{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__data_science","trajectory_id":"R","attempt_index":0,"archetype_slug":"invariant_mode_decomposition_design","domain_slug":"data_science","decision":"CANDIDATE","problem_id":"hidden_coupled_drift_in_feedback_model","causal_lever_id":"transition_mode_targeted_drift_control","proposal":{"problem":"A production prediction system updated through user-response or retraining feedback can suffer coordinated drift across features, predictions, and outcomes while univariate drift checks and aggregate accuracy remain apparently acceptable. The hidden coupled direction may grow until calibration or subgroup performance fails.","actors_substrate":["model-owning data-science team","production prediction pipeline","users or subjects represented in feedback data","risk and domain reviewers","time-windowed feature, prediction, intervention, and delayed-outcome records"],"observable_state":"Successive deployment windows show repeatable joint movement among correlated features, score distributions, interventions, and outcomes; marginal alerts are weak while a fitted transition mode grows, persists, or rotates.","consequence":"The team reacts to individual metrics after damage appears, suppresses symptoms without addressing the coupled trajectory, or continues trusting an obsolete reduced representation.","affected_objective":"Maintain calibrated, reliable, and equitable production predictions while detecting consequential feedback-driven degradation early.","structural_mapping":[{"archetype_element":"many-variable transformation","domain_realization":"A locally fitted transition operator maps one window's standardized pipeline state to the next window's state.","claim_kind":"HYPOTHESIS"},{"archetype_element":"invariant directions","domain_realization":"Approximately invariant combinations of feature, prediction, intervention, and outcome summaries describe recurring deployment trajectories.","claim_kind":"HYPOTHESIS"},{"archetype_element":"modal gain","domain_realization":"Each mode's estimated eigenvalue indicates decay, persistence, oscillation, or growth across windows.","claim_kind":"INFERENCE"},{"archetype_element":"action relevance","domain_realization":"Backtests and bounded perturbation simulations rank modes by effects on calibration, subgroup error, and operational load rather than gain alone.","claim_kind":"HYPOTHESIS"},{"archetype_element":"residual and drift governance","domain_realization":"Out-of-sample reconstruction error, mode rotation, and spectral-gap checks determine whether the modal representation remains usable.","claim_kind":"INFERENCE"}],"component_map":[{"component":"Transformation Scope","status":"direct","domain_realization":"One-version, one-policy deployment periods with a window-to-window transition operator."},{"component":"State-Vector Definition","status":"direct","domain_realization":"Standardized aggregates for features, scores, interventions, delayed outcomes, calibration, and subgroup errors, excluding post-window information."},{"component":"Invariant Mode Basis","status":"direct","domain_realization":"Eigenvectors of the fitted local transition operator, traced to original metrics."},{"component":"Modal Gain Spectrum","status":"direct","domain_realization":"Eigenvalues with bootstrap uncertainty and conditioning diagnostics."},{"component":"Dominant Mode Selection Rule","status":"adapted","domain_realization":"Retain modes exceeding a preregistered combination of persistence, outcome sensitivity, and validated residual reduction."},{"component":"Stable/Unstable Mode Partition","status":"direct","domain_realization":"Classify modes by gain relative to the discrete-time stability boundary, with uncertainty and marginal status."},{"component":"Modal Intervention Map","status":"adapted","domain_realization":"Map feasible pipeline controls—refresh cadence, sampling weights, thresholds, or feedback exposure—to estimated modal movement."},{"component":"Reconstruction Residual Check","status":"direct","domain_realization":"Test held-out next-window reconstruction error and inspect structured residuals by subgroup and regime."},{"component":"Mode Drift Monitor","status":"direct","domain_realization":"Track mode angles, gains, ordering, and residuals after deployment."},{"component":"Interpretation Scope Contract","status":"direct","domain_realization":"Declare the model descriptive and locally predictive, not proof that a mode or metric causes outcomes."},{"component":"Mode-Coupling Register","status":"direct","domain_realization":"Record near-degenerate, non-orthogonal, ill-conditioned, and perturbation-linked modes."},{"component":"Local Linearization Window","status":"direct","domain_realization":"Bound use to observed state ranges, unchanged model/policy versions, and a specified recent time horizon."},{"component":"Spectral Gap Threshold","status":"direct","domain_realization":"Require bootstrap-supported separation between retained and omitted modes; otherwise suspend reduced-mode decisions."}],"mechanism_dispositions":[{"slug":"eigendecomposition_workflow","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Decompose the explicit fitted transition matrix, retaining conditioning and uncertainty checks.","counterfactual_removal":"Without transition eigenpairs, growth and persistence cannot be attributed to invariant deployment directions."},{"slug":"modal_sensitivity_sweep","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Use bounded simulations and historical counterfactual backtests, not live uncontrolled perturbations.","counterfactual_removal":"Gain alone would not identify which modes affect decision objectives or which feasible controls reach them."},{"slug":"modal_stability_analysis","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Classify uncertain eigenvalues within the declared local window.","counterfactual_removal":"The proposal would lose its early-warning distinction between decaying and growing coupled drift."},{"slug":"mode_shape_testing","disposition":"incompatible","contribution_type":"NONE","adaptation_or_rejection":"Physical excitation and sensor placement do not match an instrumented data pipeline with an estimable operator.","counterfactual_removal":"No change; empirical validation is supplied by held-out transitions and residual tests."},{"slug":"network_spectral_centrality_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"The state is a temporal metric vector, not a node-importance network.","counterfactual_removal":"No change; centrality scores would answer a different problem."},{"slug":"power_iteration_probe","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"A dominant-only estimate is insufficient because secondary unstable and coupled modes matter; the bounded pilot can fit the full operator.","counterfactual_removal":"No change to the full-spectrum pilot."},{"slug":"principal_component_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Variance directions do not identify window-to-window dynamics and could omit low-variance consequential drift.","counterfactual_removal":"No change; covariance decomposition is not the causal lever."},{"slug":"reduced_order_model","disposition":"selected_supporting","contribution_type":"TEST_DESIGN","adaptation_or_rejection":"Use only as an offline surrogate for bounded control simulations after fidelity gates pass.","counterfactual_removal":"Mode detection survives, but cheap and safe comparison of candidate controls becomes harder."},{"slug":"residual_reconstruction_test","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Evaluate temporally held-out windows and subgroup-structured residuals.","counterfactual_removal":"There would be no hard check that omitted behavior remains within the consequence-weighted error budget."},{"slug":"singular_value_decomposition","disposition":"selected_supporting","contribution_type":"OPERATIONAL","adaptation_or_rejection":"Use for numerical conditioning and non-normality diagnostics, not as a substitute for temporal eigenmodes.","counterfactual_removal":"The core chain remains, but fragile or transiently amplified fits are less likely to be detected."},{"slug":"spectral_decomposition_report","disposition":"selected_supporting","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Publish traceability, uncertainty, couplings, prohibited causal readings, and validity bounds.","counterfactual_removal":"Analysts could operationalize tentative or entangled modes as independent causal facts."},{"slug":"spectral_gap_monitor","disposition":"selected_load_bearing","contribution_type":"OPERATIONAL","adaptation_or_rejection":"Monitor gap uncertainty jointly with mode rotation, residual growth, and version changes.","counterfactual_removal":"A formerly useful basis could silently remain in service after its separation or identity disappears."}],"causal_chain":["Feedback and retraining couple several production metrics across time.","A locally valid transition operator represents their joint window-to-window evolution.","Decomposition exposes persistent or growing combinations hidden by marginal monitors.","Sensitivity analysis identifies consequential modes and feasible controls that reach them.","A bounded control applied to a validated mode reduces its projected growth or consequence.","Residual, gap, and rotation gates halt use when the approximation loses validity."],"baseline":"Dashboard univariate drift tests, aggregate accuracy/calibration checks, and reactive retraining after a threshold breach.","nearest_rival":"A multivariate change-point or generic anomaly detector that flags joint deviation but does not estimate invariant growth directions or control leverage.","authority_safety":{"affected_parties":["people receiving predictions or decisions","subgroups whose errors may be hidden in aggregates","model operators and downstream decision teams"],"decision_authority":"The accountable model owner may authorize analysis; production changes require the existing domain, risk, and change-control authorities.","authorized_first_step":"Run a retrospective, read-only pilot on one unchanged deployment regime: fit on earlier windows, preregister stability, gap, residual, and consequence thresholds, and evaluate on later windows against baseline and rival alerts.","excluded_actions":["automatic production control from modal scores","deliberate harmful user exposure to excite modes","causal claims from eigenvectors alone","dropping subgroup metrics because aggregate reconstruction is good","extrapolation beyond the declared state or version window"],"halt_rollback":"Stop and revert to ordinary monitoring if held-out residual or subgroup error exceeds budget, modes are ill-conditioned, the gap lacks support, modes rotate beyond tolerance, or a pipeline/policy regime changes."}},"negative_tests":{"strongest_counterevidence":"Historical failures may be abrupt exogenous shocks or label-delay artifacts with no stable precursor; univariate or change-point monitors may match or outperform modal alerts after honest temporal validation.","analogy_break":"Unlike a fixed physical system, a deployed model is reflexive: interventions, retraining, and user adaptation can change the operator itself, so eigenmodes need not remain invariant or causal.","failure_condition":"No sufficiently stationary/version-consistent window yields a well-conditioned operator whose held-out reconstruction and mode identity meet preregistered tolerances.","problem_falsifier":"Across documented incidents, consequential degradation is explained by single observable variables or abrupt shocks, with no repeatable coupled trajectory that precedes failure and no advantage over marginal monitoring.","intervention_falsifier":"A validated growing mode predicts degradation, but feasible controls chosen by the sensitivity map do not reduce its held-out gain or consequence relative to baseline/rival controls within uncertainty.","risks":["Spurious modes from short, noisy, autocorrelated windows","Leakage from delayed outcomes into earlier state vectors","Aggregate fidelity masking subgroup harm","Non-normal transient growth misread as eigenvalue stability","Monitoring-induced overconfidence or unnecessary retraining","Feedback changes caused by acting on the measurement"]},"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":0.78,"generator_notes":"Closed-book structural inference from the supplied packet. Empirical effectiveness, stationarity, and control responsiveness remain hypotheses pending the bounded retrospective test."}