{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__futurism_foresight","trajectory_id":"R","attempt_index":0,"archetype_slug":"invariant_mode_decomposition_design","domain_slug":"futurism_foresight","decision":"CANDIDATE","problem_id":"coupled_driver_early_warning_failure","causal_lever_id":"cross_impact_modal_drift_control","proposal":{"problem":"Strategic-foresight teams monitor trends and weak signals separately even when developments propagate through coupled drivers. Consequently, a reinforcing or weakly damped combination can approach strategic significance while every constituent indicator remains below its individual escalation threshold. This is a recognizable early-warning failure, conditional on the cross-impact relation being stable enough to approximate as a repeated local transformation.","actors_substrate":["foresight analysts maintaining a bounded driver set","domain experts estimating cross-impacts","strategy owners consuming warnings","communities, employees, customers, and partners exposed to resulting decisions","a time-indexed cross-impact matrix and driver-state vector"],"observable_state":"Archived scanning cycles show groups of drivers moving or reinforcing together, missed developments preceded by no single threshold breach, and changing scenario outcomes under small coordinated changes to several drivers.","consequence":"The organization escalates interacting threats or opportunities late, overweights conspicuous standalone trends, and may act on a simplified scenario after its structural basis has drifted.","affected_objective":"Increase decision-relevant warning lead time while limiting false escalation and preserving plural-futures interpretation.","structural_mapping":[{"archetype_element":"many visible coordinates conceal action-relevant directions","domain_realization":"Separately named trends conceal combinations of drivers through which cross-impacts accumulate.","claim_kind":"INFERENCE"},{"archetype_element":"transformation with persistent, growing, decaying, or oscillatory modes","domain_realization":"A calibrated cross-impact update matrix approximates how bounded driver states affect the next assessment period; repeated application yields candidate propagation modes.","claim_kind":"HYPOTHESIS"},{"archetype_element":"rank modes by scalar response and consequence","domain_realization":"Eigenvalue magnitude, sensitivity to strategic outcomes, and persistence jointly determine which driver bundles merit escalation.","claim_kind":"HYPOTHESIS"},{"archetype_element":"retain residual and drift visibility","domain_realization":"Out-of-sample reconstruction error, mode rotation, and spectral-gap erosion determine whether the modal warning remains usable.","claim_kind":"INFERENCE"},{"archetype_element":"local interpretation rather than universal prediction","domain_realization":"Modes describe behavior only for the stated driver definitions, elicitation protocol, horizon, and operating regime; they are not forecasts or causal laws.","claim_kind":"INFERENCE"}],"component_map":[{"component":"Transformation Scope","status":"adapted","domain_realization":"One decision question, horizon, assessment cadence, bounded driver set, and calibrated cross-impact update rule."},{"component":"State-Vector Definition","status":"adapted","domain_realization":"Standardized driver intensities with explicit direction, scale, provenance, and missing-data treatment."},{"component":"Invariant Mode Basis","status":"adapted","domain_realization":"Eigenvectors interpreted as candidate coupled-driver propagation patterns."},{"component":"Modal Gain Spectrum","status":"direct","domain_realization":"Eigenvalues report local amplification, damping, persistence, reversal, or oscillation per update."},{"component":"Dominant Mode Selection Rule","status":"adapted","domain_realization":"Retain modes only when gain or persistence and outcome sensitivity exceed preregistered thresholds."},{"component":"Stable/Unstable Mode Partition","status":"adapted","domain_realization":"Classify discrete-time modes by eigenvalue magnitude, with a separate marginal band reflecting estimation uncertainty."},{"component":"Modal Intervention Map","status":"adapted","domain_realization":"Map feasible monitoring or preparedness actions to the original drivers comprising each selected mode."},{"component":"Reconstruction Residual Check","status":"direct","domain_realization":"Compare held-out driver transitions and warning labels with reconstructions from retained modes; inspect structured residuals."},{"component":"Mode Drift Monitor","status":"direct","domain_realization":"Track mode alignment, ordering, gains, and residuals across assessment cycles."},{"component":"Interpretation Scope Contract","status":"adapted","domain_realization":"State that outputs are conditional warning structures, not probabilities, inevitabilities, or mandates."},{"component":"Mode-Coupling Register","status":"direct","domain_realization":"Record near-degenerate, non-orthogonal, or intervention-coupled modes that cannot be acted upon independently."},{"component":"Local Linearization Window","status":"adapted","domain_realization":"Specify the horizon, state range, matrix vintage, and institutional regime over which linear propagation is tested."},{"component":"Spectral Gap Threshold","status":"direct","domain_realization":"Require uncertainty-adjusted separation between retained and dropped modes; otherwise report an unresolved subspace."}],"mechanism_dispositions":[{"slug":"eigendecomposition_workflow","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Decompose the explicit, calibrated cross-impact update matrix; use conditioning diagnostics for non-normal cases.","counterfactual_removal":"No invariant propagation modes or gains would exist to drive the proposed warning logic."},{"slug":"modal_sensitivity_sweep","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Perturb modal coordinates and feasible actions within the local window; rank changes in scenario outcomes and log cross-effects.","counterfactual_removal":"Spectral prominence could not be distinguished from decision leverage."},{"slug":"modal_stability_analysis","disposition":"selected_load_bearing","contribution_type":"CORE_CAUSAL","adaptation_or_rejection":"Classify growth, decay, reversal, and oscillation under the chosen discrete update and uncertainty band.","counterfactual_removal":"The method could not identify reinforcing or weakly damped bundles before surface thresholds breach."},{"slug":"mode_shape_testing","disposition":"incompatible","contribution_type":"NONE","adaptation_or_rejection":"Physical excitation and sensor-response fitting do not match elicited strategic drivers; held-out temporal validation supplies the empirical check instead.","counterfactual_removal":"No effect; it is not in the causal chain."},{"slug":"network_spectral_centrality_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Node-importance scores answer which driver is central, not which driver combination propagates or destabilizes.","counterfactual_removal":"No effect; removing it avoids replacing modal warning with a ranking."},{"slug":"power_iteration_probe","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Dominant-only estimation hides marginal, oscillatory, and near-degenerate modes; the bounded matrix permits fuller decomposition.","counterfactual_removal":"No effect unless matrix scale later makes full decomposition infeasible."},{"slug":"principal_component_analysis","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Variance modes in observations do not establish propagation modes of the cross-impact update.","counterfactual_removal":"No effect; high variance is not the proposed causal lever."},{"slug":"reduced_order_model","disposition":"selected_supporting","contribution_type":"OPERATIONAL","adaptation_or_rejection":"Use a retained-mode surrogate only for bounded comparative warning simulations, never as a single-future predictor.","counterfactual_removal":"The causal diagnosis remains, but rapid action sweeps and the bounded pilot become materially harder."},{"slug":"residual_reconstruction_test","disposition":"selected_load_bearing","contribution_type":"TEST_DESIGN","adaptation_or_rejection":"Evaluate held-out transitions and warning-relevant residual structure across retained-mode counts.","counterfactual_removal":"There would be no hard check that compression preserves low-salience but consequential behavior."},{"slug":"singular_value_decomposition","disposition":"considered_rejected","contribution_type":"NONE","adaptation_or_rejection":"Singular vectors may diagnose transient amplification if non-normality is severe, but paired input-output directions are not invariant repeated-update modes.","counterfactual_removal":"No effect in the initial test; severe non-normality would instead halt the eigenmode interpretation."},{"slug":"spectral_decomposition_report","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Publish driver loadings, gains, uncertainty, couplings, residuals, scope limits, and prohibited interpretations.","counterfactual_removal":"Decision users could mistake conditional descriptive modes for forecasts, causal laws, or action mandates."},{"slug":"spectral_gap_monitor","disposition":"selected_load_bearing","contribution_type":"SAFETY_GUARDRAIL","adaptation_or_rejection":"Re-estimate gap, mode alignment, and residuals each cycle; alarm on uncertainty-adjusted threshold crossings.","counterfactual_removal":"A once-valid reduced warning model could continue operating after its mode separation or identity disappears."}],"causal_chain":["Separate trend thresholds conceal coordinated movement across drivers.","A calibrated cross-impact update represents local coupling over one assessment interval.","Decomposition exposes candidate coupled directions and their gains.","Stability and outcome-sensitivity tests identify reinforcing, persistent, decision-relevant bundles.","Residual, conditioning, gap, and drift gates suppress unsupported simplification.","Analysts escalate the bundle and evaluate reversible preparedness actions before individual indicators would trigger."],"baseline":"Ordinary horizon scanning: collect weak signals, score or trend each driver separately, and escalate individual indicators crossing salience, likelihood, or impact thresholds.","nearest_rival":"Cross-impact analysis followed by scenario construction using pairwise interactions, but without invariant-mode ranking, stability classification, residual reconstruction, or mode-drift gates.","authority_safety":{"affected_parties":["people represented by monitored drivers or data","groups exposed to preparedness or resource-allocation decisions","analysts and experts whose judgments populate the matrix","strategy owners receiving escalations"],"decision_authority":"A designated foresight governance panel may authorize analytical pilots; accountable strategy owners retain authority over operational decisions, subject to existing legal and participatory review.","authorized_first_step":"Run a preregistered, shadow-mode backtest on one decision question, one fixed driver set, and archived consecutive assessment cycles; compare warning lead time, false escalations, residuals, and mode stability against the baseline and nearest rival without changing live policy.","excluded_actions":["automated resource allocation or public warning","treating modes as event probabilities or inevitable futures","targeting people or communities because they load on a mode","using results outside the registered horizon or regime","suppressing minority scenarios solely because their modal gain is small"],"halt_rollback":"Stop and withdraw the model from decision use if conditioning is unacceptable, the gap falls below threshold, modes drift beyond tolerance, residuals exceed budget, stakeholder harm appears, or performance fails the preregistered comparison; revert to the ordinary scanning record and retain results only for audit."}},"negative_tests":{"strongest_counterevidence":"Cross-impact judgments may encode inconsistent narratives rather than a temporally valid operator; small elicitation changes could rotate the modes, and discontinuities or strategic agency could dominate any linear propagation.","analogy_break":"Unlike a physical linear system, futures drivers are constructed categories whose meanings, boundaries, and relations change through policy, reflexivity, surprise, and observer interpretation. Eigenmodes are therefore conditional summaries, not natural structures guaranteed to persist.","failure_condition":"The proposal fails if the matrix is ill-conditioned or nonstationary, no uncertainty-adjusted spectral separation exists, selected modes do not reconstruct held-out behavior within budget, or bundle warnings create more false escalation without useful lead time.","problem_falsifier":"In archived cycles, consequential misses are not preceded by recurring coupled-driver patterns, and interaction-aware bundles provide no incremental warning information over the best individual driver or ordinary scan.","intervention_falsifier":"Coupled patterns exist, but in blinded held-out cycles the modal workflow does not improve preregistered warning lead time or decision-relevant discrimination over cross-impact scenarios, or its modes and residuals fail the stability gates.","risks":["false precision from subjective matrix entries","reification of constructed drivers","mode instability or swapping near degeneracy","non-normal transient effects missed by eigenvalue stability","majority expert assumptions suppressing marginalized futures","gaming of escalations once thresholds become known","premature action on a descriptive association"]},"null_rationale":null,"classification":{"candidate_kind":"DOMAIN_TRANSFER","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":"Candidate is conditional on constructing and validating an explicit local cross-impact update operator. The packet supports structural plausibility but provides no empirical performance evidence or prior-art search."}