{"schema_version":1,"assessment_id":"eoa_inverse_innovation_exp03_opportunity320_20260801","source_experiment_id":"eoa_inverse_innovation_exp03_full320_20260801","cell_id":"invariant_mode_decomposition_design__disaster_management","archetype_slug":"invariant_mode_decomposition_design","domain_slug":"disaster_management","title":"Shadow-mode detection of coupled disaster-service deterioration","opportunity_summary":"Evaluate whether locally estimated dynamic modes can reveal consequential multi-service deterioration earlier or more accurately than separate indicators and predefined cascade rules, and whether the resulting resource-package rankings improve consequence-weighted outcomes in retrospective or tabletop evaluation without influencing live operations.","adopter_authorizer":"Emergency operations center analysts are the prospective users; the legally designated incident command or emergency-management authority, together with relevant data owners, authorizes testing and retains all allocation and operational authority.","scores":{"meaningful_impact":{"score":4,"rationale":"If the proposed hidden combinations are real and actionable, earlier recognition could reduce cross-service disruption and unmet needs affecting shelters, health services, utilities, transport, and communications. The prevalence and achievable outcome magnitude are not established."},"stakeholder_pull":{"score":2,"rationale":"The packet identifies affected operators, analysts, and decision authorities but supplies no evidence of expressed demand, procurement interest, budget ownership, or dissatisfaction with current dashboards and expert judgment."},"incremental_advantage":{"score":3,"rationale":"The proposal makes a testable claim of earlier or more accurate warning and better resource ranking versus separate indicators and rule-based dependency models, but neither forecasting advantage nor intervention benefit has been observed."},"distinctiveness_plausibility":{"score":3,"rationale":"The combination of locally estimated modes, consequence-weighted resource sensitivity, explicit validity gates, and withdrawal governance is sufficiently specific to compare with rivals, but prior art is unsearched and may already contain similar dynamic cascade methods."},"technical_implementability":{"score":3,"rationale":"A bounded retrospective or tabletop implementation using time-indexed service records is technically conceivable, with explicit residual, conditioning, spectral-gap, and drift checks. Sparse reporting, nonlinear thresholds, regime changes, non-normality, and mode swapping may prevent stable estimation."},"adoption_authority_feasibility":{"score":4,"rationale":"The packet identifies incident command as the decision authority, limits analysts to shadow recommendations, and specifies approval and rollback conditions. Feasibility is reduced by likely coordination across multiple service operators and data owners."},"evidence_readiness":{"score":3,"rationale":"The candidate supplies separate problem and intervention falsifiers, named comparators, held-out evaluation, and safe test boundaries. It supplies no incident dataset, measured baseline, repeatability evidence, or demonstrated intervention effect."},"safety_net_benefit":{"score":4,"rationale":"The intended benefit directly concerns continuity of essential services and population support, while shadow use, prohibited actions, field-report precedence, and withdrawal rules provide substantial safeguards. A false ranking could still divert scarce resources from visible urgent needs."},"scalability":{"score":2,"rationale":"Models are explicitly local to a hazard phase, geography, topology, and command regime, so each expansion may require new data agreements, validation, thresholds, and monitoring. Regime sensitivity limits straightforward replication."}},"score_confidence":"MODERATE","costs":{"first_evidence":{"band_2026_usd":"50K_TO_250K","scope":"One preregistered retrospective or tabletop shadow study covering one incident type, one geography, and a short fixed horizon, including data preparation, model construction, comparator implementation, blinded evaluation, and partner review.","confidence":"MODERATE","assumptions":["Relevant historical or tabletop service-status records can be accessed under existing approvals.","No new sensors, operational dispatch integration, or live intervention is included.","The study uses a small cross-functional team and a limited number of service streams."]},"initial_deployment_startup":{"band_2026_usd":"250K_TO_1M","scope":"Prepare a single jurisdiction and incident type for repeatable shadow operation, including data-sharing work, feed integration, data-quality controls, model governance, security review, analyst workflow design, exercises, and acceptance testing.","confidence":"LOW","assumptions":["Existing emergency-management and partner systems expose usable data without major replacement.","The system remains advisory and does not automate dispatch, aid eligibility, or resource denial.","Several infrastructure and service operators must coordinate but no major hardware acquisition is required."]},"operational_launch":{"band_2026_usd":"1M_TO_5M","scope":"Launch disaster-ready shadow decision support across the relevant service partners in one jurisdiction, including resilient integrations, documented operating procedures, training, exercises, independent validation, monitoring, incident support, and contingency arrangements.","confidence":"LOW","assumptions":["Launch must meet disaster-response reliability and security expectations.","Multiple service operators contribute feeds and participate in exercises.","Human incident command remains the sole operational authority.","The estimate excludes expansion to multiple jurisdictions or hazard families."]},"annual_recurring":{"band_2026_usd":"250K_TO_1M","scope":"Maintain one-jurisdiction capability through data-feed support, analyst and engineering labor, periodic exercises, recalibration, drift and conditioning monitoring, governance review, security maintenance, and incident-period support.","confidence":"LOW","assumptions":["The number of integrated services remains limited and stable.","Material topology, policy, or hazard changes trigger revalidation within this recurring scope rather than a new deployment.","Existing emergency operations personnel provide part of the operational staffing."]}},"research_burden":"HIGH","earliest_credible_horizon":"3_TO_12_MONTHS","pipeline_gates":{"recognizable_externally_supportable_problem":{"status":"UNCERTAIN","reason":"The packet clearly describes a recognizable mechanism and observable consequence, but supplies no external evidence that hidden recurrent combinations occur often enough or outperform existing recognition practices."},"identifiable_adopter_or_authorizer":{"status":"YES","reason":"Emergency operations center analysts are identifiable users, while the legally designated incident command or emergency-management authority and relevant data owners are explicitly assigned authorization roles."},"distinct_testable_incremental_claim":{"status":"YES","reason":"The candidate can be tested for earlier or more accurate warning and better consequence-weighted resource rankings against separate indicators and a predefined rule-based cascade model."},"bounded_next_evidence_step":{"status":"YES","reason":"The authorized first step is restricted to one incident type, one geography, and a short horizon in retrospective or tabletop shadow mode, with held-out comparisons and no live allocation changes."},"no_unresolved_safety_or_authority_stop":{"status":"YES","reason":"Operational authority remains with incident command; automated dispatch, protected-trait weighting, deliberate disruption, and unsupported extrapolation are excluded; explicit halt and rollback conditions restore ordinary procedures."},"implementation_cost_scope_and_range":{"status":"UNCERTAIN","reason":"A broad range can be framed for a one-jurisdiction shadow capability, but the packet does not specify feed count, data condition, integration architecture, security obligations, staffing, or partner contracting requirements."}},"blocking_evidence":["No sealed evidence establishes that stable, identifiable hidden modes recur within bounded disaster phases.","No held-out comparison demonstrates earlier or more accurate warning than separate indicators or predefined cascade rules.","No blinded simulation or shadow result shows that high-modal-leverage resource packages improve consequence-weighted outcomes.","Prior art is unsearched, so the claimed incremental distinction from existing disaster cascade and resilience modeling is unknown.","Data availability, reporting delay, missingness, strategic reporting, and cross-operator access have not been characterized.","No evidence demonstrates stakeholder demand, workflow acceptance, or willingness to fund and govern the capability."],"next_evidence_step":"With data-owner and incident-command approval, preregister a retrospective or tabletop shadow test for one incident type, one geography, and a short fixed horizon. Compare the modal method with separate-indicator thresholds and the predefined rule-based cascade rival on held-out warning lead time, prediction error, calibration, and blinded consequence-weighted resource rankings. Falsify progression if no stable, well-conditioned mode passes residual, gap, identity, and drift gates or if it adds no warning or ranking advantage; do not alter live deployment.","research_questions":["Do bounded disaster phases contain recurrent, stable, identifiable coupled modes after accounting for reporting artifacts and policy changes?","Does the modal method improve held-out warning lead time or accuracy over separate indicators and predefined cascade rules?","Do resource packages ranked as high modal leverage improve consequence-weighted outcomes in blinded simulation or shadow evaluation?","How sensitive are modes and rankings to delayed data, missing reports, consequence weights, non-normal transient growth, and topology changes?","Can inequitable historical service patterns be prevented from becoming implicit priority weights while preserving useful state information?","Which existing disaster-management methods already combine empirical dynamic modes, intervention sensitivity, and withdrawal governance?","Will incident command, analysts, operators, and data owners accept, govern, and fund a strictly advisory workflow?"] ,"recommendation":"PARTNERED_RESEARCH","uncertainty_constraints":["Closed-book assessment provides no external validation of problem prevalence, stakeholder pull, market size, prior art, or realized impact.","The candidate is a hypothesis and supports only retrospective or tabletop shadow evaluation as the first step.","Any credible study requires cooperation from incident command, data owners, and multiple service operators.","Estimated modes may be short-lived correlations because disasters contain adaptive behavior, nonlinear thresholds, unique shocks, and policy changes.","Cost bands are resource-equivalent ranges conditioned on a single-jurisdiction advisory scope and unspecified data-system complexity.","Results must not be generalized beyond the tested hazard phase, geography, topology, or command regime without revalidation."],"closed_book_prior_art_boundary":"Prior-art status is explicitly UNSEARCHED. This assessment cannot determine novelty, prevalence, market position, or whether empirical mode decomposition, dynamic cascade forecasting, resource-sensitivity ranking, or comparable governance controls already exist in disaster-management practice or research."}