q: Does the top-decile carrying-window concentration survive a near-MILP-optimal re-solve of a held-out subset of the realized DSN request stream, separating a heavy tail intrinsic to the demand-supply geometry from the fingerprint of the incumbent scheduler's suboptimality?

facet: identification

raised_by: dantzig

priority: high

query_terms: Deep Space Network scheduling MILP optimality gap

query_terms: oversubscribed antenna scheduling re-solve held-out

query_terms: scheduling heuristic suboptimality versus structural concentration

status: open


q: Can the dual/shadow price on each binding complex-band-epoch capacity constraint be extracted at an optimized DSN allocation, and do the carrying windows coincide with the high-shadow-price (genuinely capacity-binding) windows rather than windows the incumbent discipline merely deprioritized?

facet: measurement

query_terms: shadow price antenna capacity Deep Space Network

query_terms: dual price binding constraint scheduling congestion

query_terms: marginal value of ground station MILP DSN


q: Does the heavy tail and top-decile concentration persist on a de-padded 'true need' demand series (executed-versus-requested duration ratios, or a padding-capped window), settling whether the tail is a primitive of true science demand or an artifact of strategic scheduler-induced request padding?

facet: empirics

query_terms: executed versus requested antenna time padding DSN

query_terms: strategic request inflation scheduling hedging

query_terms: de-padded demand series queue heavy tail


q: For each estimand, what fraction of loss is attributable to binding capacity (Ax<=b binds at the physical limit) versus suboptimal scheduling (a feasible reallocation would have served the dropped pass), shown by a held-out MILP re-solve reporting the optimal-versus-realized objective gap as the avoidable-loss estimate?

query_terms: capacity-binding versus avoidable loss MILP decomposition

query_terms: optimal versus realized objective gap scheduling


q: What is the gap between the penalty measured on padded requests and the penalty a Dantzig-Wolfe decomposition with a published per-complex-band-epoch shadow-price congestion charge would induce, sizing how much of the heavy tail is genuine physical contention versus an artifact of un-priced padding a coordinating dual would eliminate?

facet: mechanism

query_terms: Dantzig-Wolfe decomposition congestion pricing scheduling

query_terms: shadow price congestion charge demand padding

query_terms: coordinating dual price oversubscribed resource


q: Fitting the convex load-response separately to the deployed-scheduler output and to a re-solved near-optimal allocation on the same demand, is the convexity a property of the feasible frontier (ordinary increasing marginal cost at a binding capacity limit) or an artifact of where the current discipline rations, so the Taleb fragility reading can be distinguished from marginal-cost behavior?

query_terms: convex load response capacity frontier marginal cost

query_terms: optimal value function convexity binding capacity

query_terms: fragility signature versus increasing marginal cost queue


q: Where in the DSN data is the backlog stock and unmet-downlink stock measured as a time-integrated level, and can its realized rise-and-drain trajectory be shown across the multi-year log rather than inferring the feedback loop from a snapshot of pass-loss against instantaneous concurrent load?

raised_by: forrester

query_terms: backlog stock time-integrated level system dynamics

query_terms: unmet downlink accumulation trajectory DSN

query_terms: stock-and-flow bathtub dynamics scheduling backlog


q: Can the logs show that a mission's requested-duration padding and submission lead-time actually rise as that mission's own recent backlog rises (closing the reinforcing feedback edge), rather than asserting the loop as narration behind a convex curve an exogenous bursty arrival process would equally produce?

query_terms: lagged padding regressed on recent backlog DSN

query_terms: reinforcing feedback edge operator response scheduling

query_terms: exogenous bursty arrival versus endogenous hedge loop


q: What is the dominant request-to-allocation loop delay measured in the data, and does the design distinguish concentration produced by that delay outrunning the window from concentration that is simply the fixed celestial-mechanics geometry of overlapping viewing windows, which carries no feedback (a geometry-only placebo)?

query_terms: request to allocation loop delay measurement scheduling

query_terms: geometry-only placebo viewing window concentration

query_terms: delay-generated versus deterministic geometry concentration


q: In a within-mission fixed-effects panel regressing this cycle's padding on prior-cycle realized pass-loss at the same geometry-phase window, what lag-1 and lag-2 coefficient and sign separate an exogenous geometry-demand collision (lag near zero/negative) from an endogenous deferral-hedge loop (positive, geometrically decaying), showing whether the cross-sectional logistic reversed the causal arrow?

priority: normal

query_terms: within-mission fixed effects lagged panel padding pass-loss

query_terms: lag-1 coefficient sign endogenous deferral loop

query_terms: reverse causality cross-sectional scheduling demand


q: After a window is relieved (added aperture, arraying, logged policy change), does requested load decay toward the new capacity (open-loop scarcity relieved) or rebound to saturation as missions re-pad (closed-loop set-point restoration), and what rebound fraction and settling time confirm a loop gain near one showing the carrying windows are a moving controller artifact, not a fixed physical property the aperture lever can durably relieve?

facet: rival

query_terms: pre-post intervention rebound fraction scheduling capacity

query_terms: policy resistance set-point restoration backlog

query_terms: loop gain settling time relieved window saturation


q: Conditioning the load-response on the executed-versus-requested ratio (a proxy for instantaneous hedge gain), can the observed convexity be decomposed into a physical service-saturation component and an endogenous hedge-amplification component, and does the convexity survive once the hedge component is held fixed, locating the fragility in the loop or in the antennas?

query_terms: executed requested ratio hedge gain decomposition

query_terms: convexity loop gain versus physical saturation

query_terms: load response differencing hedge amplification


q: What is the realized exceedance count N above the chosen DSN peaks-over-threshold cutoff, and does a parametric bootstrap show the Clauset-Shalizi-Newman likelihood-ratio test can in fact discriminate a true power-law/GPD tail from a log-normal or truncated-exponential at that N and significance level, rather than the indistinguishability small samples produce?

raised_by: taleb

query_terms: peaks over threshold exceedance count power analysis

query_terms: Clauset Shalizi Newman likelihood ratio test power small sample

query_terms: generalized Pareto shape parameter discrimination log-normal exponential


q: What is the gap between requested-and-logged demand and true latent demand during the highest-concurrency windows, cross-checking SPS schedule logs against mission downlink-requirement records and NTRS loading reports, and does it show the heavy-tail and concentration estimates are not biased toward the thin-tailed null because the worst windows are the ones the log under-records?

query_terms: latent demand versus logged demand self-censoring queue

query_terms: abandonment deadline-driven queue undersampled tail

query_terms: DSN loading report versus request log reconciliation


q: Where does realized DSN concurrent-load support actually end, and does an out-of-sample backtest (fit the convex load-response on early/low-load cycles, predict loss in held-out high-load cycles) show the fitted second-order response holds outside the fitting range, rather than reading in-sample curvature as a forecast of a crewed-exploration regime the data do not contain?

query_terms: out-of-sample backtest convex load response extrapolation

query_terms: concurrent load support ceiling DSN human exploration

query_terms: second-order response held-out high-load cycle queue saturation


q: In a leave-one-out / jackknife and top-k-exceedance deletion on the GPD fit, what is the smallest set of pass-loss records whose removal drops the shape parameter to insignificance and collapses the top-decile loss share back toward ten percent, what fraction of total exceedances is that, and does the surviving tail still reject the exponential, settling whether a single-digit number of windows carries the heavy-tail result?

query_terms: jackknife leave-one-out heavy tail robustness GPD

query_terms: top-k exceedance deletion power law influential points

query_terms: smallest deletion set shape parameter insignificance


q: From mission link-budget and telemetry records, what is the distribution of the requested-versus-realized data-rate ratio behind the lost-downlink dependent variable, and is it thin-tailed and non-drifting, so a multiplicative, fat-tailed, or calibration-drifting nominal-rate error does not mechanically manufacture a heavy upper tail in lost-downlink with no contention mechanism present?

query_terms: nominal data rate measurement error model link budget

query_terms: requested versus realized data rate ratio distribution

query_terms: calibration drift multiplicative error heavy tail artifact


q: By parametric bootstrap on a simulated light-tailed-plus-calibration-drift DSN log that reproduces the arrival burstiness and skewed service, what is the false-positive rate of the pre-registered Clauset-Shalizi-Newman decision rule against a null with no heavy-tailed contention mechanism, demonstrating the rule does not spuriously accept H1 on noise-only synthetic data?

query_terms: parametric bootstrap false positive power law null

query_terms: preasymptotic fragility CSN test light-tailed null

query_terms: synthetic burstiness skewed service spurious heavy tail


q: Do this candidate's DSN logs record priority class, escalation flags, and per-request negotiated downgrades so the allocation rule-in-use can be entered as an explicit covariate, and can two requests matched on geometry, band, and load but facing different priority/deferral rules be constructed to estimate the rule's separate contribution to pass-loss?

raised_by: ostrom

query_terms: DSN schedule priority class escalation field schema

query_terms: rule-in-use covariate IAD allocation scheduling

query_terms: matched requests differing priority rule pass-loss


q: Do the logs pair each request with its executed pass so requested-minus-executed padding and per-mission escalation frequency are directly measurable, and does the loss concentration empirically track the breakdown of bargaining discipline (a monitoring-and-enforcement failure) rather than the geometry windows the hypothesis nominates?

query_terms: requested minus executed duration padding measure

query_terms: escalation frequency per mission bargaining discipline

query_terms: common-pool resource monitoring sanction loss concentration


q: Can the design decompose localized loss in each high-concentration window into genuine physical oversubscription versus the deferral discipline that resolved the conflict (a matched-rule or counterfactual-rule contrast within a fixed geometry window), so NASA is told whether a window needs a new antenna or merely a different rule, avoiding the panacea error?

facet: governance

query_terms: per-window decomposition physical oversubscription versus rule artifact

query_terms: counterfactual allocation rule fixed geometry window

query_terms: panacea diagnosis aperture versus governance remedy DSN


q: Partitioning the SPS schedule-archive revision history into forecast / mid-range negotiation / near-real-time stages, is the heavy-tail concentration produced at the rules-on-paper stage (published priority discipline) or only at the rules-in-use stage (realized negotiation and de-conflict trades), telling a fragile rule from a fragile institution?

query_terms: SPS schedule revision history stage partition

query_terms: rules-on-paper versus rules-in-use concentration stage

query_terms: collective-choice negotiation de-conflict trade DSN log


q: Re-estimating concentration (tail fit, Gini, top-decile share) separately by complex-band-phase stratum (S/X/Ka x complex x mission phase), is the heavy tail a shared structural property or a pooling artifact in which one congested Ka-band stratum drives an apparently network-wide fat tail?

query_terms: stratified tail estimation band complex phase DSN

query_terms: pooling artifact heterogeneous resource unit concentration

query_terms: Ka-band congestion stratum network-wide fat tail


q: Identifying which appropriators sit at the collective-choice table for the carrying windows (who can submit, escalate, and trade), does realized loss concentrate on appropriators excluded from or weakly represented in the mid-range negotiation, distinguishing a contention problem solvable by aperture from a representation problem solvable only by changing who governs the window?

query_terms: user community boundary appropriator DSN negotiation

query_terms: representation-weighted loss polycentric scheduling

query_terms: weakly represented mission loss concentration governance


q: Which exact DSN scheduling-log and mission downlink-requirement/criticality fields would falsify the geometry exclusion restriction by showing geometry affecting pass-loss through the mission's own declared pass value rather than through contention?

raised_by: rubin

query_terms: exclusion restriction instrument geometry pass value falsify

query_terms: criticality record downlink requirement field DSN

query_terms: geometry confounder science value contention identification


q: Which SPS schedule-archive fields separate hedged/padded requested-hours from genuine need so the interference-aware exposure mapping (others' realized concurrent-load exposure) is well-defined and free of hidden versions of treatment?

query_terms: exposure mapping partial interference SUTVA scheduling

query_terms: padded versus genuine requested hours SPS field

query_terms: well-defined treatment hidden versions request log


q: What is the realized cross-tabulation of donor cells (declination x band x phase x complex) under both low- and high-load regimes, and do encounter/EDL windows ever occur under low load, so the no-common-support cells can be reported before fitting and the estimand narrowed to the overlap sub-population?

query_terms: common support positivity overlap covariate cells scheduling

query_terms: donor cell declination band phase complex load regime

query_terms: encounter EDL window low-load overlap extrapolation


q: From an independent out-of-stream record (off-log negotiation/withdrawal trail, service-agreement amendments, pre-submission coordination logs), is the entry/retention propensity for a request to appear as a logged loss flat across geometry windows, breaking the survivorship/selection rival in which concentration is a property of the log's entry-and-retention process, not of physical contention? Which dataset settles it?

query_terms: off-log withdrawal pre-coordination trail survivorship selection

query_terms: entry retention propensity logged loss geometry window

query_terms: service agreement amendment descope record DSN


q: What outcome-independent request-level criticality covariate (mission science-criticality declarations or instrument-cadence records, measurable before the schedule outcome, NOT requested duration) can break the geometry-criticality confound that varies request-to-request within a mission-epoch and so survives mission and epoch fixed effects?

query_terms: request-level criticality covariate outcome-independent

query_terms: instrument cadence science plan criticality declaration

query_terms: within-mission-epoch confounder fixed effects geometry criticality


q: Producing the geometry-by-criticality cross-tabulation with served/lost counts per cell, do the off-diagonal cells (geometry-tight-but-not-high-criticality, geometry-loose-but-high-criticality) carry enough donor units under both arms for the geometry placebo to be identified, or are tight geometry and high criticality collinear so overlap fails and the rival is observationally equivalent?

query_terms: geometry by criticality cross-tabulation served lost

query_terms: off-diagonal donor cells instrument overlap deep space

query_terms: collinear tight geometry high criticality identification fail


q: What fraction of submitted requests, executed passes, and dropped/expired requests is captured in raw DSN service-catalog and SPS schedule-archive records versus inferred from aggregate loading reports, and is that capture rate uniform across complexes, bands, and epochs or does it drop precisely in the high-contention windows where H1 lives (a coverage census before the Gini)?

raised_by: mcdowell

query_terms: coverage census capture rate DSN schedule archive

query_terms: non-random missingness high contention window Gini

query_terms: raw log versus aggregate loading report inference


q: Is there an independent reconciling field (priority class, criticality flag, post-hoc mission acknowledgment) separating genuine unmet demand from a hedge pass never intended to fly, and what is the top-decile concentration after every sub-threshold request is reclassified as non-loss?

query_terms: hedge request reclassification non-loss sensitivity

query_terms: declared versus real demand reconciling field DSN

query_terms: top-decile concentration after hedge stripping


q: Naming a second independently-produced record of the same passes (mission-ops executed-pass logs, station downtime/maintenance logs, or published NTRS loading tabulations at matching granularity), do the counted pass-loss events reconcile window-by-window, and where they disagree which record is missing the loss and does the disagreement cluster on the geometry-phase windows H1 nominates as carriers?

query_terms: two-source reconciliation executed-pass station downtime log

query_terms: window-by-window pass-loss reconciliation NTRS loading

query_terms: independent record disagreement clustering carrying window


q: Which single SPS stage row (forecast vs negotiated vs realtime) is the canonical 'request', what de-duplication/supersession rule proves the count is stable under split/merge/re-negotiation, and which of the three re-stated stage durations is the 'requested duration' denominator, so the request count, every wait-time, and the top-decile concentration are not exposed to double-counting of superseded entries?

query_terms: canonical request stage supersession de-duplication SPS

query_terms: cross-stage request identity key forecast negotiated realtime

query_terms: double counting superseded schedule entry wait time


q: Will the computed lost-downlink (from requested duration, band, and nominal data rate) be reconciled against the DSN independent executed-pass returned-bits / link-margin / station-monitor record, with the discrepancy rate reported, and does the heavy tail and top-decile concentration survive when loss is anchored to measured executed-pass accounting rather than a nominal rate inferred from a hedged request?

query_terms: executed-pass returned data volume accounting reconciliation

query_terms: nominal rate versus measured returns discrepancy DSN

query_terms: lost downlink anchored measured executed pass tracking data


q: Fitting the wait-time and loss tails epoch-by-epoch against the documented antenna-complement and logging-convention changes (NTRS loading reports plus DSN configuration record), are the heavy-tail exponent and top-decile concentration stable WITHIN a fixed-configuration regime rather than a regime-mixture artifact of pooling across antenna-complement breaks and a shifting logging floor?

query_terms: DSN antenna complement timeline 70m 34m arraying epochs

query_terms: epoch-segmented tail fit fixed configuration regime

query_terms: logging convention floor change non-stationary catalog power law


q: Does the top-decile concentration survive when re-derived from a structural two-adapting-wills model of schedulers and requesters and remain stable across SPS epochs in which the priority discipline or padding behavior demonstrably changed, or does the concentration move with the discipline (proving the windows are an artifact of the current game, not a structural feature)?

raised_by: beaufre

query_terms: epoch-stability concentration changed scheduling discipline

query_terms: two-player scheduler requester adaptation model

query_terms: discipline-regime change top-decile Gini re-estimation


q: Which covariate decomposition or counterfactual re-weighting classifies each carrying window as geometry-durable (celestial-mechanics-set: declination, viewing overlap, fixed-longitude complex placement) versus mission-phase-mix-contingent, so a planner knows which localized windows to harden and which will dissolve when the configuration changes?

query_terms: geometry-durable versus mix-contingent window classification

query_terms: variance decomposition geometry coefficient mission phase share

query_terms: counterfactual re-weighting hold geometry perturb mix


q: When the scheduling-discipline lever is re-run on logs from a regime that protected a deferred class, does the relief persist or does the relieved window reappear elsewhere because requesters re-optimize padding against the new discipline, leaving aggregate lost downlink unchanged (the second-order recoil quantified even ordinally)?

query_terms: second-order recoil protected deferred class re-gaming

query_terms: padding best-response changed discipline displacement

query_terms: aggregate lost downlink pre-post protection bullwhip


q: Can multi-cycle DSN scheduling logs exhibit cross-cycle migration of the top-decile carrying windows (a Markov/transition or persistence statistic on top-decile windows), demonstrating the concentration is a stable structural invariant rather than a moving equilibrium a single-epoch fixed-effects design freezes by construction?

query_terms: cross-cycle carrying window migration persistence statistic

query_terms: top-decile window identity Markov transition multi-cycle

query_terms: structural invariant versus moving equilibrium scheduling


q: What logged observable would falsify the classification of DSN contention as indirect attrition (deferral, padding, priority discipline) versus a direct seizure of a decisive window (wholesale surrender of low-priority passes to guarantee one critical window, or a complex held in reserve as a credible threat), so the same Gini number cannot pass for two distinct generative stories?

query_terms: indirect attrition versus direct seizure log signature scheduling

query_terms: falsifying observable contention resolution mode

query_terms: decisive window seizure reserve complex threat


q: For each of the three remedies (added aperture, scheduling-discipline change, demand shaping), how much does it expand served missions' freedom of action versus transfer maneuver room to the most padding-aggressive missions who re-optimize against the new rule, and on what back-tested evidence is a discipline change a recovery of science return rather than a redistribution to the heaviest padders?

query_terms: freedom-of-action ledger remedy redistribution recovery

query_terms: padding-aggressive mission back-test post-rule logs

query_terms: distributive maneuver room scheduling discipline change


q: What is the purposive sampling rule (extreme-tail only vs also off-tail confirming/disconfirming), the qualitative datum type (anomaly reports, scheduling correspondence, operator phase narratives), and the stopping rule (thematic saturation vs fixed n per stratum) for the explanatory-sequential qualitative strand connected at sampling to the identified carrying windows, so a replicator can reconstruct it?

raised_by: creswell

query_terms: purposive sampling rule explanatory sequential mixed methods

query_terms: qualitative datum anomaly report operator narrative DSN

query_terms: thematic saturation stopping rule joint display carrying window


q: Is mission-declared criticality a measured quantitative field or a coded qualitative judgment, which records populate a joint-display column pairing each top-decile window's bit-volume loss, criticality-weighted loss, and the qualitative theme of why its loss mattered, and at how many windows do the volume and meaning rankings diverge?

query_terms: joint display bit-volume criticality-weighted qualitative theme

query_terms: criticality measured field versus coded judgment DSN

query_terms: volume versus meaning ranking divergence count window


q: What paradigm does the design declare (pragmatist or transformative), which log field records the requesting mission's priority class or institutional standing, and would a strata-by-priority-class breakdown of where loss concentrates change which intervention the analysis recommends?

query_terms: pragmatist transformative paradigm declaration mixed methods

query_terms: priority class institutional standing log field mission

query_terms: strata by priority class loss concentration intervention


q: MODERATOR MISSING ANGLE: No panel seat tested the realized-science-value-per-bit calibration of the dependent variable against an independent program-value or decadal-priority source; the entire design rests on lost-downlink bits and criticality-weighted bits as the proxy for lost science return, yet no thinker grounded whether bits map to mission/program value with an external benchmark (e.g., decadal survey science traceability, mission level-1 requirements). Phase 1 must establish whether an independent science-value weighting exists and whether the concentration result is robust to it, because if value and volume diverge the aperture-aiming policy conclusion inverts.

raised_by: moderator

query_terms: science value per bit downlink mission program value weighting

query_terms: decadal survey science traceability mission level-1 requirement

query_terms: value versus volume divergence telemetry lost science return