Negotiation is a very common interaction between agents. Many common negotiation protocols work with cardinal utilities, even though ordinal preferences, which only rank the outcomes, are easier to elicit from humans. In this work, we focus on negotiation with ordinal preferences over a finite set of outcomes. We study an intuitive protocol for bilateral negotiations, where the two parties make offers alternately. We analyze the negotiation protocol under two settings: First, we consider the full information setting, where each party is fully aware of the other party's preference order. For this case, we provide elegant strategies that specify a sub-game perfect equilibrium. In addition, we show how the studied negotiation protocol almost completely implements a known bargaining rule. Second, we analyze the complementary no-information setting where neither party knows the preference order of the other party. For this case, we provide a Maxmin strategy and show that every pair of Maxmin strategies specifies a robust-optimization equilibrium. Finally, through a human study ($N=150$), we empirically study the practical relevance of our full information analysis to people engaging in negotiations with each other and/or with an automated agent using the studied protocol. Surprisingly, our results indicate that people tend to arrive at the equilibrium outcomes despite frequently departing from the proposed strategies. In addition, in contrast to commonly held beliefs, we find that an equilibrium-following agent performs very well with people.

协商是代理之间非常常见的交互。许多常见的谈判协议都适用于基数效用，尽管仅对结果进行排序的顺序偏好更容易从人类那里获得。在这项工作中，我们重点关注对一组有限结果的顺序偏好的谈判。我们研究了一种直观的双边谈判协议，双方轮流提出报价。我们在两种设置下分析协商协议：首先，我们考虑完整信息设置，其中各方都充分了解另一方的优先顺序。对于这种情况，我们提供了指定子博弈完美均衡的优雅策略。此外，我们还展示了所研究的协商协议如何几乎完全实现已知的讨价还价规则。其次，我们分析互补的无信息环境，其中双方都不知道另一方的偏好顺序。对于这种情况，我们提供了一个 Maxmin 策略，并表明每对 Maxmin 策略都指定了一个稳健优化均衡。最后，通过人类研究（ $N=150$ ），我们实证研究了我们的完整信息分析与使用所研究协议进行相互谈判和/或与自动化代理进行谈判的实际相关性。令人惊讶的是，我们的结果表明，尽管人们经常偏离所提出的策略，但仍倾向于达到均衡结果。此外，与普遍持有的信念相反，我们发现均衡跟随主体与人相处得很好。