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nhra_gt.agents.negotiation

Negotiation and Debate Loop logic.

Models multi-round interactions between agents to reach consensus or deadlock.

Classes

DebateLoop

Manages a structured negotiation between two agents.

Source code in src/nhra_gt/agents/negotiation.py 
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class DebateLoop:
    """
    Manages a structured negotiation between two agents.
    """

    def __init__(self, agent_a: Agent, agent_b: Agent, max_rounds: int = 3):
        self.agent_a = agent_a
        self.agent_b = agent_b
        self.max_rounds = max_rounds

    def negotiate(self, state: State, params: Params, rng: Any) -> tuple[dict[str, Any], list[str]]:
        """
        Runs the debate loop and returns final strategies and the transcript.
        """
        transcript = []
        transcript.append("--- BEGIN NEGOTIATION ---")

        # Round 1: Initial Positions
        move_a = self.agent_a.decide(state, params, rng)
        move_b = self.agent_b.decide(state, params, rng)

        transcript.append(f"Agent A ({move_a.get('RATIONALE', 'No rationale')})")
        transcript.append(f"Agent B ({move_b.get('RATIONALE', 'No rationale')})")

        # Subsequent rounds would involve 'persuasion' if we had a full LLM loop
        # For now, we resolve the joint move using the standard combination rules

        final_strategies = {}
        # Simple merging (State usually takes precedence on internal ops, Cth on funding terms)
        final_strategies.update(move_a)
        final_strategies.update(move_b)

        transcript.append("--- END NEGOTIATION ---")
        return final_strategies, transcript

Functions

negotiate(state, params, rng)

Runs the debate loop and returns final strategies and the transcript.

Source code in src/nhra_gt/agents/negotiation.py 
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def negotiate(self, state: State, params: Params, rng: Any) -> tuple[dict[str, Any], list[str]]:
    """
    Runs the debate loop and returns final strategies and the transcript.
    """
    transcript = []
    transcript.append("--- BEGIN NEGOTIATION ---")

    # Round 1: Initial Positions
    move_a = self.agent_a.decide(state, params, rng)
    move_b = self.agent_b.decide(state, params, rng)

    transcript.append(f"Agent A ({move_a.get('RATIONALE', 'No rationale')})")
    transcript.append(f"Agent B ({move_b.get('RATIONALE', 'No rationale')})")

    # Subsequent rounds would involve 'persuasion' if we had a full LLM loop
    # For now, we resolve the joint move using the standard combination rules

    final_strategies = {}
    # Simple merging (State usually takes precedence on internal ops, Cth on funding terms)
    final_strategies.update(move_a)
    final_strategies.update(move_b)

    transcript.append("--- END NEGOTIATION ---")
    return final_strategies, transcript