{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp06_four_proposal_generalization60_20260803","cell_id":"predictive_residual_processing__futurism_foresight","arm":"COMPLETE_PROPOSAL_PORTFOLIO","candidate_id":"prp-foresight-scenario-residual-exchange-001","proposal_index":1,"version":0,"title":"Scenario-Residual Exchange for Distributed Horizon Scanning","problem":"A distributed foresight network sends a central strategy team complete weekly assessments for every monitored driver. Most packets restate expected trajectories, yet analysts must read them to discover the few observations that contradict a scenario assumption, change a cross-impact, or reveal an unrepresented actor. The network lacks a shared, versioned expectation against which a deviation can be encoded, reconstructed, audited, and used to revise the scenario model.","actors":["Local horizon-scanning analysts who observe and classify evidence","Scenario stewards who maintain expected driver states and uncertainties","Central foresight analysts who reconstruct and assess updates","Strategic decision owners who commission investigations or preparedness reviews","An independent audit reviewer who samples full source packets","People and groups represented in scanned evidence, especially those exposed to consequential decisions"],"observable_state":"For a defined scanning cycle, intake logs show complete driver-assessment packets entering a review queue; packet comparison shows repeated expected content; queue timestamps show whether review demand exceeds the declared analyst-attention budget; and later review can identify whether material assumption-breaking evidence was present in packets that were reviewed late or summarized away.","consequence":"When predictable packets consume the review budget, model-breaking observations can reach scenario stewards after the relevant planning window, while repeated baseline material still incurs collection, transmission, and review costs. Exception-only reporting without a governed baseline can worsen the problem by making silence ambiguous and by suppressing evidence that challenges the shared scenario assumptions.","affected_objective":"Maintain timely, auditable awareness of changes relevant to plural futures within a fixed analyst-attention budget, without allowing the current scenario model to erase rare, minority, rights-relevant, or safety-relevant evidence.","intervention":"Define a weekly prediction target for each monitored driver: direction, pace, geographic spread, affected actors, cross-driver relationships, evidence confidence, and implications for named scenarios over an explicit horizon. Before evidence intake, each scenario steward publishes a versioned expected-state vector with uncertainty, scope, expiry, and ownership. Local analysts retain each full source capsule but compare their observed driver assessment with the matching expected vector and encode a structured residual: faster or slower movement, reversed direction, new actor or geography, altered cross-impact, assumption violation, or missing observation. A precision-and-consequence gate routes residual cards within the attention budget, attaching source provenance, model version, uncertainty, and a pointer to the full capsule. The central team reconstructs the current driver assessment from its synchronized expected vector plus the residual. Validated residuals may inform a separately approved model update; they do not automatically change scenarios or strategy. Heartbeats distinguish an expected week from missing reporting. Random and risk-stratified full packets are independently reviewed, and a scheduled full-state reconciliation checks reconstruction. Version mismatch, stale expectations, structured drift, audit disagreement, protected-signal classes, or an exceeded residual-error budget switches the affected driver to full-packet review.","structural_mapping":[{"archetype_element":"Prediction target and observation boundary","domain_realization":"A weekly, structured driver-state assessment over a named horizon, not prediction of a single future and not the raw text of every source."},{"archetype_element":"Generative model state, scope, and horizon","domain_realization":"The versioned set of scenario-conditioned expectations for driver direction, pace, actors, geography, interactions, and uncertainty, bounded to specified drivers and time horizons."},{"archetype_element":"Expected and actual behavior","domain_realization":"The steward records the expected driver-state vector before intake; a scanner records the observed vector and its full evidence capsule afterward."},{"archetype_element":"Prediction comparator and error signal","domain_realization":"A declared field-by-field comparison produces signed or categorical residuals such as acceleration, reversal, novel actor, new coupling, or assumption break."},{"archetype_element":"Precision weighting and residual budget","domain_realization":"Residual priority combines source reliability, observation uncertainty, potential consequence, perspective coverage, and analyst-review cost; suppressed residual mass is logged against a declared budget."},{"archetype_element":"Residual propagation channel","domain_realization":"The constrained central-review queue receives residual cards with reconstruction context instead of every complete routine assessment."},{"archetype_element":"Confidence and uncertainty state","domain_realization":"Prediction confidence, observation confidence, source quality, and unresolved disagreement remain separate fields rather than collapsing into one alert score."},{"archetype_element":"Update rule","domain_realization":"Validated residuals enter a review that can revise a parameter, threshold, scenario assumption, driver boundary, or model structure; operational routing and model learning occur on separate cadences."},{"archetype_element":"Model synchronization and provenance","domain_realization":"Every residual carries the expected-state version, driver identifier, horizon, source, timestamp, transformation history, and analyst attribution; incompatible versions are rejected."},{"archetype_element":"Freshness, drift, and validity","domain_realization":"Expected vectors expire, while sustained directional or segment-specific residual structure triggers review even when individual residuals remain below an escalation threshold."},{"archetype_element":"Reconstruction requirement","domain_realization":"A central analyst must be able to recover the complete structured driver assessment from the matching expectation and residual, with the original evidence capsule available on demand."},{"archetype_element":"Raw audit and full-state resynchronization","domain_realization":"An independent reviewer examines random and risk-stratified complete packets, and the network periodically reconciles every driver against a full current-state assessment."},{"archetype_element":"Fallback and decompression","domain_realization":"Audit disagreement, staleness, version mismatch, drift, missing heartbeat, or excessive reconstruction error restores complete-packet routing for the affected scope."},{"archetype_element":"Safety-critical bypass","domain_realization":"Evidence involving imminent physical harm, rights restriction, conflict escalation, severe distributional harm, protected disclosures, or an explicitly designated high-consequence assumption travels in full regardless of predictability."},{"archetype_element":"Attention and bandwidth budget","domain_realization":"The steering group declares the analyst-hours and review slots available per cycle and counts model maintenance, auditing, fallback, and residual review against the same budget."}],"mechanism_mapping":[{"mechanism_slug":"event_triggered_residual_reporting","role":"Routes a driver update when its precision-weighted structured deviation crosses the applicable threshold, while heartbeats make non-reporting interpretable.","counterfactual_removal":"Without it, the system remains a full-packet workflow or an informal exception practice rather than a residual channel."},{"mechanism_slug":"precision_weighted_error_gate","role":"Ranks residuals by reliability, uncertainty, consequence, perspective coverage, and review cost while retaining suppressed items for audit.","counterfactual_removal":"Magnitude-only ranking could bury small but reliable or consequential changes and would not govern the finite attention budget."},{"mechanism_slug":"model_version_checksum_handshake","role":"Confirms that scanner and central analyst use the same expected-state definition and tags each residual with its baseline.","counterfactual_removal":"A well-formed residual could be reconstructed against the wrong scenario baseline and silently change meaning."},{"mechanism_slug":"periodic_full_state_resynchronization","role":"Reconciles complete driver assessments on a cadence or early drift trigger, bounding accumulated semantic divergence.","counterfactual_removal":"Omitted or misunderstood residuals could compound indefinitely between distributed scanning desks and the central team."},{"mechanism_slug":"shadow_raw_channel_sampling","role":"Sends random and risk-stratified complete evidence packets to an independent reviewer to detect what the expectation model suppresses.","counterfactual_removal":"The residual stream would grade itself and could not reveal evidence its comparator never recognized as relevant."},{"mechanism_slug":"model_drift_monitoring","role":"Tracks residual distributions, audit disagreement, missing observations, and expectation expiry by driver and source segment.","counterfactual_removal":"Slow regime change could be normalized as routine variation until the scenario model was materially stale."},{"mechanism_slug":"raw_signal_fallback_switch","role":"Restores full-packet review for affected drivers when compatibility, confidence, freshness, safety, or reconstruction conditions fail.","counterfactual_removal":"A failed model would continue filtering evidence precisely when fuller context was needed."},{"mechanism_slug":"prediction_error_review","role":"Separates operational escalation from slower decisions about whether the evidence, model, threshold, action, or scanning boundary should change.","counterfactual_removal":"Residuals could create alerts without producing attributable learning, or could contaminate the model through automatic self-confirming updates."},{"mechanism_slug":"residual_comparison_test","role":"Compares residual structure with a simple rival expectation and with independently reviewed raw samples to distinguish noise from misspecification.","counterfactual_removal":"The team could label patterned model failure as harmless noise merely because individual deviations were small."},{"mechanism_slug":"surprise_to_action_bridge","role":"Assigns each validated high-consequence residual to a named owner and a bounded action such as evidence expansion, scenario stress test, or preparedness review.","counterfactual_removal":"Material deviations could stop at a dashboard without acknowledgement, investigation, or decision relevance."}],"causal_chain":["Scenario stewards publish bounded, uncertain, versioned expectations before the weekly evidence window.","Scanning analysts capture full evidence and translate it into the same structured driver-state representation.","The comparator calculates model-relative, reconstructive residuals rather than generic novelty scores.","Precision and consequence weighting allocate scarce review slots while protected classes bypass suppression.","Central analysts receive the matching baseline identity and residual, reconstruct the current assessment, and request full context when needed.","Validated surprises are assigned to an owner and a defined investigation or scenario-stress action.","A separate prediction-error review decides whether the model, data treatment, threshold, action, or scanning boundary should change.","Independent raw sampling and periodic full-state reconciliation measure omissions and semantic reconstruction error.","Drift, incompatibility, missingness, protected signals, or budget violations suspend residual-only routing and restore complete review."],"baseline":"The same distributed network submits complete weekly driver assessments, which central analysts triage using ordinary tags and stated priority. All packets remain available, but no precommitted shared expectation, reconstructive residual, model-version gate, independent suppression audit, or residual-triggered fallback controls what receives attention.","nearest_rivals":["A conventional horizon-scanning repository with keyword tags, relevance scores, and analyst alerts; it ranks documents but does not reconstruct driver state from a shared expectation plus residual.","An anomaly-detection feed that flags statistically unusual sources or terms; it can surface novelty but does not represent scenario-relative direction, uncertainty, synchronization, or model learning.","An informal report-by-exception rule against last week's status; it reduces reporting but leaves the baseline, silence semantics, error budget, raw audit, and fallback unspecified.","A periodic scenario-refresh workshop; it can revise assumptions with full context but does not continuously encode and route intervening prediction errors within a weekly attention budget.","A simple change log between successive driver assessments; it transmits differences from the prior state but not deviations from a forward, scoped expectation with uncertainty and consequence weighting."],"remaining_contrastive_claim":"The proposal should be preferred over these rivals only if a synchronized scenario expectation plus structured residual lets reviewers recover decision-relevant driver state while reallocating attention, and if independent raw audits, full-state reconciliation, and fallback keep consequential omissions within a predeclared tolerance after all maintenance costs are counted.","authority_safety":{"decision_authority":"The foresight steering group may define the pilot scope, attention budget, protected bypass classes, and acceptance thresholds. Scenario stewards may propose model changes, but strategic decision owners retain authority over preparedness or policy choices, and affected-domain experts can contest classifications or request full context.","authorized_first_step":"Run a read-only retrospective and shadow-mode comparison on one bounded, already-authorized scan corpus. Continue delivering all packets through the baseline workflow while the prototype independently creates expectations, residuals, reconstructions, audit samples, and fallback events.","excluded_actions":["Suppressing or delaying any live evidence packet during the first test","Automatically changing an official scenario, forecast, strategy, resource allocation, or policy","Using residual scores to evaluate individual analyst performance","Removing, downgrading, or identifying dissenting and minority perspectives because they conflict with the baseline","Publishing source material or personal data beyond existing access permissions","Allowing the model to update itself from residuals without an attributable human review","Treating a matching model checksum as evidence that the shared model is correct"],"halt_rollback":"Halt residual processing for the affected scope if a protected signal is suppressed, a heartbeat or version check fails, reconstruction exceeds its predeclared tolerance, the audit path is not independent, or total pilot workload exceeds its budget. Roll back by routing complete packets, restoring the last frozen expectation and threshold versions, preserving the comparison log, and requiring steering-group review before another shadow run."},"negative_tests":{"strongest_counterevidence":"In the shadow comparison, reviewers using reconstructed assessments plus residual cards miss assumption changes, cross-driver interactions, or underrepresented-source evidence that reviewers using complete packets identify, while model preparation, synchronization, auditing, and fallback consume as much or more analyst time than the full-packet baseline.","problem_falsifier":"The inferred problem is falsified for the selected setting if complete packets consistently fit within the declared review budget and planning window, repeated expected content is not a material share of review work, or delayed material signals are attributable to absent evidence or decision blockage rather than intake saturation.","intervention_falsifier":"The intervention is falsified if blinded reviewers cannot reconstruct the decision-relevant driver assessment within predeclared semantic tolerances; protected or stratified source classes have worse omission outcomes than the baseline; silence cannot be distinguished from missing reporting; residuals show persistent structure after permitted model revision; or total routing, maintenance, audit, and fallback cost is not lower than full review at the required fidelity.","risks":["Model lock-in could make the shared scenario assumptions a self-confirming filter.","Consequence and precision weights could systematically discount unfamiliar sources, regions, disciplines, or minority perspectives.","Analysts could game classifications to obtain attention or avoid scrutiny.","Residual cards could expose sensitive atypical behavior even when routine source material is suppressed.","Frequent false escalation could produce alarm fatigue and pressure to weaken safeguards.","Automatic learning could absorb the effects of actions taken in response to earlier residuals as if they were independent evidence.","A quiet channel could be mistaken for stability when a source, scanner, comparator, or communication path has failed.","Residual framing could strip away narrative context needed to interpret ambiguous long-range evidence."]},"next_evidence_step":"Pre-register a bounded shadow evaluation using one historical scan corpus divided into sequential evidence windows. Freeze an expectation model using only material available before each window; generate residual cards without altering the baseline record; and give blinded reviewers either the ordinary full-packet workflow or synchronized expectation-plus-residual reconstructions, with full packets available only through logged fallback. Compare review time, reconstruction agreement, identified assumption changes, cross-driver findings, protected-source coverage, false escalations, fallback frequency, audit disagreement, and total model-maintenance effort. Define semantic tolerance, protected-signal rules, and halt conditions before scoring; treat the result only as evidence about whether a live shadow pilot is warranted.","prior_art_status":"UNSEARCHED","diversity_from_prior_proposals":"Not applicable at proposal_index 1; no cross-proposal comparison context was used.","revision_record":{"parent_version":null,"progress_targets_addressed":["Initial complete proposal"],"conceptual_changes":["Instantiated predictive residual processing as a reconstructive exchange for structured scenario-driver assessments rather than as generic anomaly alerts.","Separated operational residual routing from slower, authorized scenario-model revision."],"operational_changes":["Specified shared weekly prediction targets, version checks, heartbeats, precision gating, protected bypasses, independent raw sampling, full-state reconciliation, and scoped fallback.","Limited the first step to retrospective and shadow operation with no live suppression or strategic automation."],"evidence_changes":["Defined observable queue, repetition, reconstruction, omission, coverage, workload, and audit measures.","Specified problem and intervention falsifiers and a bounded comparative evidence step."],"claim_changes":["Made no claim of novelty, prevalence, demand, or effect size.","Conditioned the contrastive claim on reconstruction fidelity, consequential-signal protection, and net attention cost."]}}