Language of Bargaining (2023)

Abstract: 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.

## introduction bilateral bargaining, in the sense of a goal-oriented negotiation between two parties, is a fundamental human social behavior that takes shape in many areas of social experience. driven by a desire to better understand this form of interaction, a rich body of work in economics and psychology has evolved to study bargaining @xcite @xcite . however, this work has seldom paid careful attention to the use of language and its fine-grained impacts on bargaining conversations; indeed, many studies operationalize bargaining as simply the back-andforth exchange of numerical values. meanwhile, there is growing interest in bargaining in nlp oriented towards the goal of building dialogue systems capable of engaging in effective negotiation @xcite . in this work, we aim to bridge these two lines of work and develop a com-putational understanding of how language shapes bilateral bargaining. to do so, building on a widely used exercise involving the bargaining over the price of a house used in negotiation education, we develop a controlled experimental environment to collect a dataset of bargaining conversations. 1 the treatment in our experiment is the manner in which subjects communicate: either through alternating, written, numeric offers (the alternating offers or ao condition) or unstructured, verbal communication (the natural language or nl condition). furthermore, to encourage naturalistic interactions, we recruit participants via behavioral labs and allow participants to negotiate in a conversational setting using audio on zoom instead of crowdingsourcing text conversations as prior work has done @xcite @xcite . in total, we collect a dataset with 230 alternating-offers negotiations and 178 natural language negotiations. in contrast with @xcite 's craigslist negotiation dataset, our natural language negotiations have an average of over 4x more turns exchanged during each conversation, so our dataset represents a richer source to explore linguistic aspects of bargaining behavior than has been presented by existing work in this area. in addition, we enrich the dataset by annotating all the conversations with a set of negotiationspecific speech acts. inspired by prior work on rhetorical strategies in negotiations @xcite @xcite , we create a simplified taxonomy of what we term bargaining acts and hire undergraduate research assistants to provide annotations. to the best of our knowledge, our dataset of speech acts in negotiations is an order of magnitude larger than existing datasets. we first provide descriptive results based on our dataset. although the ao and nl conditions are conducted via different communication mechanisms, they reach the same average agreed prices. however, 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. these observations suggest that the use of language facilitates collaboration. we also find differences in how buyers and sellers employ bargaining acts. recorded and transcribed speech provides more direct access to the intuitive attitudes and behaviors of the buyers and sellers. this enables us to identify subtle types of expression that are predictive of negotiation outcomes and reveal underlying dynamics of negotiation. other findings corroborate conclusions from @xcite , who distinguish the effectiveness of negotiators' different expressions of the same rationale. we set up prediction tasks to predict the outcome of a negotiation based on features of the conversation and analyze the important features contributing to class differentiation. our results show that liwc features provide consistently strong performance and even outperform longformer @xcite given the beginning of a negotiation. important features reveal that successful sellers drive and frame the conversation early on by using interrogative words to prompt buyers with targeted questions, while successful buyers convey their personal considerations and concerns while using negative expressions to push for lower prices. in summary, we make the following contributions: ## related work negotiation is a growing area of study in computer science. @xcite provide an excellent survey of research on negotiation dialogue systems. @xcite train recurrent neural networks to generate natural language dialogues in negotiations. @xcite propose a modular generative model based on dialogue acts. our focus is on deriving computational understanding of how language shapes negotiation. several research disciplines have studied bilateral bargaining from different perspectives and using different tools. economic theory has investigated the role of incomplete information @xcite and highlighted the role of explicit communication @xcite . @xcite and pruitt (2013) provide an overview of the psychology literature on negotiation. however, these studies tend to overlook the content of the communication, with some notable exceptions @xcite @xcite . the most related work to ours is lee and ames (2017), who study how bargaining outcomes are affected by the way a rationale is expressed. they find that expressions that hint at a constraint (e.g., "i can't pay more") are more effective at shaping a seller's views of the buyer's willingness to pay than critique rationales (e.g., "it's not worth more"). ## dataset the first contribution of our work is building the first transcript dataset of spoken natural language bargaining between lab experiment participants. our dataset extends existing datasets in four ways: 1. negotiation happens in spoken language, and is thus more fluid and natural, akin to real-world bargaining scenarios, such as price haggling in vendor markets, union negotiations, or diplomacy talks, while existing work is largely based on written exchanges @xcite @xcite ; 2. our work is the first one to introduce a control condition without the use of natural language; while contributing greatly to our understanding of negotiation, existing bargaining datasets are somewhat limited in being based on written exchanges @xcite , often in the context of a highly structured game @xcite . ## bargaining act annotation previous researchers have recognized the inherently speech-act-like character of negotiations @xcite ; twitchell et al., 2013). many or most utterances in a bargaining context can be thought of as taking some action with reference to the negotiation. here we propose and present a simplified ontology of negotiation-oriented speech acts (hereafter, bargaining acts) relevant to the present context of negotiation. two trained undergraduate research assistants annotated all transcripts according to five bargaining acts: 1) new offer, 2) repeat offer, 3) push, 4) comparison, 5) allowance, and 6) end. twenty-four transcripts were annotated by both annotators to allow agreement to be calculated. using masi distance weighting @xcite , we found a krippendorff's alpha @xcite ) of 0.72, representing a high degree of agreement for a pragmatic annotation task. figure 2 shows that new offers, pushes, and comparisons are relatively more frequent and appear more consistently in all the negotiations than allowances and repeat offers. we note in comparing buyers to sellers, we observe that buyers make on average 1 more new offers per negotiation than sellers (independent sample, heteroskedasticity robust t-test, @xmath0.02). we find no statistically significant differences between roles for the other five bargaining acts. the bargaining act annotations allow us to describe a negotiation as a sequence of offers proposed by the buyer and seller. we compare how the frequency and pattern of numerical offers differ across 1) experimental treatments (nl vs. ao) and 2) negotiation outcomes. we characterize different properties of the negotiations as well as their trajectories over the course the interaction. figure 3 reveals three general patterns on offer trajectories. first, both ao and nl bargaining feature a similar range of new offers exchanged in the early stages of the negotiation. early on, buyers in both treatments present new offers as low as 170; and sellers, as high as 270. but extreme offers are more prevalent in ao than nl bargaining. second, both the ao and nl trajectories exhibit a rhythmic pattern of low and high offers, which is familiar to real-world negotiations. the buyer's low offer is countered by the seller's high offer, which is then countered by the buyer's slightly increased low offer, and so on. third, nl bargaining takes far fewer new offers to reach agreement than ao bargaining. figure 3b clearly demonstrates that nl negotiations converge quicker, with consecutive offers converging to within $5k after 6 new offers. ao negotiations take over 40 new offer exchanges to reach a similar convergence. ## predicting negotiation outcomes finally, we set up prediction tasks to understand the relationship between the use of natural language and negotiation success. overall, our models demonstrate performance gains over majority class in most settings. surprisingly, logistic regression using bag-of-words and liwc category features outperform the neural model. we observe differentiation between classification accuracy on seller only and buyer only speech, and highlight features that explain this difference. ## conclusion in this work we design and conduct a controlled experiment for studying the language of bargaining. we collect and annotate a dataset of alternating offers and natural language negotiations. our dataset contains more turns per negotiation than existing datasets and, since participants communicate orally, our setting facilitates a more natural communication environment. our dataset is further enhanced with annotated bargaining acts. our statistical analyses and prediction experiments confirm existing findings and reveal new insights. most notably, the ability to communicate using language results in higher agreement rates and faster convergence. both sellers and buyers benefit from maintaining an active role in the negotiation and not being reactive to the other party.