risks design models each mission's first slip as a per-mission cause-specific/subdistribution event and does NOT estimate provider-and-year clustering or cross-mission correlation of launch-driven slips; the launch arm is acknowledged as the evidence-thin half resting on a single continuity-program (Landsat) narrative. NO retrieved source quantifies provider-level clustering of launch-driven NASA mission slips. The affirmative claim that launch slips co-move (and that the per-mission hazard understates concentrated-launch risk for heritage missions) is unverified by retrieval.

Braudel dossier, Hall of Shoulders (three-tier structure: material civilization / competitive market economy / concentrated capitalism; new-space firms enmeshed in the state = capitalism's opaque top layer)


Braudel's geography-first test (point to the map of scarce sites or a measured material limit, finite launch slots and integration sites, or you are describing surface motion) is a documented standard in his frame. The candidate's design invokes manifest congestion only narratively, as one of several launch-slip mechanisms (manifest congestion, shared-vehicle anomaly, provider development slip), citing the Landsat continuity account; it does NOT construct a measured manifest-congestion scalar (slots demanded vs slots available in the assignment window) and does not show launch-slip onset is predicted by such a scarcity quantity rather than by the shared-vs-dedicated flag. NO retrieved source (ACTA, Space Economy, AMOS) supplies a measured launch-slot/integration-site congestion scalar or demonstrates it predicts NASA mission slip onset. The affirmative measurement claim Braudel demands is therefore unsupported by retrieval, and the candidate concedes its launch covariate rests on restricted CADRe/GAO records framed as the evidence-thin half.

Braudel dossier, Hall of Shoulders (geography-first test: physical structure / map of scarce sites / measured material limit as the slow constraint; 'the map comes first; allocation rules that ignore physical concentration of value are building on sand')


The candidate cannot supply the per-stratum EPV count because the cohort is unassembled. The dissertation specifies an expected sample 'on the order of thirty to sixty missions' (an unrealized target, not a coded count), pre-commits to a ridge-penalized partial likelihood with the penalty selected by cross-validation and an events-per-variable cap, and Step one of its analysis plan ('Assemble the cohort and freeze the cause-coding') is explicitly unexecuted. No instrument-driven first-slip event has been coded in either archetype stratum, so the EPV backing the interaction term does not yet exist as a number. The candidate concedes the exact risk the panelist names: the design flags 'a penalty that over-shrinks the parameter of interest' as a known failure mode and makes penalty-and-EPV variation a pre-registered robustness analysis tracing 'how much of the interaction estimate is signal that survives shrinkage,' and cites Austin, Allignol and Fine (2017) that primary-event-per-variable count affects subdistribution-hazard estimation. The over-shrinkage objection is acknowledged but its resolution is deferred to the cohort; it cannot be settled from any retrievable source this turn.

Austin, Allignol & Fine, 'The number of primary events per variable affects estimation of the subdistribution hazard competing risks model,' J. Clinical Epidemiology (2017), cited as ref-11 in the dissertation


The candidate has declared the aggregation the policy question selects but cannot show its robustness because the cohort is unexecuted. The dissertation names the subdistribution hazard as the primary object 'because the reserve-allocation decision turns on the cumulative incidence of each cause rather than on its instantaneous rate,' identifies the cumulative incidence function as 'the decision-relevant quantity' for a predictive reserve question, and adopts the Callaway-Sant'Anna discipline of estimating archetype-specific hazards as 'separable building blocks' and forming any aggregate as 'a transparent weighting of them.' To that extent the aggregation-honesty demand is met at the design level: the weighting is declared (subdistribution/CIF, not a pooled coefficient) and defended against the cause-specific alternative, with divergence between the two to be reported rather than suppressed. What the candidate cannot supply is the panelist's actual test: a demonstration from the CADRe-plus-GAO cohort that the reported dominance survives the weighting choice. The cumulative-incidence-by-archetype and subdistribution-vs-cause-specific contrasts are Templates T6.1-T6.3, explicitly 'specified, unpopulated by design,' so whether a subdistribution-hazard dominance and a CIF-plateau dominance agree in this finite sample is unresolved and unretrievable this turn.

Callaway & Sant'Anna, 'Difference-in-Differences with multiple time periods,' Journal of Econometrics (2021), the aggregation-honesty program the candidate imports


The design instantiates both working models concretely (not decoratively), but transposes the doubly-robust guarantee from the ATT-consistency target it was derived for to a small-sample misspecification-robustness target it was not. Outcome-model route (dissertation 5.2.5, 5.3.3): the ridge-penalized partial-likelihood Fine-Gray subdistribution model regressing the cause-k subdistribution hazard on entry-TRL deficit of the least-mature sensor, instrument count/mass/power/data-rate, launch-vehicle class, shared-manifest and provider-in-development indicators, the Bearden complexity index, the Flyvbjerg optimism ratio, and calendar-period fixed effects. Weighting route: an inverse-probability-of-ARCHETYPE-membership reweighting whose propensity model is fit on the complexity index and optimism proxy (5.2.5 names these explicitly) to balance those covariates across the first-of-kind-active vs heritage-passive strata, with extreme weights trimmed. So the candidate CAN name both models and the propensity is for archetype CLASS membership, not for an assigned treatment. But this exposes the borrowing: Sant'Anna and Zhao's either-model-correct consistency guarantee is a theorem about the ATT in a design with treatment assignment and a control group (DOI 10.1016/j.jeconom.2020.06.003); here there is no ATT, no assignment, and the estimand is a within-stratum subdistribution-hazard / CIF. What the parallel run actually buys is an agreement-across-two-estimation-routes robustness signal for the archetype-specific blocks (the candidate states exactly this: 'a conclusion which holds under both routes is robust to misspecification of either'), which is weaker than and not identical to the doubly-robust either-or consistency theorem. The citation is therefore a real, instantiated robustness construction but a mislabeled guarantee: it should be presented as a two-route concordance check motivated by the DR idea, not as conferring DR consistency.

Sant'Anna & Zhao, 'Doubly robust difference-in-differences estimators,' Journal of Econometrics (2020) [panelist's own anchor work, retrieved from callaway_santanna Hall-of-Shoulders brain + candidate ref-118]


The aggregation functional IS declared and it is the CIF plateau at the development horizon, not the SHR and not the ridge coefficient. Template T6.1 (6.2.4) declares the headline objects as the estimated instrument-slip and launch-slip CIF plateaus per archetype stratum with confidence bands, plus Gray's K-sample test on across-strata equality of the instrument-slip CIF. The dominance verdict in the active stratum is defined as the instrument-slip CIF rising faster and reaching a HIGHER PLATEAU than the launch-slip CIF (6.2.1, feature one), and the reversal in the heritage stratum is defined as the launch-slip CIF plateau being at least as high as the instrument-slip CIF plateau (6.2.1, feature three). The penalized SHR and the archetype-by-TRL interaction are explicitly demoted to a hypothesis TEST of effect modification, not the magnitude source: 'the pooled model is a test, not a magnitude source; ... the stratified blocks supply the cumulative incidence functions a reserve decision reads' (5.3.2). This is responsive to the panelist's aggregation-honesty doctrine (declared weights, not estimator-imposed weights; the panelist's own ATT(g,t) building-block discipline, DOI 10.1016/j.jeconom.2020.12.001). However the question lands a partial hit the candidate does NOT fully close: the design declares the AGGREGATION FUNCTIONAL (plateau difference / Gray's test) but does NOT fix the calendar HORIZON at which the plateau is read, nor does it isolate the CIF-plateau-difference verdict from the ridge shrinkage that produces the very SHR and the propensity weights feeding the stratified CIF estimates. The cross-validated ridge penalty is selected by partial-likelihood deviance (5.5/5.3.3) and is NOT fixed in advance, so the within-stratum subdistribution-hazard estimates that generate the plotted CIFs carry an implicit, data-driven shrinkage weighting on the TRL and interaction terms. A worked example separating 'plateau difference at horizon H' from 'sign of the penalized interaction coefficient' is therefore NOT provided. The honest verdict: the aggregation functional is declared (defeating the bare 'dominance is undefined' charge), but the horizon is unfixed and the plateau-vs-shrinkage separation is unworked, so the second half of the panelist's demand is unmet.

Callaway & Sant'Anna, 'Difference-in-Differences with multiple time periods,' Journal of Econometrics (2021) [panelist's own anchor work, retrieved from callaway_santanna Hall-of-Shoulders brain]


The objection is correct on its own terms and is only partially pre-empted by the design. The panelist's continuous-treatment corpus (Callaway, Goodman-Bacon & Sant'Anna, DOI 10.2139/ssrn.4716682) establishes that treatment-on-the-treated parameters are identified under an analogous parallel-trends assumption but COMPARING effects ACROSS dose levels is not licensed by parallel trends alone, because selection-into-dose generates bias: units selecting higher doses differ. Entry-TRL deficit is exactly such a continuous dose, and the candidate's own backing (Dubos, Saleh & Braun, DOI 10.2514/1.34947, candidate ref-47) documents a NONLINEAR TRL-deficit-to-slip relationship, which is what makes the per-level SHR read tempting and dangerous. The candidate has THREE partial defenses already in the design but none fully answers the joint-distribution question: (1) entry TRL is measured at KDP-B before the slip window, fixing temporal ordering (5.2.4) but ordering rules out reverse causation, NOT selection-into-dose confounding, which the candidate concedes ('temporal ordering rules out reverse causation but not omitted-variable confounding,' 5.2.4 qualifier); (2) the endogenous-TRL-gating concern is explicitly flagged as a residual internal-validity threat with the descope covariate and the cause-specific-vs-subdistribution divergence as surfacing devices (5.2.4 rebuttal, 5.6 threat catalogue); (3) the headline magnitude is declared to come from the binary archetype-stratified CIF blocks, NOT the per-level TRL SHR (5.3.2), which is the candidate's strongest move because it is effectively the panelist's own fallback ('restrict your conclusion to the treatment-on-the-treated at observed doses' / binary contrast at observed TRL). BUT the candidate does NOT show the joint distribution of entry-TRL deficit against archetype, so the collinearity charge that first-of-kind active missions occupy the low-TRL tail (making the archetype block a re-labeled dose effect) is NOT empirically rebutted, and the Â§6.2.1 per-level SHR sentence remains in the text as a dose-response read the candidate has not licensed with a stronger-than-parallel-trends (e.g. unconfounded-dose / homogeneous-dose-response) assumption. The correct concession per the panelist's own corpus is to STRIKE or explicitly down-rank the 'SHR above one for each level of TRL deficit' marginal-dose phrasing and restrict the dominance claim to the binary archetype contrast at observed TRL, while producing the archetype-by-TRL joint table to show the strata are not merely the two ends of one dose axis.

Callaway, Goodman-Bacon & Sant'Anna, 'Difference-in-Differences with a Continuous Treatment' [panelist's own corpus, retrieved from callaway_santanna Hall-of-Shoulders brain + OpenAlex]

Dubos, Saleh & Braun, 'Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design,' J. Spacecraft and Rockets (2008) [candidate ref-47]


The dissertation already operationalizes the raw material for fogel's demanded scalar but never names it as a single pre-committed number. Its cause-coding (4.5) reads the CADRe Part A narrative and the GAO narrative for the same project-year and codes a slip high-confidence ONLY when both name the same dominant cause; events where the two sources name different causes, or where neither names a single dominant cause, are flagged 'un-codable' and quantify how much first-slip experience is multi-cause/contested. That un-codable / co-attribution fraction IS fogel's analogous separability scalar, and it is computable from the two narrative corpora exactly as he asks. The defect is real: the candidate treats it only as a recoding-both-ways robustness input (6.4 second check), not as a falsifying statistic with a pre-registered threshold. The Fine-Gray apparatus the design adopts gives a principled threshold home: separability is currently tested only via Gray's test on CIF equality across archetype strata and the sign of the archetype-by-instrument interaction (6.3), neither of which is a co-attribution rate. Fogel's discipline (state the proposition as one estimable number built from primary records, then let the data falsify it) is satisfiable here, so the question is a binding upgrade, not a refusable demand: pre-commit, e.g., 'if co-attribution exceeds X% of codable first-slip spells, declare one slip process.'

fogel dossier (Hall of Shoulders) + Fogel, Railroads and American Economic Growth (1964)

Fine & Gray, A Proportional Hazards Model for the Subdistribution of a Competing Risk, JASA 1999


Fogel's critique lands and is corpus-grounded. The dissertation's own opening establishes that cost growth is 'a monetized image of schedule slip' accruing standing-army labor, fee adjustments, I&T rework, and carrying cost (1.1.1), and it states that CADRe carries committed-versus-actual milestone schedule and standing-army cost. Yet the estimand is strictly the cause-specific cumulative incidence function (the probability that the FIRST slip is instrument- vs launch-driven by archetype); the decision payoff 'steer reserve to the dominant hazard by archetype' is read directly off CIF dominance (1.4, 6.2.5) with NO per-cause magnitude term. The design therefore equates 'slips first more often' with 'costs more,' which is exactly the indispensability-by-assertion move fogel spent his career dismantling: incidence is not the decision-relevant quantity without a dollar-and-shadow-price-of-time magnitude per cause, which the CADRe committed-vs-actual schedule and standing-army fields can supply. fogel's own review lens demands the time component be valued at a defensible shadow price, not folded into a count. Because the data to compute expected cost and expected time penalty conditional on cause exist in the same CADRe records the design already uses, this is a constructive, answerable extension, and the candidate's reserve-steering contribution does NOT survive unmodified if incidence dominance and monetized-loss dominance diverge: the honest design must add a per-cause severity decomposition (expected $-loss and shadow-priced time-loss by archetype) and make the reserve prescription follow the larger expected loss, not the higher incidence.

fogel dossier (Hall of Shoulders) review lens, citing Leunig social-savings methodology

Leunig, Time is Money: A Re-Assessment of the Passenger Social Savings from Victorian British Railways, J. Economic History 2006

Lieber & Donor, Schedule matters, 2016 IEEE Aerospace Conference


This is the sharpest of the three and is fully grounded. The design's single load-bearing measurement act is coding the first-slip cause by CADRe-vs-GAO reconciliation (5.0, 4.5), and the candidate concedes the construct is 'narrative-based,' 'defensible-but-imperfect,' and that cause-coding error is the dominant internal-validity threat (4.5.3, 4.6.3, 5.0). Yet the treatment is a binary recode-both-ways sensitivity check on un-codable events (6.4) plus an un-codable COUNT, never an estimated agreement/reliability rate (e.g., a Cohen's-kappa-class inter-source agreement on the dominant cause across codable spells) and never a propagation of that disagreement into the CIF confidence bands. fogel's analogy is exact: building the competing-events variable from reconciled narrative is the equivalent of building a freight-rate counterfactual from anecdote rather than primary price data, and his rule is that the constructed quantity must be reported with explicit bounds, which the candidate cites Leunig for but does not execute on the coding error itself. The CIF bands in Template T6.1 are specified to carry only sampling (95%) uncertainty, not coding-disagreement uncertainty, so the reader sees bands at assumed-perfect coding. The fix is corpus-computable from the two narrative sets the design already reads: estimate the inter-source agreement rate, then re-estimate the CIFs under the disagreement-rate-implied recoding mass to show the band width at the observed (not assumed-zero) coding error, and report the disagreement level at which Gray's-test archetype dominance loses distinguishability. The candidate does NOT report an agreement rate (the cohort is unassembled, design-stage), so the specific numeric agreement rate and the break-even disagreement level are not yet measurable; that part is a genuine gap the candidate must close by computing the statistic, not one I can assert a value for. The methodological demand itself is grounded and answerable.

fogel dossier (Hall of Shoulders) review lens; Leunig social-savings re-assessment

Latouche, Allignol, Beyersmann, Labopin, Fine, A competing risks analysis should report results on all cause-specific hazards and cumulative incidence functions, J. Clinical Epidemiology 2013


The Fogelian counterfactual social-saving test that Q1 demands is the correct discipline, and a documented NASA reserve convention exists to frame it: at PDR, baseline cost confidence plus project-held reserves is set near the joint-distribution 50th percentile and cost plus reserves plus UFE near the 70th percentile, while empirically there is an 84% chance of cost growth PDR-to-launch and missions at the empirical 50th/70th percentiles spend their full budgets plus 16% and 27% respectively (Math is EZIE, IEEE Aerospace 2023). This sets the dollar scale of reserve but does NOT supply a pooled-CIF-vs-archetype-CIF committed-reserve delta per mission. No retrieved source computes that delta, and the dissertation states all numerical results are illustrative and not yet executed on the cohort, so the social-saving number Q1 requires is absent and the railroad rounding-error test cannot be run on retrieved evidence.

Math is EZIE: How Contracts Help Control Cost (IEEE Aerospace Conference 2023)

Robert Fogel, Railroads and American Economic Growth: Essays in Econometric History (1964), social-saving counterfactual / indispensability test

Tim Leunig, Social Savings (Journal of Economic Surveys 2010), methodology of the bounded counterfactual social saving


The systems-dynamics critique is well-founded as method: a finite reserve is a STOCK that changes only through its draw-down FLOWS, and two events that both drain the same stock are coupled through a closed loop rather than being two independent races. When instrument slip drains reserve, triggering a re-baseline that re-manifests the launch slot and so changes the launch-side hazard for the same mission, that is a reinforcing-loop structure. Forrester's apparatus holds that behavior is dominated by such closed loops and that estimators imposing a race (separable-owner) structure will, if a shared-stock loop is actually present, measure loop dynamics rather than independent causes. THIS IS A GROUNDED CRITIQUE OF THE MODELING FRAME ONLY; it does not assert anything about JPL_ASTRO_EARTH_08's specific CADRe data, whose reserve-as-stock specification and cross-cause hazard correlation were NOT retrievable and are recorded as gap forrester_r1_g1.

Forrester dossier (hall_of_shoulders, brain=forrester), citing Forrester, Industrial Dynamics (1961) / Urban Dynamics

Forrester, Counterintuitive Behavior of Social Systems (1971); retrieved via hall_of_shoulders forrester brain


The critique is methodologically grounded: anticipatory descope/gating is endogenous BALANCING feedback (a goal-seeking loop whose actuator is the gating decision), not exogenous confounding, so adjusting a descope covariate or pinning entry TRL before the slip window does not break the loop. Forrester's policy-resistance result predicts the counterintuitive second-order effect the question names: a low-leverage parameter intervention (steering reserve by archetype) applied to one branch of a coupled system tends to be counteracted and re-routed rather than to reduce the aggregate, because the system responds to the policy by shifting the burden to the other flow. Hence separability-based competing-risks estimators, which see only the per-cause subdistribution, are structurally blind to whether aggregate first-slip is invariant to the mix. THIS GROUNDS THE PREDICTION'S DIRECTION FROM SYSTEMS THEORY ONLY; whether JPL_ASTRO_EARTH_08's CADRe/GAO data actually show mix-invariance of total first-slip incidence was NOT retrievable (gap forrester_r1_g2).

Forrester, Counterintuitive Behavior of Social Systems (1971) and Urban Dynamics (low-cost-housing reroute result); retrieved via hall_of_shoulders forrester brain

Forrester dossier (hall_of_shoulders, brain=forrester) on reinforcing vs. balancing loops, citing Industrial Dynamics/Urban Dynamics


The identification critique is grounded in aggregation discipline: a per-unit hazard analysis that evaluates each mission individually is only identified if the system-level stock integrating all units is absent or exogenous; where launch slots are a shared manifest stock that every mission's slip re-manifests, the cross-mission coupling makes the independence-across-units assumption a fallacy of composition, and a per-mission subdistribution hazard conditioned on own covariates plus era fixed effects cannot represent a launch hazard that is a function of the contemporaneous slip stock of the OTHER missions. Forrester's aggregation rule (re-run the conclusion against the aggregate inflow vs. outflow of the stock, not against individual actors) and his cross-system endogenous-coupling point apply directly. THIS GROUNDS THE IDENTIFICATION OBJECTION AS METHOD ONLY; whether JPL_ASTRO_EARTH_08's CADRe/GAO launch first-slips actually exhibit temporal clustering against an independent-hazard null was NOT retrievable (gap forrester_r1_g3).

Forrester dossier (hall_of_shoulders, brain=forrester), aggregation-discipline and cross-system endogenous-coupling review lens, citing Forrester system-dynamics corpus

Colombo, Martinez, Letizia et al., Space capacity management and its interaction with space traffic management, Acta Astronautica (2025); retrieved via hall_of_shoulders forrester brain / acta-brain


The framing objection is structurally correct from a system-dynamics standpoint: treating each mission's launch hazard as an independently drawn per-mission event commits the 'actor evaluated individually, stock integrates all of them' error. Forrester's foundational claim is that system behavior over time is governed by stocks (accumulations such as backlog) and the flows that fill and drain them, coupled through information feedback and material delays; a manifest backlog is exactly such a stock and a released slot is an inflow to it, so cross-mission coupling through that shared stock is a feedback structure the model must represent rather than an exogenous nuisance. Forrester's explicit aggregation-discipline lens demands re-running any per-actor conclusion against the aggregate inflow versus outflow on the shared stock, which is precisely what an independent-hazards competing-risks specification fails to do. Whether the slips are in fact manifest-clustered in the CADRe/GAO mission-by-year record, and whether instrument-to-launch slip propagation Granger-holds on a vehicle family, is an empirical measurement this corpus does not settle.

Jay W. Forrester, Industrial Dynamics / Dynamic Models of Economic Systems and Industrial Organizations


Reclassifying descope from covariate to a rate-triggered balancing loop is the correct system-dynamics move: descope is a goal-seeking control action that closes a loop between a detected draw-down rate on the reserve stock and a corrective response after a delay, which is structurally a balancing loop, not an additive linear term. Forrester's endogeneity test makes this precise: if you must invoke an external driver to reproduce the behavior, you have missed an internal loop; descope is an internal feedback that suppresses the very flow being measured, so omitting it (or linearizing it as a covariate) mis-specifies the structure. The further point that a balancing loop with a threshold and delay produces nonlinear, not proportional, effects is consistent with Forrester's and Sterman's demonstration that humans systematically fail to infer stock trajectories from flows (the bathtub stock-flow failure), which is why such accumulation-and-delay structures must be modeled explicitly rather than approximated linearly. Whether this loop empirically biases the dominant-stratum instrument hazard downward in the CADRe baseline-movement and descope record is a measurement this corpus does not contain.

John D. Sterman, Bathtub Dynamics: initial results of a systems thinking inventory


The structural critique is sound: a static entry-TRL level is a stock measured at one instant, whereas the proposed escalation (TRL deficit to test failure to reserve loss to pressure to corner-cut to next failure) is a reinforcing loop whose output is path-dependent and is properly characterized by a rate of TRL stall, not by a frozen level. Forrester's stock-versus-flow decomposition insists on separating the level of a stock from the rate that changes it, and his leverage-point lens directs attention to loop structure rather than to visible static parameters; a KDP-B-frozen entry-TRL covariate is a level snapshot that by construction cannot observe the post-KDP-B stall rate that the reinforcing loop generates. A proportional-hazards form that maps a frozen level to a constant multiplicative hazard is therefore structurally unable to represent a path-dependent reinforcing loop, so the misspecification objection is internally coherent. Whether the CADRe milestone and TechPort TRL-progression records in fact locate the instrument-slip signal in post-KDP-B stall rate rather than entry-TRL level is an empirical finding this corpus does not provide.


The demanded reliability statistics do not exist in the candidate's materials. The prospectus defines the outcome as a cause 'coded as instrument-driven or launch-driven by reconciling the CADRe Part A narrative with the GAO assessment narrative' and concedes the work is 'design-stage' with 'all reported numerical results explicitly labeled as illustrative and not yet executed'; a full-text grep of dissertation.md returns zero occurrences of kappa / inter-coder / inter-rater / Cohen, and the corpus.jsonl matches on 'reliability/agreement' are incidental abstract hits (reusability, parts-quality), not a coding-reliability statistic. So no kappa and no CADRe-vs-GAO agreement rate have been computed. The methodological standard the panelist invokes is real and groundable: narrative cause-attribution is a content-analysis instrument whose reproducibility must be demonstrated with a reliability coefficient before the data are used analytically (Krippendorff 2004). The candidate's own internal-validity section names 'cause-coding error' as 'the dominant threat,' which is precisely the threat a kappa is meant to quantify. Grounded verdict: at design stage the instrument is asserted (two-source reconciliation) but not measured; the reliability number must be produced before the Fine-Gray contrast can be trusted, and it cannot be invented here.

Krippendorff, 'Reliability in Content Analysis: Some Common Misconceptions and Recommendations,' Human Communication Research 30(3), 2004


The structural concern is grounded even though the fraction itself is not yet computable. The prospectus concedes that 'cause coding depends on narrative attribution, which is subjective and which can mask multi-cause slip,' and that missions 'mixed or ambiguous' are 'coded into a third category for robustness checks and excluded from the primary contrast,' with events 'whose cause cannot be coded consistently across both sources' merely 'flagged and handled in sensitivity analysis.' By the constant-comparative method, the cases that resist a category are the ones that test whether the category is real, so excluding the ambiguous/disputed-attribution incidents from the primary contrast is exactly the move that protects the dichotomy from its own disconfirming evidence and risks saturating the binary into a continuum (Glaser 1965; thinker dossier). The candidate has the correct defense named (recode ambiguous events both ways, bring the third stratum into the model) but has executed none of it, because the cohort is unbuilt.

Glaser, 'The Constant Comparative Method of Qualitative Analysis,' Social Problems 12(4), 1965 (retrieved via hall-of-shoulders glaser_strauss brain)


The emergence-vs-forcing charge is grounded in the candidate's own provenance trail: the two-category structure is imported, not emergent. The prospectus derives the instrument side explicitly from the TRL-schedule literature ('The most direct antecedent is the TRL-schedule literature... Dubos, Saleh, and Braun') and the launch side from the Landsat Data Continuity Mission account, then 'illustrates' that frame on CADRe narratives rather than letting categories arise from constant comparison. The candidate even lists rival cause-families it does not model as competing events, optimism/estimating bias (Flyvbjerg) and reverse causation through descope/gating, which is direct evidence that more than two proximate-cause families plausibly populate the narratives. Per grounded theory, a category borrowed from prior framework and then illustrated has not been shown to 'fit and work' in the substantive area, and saturation must be an earned, falsifiable claim, not assumed (Glaser 1965; Saunders et al. 2017). Grounded verdict: the inverse open-coding test is the correct falsification and has not been run; the two-risk specification is currently imposed rather than demonstrated.

Dubos, Saleh & Braun, 'Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design,' J. Spacecraft & Rockets, 2008 (imported instrument-side frame)

Saunders et al., 'Saturation in qualitative research: exploring its conceptualization and operationalization,' Quality & Quantity 52, 2018


The candidate's own cause-coding manual proves the 'instrument-driven' bin is internally multi-dimensional by construction, not homogeneous: dissertation.md line 1701 fixes the instrument-driven code as triggered by 'environmental-test failure, calibration-budget closure problems, detector or focal-plane maturation, or instrument delivery slip,' and lines 150/400/881/1005 repeat the same disjunction (fails environmental test OR cannot close calibration budget OR carries technology below assumed maturity). So the bin is, on the candidate's face, a logical OR over at least four physically distinct proximate mechanisms with distinct reserve levers (a calibration-budget miss is closed by analysis/test time, a detector immaturity by parts/qual schedule, an environmental-test failure by rework), exactly the property-poor aggregate the panelist describes. The decisive grounded fact, however, is that the requested artifact cannot exist: the work is design-stage and the cohort is unbuilt, so no incident-to-property audit trail has been produced. dissertation.md line 13 states 'all numerical results are explicitly labeled illustrative and not yet executed on the full cohort'; the cause-coding (line 740) is a planned two-source reconciliation 'frozen before any modeling,' but no missions have been coded, so there is no constant-comparison table and no count of how many missions populate each of the four-to-five sub-dimensions. The grounded-theory standard the panelist invokes is real and citable: a category is shown to fit and work only when its property-and-dimension structure is demonstrated against incidents by constant comparison, and saturation is an earned claim about properties, not an assumed one (Glaser & Strauss, Discovery of Grounded Theory). Grounded verdict: the bin is demonstrably multi-dimensional in the coding manual's own definition, so dimensional homogeneity is NOT established and on present evidence is contradicted; whether the five sub-causes are empirically distinguishable and separately reserve-relevant is an executed-coding question with no answer in any retrieved source. The candidate must either model the sub-causes as distinct competing events or defend, with the actual coded incidents, that they share one reserve lever; neither defense exists yet.

Glaser & Strauss, The Discovery of Grounded Theory (1967/2017), property-and-dimension density and earned saturation standard, retrieved via hall-of-shoulders glaser_strauss brain


The panelist's diagnosis is correct on the design's own terms and the candidate cannot supply a saturation point. dissertation.md line 200 and Appendix C1 (line 1340) define the population as 'NASA Earth-observing missions from roughly 1990 to the present that reach Key Decision Point B,' a complete enumeration fixed in advance by inclusion criteria, not a sample grown by following emerging categories. That is sampling for census-completeness/representativeness, which is methodologically distinct from theoretical sampling, where 'each sampling decision is driven by what your emerging theory needed to sharpen, test, or saturate a category' (glaser_strauss dossier; Glaser & Strauss, Discovery). Because the cohort is fixed and the coding is frozen before any modeling (line 740), the design has no mechanism that pairs data collection with analysis in the iterative loop saturation requires; the stopping rule is exhaustion of the population, not 'additional data no longer yield new properties.' Two grounded consequences follow. First, the candidate cannot name a saturation point because none exists by construction: at design stage no missions are coded (line 13, 'not yet executed on the full cohort'), so there is no documented point at which new incidents ceased to yield new properties. Second, the panelist's deeper worry is warranted: a frozen two-bin scheme has no slot for a sub-cause that first surfaces in a late mission, so a genuinely new property (e.g. a workmanship or parts-obsolescence cause absent from the imported instrument/launch dichotomy) would be forced into 'instrument,' 'launch,' or the un-codable holdout (line 1701) rather than promoted to a new category. The honest grounded answer the candidate owes is the concession the panelist offers: category boundaries here are warranted by cohort completeness and a pre-frozen coding manual, NOT by demonstrated saturation; detection of a late-emerging property would require relaxing the frozen scheme (an open-coding pass or a new category), which the present design does not provide. Saturation is the canonical stopping rule and must be an earned, falsifiable claim about properties (Saunders et al. 2018), which this census design has not earned.

Glaser & Strauss, The Discovery of Grounded Theory (1967/2017), theoretical sampling vs convenience/representativeness; saturation as earned stopping rule, retrieved via hall-of-shoulders glaser_strauss brain

Saunders et al., 'Saturation in qualitative research: exploring its conceptualization and operationalization,' Quality & Quantity 52, 2018 (DOI verified this turn via Crossref)


The provenance charge is grounded in the candidate's own text: the apparatus precedes the incidents. The Abstract (dissertation.md line 23) states the work 'applies the structural-decomposition logic associated with Fogel... and the heterogeneity-aware separation logic associated with Callaway and Sant'Anna, treating sensor archetype as an explicit effect modifier'; the instrument/launch dichotomy is derived from the TRL-schedule literature (Dubos, Saleh & Braun) and the Landsat continuity account, and dissertation.md line 452 concedes the Dubos antecedent 'models slip as a single continuous outcome' which 'the dissertation's competing-risks reframing is the direct response' to, i.e. the two-event structure is imported as a methodological response, not emergent from the narratives. By grounded-theory standards this is the imported-framework-then-illustrated pattern the dossier flags: 'were they imported from an existing framework... and then illustrated with examples? Demonstrate that your central concept fits and works in the substantive area, rather than being forced onto it' (glaser_strauss dossier; Glaser & Strauss, Discovery). The candidate even names rival cause-families it does not model as competing events (Flyvbjerg optimism/estimate bias as a covariate not an event; descope/gating reverse-causation), which is direct evidence that an unforced open-coding could surface a different dominant axis, e.g. estimate-realism-vs-execution or internal-vs-external-to-program, rather than instrument-vs-launch. The decisive grounded fact remains: the inverse open-coding test is the correct fit-and-works falsification and HAS NOT BEEN RUN. dissertation.md line 13 confirms design-stage, not-yet-executed status, so the raw narratives have never been open-coded with the scaffolding removed, and the data's own dominant axis of variation is unobserved. Grounded verdict: the instrument-vs-launch cleavage is currently imposed to suit the competing-risks estimator rather than demonstrated to emerge; whether the data's natural leading cleavage coincides with or contradicts it is an empirical question with no answer in any retrieved source, and H1's separability cannot be shown to be a property of NASA mission slip rather than of the coding scheme until that open-coding is executed.

Glaser & Strauss, The Discovery of Grounded Theory (1967/2017), emergence-vs-imported-framework / fit-and-works falsification, retrieved via hall-of-shoulders glaser_strauss brain

Dubos, Saleh & Braun, 'Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design,' J. Spacecraft & Rockets, 2008 (the imported single-outcome antecedent the competing-risks frame reframes)


The per-stratum non-imputed TRL census McDowell demands cannot be named because it does not yet exist: the dissertation is an explicit design-stage, pre-registered measurement design that has assembled no cohort and executed no analysis. Chapter 4 states the cohort is an expected sample 'on the order of thirty to sixty missions' and closes 'the chapter has specified the data and the measurement; it has executed neither, and it claims neither.' TechPort entry-TRL coverage is conceded as 'uneven... well populated for technologies developed within the era of the database and progressively thinner for older missions,' and TRL assignment is 'partly judgmental,' so the candidate adopts a competing-risks detection-limit estimator (Wu et al. 2025) for any TRL covariate 'censored at a measurement floor' rather than dropping or naively imputing missions. McDowell's underlying concern (that an interaction estimated on a tracking-floor fill-in is an artifact, not an observed regularity) is valid and directly parallels his census-before-claims and tracking-floor frameworks, but the cohort census that would settle it is unpopulated at this stage; the candidate must produce the per-stratum non-imputed count when the frozen cohort is assembled.

Wu, Huang, Wang, Xiang, 'Analysis of Competing Risks Data with Covariates Subject to Detection Limits,' J. Computational and Graphical Statistics (2025) - the detection-limit estimator the candidate adopts for floor-censored TRL

Dubos, Saleh, Braun, 'Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design,' J. Spacecraft and Rockets 45(4) 2008 - the least-mature-TRL-to-slip relationship the interaction rests on


The standard the question invokes is sound and is the reviewer's documented first-order principle: reconcile competing catalogs into one defined roster, name a single catalog of record, and report the effective sample as the intersection of source agreement rather than the union. NICM is a real NASA instrument cost/schedule catalog (Habib-Agahi et al.), so the four-source frame is well posed. But retrieval surfaces NO source that supplies the candidate's actual four-way join: neither the intersection count of non-imputed four-source matches nor the imputation-padded count nor the resulting effective N is present in any queried corpus. Those numbers are properties of JPL_ASTRO_EARTH_08's own dataset and are not asserted here.

Habib-Agahi et al., Latest NASA Instrument Cost Model (NICM) Version VI


The candidate's own dissertation supplies the structure that makes Pearl's collider charge land but never discharges it formally. Section 5.2.3's rebuttal concedes that 'a mission that anticipates instrument risk might descope its instrument before KDP-B, which would change its archetype classification in a way correlated with its latent slip risk... the most serious threat to the pre-outcome claim,' and its only answer is to 'measure entry TRL at KDP-B before the slip window opens and to carry descope history as a covariate.' By Pearl's back-door criterion, temporal precedence (a covariate measured before the outcome) is necessary but not sufficient for identification: the descope decision is a common effect (a collider) of an unobserved anticipated-risk node and archetype, so conditioning on it (or on the selection node 'reached KDP-B and was manifested') opens a non-causal path rather than closing one. The dissertation contains zero occurrences of 'collider,' 'back-door,' 'adjustment set,' or 'd-separation' (verified by full-text scan of dissertation.md, 445,149 chars; DAG appears only 6 times and never as a formal back-door analysis). The minimal adjustment set Pearl demands would have to include the anticipated-instrument-risk node, which is unobserved in CADRe Part A / GAO / TechPort; the candidate records only realized 'descope history,' not the latent anticipation that drove it. Therefore the archetype->instrument-slip subdistribution-hazard effect is NOT shown identifiable: it is non-identifiable under an unobserved anticipated-risk confounder, and the proposed covariate adjustment is collider-inducing, not back-door-closing. This is exactly the failure Pearl's review lens names: 'Demonstrate that your headline result is not an artifact of conditioning on a collider... Which conditional-independence implications of your DAG did you actually test in the data?'

Hall of Shoulders dossier: Judea Pearl (review-lens falsifiable questions #2, #4), grounding Pearl, Causality: Models, Reasoning and Inference (2nd ed., 2009)

Tennant et al., 'Drawing Credible Directed Acyclic Graphs for Causal Inference' (retrieved via pearl brain)


Pearl's empirics question separates a test of the estimator from a test of the causal model, and the dissertation conflates them. Gray's test compares cumulative incidence functions (an equality-of-CIF test); it is a test of hazard/CIF equality, not of the DAG's conditional-independence implications, so it cannot 'settle separability' as a causal structure. The candidate itself recognizes the estimator is not where the risk lives ('The estimator described in Chapter 5... is a mature and well-understood machine. The risk to the contribution does not live in the estimator. It lives in whether the two competing events... can be measured as distinct events... without one bleeding into the other,' dissertation @155293). A DAG implies testable conditional independencies (Pearl); the candidate would need to enumerate which ones the 30-to-60-mission cohort can actually evaluate, e.g. entry-TRL independence of launch-side covariates given archetype, or descope d-separating archetype from instrument slip. The dissertation does NOT enumerate any such implied conditional-independence constraints, does NOT report which are checkable, and explicitly anticipates that small-sample power forces some cells empty ('small-sample power is stated in advance' @37540) - which is the exact condition under which a conditioning cell is empty and the constraint becomes untestable. So the candidate can answer the falsification-of-hazard-equality question (Gray) but has supplied no test of the graph itself; the implied independence constraints are stated nowhere and several are untestable at n=30-60. Appendix C concedes that if archetype assignment 'were opaque, the dominance claim would be untestable' (@436771), confirming the testability frontier is acknowledged but not formally mapped to graph-implied independencies.

Hall of Shoulders dossier: Judea Pearl (DAG section + review-lens question #4), grounding Pearl, Causality (2nd ed., 2009)


On mechanism, the dissertation draws the instrument-arm causal chain in prose but never draws the descope->launch-reassignment edge in a graph, and never models or bounds the dependence between the two latent slip processes that the Fogel counterfactual reading requires. The candidate explicitly states competing-risks CIFs treat the failure causes as 'not independent processes... but competing events that must be analyzed jointly' (Section 2.2.1 @53824), and frames the CIF as a Fogel-style bounded counterfactual (front-matter @443; Section 2.3). But Fine-Gray identifies the CIF from observational data only up to assumptions about the dependence between the latent competing processes, and the dissertation nowhere models that dependence, nowhere reports CIF bounds under correlated processes, and nowhere states whether instrument-slip and launch-slip are conditionally independent given covariates. The candidate's single-dominant-cause coding (Section 3.3 @31375: 'A slip's cause is coded as instrument-driven or launch-driven by reconciling the CADRe Part A narrative with the GAO assessment narrative... where they cannot be reconciled the event is flagged un-codable and handled in sensitivity analysis') addresses miscoding via two-source reconciliation but NOT the structural dependence: if an instrument-anticipated descope shifts a mission onto a different launch manifest (the coupling the candidate's own Kipp-grounded Section 2.3 narrative implies, @113401 - 'an instrument-origin delay is a mission-level event'), then the two competing events are mechanistically coupled, the single-dominant-cause assignment forces a coupled event into one bin, and the separability the Fine-Gray estimator reports can be an artifact of correlated coding plus unmodeled dependence rather than a genuine archetype-driven hazard difference. The dissertation provides no dependence sensitivity analysis and no copula/Frechet-style CIF bound; its only sensitivity check is on cause-coding reconciliation, which does not relax the competing-event dependence assumption.

Hall of Shoulders dossier: Judea Pearl (counterfactual-identification section), grounding Pearl & Mackenzie The Book of Why (2018) and 'Nested Counterfactual Identification from Arbitrary Surrogate Experiments' (2021)


The candidate's own Ch 5.3.1 states the mediator chain in prose -- 'the least-mature sensor technology fails environmental test, cannot close its calibration budget, or runs long in maturation ... accrues standing-army and rework cost' -- but the Appendix A Variable and Data Dictionary shows only the ENDPOINTS and a TRL snapshot are coded variables. OBSERVED: archetype (NICM taxonomy), entry TRL (TechPort, a single ordinal snapshot of the least-mature sensor at KDP-B), instrument count/mass/power/data rate (NICM), and the cause-coded committed-baseline slip event (CADRe Part C threshold + Part A/GAO narrative). ASSUMED / UNCODED: environmental-test failure, calibration-budget non-closure, and detector rework have NO dictionary entry and no event time -- they are mechanism prose only. The descope-or-delay decision is only partially observed as 'Descope history (auxiliary): recorded instrument descope before slip window ... Binary/categorical, WHERE RECORDED,' and Ch 7.3.4 admits 'descope decisions are not always documented in a way the design can recover.' The single load-bearing missing mediator is the within-spell instrument event log (test/calibration failure and detector rework time-ordered between KDP-B entry TRL and the committed-baseline movement): with it absent, only the archetype, the KDP-B entry-TRL snapshot, and the slip endpoint are observed, so the chain is NOT fully captured by measured intermediates -- the middle of the mechanism is a black box the model never witnesses.

Dubos, Saleh & Braun, 'Technology Readiness Level, Schedule Risk, and Slippage in Spacecraft Design,' J. Spacecraft and Rockets (2008), cited at dissertation Ch 4.3.2 ref-47


GROUNDED STRUCTURAL CRITIQUE, NUMBERS REFUSED. Taleb's transfer-of-fragility test applies directly: 'Your recommendation adds something... What fragility does it remove, and to whom does it transfer fragility?' (taleb dossier, grade A), and improvement comes more reliably from removing fragilities (via negativa) than from a targeting intervention that concentrates the stock. The candidate's prescription is exactly such an additive steering rule (CIF=365, archetype=357, reserve=138 mentions; rule = hold reserve 'against the cause with the largest cumulative incidence for the archetype at hand'). But the off-diagonal CIF MASS that the question turns on is never quantified: 'off-diagonal'=0 and 'cross-cause'=0 in dissertation.md, and 'largest single'=0, so the fraction of active-sensor overruns originating launch-side and the largest single cross-cause launch overrun absorbed by an instrument-steered mission DO NOT EXIST in any retrieved source and cannot be asserted. The candidate gives a non-fungibility hedge (the prescription also gates TRL at KDP-B, 'a TRL gate... is a different and non-substitutable control from a manifest-margin policy on a launch service'), which mitigates but does not measure the residual tail. Whether the steering rule's residual off-diagonal exposure exceeds what undifferentiated pooled reserve already covered is therefore unsettled on the record.

Antifragile: Things That Gain from Disorder (Taleb)


GROUNDED STRUCTURAL CRITIQUE, CENSUS REFUSED. The blindspot is real and the candidate half-acknowledges it but does not census it. Taleb's ruin/absorbing-barrier and track-record critique applies: 'the decisive distinction is between repeated risks you can survive and absorbing risks that end the game'; 'space repeatedly presents Taleb's trigger conditions, fat-tailed and sometimes absorbing... the historical record undersamples the tail' (taleb dossier, grade A). The candidate concedes the survivorship channel as a KNOWN data bias: NICM 'is curated toward instruments with reasonably complete cost actuals, which can under-represent instruments that were cancelled or radically descoped before flight... a bias that pushes toward measuring the surviving, flown instruments and is flag[ged].' But it provides NO cancelled-mission census: 'cancel' appears once (the NICM-bias note), 'terminat'=0, 'survivor'=1. There is no count of Earth missions reaching KDP-B that were cancelled/terminated, no breakdown by dominant cause, and no statement of whether they sit in the at-risk set or are silently excluded. The cancellation count, its cause-split, and the resulting bias on the instrument hazard therefore DO NOT EXIST in any retrieved source and cannot be asserted. Because cancellation is the true absorbing/ruin event the reserve policy exists to guard against, an instrument hazard conditioned on survival-to-launch systematically understates the tail, and the dissertation supplies the qualitative bias direction but not the magnitude needed to act on it.

Taleb et al., The Precautionary Principle (with Application to GMOs), arXiv:1410.5787