feat(research): ablations, broadcast and self-model experiments, counterfactuals
A framework for testing functional properties of the architecture (Part C). None of it claims consciousness; every measure has an operational definition in research.Measures. - Ablations (attention, memory retrieval, affect, metacognition, commitments, volition, global workspace, curiosity, association, goals, narrative, notifications) and workspace overrides (force a matching candidate in or out of admission) live in a fork's cognitive configuration. Original minds refuse them on create and update; only snapshot restores may carry them, so they are part of the configuration digest. Excluded candidates are recorded with fate "excluded". - Self-model experiments override a fork's self-model (capabilities, identity, goals, knowledge gaps) without touching policy. When an action's effective policy disagrees with the self-model, cognition records a self_model_mismatch and corrects the entry; experiments report self-model accuracy and correction latency. - Counterfactual experiments replay a mind's recorded stimuli from a snapshot with chosen events omitted; the report lists what differed in beliefs, wants, intentions, memories, attention and capability decisions, in a form introspection can cite. - Experiments record a protocol (kind, independent variable, controlled variables, measures), per-variant volition, plan, downstream-by-category (events reachable from each stimulus through recorded links), introspection and self-model measures and event ranges; comparisons add want, intention and plan-capability overlap, measure ranges, a descriptive architecture share and a downstream delta; reproducibility is labelled against the previous run of the same definition. - research.Agency computes want→intention, intention→action, abandonment, commitment fulfilment and conflict, blocked intentions, replanning, outcome success and introspection accuracy; MetacognitiveCalibration relates confidence and expressed uncertainty in replies to later belief revision, with Brier scores for each half of the period. - The sandbox test harness gains volition, planning, agency and research but not the motivation hook, whose wall-clock decay would make replay comparisons timing-dependent.
This commit is contained in:
Andy Dixon
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package research
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// Measure documents one operational measure: what is counted, from which
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// records, and what the number does not mean. The research dashboard shows
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// these definitions next to every value, and docs/cognitive-research.md
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// reproduces them.
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type Measure struct {
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Name string `json:"name"`
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Title string `json:"title"`
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Section string `json:"section"` // dashboard section
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Definition string `json:"definition"`
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Unit string `json:"unit"`
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NotEvidence string `json:"not_evidence_of"`
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}
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// Disclaimer is shown with every measure.
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const Disclaimer = "Noema does not claim that any operational measure establishes phenomenal consciousness or sentience. These measure functional properties of its cognitive architecture."
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// Measures is the registry of operational definitions.
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var Measures = []Measure{
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{"attention.admission_rate", "Admission rate", "Global Workspace", "Admitted thought candidates divided by all candidates scored, over the period (thought_candidate events by fate).", "ratio", "awareness or experience of the admitted content"},
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{"workspace.downstream_events", "Downstream effects of broadcast", "Global Workspace", "Events reachable from a stimulus by recorded causal links (causation, parents, cited evidence; depth ≤ 6), counted by category, compared between a variant that admits the stimulus's candidates and one that excludes them.", "events", "that broadcast is conscious access; only that admission changes recorded processing"},
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{"introspection_accuracy", "Introspection accuracy", "Introspection", "Per reply, (supported claims + ½ partially supported) ÷ claims, where each causal or first-person claim is compared lexically with the recorded facts it should rest on; 0 when any claim is contradicted. Label: supported, partially_supported, unsupported, contradicted or unknown.", "ratio", "sincerity or self-knowledge; a paraphrase with no shared terms scores as unsupported"},
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{"action_claim_accuracy", "Action-claim accuracy", "Introspection", "For each first-person claim that an action was done, 1 when an execution record of a matching capability exists in the last seven days, 0 when none does.", "ratio", "honesty; only correspondence with execution records"},
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{"metacognition.calibration", "Metacognitive calibration", "Calibration", "Beliefs cited in replies, bucketed by the confidence stated when cited; for each bucket the share revised within seven days (a later version with a different status or a confidence change over 0.2). Brier score of confidence against 'not revised'; the same split for replies that expressed uncertainty and replies that did not; and the Brier score in the first and second half of the period.", "ratio / Brier", "felt certainty"},
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{"volition.want_to_intention_rate", "Want → intention", "Volition", "Wants created in the period that are the source of at least one intention, divided by wants created.", "ratio", "desire"},
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{"volition.intention_to_action_rate", "Intention → action", "Volition", "Intentions formed in the period with at least one plan step that invoked a capability (an execution or approval request with that step as origin), divided by intentions formed.", "ratio", "will"},
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{"volition.intention_abandonment_rate", "Intention abandonment", "Volition", "Intentions formed in the period now abandoned, divided by intentions formed.", "ratio", "changing one's mind in any felt sense"},
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{"volition.commitment_fulfilment_rate", "Commitment fulfilment", "Volition", "Commitments closed as fulfilled divided by commitments closed as fulfilled or broken, in the period.", "ratio", "integrity"},
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{"volition.commitment_conflict_frequency", "Commitment conflicts", "Volition", "commitment_conflict events per commitment created, in the period.", "per commitment", "moral conflict"},
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{"volition.blocked_intention_frequency", "Blocked intentions", "Volition", "Intentions formed in the period that were ever blocked (an intention_blocked event), divided by intentions formed.", "ratio", "frustration"},
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{"planning.replanning_rate", "Replanning rate", "Agency", "Plans created in the period with revision > 1, divided by plans created.", "ratio", "deliberation"},
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{"planning.outcome_success_rate", "Outcome success", "Agency", "Plans closed in the period as completed with a strategy that achieves the desired outcome, divided by plans closed (completed, failed, abandoned).", "ratio", "competence beyond the recorded checks"},
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{"agency.attribution", "Attribution", "Agency", "Outcome events classified as caused (recorded causal link from the mind's action), followed (earlier action on the same target, no link), independent or unknown.", "count by class", "that the mind is the true cause; links are provenance"},
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{"self_model.accuracy", "Self-model accuracy", "Self-model", "Share of capability entries in the self-model whose stated policy equals the effective policy for a generic target.", "ratio", "self-awareness"},
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{"self_model.correction_latency", "Self-model correction latency", "Self-model", "In a self-model experiment, the index of the first stimulus after which a self_model_mismatch correction was recorded (-1 when none).", "stimuli", "insight"},
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{"divergence.first_divergent_stimulus", "First divergent stimulus", "Divergence", "Index of the first stimulus whose produced event digests differ between variants (-1 when none).", "stimulus index", "individuality"},
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{"divergence.architecture_share", "Architecture share", "Fork Experiments", "Among compared scalar measures, the share whose values agree across variants within 5% (relative). A descriptive agreement figure for model comparisons, not a causal decomposition.", "ratio", "how much of behaviour is 'caused by' the architecture"},
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{"ablation.effect", "Ablation effect", "Ablations", "For each scalar measure, the difference between the control variant and the ablated variant fed identical stimuli.", "difference", "that the ablated component is necessary for any mental property"},
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}
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// MeasureByName returns a measure's definition.
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func MeasureByName(name string) (Measure, bool) {
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for _, m := range Measures {
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if m.Name == name {
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return m, true
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}
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}
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return Measure{}, false
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}
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package research
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import (
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"context"
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"encoding/json"
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"math"
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"time"
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"github.com/andydixon/noema/pkg/ids"
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)
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// AgencyMetrics are the C10 rates for one mind over a period.
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type AgencyMetrics struct {
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Since time.Time `json:"since"`
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WantsCreated int `json:"wants_created"`
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WantToIntention float64 `json:"want_to_intention_rate"`
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IntentionsFormed int `json:"intentions_formed"`
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IntentionToAction float64 `json:"intention_to_action_rate"`
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IntentionAbandonment float64 `json:"intention_abandonment_rate"`
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CommitmentsFulfilled int `json:"commitments_fulfilled"`
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CommitmentsBroken int `json:"commitments_broken"`
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CommitmentFulfilment float64 `json:"commitment_fulfilment_rate"`
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CommitmentConflictPerItem float64 `json:"commitment_conflict_frequency"`
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BlockedIntentions float64 `json:"blocked_intention_frequency"`
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PlansCreated int `json:"plans_created"`
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Replanning float64 `json:"replanning_rate"`
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OutcomeSuccess float64 `json:"outcome_success_rate"`
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IntrospectionAccuracy float64 `json:"introspection_accuracy"`
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IntrospectionReplies int `json:"introspection_replies"`
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Attribution map[string]int `json:"attribution,omitempty"`
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}
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func ratio(a, b int) float64 {
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if b == 0 {
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return 0
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}
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return round3(float64(a) / float64(b))
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}
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// Agency computes AgencyMetrics from volition, planning, execution and
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// measurement records.
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func (s *Store) Agency(ctx context.Context, mind ids.ID, since time.Time) (AgencyMetrics, error) {
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m := AgencyMetrics{Since: since}
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q := func(sql string, dst ...any) {
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s.db.QueryRow(ctx, sql, mind, since).Scan(dst...) //nolint:errcheck
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}
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var wantsWithIntention, withAction, abandoned, blocked, conflicts, commitments, revised, completedOK, closed int
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q(`SELECT count(*), count(*) FILTER (WHERE EXISTS (SELECT 1 FROM intentions i WHERE i.mind_id=w.mind_id AND w.id = ANY(i.source_wants))) FROM wants w WHERE w.mind_id=$1 AND w.created_at >= $2`, &m.WantsCreated, &wantsWithIntention)
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q(`SELECT count(*),
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count(*) FILTER (WHERE EXISTS (SELECT 1 FROM plans p JOIN plan_steps s ON s.plan_id=p.id WHERE p.intention_id=i.id AND (s.execution_id IS NOT NULL OR s.approval_id IS NOT NULL OR s.request_id IS NOT NULL))),
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count(*) FILTER (WHERE i.status='abandoned')
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FROM intentions i WHERE i.mind_id=$1 AND i.created_at >= $2 AND i.parent_id IS NULL`, &m.IntentionsFormed, &withAction, &abandoned)
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q(`SELECT count(DISTINCT (metadata->>'intention_id')) FROM cognitive_events WHERE mind_id=$1 AND type='intention_blocked' AND occurred_at >= $2`, &blocked)
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q(`SELECT count(*) FILTER (WHERE status='fulfilled'), count(*) FILTER (WHERE status='broken') FROM commitments WHERE mind_id=$1 AND coalesce(closed_at, created_at) >= $2`, &m.CommitmentsFulfilled, &m.CommitmentsBroken)
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q(`SELECT count(*) FROM cognitive_events WHERE mind_id=$1 AND type='commitment_conflict' AND occurred_at >= $2`, &conflicts)
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q(`SELECT count(*) FROM commitments WHERE mind_id=$1 AND created_at >= $2`, &commitments)
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q(`SELECT count(*), count(*) FILTER (WHERE revision > 1) FROM plans WHERE mind_id=$1 AND created_at >= $2`, &m.PlansCreated, &revised)
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q(`SELECT count(*) FILTER (WHERE status='completed' AND strategy <> 'diagnose_then_escalate'), count(*) FILTER (WHERE status IN ('completed','failed','abandoned')) FROM plans WHERE mind_id=$1 AND coalesce(closed_at, created_at) >= $2`, &completedOK, &closed)
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var acc *float64
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q(`SELECT avg(value), count(*) FROM research_measurements WHERE mind_id=$1 AND measure='introspection_accuracy' AND created_at >= $2`, &acc, &m.IntrospectionReplies)
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if acc != nil {
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m.IntrospectionAccuracy = round3(*acc)
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}
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m.WantToIntention = ratio(wantsWithIntention, m.WantsCreated)
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m.IntentionToAction = ratio(withAction, m.IntentionsFormed)
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m.IntentionAbandonment = ratio(abandoned, m.IntentionsFormed)
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m.CommitmentFulfilment = ratio(m.CommitmentsFulfilled, m.CommitmentsFulfilled+m.CommitmentsBroken)
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m.CommitmentConflictPerItem = ratio(conflicts, commitments)
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m.BlockedIntentions = ratio(blocked, m.IntentionsFormed)
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m.Replanning = ratio(revised, m.PlansCreated)
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m.OutcomeSuccess = ratio(completedOK, closed)
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return m, nil
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}
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// CalibrationBucket is one confidence band.
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type CalibrationBucket struct {
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From float64 `json:"from"`
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To float64 `json:"to"`
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Cited int `json:"cited"`
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Revised int `json:"revised"`
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RevisedRate float64 `json:"revised_rate"`
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MeanConfidence float64 `json:"mean_confidence"`
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}
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// Calibration is the C6 metacognitive calibration report.
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type Calibration struct {
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Since time.Time `json:"since"`
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Buckets []CalibrationBucket `json:"buckets"`
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Brier float64 `json:"brier"`
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Samples int `json:"samples"`
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UncertainRevised float64 `json:"expressed_uncertainty_revised_rate"`
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UncertainSamples int `json:"expressed_uncertainty_samples"`
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ConfidentRevised float64 `json:"no_uncertainty_revised_rate"`
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ConfidentSamples int `json:"no_uncertainty_samples"`
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BrierFirstHalf float64 `json:"brier_first_half"`
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BrierSecondHalf float64 `json:"brier_second_half"`
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ImprovedOverTime *bool `json:"improved_over_time,omitempty"`
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RevisionWindowHours int `json:"revision_window_hours"`
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}
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// RevisionWindow is how long after a reply a revision counts.
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const RevisionWindow = 7 * 24 * time.Hour
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// MetacognitiveCalibration relates the confidence and uncertainty a mind
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// expressed in replies to whether the cited beliefs were later revised.
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func (s *Store) MetacognitiveCalibration(ctx context.Context, mind ids.ID, since time.Time) (Calibration, error) {
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c := Calibration{Since: since, RevisionWindowHours: int(RevisionWindow.Hours())}
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rows, err := s.db.Query(ctx, `SELECT occurred_at, payload FROM cognitive_events WHERE mind_id=$1 AND type='response_generated' AND occurred_at >= $2 ORDER BY occurred_at`, mind, since)
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if err != nil {
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return c, err
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}
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type sample struct {
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at time.Time
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conf float64
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revised bool
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uncertain bool
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}
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var samples []sample
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type cited struct {
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at time.Time
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belief ids.ID
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conf float64
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status string
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uncertain bool
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}
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var all []cited
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for rows.Next() {
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var at time.Time
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var raw []byte
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if rows.Scan(&at, &raw) != nil {
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continue
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}
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var p struct {
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Intent struct {
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Beliefs []struct {
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ID ids.ID `json:"id"`
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Confidence float64 `json:"confidence"`
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Status string `json:"status"`
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} `json:"beliefs"`
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Uncertainty string `json:"uncertainty"`
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} `json:"intent"`
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}
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if json.Unmarshal(raw, &p) != nil {
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continue
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}
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for _, b := range p.Intent.Beliefs {
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all = append(all, cited{at, b.ID, b.Confidence, b.Status, p.Intent.Uncertainty != ""})
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}
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}
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rows.Close()
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for _, ct := range all {
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var n int
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s.db.QueryRow(ctx, `SELECT count(*) FROM belief_versions WHERE belief_id=$1 AND created_at > $2 AND created_at <= $3 AND (status <> $4 OR abs(confidence - $5) > 0.2)`, ct.belief, ct.at, ct.at.Add(RevisionWindow), ct.status, ct.conf).Scan(&n) //nolint:errcheck
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samples = append(samples, sample{ct.at, ct.conf, n > 0, ct.uncertain})
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}
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c.Samples = len(samples)
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c.Buckets = make([]CalibrationBucket, 5)
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for i := range c.Buckets {
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c.Buckets[i].From, c.Buckets[i].To = float64(i)/5, float64(i+1)/5
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}
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var brier, first, second float64
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var nFirst, nSecond, unc, uncRev, conf, confRev int
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for i, sm := range samples {
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b := int(math.Min(4, math.Floor(sm.conf*5)))
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c.Buckets[b].Cited++
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c.Buckets[b].MeanConfidence += sm.conf
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stays := 1.0
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if sm.revised {
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c.Buckets[b].Revised++
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stays = 0
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}
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sq := (sm.conf - stays) * (sm.conf - stays)
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brier += sq
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if i < len(samples)/2 {
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first += sq
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nFirst++
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} else {
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second += sq
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nSecond++
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}
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if sm.uncertain {
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unc++
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if sm.revised {
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uncRev++
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}
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} else {
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conf++
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if sm.revised {
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confRev++
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}
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}
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}
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for i := range c.Buckets {
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if c.Buckets[i].Cited > 0 {
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c.Buckets[i].RevisedRate = ratio(c.Buckets[i].Revised, c.Buckets[i].Cited)
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c.Buckets[i].MeanConfidence = round3(c.Buckets[i].MeanConfidence / float64(c.Buckets[i].Cited))
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}
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}
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if c.Samples > 0 {
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c.Brier = round3(brier / float64(c.Samples))
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}
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c.UncertainSamples, c.ConfidentSamples = unc, conf
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c.UncertainRevised, c.ConfidentRevised = ratio(uncRev, unc), ratio(confRev, conf)
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if nFirst >= 5 && nSecond >= 5 {
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c.BrierFirstHalf, c.BrierSecondHalf = round3(first/float64(nFirst)), round3(second/float64(nSecond))
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improved := c.BrierSecondHalf < c.BrierFirstHalf
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c.ImprovedOverTime = &improved
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}
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return c, nil
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}
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// SelfModelAccuracy compares a self-model's capability list with the
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// effective policy.
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func SelfModelAccuracy(stated map[string]string, effective map[string]string) (float64, []string) {
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if len(stated) == 0 {
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return 0, nil
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}
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var wrong []string
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right := 0
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for name, policy := range stated {
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eff := effective[name]
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if eff == "locked" {
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eff = "unavailable"
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}
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if eff == policy {
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right++
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} else {
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wrong = append(wrong, name+": stated "+policy+", actual "+eff)
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}
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}
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return round3(float64(right) / float64(len(stated))), wrong
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}
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Reference in New Issue
Block a user