A Dynamic Strategy Coach for Effective Negotiation

TL;DR

This paper introduces a machine-in-the-loop negotiation coach that analyzes text-based bargaining dialogs in real time, recommending tactics to improve seller outcomes, and demonstrates a 60% profit increase in evaluations.

Contribution

It develops a dynamic negotiation strategy coach that predicts effective tactics based on dialog context, enhancing human negotiation performance.

Findings

Seller profits increased by nearly 60%.

The system effectively predicts context-dependent negotiation tactics.

Real-time recommendations improve negotiation outcomes.

Abstract

Negotiation is a complex activity involving strategic reasoning, persuasion, and psychology. An average person is often far from an expert in negotiation. Our goal is to assist humans to become better negotiators through a machine-in-the-loop approach that combines machine's advantage at data-driven decision-making and human's language generation ability. We consider a bargaining scenario where a seller and a buyer negotiate the price of an item for sale through a text-based dialog. Our negotiation coach monitors messages between them and recommends tactics in real time to the seller to get a better deal (e.g., "reject the proposal and propose a price", "talk about your personal experience with the product"). The best strategy and tactics largely depend on the context (e.g., the current price, the buyer's attitude). Therefore, we first identify a set of negotiation tactics, then learn to predict the best strategy and tactics in a given dialog context from a set of human-human bargaining dialogs. Evaluation on human-human dialogs shows that our coach increases the profits of the seller by almost 60%.