Build Consensus

Collective agreement across distributed agents w/o central authority — scout advocacy, threshold quorum sensing, commit dynamics from honeybee swarm decisions.

Use When

• Group must decide between many options w/o designated leader
• Centralized decision = bottleneck or single point of failure
• Stakeholders diff info/perspectives must be integrated
• Past decisions suffered groupthink (premature conv) or analysis paralysis (no conv)
• Designing auto systems needing consensus (distributed DBs, multi-agent AI)
• Complements coordinate-swarm when coordination needs explicit collective decisions

In

• Required: Decision (binary, select from N, param set)
• Required: Participating agents (team, services, voters)
• Optional: Known options w/ prelim quality assessments
• Optional: Urgency (time budget)
• Optional: Acceptable err rate (group occasionally pick 2nd-best?)
• Optional: Current failure mode (groupthink, deadlock, flip-flop)

Do

Step 1: Generate Proposals — Independent Scouting

Decision space explored before advocacy begins.

1. Assign scouts to independently explore:
   • Each scout evaluates w/o knowing others' findings
   • Independent eval prevents early herding → popular-but-mediocre
   • Scout count: min 3 per serious option (reliability)
2. Scouts produce structured assessments:
   • Option ID
   • Quality score (normalized 0-100 or categorical: poor/fair/good/excellent)
   • Key strengths + risks
   • Confidence (how thoroughly evaluated?)
3. Aggregate reports w/o filter — all above min quality enter advocacy

→ Independently evaluated proposals w/ scores + assessments. No option eliminated by single evaluator; perspective diversity preserved.

If err: Scouts converge on same option w/o independent eval → scouting not truly independent. Rerun w/ explicit info barriers. Too many survive → raise min threshold. Too few → lower or add scouts.

Step 2: Advocacy Dynamics (Waggle Dance)

Scouts advocate preferred options, intensity proportional to quality.

1. Each scout advocates top-rated:
   • Intensity proportional to quality (better → more vigorous)
   • Public — all observe
   • Present evidence + quality, not just pref
2. Uncommitted observe + evaluate:
   • Follow up by inspecting independently
   • Own inspection confirms → join advocacy
   • Inspection shows lower quality → don't join
3. Cross-inspection dynamics:
   • Weaker advocates naturally lose followers as agents verify
   • Stronger gain through confirmed quality
   • Self-correcting: exaggerated advocacy fails verification

Advocacy Dynamics:
┌─────────────────────────────────────────────────────────┐
│ Scout A advocates Option 1 (quality 85) ──→ ◉◉◉◉◉     │
│ Scout B advocates Option 2 (quality 70) ──→ ◉◉◉        │
│ Scout C advocates Option 3 (quality 45) ──→ ◉           │
│                                                         │
│ Uncommitted agents inspect:                             │
│   Agent D inspects Option 1 → confirms → joins ◉◉◉◉◉◉  │
│   Agent E inspects Option 2 → confirms → joins ◉◉◉◉    │
│   Agent F inspects Option 3 → disagrees → inspects Opt 1│
│                               → confirms → joins ◉◉◉◉◉◉◉│
│                                                         │
│ Over time: Option 1 advocacy grows, Option 3 fades      │
└─────────────────────────────────────────────────────────┘

→ Advocacy for best option(s) grows as agents verify. Weaker fades. Group converges naturally w/o any agent dictating.

If err: No convergence (2 options neck-and-neck) → genuinely equivalent, proceed to quorum w/ either or tiebreaker. Converges too fast on mediocre → increase eval independence (more scouts, stricter barriers) + mandatory cross-inspection.

Step 3: Quorum Threshold + Commit

Commit threshold → collective action.

1. Set quorum:
   • Simple: 50% + 1
   • Important: 66-75%
   • Critical/irreversible: 80%+
   • Rule: higher stakes → higher quorum → slower but more reliable
2. Monitor commit accumulation:
   • Track # committed per option over time
   • Transparent (all see state)
   • No commit withdrawal mid-cycle (prevents oscillation)
3. Quorum reached:
   • Winning option = collective decision
   • Losers ack (no rogue agents)
   • Implement immediately — delay erodes commit

→ Clear quorum moment, enough agents independently committed. Legitimate because emerged from independent eval, not authority.

If err: Quorum never reached in time → escalate Step 4. Reached but agents unhappy → advocacy too short, committed w/o adequate eval. Wrong consensus (discovered after) → independent scouting insufficient, increase scout diversity + eval thoroughness next cycle.

Step 4: Deadlock Resolution

Break gridlock when natural process stalls.

1. Diagnose type:
   • Genuine tie: Equally good → flip coin; delay cost exceeds picking "wrong" equal
   • Info deficit: Can't eval well → invest more scouting before re-advocacy
   • Faction: Entrenched subgroups refuse to cross-inspect → mandatory rotation, advocates inspect opposing
   • Option proliferation: Too many fragment commit → eliminate bottom 50%, re-advocate
2. Apply resolution:
   • Tie: random or merge if compatible
   • Deficit: time-boxed scouting extension
   • Faction: forced cross-inspection round
   • Proliferation: ranked elimination tournament
3. After res, reset quorum clock, re-run Step 3

→ Deadlock resolved via intervention. Visible + accepted as fair process even if indiv preferred diff outcome.

If err: Deadlocks recur on same decision → framing wrong. Step back: decomposable into smaller independent decisions? Scope reduction? "Try both and see"? Sometimes best consensus = "time-boxed experiment".

Step 5: Consensus Quality

Eval whether process produced good decision, not just decision.

1. Post-decision:
   • Winning option independently verified by ≥N agents?
   • Speed appropriate (not too fast/groupthink, not too slow/paralysis)?
   • Process surfaced info missed by single decider?
   • Agents committed to impl or merely compliant?
2. Health metrics:
   • Time to quorum: decreasing = learning; increasing = complexity/dysfunction
   • Scout-to-commit ratio: scouting per commit. High = difficult or low trust
   • Post-decision regret rate: how often group wishes diff?
3. Feed learnings back:
   • Adjust thresholds based on importance + past accuracy
   • Adjust scout count based on complexity
   • Adjust time budgets based on historical time-to-quorum

→ Feedback loop improves quality over time. Group learns to scout better, advocate honestly, commit confidently.

If err: Poor metrics (high regret, slow) → audit for structural fails: insufficient scout diversity, advocacy w/o verification, thresholds too low. Rebuild failing stage vs overhauling whole.

Check

• Proposals via independent scouting (no herding)
• Advocacy proportional to assessed quality
• Uncommitted verified advocated options
• Quorum appropriate for importance
• Quorum reached + implemented promptly
• Deadlock mechanism available (even if unused)
• Post-decision quality assessment done

Traps

• Skip independent scouting: Jump to advocacy → groupthink. Consensus quality = eval quality
• Equal advocacy, unequal options: Same advocacy regardless of quality → random selection. Must be proportional
• Commit withdrawal: Un-commit → oscillation. Once committed in cycle, stay until resolves
• Consensus = unanimity confusion: Consensus = sufficient agreement, not total. Waiting 100% = permanent deadlock
• Ignore losing side: Losers have info group needs. Concerns should inform impl even if don't block

→

• coordinate-swarm — foundational coordination framework supporting signal-based consensus
• defend-colony — collective defense often needs rapid consensus under threat
• scale-colony — consensus mechanisms adapt when group size changes significantly
• dissolve-form — morphic controlled dismantling; consensus before dissolution critical
• plan-sprint — sprint planning involves team consensus on scope
• conduct-retrospective — retrospectives = consensus-building about process improvement
• build-coherence — AI self-app variant; maps bee democracy to single-agent multi-path reasoning