Game Over: Facing the AI Negotiator

Think about your most recent negotiation experience. You prepared (hopefully) by assessing your interests and potential agreement options and by thinking about your ‘Best Alternative to a Negotiated Agreement’ (BATNA). You sat down at the negotiation table, unsure whether a ‘Zone of Possible Agreement’ (‘ZOPA’) or ‘Bargaining Range’ exists and what its extension could be. You engaged in back-and-forth communication with the other side, attempting to devise creative solutions and, at the same time, to convince them of the merits of your preferred option. Maybe you came to an agreement, maybe not. It might have been quite stressful, especially if you and your negotiation partner felt strongly about certain issues and emotions flew high.

This kind of negotiation experience could soon be a thing of the past. Increasingly, AI applications are used to assist or even replace human negotiators. In consequence, it is no longer entirely correct to say that negotiations are a process in which people attempt to solve a problem. Clearly some accommodation of the ‘PPP Model of Negotiations’ is warranted to account for the changes in negotiation practice associated with the rise and use of smart algorithms.

In a recent essay (forthcoming in the University of Chicago Law Review Online), I argue that AI applications will put an end to negotiation processes as we know them. The typical back-and-forth communication and haggling in a state of information insecurity will no longer be characteristic of negotiations. AI applications will increase the information level of the parties and drastically reduce transaction costs. Negotiators will have a clearer view of the ZOPA and its extension. This suggests that they should be able to come to a fair agreement quickly: just split the pie.

But information asymmetries will persist. Large companies will be better informed about relevant factors than their opponents, and they will have more sophisticated AI tools at their disposal. These negotiators will devise and execute sophisticated negotiation strategies with precision, capturing the lion’s share of the cooperative surplus. And they will automate the negotiation process, creating favorable non-agreement alternatives for themselves, and presenting their opponents with a limited set of options from which they can choose. In essence, their opponents will be able to choose how they are checkmated. Game over.

Horst Eidenmueller is Statutory Professor for Commercial Law at the University of Oxford and Professorial Fellow of St. Hugh’s College, Oxford.

The paper is available here.

This post is published as part of the series 'How AI Will Change the Law'.