Factors’ Influence on Human–Computer Negotiation Results—A Systematic Evaluation

Abstract

Artificial intelligence (AI) and computer agents are increasingly shaping daily decision-making and commercial interactions. This study investigates the influence of computer agents’ attributes on negotiation results and proposed a systematic method to evaluate the negotiation outcomes. Specifically, it examines the effects of negotiation timespan (synchronous vs. asynchronous), concession tactics, and issue-search mechanisms on both economic and perceptual results in human-agent negotiation. In an experiment, human buyers negotiated purchase of mobile plan contracts with computer agents programmed with one of three concession tactics (conceding, neutral, or competitive) and one of two issue search mechanisms (breadth-first or depth-first). Negotiations occurred under either synchronous or asynchronous timeframes. The experimental results suggest that on the group (dyad) level, timespan has marginal effects on agreement rate, while tactic has a significant impact. On the individual level, agents’ tactics have significant effects on the objective outcomes, while search mechanisms have a significant influence on the subjective outcomes.