Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
arXiv:2607.05863v1 Announce Type: new Abstract: Negotiation is a fundamental strategic interaction in management science, characterized by agents attempting to reach agreements while protecting private information, such as reservation costs and hidden valuations. A prevalent yet complex scenario involves a single seller negotiating concurrently with multiple buyers, each possessing heterogeneous, private budgets. In such settings, constrained by a limited number of communication turns, the seller must balance exploring the broader market to discover the highest valuation with concentrating suf
The rapid advancement and integration of large language models (LLMs) are leading to their application in complex strategic interactions like multi-party negotiations, requiring novel training methodologies.
This research outlines a pathway for LLMs to handle sophisticated bargaining scenarios, which is crucial for automating complex business and strategic decisions where private information and concurrent interactions are common.
LLMs move beyond simple conversational agents to become sophisticated negotiators capable of strategic interaction in multi-seller, multi-buyer markets, potentially influencing market efficiencies and pricing.
- · Businesses with complex supply chains
- · AI software providers
- · E-commerce platforms
- · Large language model developers
- · Human negotiation consultants
- · Traditional algorithmic trading systems
- · Less adaptable human sales teams
- · Inefficient marketplaces
Increased efficiency and potentially fairer outcomes in multi-buyer market negotiations due to LLM-driven agents.
Disruption of industries reliant on human negotiation expertise and the emergence of new AI-mediated market structures.
Potential for AI agents to collude or manipulate markets through advanced bargaining techniques, necessitating new regulatory frameworks.
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