Bargaining is an important and unique part of negotiation between humans. As
LLM-driven agents learn to negotiate and act like real humans, how to evaluate
agents' bargaining abilities remains an open problem. For the first time, we
formally described the Bargaining task as an asymmetric incomplete information
game, defining the gains of the Buyer and Seller in multiple bargaining
processes. It allows us to quantitatively assess an agent's performance in the
Bargain task. We collected a real product price dataset, AmazonHistoryPrice,
and conducted evaluations of various LLM agents' bargaining abilities. We find
that playing a Buyer is much harder than a Seller, and increasing model size
can not effectively improve the Buyer's performance. To address the challenge,
we propose a novel approach called OG-Narrator that integrates a deterministic
Offer Generator to control the price range of Buyer's offers, and an LLM
Narrator to create natural language sentences for generated offers.
Experimental results show that OG-Narrator improves the buyer's deal rates from
26.67% to 88.88% and brings a ten times of multiplication of profits on all
baselines, even a model that has not been aligned.

描述并量化了 LLM 驱动代理在具有不完全信息的买卖任务中的议价能力，并通过提出一种集成了确定性商品生成器和自然语言生成器的新方法 OG-Narrator 来提高买家的交易成功率和利润。

LLM 的谈判能力评估：基准与买方增强方法

Measuring Bargaining Abilities of LLMs: A Benchmark and A  Buyer-Enhancement Method

Leveraging an established exercise in negotiation education, we build a novel
dataset for studying how the use of language shapes bilateral bargaining. Our
dataset extends existing work in two ways: 1) we recruit participants via
behavioral labs instead of crowdsourcing platforms and allow participants to
negotiate through audio, enabling more naturalistic interactions; 2) we add a
control setting where participants negotiate only through alternating, written
numeric offers.Despite the two contrasting forms of communication, we find that
the average agreed prices of the two treatments are identical. But when
subjects can talk, fewer offers are exchanged, negotiations finish faster, the
likelihood of reaching agreement rises, and the variance of prices at which
subjects agree drops substantially. We further propose a taxonomy of speech
acts in negotiation and enrich the dataset with annotated speech acts. We set
up prediction tasks to predict negotiation success and find that being reactive
to the arguments of the other party is advantageous over driving the
negotiation.

本文介绍了使用语言塑造双边谈判的新颖数据集及其内部的的语言功能元素，以及利用行为实验室中的参与者在音频中进行协商得出的结果，研究了不同通讯方式下的谈判结果，发现在能够说话的情况下，交换数量较少，谈判结束更快，达成协议的可能性更大，同意价格的方差显著降低，而在预测成功谈判方面，被动反应他方论点较主动牵引更优势。

谈判语言学

Language of Bargaining

In large scale collective decision making, social choice is a normative study
of how one ought to design a protocol for reaching consensus. However, in
instances where the underlying decision space is too large or complex for
ordinal voting, standard voting methods of social choice may be impractical.
How then can we design a mechanism - preferably decentralized, simple,
scalable, and not requiring any special knowledge of the decision space - to
reach consensus? We propose sequential deliberation as a natural solution to
this problem. In this iterative method, successive pairs of agents bargain over
the decision space using the previous decision as a disagreement alternative.
We describe the general method and analyze the quality of its outcome when the
space of preferences define a median graph. We show that sequential
deliberation finds a 1.208- approximation to the optimal social cost on such
graphs, coming very close to this value with only a small constant number of
agents sampled from the population. We also show lower bounds on simpler
classes of mechanisms to justify our design choices. We further show that
sequential deliberation is ex-post Pareto efficient and has truthful reporting
as an equilibrium of the induced extensive form game. We finally show that for
general metric spaces, the second moment of of the distribution of social cost
of the outcomes produced by sequential deliberation is also bounded.

本研究提出了顺序协商的方法作为一个分布式的，可扩展的机制来解决大规模集体决策的问题，并在中位图上证明了该方法的有效性。