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"""
consensus_verification.py
A deterministic model for consensus emergence in multi-agent systems.
"""
import hashlib
import numpy as np
from dataclasses import dataclass
from typing import Dict, List
@dataclass(frozen=True)
class Claim:
"""Represents a proposition with a unique identifier and a clarity score."""
identifier: str # Hash of the canonical statement
clarity: float # 0.0 (ambiguous) to 1.0 (clear)
class ConsensusEngine:
"""
Simulates a network of agents converging on belief states through
iterative local updates.
"""
def __init__(self, agent_count: int = 10):
self.agent_count = agent_count
self.agents = [{"id": i, "beliefs": {}} for i in range(agent_count)]
def process(self, claims: List[Claim]) -> Dict[str, float]:
"""
Executes the consensus process.
Returns a mapping from each claim identifier to its final acceptance ratio.
"""
# 1. Initialize agent beliefs stochastically based on claim clarity.
for agent in self.agents:
for claim in claims:
accept_prob = 0.1 + (0.9 * claim.clarity)
agent["beliefs"][claim.identifier] = 1 if np.random.random() < accept_prob else 0
# 2. Iterate: agents adjust beliefs towards network average.
for _ in range(5):
network_avg = self._average_beliefs(claims)
for agent in self.agents:
for claim in claims:
current = agent["beliefs"][claim.identifier]
social_influence = network_avg[claim.identifier] - current
agent["beliefs"][claim.identifier] = np.clip(current + social_influence * 0.3, 0, 1)
# 3. Calculate and return final consensus.
final_state = {}
for claim in claims:
total_acceptance = sum(a["beliefs"][claim.identifier] for a in self.agents)
final_state[claim.identifier] = round(total_acceptance / self.agent_count, 3)
return final_state
def _average_beliefs(self, claims: List[Claim]) -> Dict[str, float]:
avg = {}
for claim in claims:
total = sum(a["beliefs"].get(claim.identifier, 0) for a in self.agents)
avg[claim.identifier] = total / self.agent_count
return avg
def run_demonstration():
"""
Demonstrates the model's convergence on a small set of claims.
"""
# Define claims with varying clarity.
claim_data = [
("Water boils at 100°C at sea level.", 0.99),
("Democracy requires informed participation.", 0.75),
("Abstract concepts influence material outcomes.", 0.40),
]
claims = [
Claim(identifier=hashlib.sha256(text.encode()).hexdigest()[:16], clarity=score)
for text, score in claim_data
]
print("Claims:")
for c in claims:
print(f" ID:{c.identifier} | Clarity:{c.clarity}")
# Run the engine.
engine = ConsensusEngine(agent_count=10)
result = engine.process(claims)
print("\nConsensus Results (Acceptance Ratio):")
for claim_id, ratio in result.items():
print(f" {claim_id}: {ratio}")
if __name__ == "__main__":
run_demonstration() |