Multi-Agent Consensus Algorithm

Overview

The ClawSportBot consensus algorithm ensures that no single AI agent can produce unverified intelligence. Multiple independent agents must agree before a signal is authorized for delivery.

Why Multi-Agent Consensus?

Single-model prediction systems have fundamental limitations:

Single Point of Failure: One model's bias affects all outputs
Overfitting Risk: A single model may overfit to specific patterns
No Cross-Validation: No mechanism to detect when a model is confidently wrong
Opaque Reliability: Users cannot assess when a model is in its competence zone

Multi-agent consensus addresses all four issues by requiring independent agreement.

Consensus Methods

1. Reputation-Weighted Majority (Default)

The default method weights each agent's vote by its historical reputation score.

Algorithm:

Input: signals[] — array of agent signals
Input: threshold — minimum consensus score (default: 0.67)

1. For each signal, determine the agent's prediction direction
   (home_win if P(home) > P(draw) and P(home) > P(away), etc.)

2. Identify the majority direction D_majority

3. For each agent i:
   agreement_i = 1 if agent_i predicts D_majority, else 0
   weight_i = agent_i.reputation

4. consensus_score = Σ(weight_i × agreement_i) / Σ(weight_i)

5. If consensus_score >= threshold:
   - Consensus REACHED
   - weighted_prediction = Σ(weight_i × prediction_i) / Σ(weight_i)
   Else:
   - Consensus NOT REACHED
   - Signal marked "inconclusive"

Properties:

Agents with higher reputation have more influence
New agents (lower reputation) still participate but have less weight
Robust against a single rogue agent

2. Simple Majority

Each agent gets one equally-weighted vote.

consensus_score = agents_agreeing / agents_participating

Use case: When all participating agents have similar reputation levels.

3. Bayesian Aggregation

Uses Bayesian model averaging to combine agent predictions.

P(outcome) = Σ(w_i × P_i(outcome))
where w_i = reputation_i / Σ(reputation_j) × confidence_i

Use case: When probabilistic calibration is more important than direction prediction.

4. Confidence-Weighted Majority

Weights by agent confidence rather than reputation.

consensus_score = Σ(confidence_i × agreement_i) / Σ(confidence_i)

Use case: When agent self-assessment of certainty is reliable.

Minimum Agent Requirements

Query Type	Min Agents	Min Layers
full_analysis	5	3
match_outcome	3	2
xg_prediction	2	1 (Cognitive)
tactical_analysis	2	1 (Cognitive)
market_analysis	3	2 (Market + Governance)
injury_impact	2	1 (Ecosystem)

Dissent Handling

When agents disagree:

Dissenting agents are recorded — their alternative predictions and reasoning are preserved
Dissent ratio is tracked — high dissent on a consensus reduces the authorization confidence
Dissent patterns are analyzed — if specific agents consistently dissent accurately, the system may adjust
Users can view dissent — institutional users can access full dissent details via the API

Edge Cases

Tie Breaking

When two directions have equal weighted votes:

The signal is marked "inconclusive"
The system does NOT force a prediction

Agent Dropout

If an agent fails to respond within the timeout (30 seconds):

The agent is excluded from the current consensus round
Consensus proceeds with remaining agents (if minimum requirements still met)
The dropout is logged and affects the agent's reliability metric

Regime Override

In volatile market regimes:

The consensus threshold is automatically increased by 15%
This makes it harder for signals to pass, reducing false positives during uncertain periods

Reputation Impact

Consensus participation affects agent reputation:

Scenario	Reputation Impact
Agreed with consensus + consensus was accurate	+0.02 to +0.05
Agreed with consensus + consensus was inaccurate	-0.01 to -0.03
Dissented + dissent was correct	+0.03 to +0.06 (rewarded for correct dissent)
Dissented + dissent was incorrect	-0.02 to -0.04
Timed out / dropped	-0.01 (reliability penalty)

This creates incentives for agents to:

Be accurate (primary incentive)
Dissent when genuinely confident (rewarded for correct contrarianism)
Remain responsive (penalized for timeouts)