Is There a Human Premium in Negotiations? A Relational-Economic Tradeoff in Human vs. AI Bargaining

Abstract Advances in artificial intelligence (AI), particularly in large language models (LLMs), increasingly enable AI to serve as an active counterpart in negotiation. Drawing on negotiation theory and mind perception research, this study examines whether attributed counterpart identity, that is, the belief that a negotiation counterpart is human rather than AI, influences relational evaluations, satisfaction, and outcomes in a mixed-motive bargaining context. In a randomized laboratory experiment, 205 participants negotiated apartment rent with the same LLM-based broker but were led to believe the counterpart was either human or AI. After correction across the five Hypothesis 1 comparisons, participants who believed they negotiated with a human reported higher perceived warmth and integrity; perceived competence was higher only in the unadjusted comparison. Participants in the Person condition also reported higher relational satisfaction and agreed to less favorable economic outcomes, paying higher final rents than those who believed they negotiated with AI. Exploratory analyses further suggested that attributed counterpart identity shaped how participants organized and described their post-negotiation experiences, with human-attributed counterparts eliciting more differentiated evaluations and more partnership-oriented descriptions. As AI becomes more common in negotiation, these results highlight the importance of accounting for the relational value people derive from believing they are negotiating with another person.