The Promise and Peril of Automated Negotiators

Abstract

Innovations in artificial intelligence are enabling a new class of applications that can negotiate with people through chat or spoken language. Developed in close collaboration with behavioral science research, these algorithms can detect, mimic, and leverage human psychology, enabling them to undertake such functions as the detection of common mistakes made by novice negotiators. These algorithms can simulate the cognitive processes that shape human negotiations and make use of these models to influence negotiated outcomes. This article reviews some of the scientific advances enabling this technology and discusses how it is being used to advance negotiation research, teaching, and practice.

The need for a female perspective in designing agent-based negotiation support

“…By simulating and analyzing different negotiation strategies and offering patterns, these systems structure negotiations and determine optimal decision-making [2,22]. Then, negotiations are regarded mathematical problems to be targeted through systematic mechanisms, classical economic, and rational principles, such as game theory [13].…”

“…One major distinction between the different designs is the degree of freedom in communication. Interactivity in existing NTSs ranges from passive user interaction (such as a personalized voice-overs that articulate the negotiator's thoughts at each step [9]), or scripted interaction (where participants can choose their speech acts from a set of options in a menu [13]), to no textual input at all [22]. Others choose semi-interactive methods, in which participants react to an offer by selecting an option -i.e., accept, propose a counter-offer, or walk away -complement by argumentation, either in written or verbal format [40].…”

Teaching Reverse Appraisal to Improve Negotiation Skills

“…[6] but also through observing nonverbal cues, especially emotional expressions [8,9,10]. Emotional expressions have been argued to serve important social functions, including communicating mental states, such as goals and preferences to others [11,12]. But unlike explicit verbal statements, emotional expressions are ambiguous and observers must attend to the context to recover their meaning [13].…”

The Case for Negotiation Robots in simulated workplace negotiations A Theoretical Approach

“…It is possible that this lack of negotiation may be a contributing factor to the gender wage gap. There are efforts underway to design artificially intelligent (AI) negotiators who can train individuals in the art of negotiation or can act as proxies in negotiations (Gratch, 2021). Job interviews and salary negotiations have traditionally taken place over face-to-face (F2F) communication.…”

“…Figure 1: IAGO Interfaceadjusted to simulate negotiation scenarios such as job negotiations(Johnson & Gratch, 2020). Modifications to the base IAGO algorithm have used enhanced emotional recognition to enrich interactions with IAGO(Mell et al, 2018;Johnson & Gratch, 2020;Gratch, 2021). Frameworks such as CaSiNo use a text-based approach to simulating negotiations in virtual space with phrases common to real-life negotiations(Chawla et al, 2021).…”