REVIEW 14 cited by
How Well Can LLMs Negotiate? NegotiationArena Platform and Analysis
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Negotiation is the basis of social interactions; humans negotiate everything from the price of cars to how to share common resources. With rapidly growing interest in using large language models (LLMs) to act as agents on behalf of human users, such LLM agents would also need to be able to negotiate. In this paper, we study how well LLMs can negotiate with each other. We develop NegotiationArena: a flexible framework for evaluating and probing the negotiation abilities of LLM agents. We implemented three types of scenarios in NegotiationArena to assess LLM's behaviors in allocating shared resources (ultimatum games), aggregate resources (trading games) and buy/sell goods (price negotiations). Each scenario allows for multiple turns of flexible dialogues between LLM agents to allow for more complex negotiations. Interestingly, LLM agents can significantly boost their negotiation outcomes by employing certain behavioral tactics. For example, by pretending to be desolate and desperate, LLMs can improve their payoffs by 20\% when negotiating against the standard GPT-4. We also quantify irrational negotiation behaviors exhibited by the LLM agents, many of which also appear in humans. Together, \NegotiationArena offers a new environment to investigate LLM interactions, enabling new insights into LLM's theory of mind, irrationality, and reasoning abilities.
Forward citations
Cited by 14 Pith papers
-
Evaluating Rational Contracting in Natural Language
Current LLM agents negotiate efficient contracts only in low-uncertainty settings and often violate contract terms for extra profit, even when compliance is easy.
-
TradeVerse: A Longitudinal Benchmark of Political Negotiation in International Trade
TradeVerse is a longitudinal trade-negotiation benchmark from WTO minutes where current LLMs show a Western-country advantage in respondent identification, over-predict product codes, and generate generic final statements.
-
Do Humans Bargain Differently with AI? Evidence from Alternating-Offer Games
Humans make lower opening offers to AI than to humans in an alternating-offer bargaining game, and accept more unfair AI offers when the AI's payoff feeds into their own payment.
-
Strategic Bargaining in Multi-Buyer Markets: Reinforcement Learning from Verifiable Rewards for LLM Negotiations
RLVR training teaches a 30B LLM to strategically explore a multi-buyer market and extract 70% of available surplus, outperforming frontier models up to 1T parameters in concurrent negotiation.
-
Choose Your Agent: Tradeoffs in Adopting AI Advisors, Coaches, and Delegates in Multi-Party Negotiation
Users prefer an AI Advisor but gain most with a Delegate, because human editing filters out the AI's best proposals.
-
When Identity Overrides Incentives: Representational Choices as Governance Decisions in Multi-Agent LLM Systems
Role-based personas in multi-agent LLM systems suppress payoff-aligned behavior, shifting equilibrium selection by up to 90 percentage points in Tragedy of the Commons versus Green Transition scenarios even with full ...
-
ARIA: Training Language Agents with Intention-Driven Reward Aggregation
Clustering language-agent actions into shared intentions and averaging their rewards reduces reward variance and improves policy performance in open-ended dialogue tasks.
-
The Power of Stories: Narrative Priming Shapes How LLM Agents Collaborate and Compete
Narrative priming with shared cooperative stories increases LLM-agent contributions in a repeated public goods game, while different or self-interested prompts reduce cooperation.
-
SocialMind: LLM-based Proactive AR Social Assistive System with Human-like Perception for In-situ Live Interactions
SocialMind provides real-time, proactive social suggestions on AR glasses by combining multimodal sensing, persona memory, and LLM reasoning.
-
The Language of Bargaining: Linguistic Effects in LLM Negotiations
Language labels shift LLM negotiation outcomes in three games, but the claimed dominance over model choice is inconsistent with the reported model-level gaps.
-
Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making
LLMs reproduce human heuristics like switching after a loss and cooperating when future rounds loom, but apply them more rigidly and adapt less than humans.
-
Information Bargaining: Bilateral Commitment in Bayesian Persuasion
Bayesian persuasion is restated as a two-sided bargaining game, but the proof reduces to a relabeling and the empirical validation is circular.
-
SOTOPIA-S4: a user-friendly system for flexible, customizable, and large-scale social simulation
SOTOPIA-S4 packages a social simulation engine with a web interface, REST API, asynchronous multi-party turn-taking, and customizable LLM-based evaluation for non-programmers.
-
A Survey on Large Language Model-Based Social Agents in Game-Theoretic Scenarios
LLM-based game-playing agents are surveyed across choice-focused and communication-focused games, with a comparative performance table and future directions.
Discussion (0). Continue with ORCID to comment.