{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__operations_research","trajectory_id":"R","attempt_index":0,"archetype_slug":"invariant_mode_decomposition_design","domain_slug":"operations_research","decision":"CANDIDATE","problem_id":"hidden_unstable_congestion_modes_in_coupled_service_networks","causal_lever_id":"damp_action_relevant_congestion_modes","proposal":{"problem":"In a coupled service network, queues interact through routing, shared capacity, and control rules. Average utilization and queue-by-queue alarms can appear acceptable while a combination of workloads grows toward network-wide congestion; local capacity changes may merely displace backlog. The independently recognizable problem is detecting and controlling these hidden unstable congestion patterns before service failure.","actors_substrate":["service-network customers and priority classes","queue and facility operators","routing and capacity planners","linked queues, servers, buffers, and routing rules","operations-research model owners"],"observable_state":"Repeated queue-length or workload vectors show correlated growth, oscillation, or backlog migration that individual-queue thresholds and average utilization do not explain.","consequence":"Delayed detection permits a coupled congestion pattern to amplify, causing missed service targets, spillover among queues, and potentially ineffective local capacity interventions.","affected_objective":"Minimize delay and service-level violations while preserving throughput, feasibility, and equitable treatment across customer classes.","structural_mapping":[{"archetype_element":"Transformation acting on coupled variables","domain_realization":"A fitted one-step workload transition or local Jacobian maps the network workload vector under fixed routing and service policy. HYPOTHESIS: a locally valid operator can be estimated from the bounded pilot data.","claim_kind":"HYPOTHESIS"},{"archetype_element":"Invariant directions with scalar response","domain_realization":"Eigenmodes represent joint queue-workload patterns; eigenvalues characterize local decay, persistence, oscillation, or growth.","claim_kind":"INFERENCE"},{"archetype_element":"Action relevance differs from raw magnitude","domain_realization":"A modest mode may strongly affect delay or violations, while a large mode may have little operational consequence; perturbation ranks modes by objective sensitivity.","claim_kind":"INFERENCE"},{"archetype_element":"Residual and regime limits","domain_realization":"Out-of-sample reconstruction error, structured residuals, spectral-gap loss, and mode rotation delimit when the modal model may guide decisions.","claim_kind":"INFERENCE"}],"component_map":[{"component":"Transformation Scope","status":"direct","domain_realization":"One-step workload evolution under a fixed routing, arrival, and service-policy regime."},{"component":"State-Vector Definition","status":"direct","domain_realization":"Normalized queue workloads by facility and customer class, with units and aggregation fixed before fitting."},{"component":"Invariant Mode Basis","status":"direct","domain_realization":"Eigenvectors of the estimated workload-transition operator."},{"component":"Modal Gain Spectrum","status":"direct","domain_realization":"Eigenvalues with uncertainty estimates and modulus-based growth classification."},{"component":"Dominant Mode Selection Rule","status":"adapted","domain_realization":"Retain modes exceeding an objective-sensitivity threshold or required to satisfy an out-of-sample residual budget."},{"component":"Stable/Unstable Mode Partition","status":"direct","domain_realization":"Classify modes as decaying, marginal, growing, or oscillatory inside the fitted regime."},{"component":"Modal Intervention Map","status":"adapted","domain_realization":"Map feasible routing, staffing, and capacity adjustments to their predicted changes in action-relevant modal gains."},{"component":"Reconstruction Residual Check","status":"direct","domain_realization":"Reconstruct held-out workload transitions and inspect both error magnitude and structured queue-level residuals."},{"component":"Mode Drift Monitor","status":"direct","domain_realization":"Re-estimate mode angles, ordering, gains, and residuals on successive windows."},{"component":"Interpretation Scope Contract","status":"direct","domain_realization":"Permit local operational prediction only within stated demand, routing, staffing, and workload ranges; prohibit causal claims from decomposition alone."},{"component":"Mode-Coupling Register","status":"direct","domain_realization":"Record near-degenerate, ill-conditioned, or intervention-linked modes whose effects cannot be treated independently."},{"component":"Local Linearization Window","status":"direct","domain_realization":"The observed workload and policy range used to fit and validate the local operator."},{"component":"Spectral Gap Threshold","status":"direct","domain_realization":"Minimum separation between retained and omitted modes required for stable selection and continued use."}],"mechanism_dispositions":[{"slug":"eigendecomposition_workflow","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Decompose the explicit fitted local workload operator, retaining conditioning diagnostics.","counterfactual_removal":"Without modes and gains, hidden joint growth directions cannot be identified."},{"slug":"modal_sensitivity_sweep","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Perturb feasible controls and modal coordinates locally, scoring delay and violation responses while logging cross-effects.","counterfactual_removal":"The analysis could describe modes but could not identify operational leverage."},{"slug":"modal_stability_analysis","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Partition workload modes relative to the discrete-time stability boundary within the validation window.","counterfactual_removal":"The proposal loses its early-warning distinction between harmless variation and growing congestion."},{"slug":"mode_shape_testing","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Physical excitation testing is mismatched; historical transitions and a fitted operator are available, and deliberately exciting congestion is unsafe.","counterfactual_removal":"No material change; empirical validation is supplied by held-out passive observations."},{"slug":"network_spectral_centrality_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Node-importance ranking answers a different question from dynamic workload stability and may mistake connectivity for congestion leverage.","counterfactual_removal":"No change to the dynamic causal chain."},{"slug":"power_iteration_probe","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"A dominant-only estimate could miss an objective-sensitive secondary mode; the bounded network permits a full decomposition.","counterfactual_removal":"No change because full-spectrum analysis supplies the required modes and gap."},{"slug":"principal_component_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Variance directions need not be transition-invariant or predictive of growth.","counterfactual_removal":"No change; covariance structure is not the proposed causal lever."},{"slug":"reduced_order_model","disposition":"selected_supporting","contribution_type":"OPERATIONAL","adaptation_or_rejection":"Use a validated retained-mode surrogate only for rapid, reversible policy screening inside the fitted window.","counterfactual_removal":"Identification remains possible, but bounded comparison of many candidate controls becomes slower."},{"slug":"residual_reconstruction_test","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Test retained modes on held-out transitions and reject reductions with excessive or structured residuals.","counterfactual_removal":"Omitted congestion behavior could be hidden by an apparently clean modal summary."},{"slug":"singular_value_decomposition","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"SVD is reserved as a diagnostic fallback if non-normality makes eigenvectors unusable; singular directions do not provide repeated-dynamics stability verdicts.","counterfactual_removal":"No change under the explicit well-conditioned-operator hypothesis."},{"slug":"spectral_decomposition_report","disposition":"selected_supporting","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Document mode meanings, uncertainty, coupling, residuals, prohibited interpretations, and validity limits for decision users.","counterfactual_removal":"The mathematics remains, but misuse risk rises and operational authorization is hard-gated."},{"slug":"spectral_gap_monitor","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Monitor retained/omitted separation, mode rotation, gain drift, and residual growth on scheduled refits.","counterfactual_removal":"A once-valid controller could continue after mode identity or dominance changes."}],"causal_chain":["Coupled routing and capacity rules generate a local workload-transition operator.","Its unstable or weakly damped eigenmodes reveal joint backlog patterns hidden by queue-level averages.","Sensitivity analysis links feasible controls to changes in consequential modal gains.","A reversible control predicted to reduce those gains should reduce subsequent modal amplitude and service violations.","Residual, drift, conditioning, and spectral-gap checks determine whether that interpretation remains usable."],"baseline":"Continue queue-by-queue threshold monitoring and make local staffing or routing adjustments at the visibly worst queue.","nearest_rival":"Use simulation or optimization directly on the full queue network, selecting policies by predicted delay without an explicit modal decomposition.","authority_safety":{"affected_parties":["customers whose service priority or route may change","frontline staff whose workload may shift","operators of upstream and downstream facilities"],"decision_authority":"The designated service-network operations manager may authorize the pilot after model-risk review and confirmation that contractual, labor, capacity, and priority constraints remain satisfied.","authorized_first_step":"Offline replay on one historical operating regime, followed—only if validation gates pass—by a time-limited shadow recommendation for one reversible routing or staffing adjustment; no automatic execution.","excluded_actions":["deliberately create congestion to excite modes","reduce legally or contractually protected service priority","deploy outside the validated workload window","automate routing or staffing changes during the first test","discard queues or classes solely because their variance is small"],"halt_rollback":"Halt if held-out residual exceeds the preset budget, residuals become structured, eigenvectors are ill-conditioned, the spectral gap falls below threshold, a recommendation violates feasibility or equity constraints, or shadow predictions degrade versus baseline; withdraw recommendations and revert to ordinary operating policy."}},"negative_tests":{"strongest_counterevidence":"The apparent network mode may be an artifact of common demand shocks or preprocessing. If a single observable bottleneck or exogenous arrival factor explains held-out backlog propagation as well as the modal operator, the decomposition adds no actionable structure.","analogy_break":"Queue dynamics can be nonlinear, policy-dependent, stochastic, and non-normal; eigenmodes need not evolve independently, and local eigenvalue stability does not guarantee global stability after a large demand shock.","failure_condition":"The approach fails if no operator has a stable validation window, modes are too ill-conditioned or near-degenerate to interpret, feasible controls cannot move the risky modes, or required residual and safety budgets cannot be met.","problem_falsifier":"Across held-out periods, backlog growth and service failures are fully explained by independent queue effects or one directly observed bottleneck, with no reproducible coupled workload pattern or backlog migration.","intervention_falsifier":"After a preregistered reversible adjustment, the targeted mode's gain or amplitude does not decline relative to matched baseline windows, or it declines without improving delay or violations and merely shifts harm to another queue or class.","risks":["Spurious modes from nonstationary demand or measurement error","Non-normal transient amplification despite apparently stable eigenvalues","Unfair redistribution of delay across customer classes","Overconfidence from in-sample residuals","Mode swapping near a small spectral gap","Local controls shifting congestion rather than damping it"]},"null_rationale":null,"classification":{"candidate_kind":"MECHANISM_COMPOSITION","prior_art_status":"UNSEARCHED","evidence_maturity":"HYPOTHESIS"},"revision_change_log":{"revision_kind":"ORIGINAL","prior_problem_id":null,"prior_causal_lever_id":null,"problem_changed":false,"causal_lever_changed":false,"conceptual_changes":[],"operational_changes":[],"repairs_addressed":[]},"confidence":0.82,"generator_notes":"Closed-book structural inference. The candidate assumes a locally estimable workload-transition operator; that assumption and all claimed operational effects require testing."}