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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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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