Language of Bargaining
This work addresses the problem of understanding communication dynamics in negotiations for researchers and educators, but it is incremental as it builds on existing datasets and methods.
The researchers tackled the problem of how language use affects bilateral bargaining by creating a novel dataset from negotiation exercises, finding that audio communication leads to faster negotiations, higher agreement rates, and reduced price variance compared to numeric offers, though average agreed prices remained identical.
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. Our work also reveals linguistic signals that are predictive of negotiation outcomes.