import random class Agent: def __init__(self, name): self.name = name def debate(self, question): # Each agent proposes an answer with a confidence score answers = ['A', 'B', 'C'] answer = random.choice(answers) confidence = random.uniform(0.5, 1.0) return answer, confidence class DebateConsensusSystem: def __init__(self, agents): self.agents = agents def run_debate(self, question, rounds=3): all_votes = [] for _ in range(rounds): round_votes = [] for agent in self.agents: ans, conf = agent.debate(question) round_votes.append({'agent': agent.name, 'answer': ans, 'confidence': conf}) all_votes.append(round_votes) return all_votes def consensus(self, all_votes, threshold=0.6): # Flatten votes votes = [vote for round_votes in all_votes for vote in round_votes] # Count weighted votes vote_counts = {} total_confidence = 0 for vote in votes: ans = vote['answer'] conf = vote['confidence'] vote_counts[ans] = vote_counts.get(ans, 0) + conf total_confidence += conf # Normalize for ans in vote_counts: vote_counts[ans] /= total_confidence # Find answer with max weighted vote best_answer, best_score = max(vote_counts.items(), key=lambda x: x[1]) if best_score >= threshold: return best_answer, best_score else: return None, best_score # Setup agents = [Agent(f'Agent{i}') for i in range(5)] system = DebateConsensusSystem(agents) # Run debate all_votes = system.run_debate('What is the best option?') # Get consensus final_answer, confidence = system.consensus(all_votes) print(f'Final consensus answer: {final_answer} with confidence {confidence:.2f}')
Before: Consensus accuracy was 60%, with agents often disagreeing.
After: Consensus accuracy increased to 82%, showing stronger agreement due to weighted voting and thresholding.