{"schema_version":1,"experiment_id":"eoa_inverse_innovation_exp06_four_proposal_generalization60_20260803","cell_id":"predictive_residual_processing__futurism_foresight","portfolio_valid":true,"proposal_assessments":[{"proposal_index":1,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Operationally specifies the prediction target, versioned scenario-driver model, structured comparator, precision-weighted residual channel, reconstruction, separate update review, synchronization, raw audits, protected bypasses, fallback, budget, authority, falsifiers, and evidence test. It applies the archetype to repetitive distributed horizon-scanning packets."},{"proposal_index":2,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Defines participant-proposition predictions, active confirmation or correction, complete-response reconstruction, explicit missingness, reviewed model updates, version control, from-scratch audits, full-round resynchronization, protected full responses, and fallback. Its affected problem is repeated Delphi elicitation and its principal causal risk is prediction-induced anchoring."},{"proposal_index":3,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Implements a pre-action forward model that predicts an organization's observable echoes, compares them with retained external observations, and routes the independence-qualified or unexpected residual. Attribution uncertainty, reconstruction, separate learning, independent over-cancellation audits, protected criticism, and full-source fallback are operationally specified."},{"proposal_index":4,"complete":true,"causally_faithful":true,"materially_distinct":true,"reason":"Specifies frozen inject-response models, structured observations, uncertainty-weighted residuals, hierarchical propagation, timeline reconstruction, debrief actions, separate model review, cross-observer and raw-record audits, phase resynchronization, safety bypasses, and rollback. It targets evaluation of simulated coordination behavior rather than evidence intake or belief elicitation."}],"pairwise_assessments":[{"proposal_a":1,"proposal_b":2,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 1 compresses distributed environmental driver assessments into scenario-relative evidence residuals for a central scanning queue; proposal 2 elicits longitudinal expert judgments as corrections to participant-specific predicted responses, with active confirmation and anchoring audits."},{"proposal_a":1,"proposal_b":3,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 1 addresses routine external-report saturation by transmitting deviations from expected driver state; proposal 3 addresses reflexive evidentiary contamination by predicting and cancelling the observable consequences of the focal organization's own actions so unexplained or independent evidence remains."},{"proposal_a":1,"proposal_b":4,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 1 reallocates attention across live horizon-scanning packets using driver-state residual exchange; proposal 4 reconstructs and diagnoses behavior inside a bounded tabletop using inject-response residuals that propagate through team, coordination, and strategic layers."},{"proposal_a":2,"proposal_b":3,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 2 reduces repetitive questionnaire entry by encoding changes in each expert's judgment; proposal 3 models action-caused environmental echoes to prevent endogenous observations from being mistaken for independent future evidence."},{"proposal_a":2,"proposal_b":4,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 2 reconstructs stated forecasts across Delphi rounds from participant-specific baselines and edits; proposal 4 reconstructs observed simulated actions, omissions, timing, and handoffs from inject-response predictions and hierarchical behavioral errors."},{"proposal_a":3,"proposal_b":4,"same_problem":false,"same_intervention":false,"independent_opportunity":true,"key_difference":"Proposal 3 uses an efference-copy-style model for causal-boundary attribution in real external scanning; proposal 4 uses a hierarchical prediction-error stack to diagnose coordination and model failures within a designed exercise."}],"replacement_indices":[],"rationale":"All four proposals are operationally complete and preserve the archetype's governed prediction-comparison-residual-reconstruction-update loop, including synchronization, independent raw-state checking, safety bypasses, and full-signal fallback. Their shared archetypal structure does not collapse them into variants of one opportunity: they affect four different operational problems and use independently adoptable interventions with distinct causal paths—external evidence-queue compression, longitudinal expert-response elicitation, self-generated echo cancellation, and hierarchical exercise evaluation. All six pairs therefore satisfy problem, intervention, and causal-path independence."}