Human Negotiators Research Articles

Systematic Literature Review of Strategic Behavior in Negotiation

Negotiation is a frequent phenomenon in everyday life. Whenever a person needs another person to solve their problems, their intertwining goals and preferences should be adjusted through negotiation. From this recurrent demand of dealing with negotiation, we constantly seek ways to improve it. One of the ways is by learning and assessing strategic behavior in negotiation. Starting from this objective, we systematically reviewed 1,490 articles to build a comprehensive outlook on negotiation behavior research progress. We use five systems of observation level to categorize the articles included in this study that are: (1) intrapersonal system, (2) interpersonal system, (3) group system, (4) organizational system, and (5) virtual system. The result showed us that the research landscape has progressed toward the development of automated negotiating agents besides observing human negotiators exclusively. We also see the emerging interplay between systems of observation level

Synchrony facilitates altruistic decision making for non-human avatars

Synchrony is a natural part of human interaction and is often associated with a variety of prosocial outcomes including affinity and rapport. The purpose of this research was to examine whether human negotiators would synchronize their movements when working with non-human avatars and if so, whether that would affect their perceptions of their negotiations. Results suggest that participants synchronized their movements with the movements of non-human negotiation partners, that greater synchrony was observed when participants engaged in an integrative negotiation compared to a distributive negotiation, and participants that engaged in synchrony reported greater affiliation with their non-human partner. Synchrony also lead to giving more to the non-human agent in an integrative negotiation whereas it had no significant impact on the outcomes in a distributive negotiation. Implications for human-machine interaction are discussed.

Artificial Intelligence and Technology in Teaching Negotiation

AbstractArtificial intelligence (AI), machine learning (ML), affective computing, and big‐data techniques are improving the ways that humans negotiate and learn to negotiate. These technologies, long deployed in industry and academic research, are now being adopted for educational use. We describe several systems that help human negotiators evaluate and learn from role‐play simulations as well as applications that help human instructors teach negotiators at the individual, team, and organizational levels. AI can enable the personalization of negotiation instruction, taking into consideration factors such as culture and bias. These tools will enable improvements not only in the teaching of negotiation, but also in teaching humans how to program and collaborate with technology‐based negotiation systems, including avatars and computer‐controlled negotiation agents. These advances will provide theoretical and practical insights, require serious consideration of ethical issues, and revolutionize the way we practice and teach negotiation.

The Effects of Experience on Deception in Human-Agent Negotiation

Negotiation is the complex social process by which multiple parties come to mutual agreement over a series of issues. As such, it has proven to be a key challenge problem for designing adequately social AIs that can effectively navigate this space. Artificial AI agents that are capable of negotiating must be capable of realizing policies and strategies that govern offer acceptances, offer generation, preference elicitation, and more. But the next generation of agents must also adapt to reflect their users’ experiences.
 The best human negotiators tend to have honed their craft through hours of practice and experience. But, not all negotiators agree on which strategic tactics to use, and endorsement of deceptive tactics in particular is a controversial topic for many negotiators. We examine the ways in which deceptive tactics are used and endorsed in non-repeated human negotiation and show that prior experience plays a key role in governing what tactics are seen as acceptable or useful in negotiation. Previous work has indicated that people that negotiate through artificial agent representatives may be more inclined to fairness than those people that negotiate directly. We present a series of three user studies that challenge this initial assumption and expand on this picture by examining the role of past experience.
 This work constructs a new scale for measuring endorsement of manipulative negotiation tactics and introduces its use to artificial intelligence research. It continues by presenting the results of a series of three studies that examine how negotiating experience can change what negotiation tactics and strategies human endorse. Study #1 looks at human endorsement of deceptive techniques based on prior negotiating experience as well as representative effects. Study #2 further characterizes the negativity of prior experience in relation to endorsement of deceptive techniques. Finally, in Study #3, we show that the lessons learned from the empirical observations in Study #1 and #2 can in fact be induced—by designing agents that provide a specific type of negative experience, human endorsement of deception can be predictably manipulated.

No Rage Against the Machine: How Computer Agents Mitigate Human Emotional Processes in Electronic Negotiations

With the proliferation of information technology and artificial intelligence in society, human users have started to engage in social interactions with computer agents. In this study, we conducted a laboratory experiment in which neurophysiological measurements were used to investigate the effect of computer agents on the affective processes and behavior of human negotiators. Participants engaged in alternating-offer bargaining over the partition of a pie with either human or computer counterparts and under different levels of urgency to reach an agreement. Overall, our data show that the subjects claimed significantly higher proportions for themselves when they made opening offers to computer agents than when bargaining with human counterparts, regardless of the degree of urgency in the negotiation. However, when the subjects responded to computer-issued offers the picture was more complex. Whereas under high-level urgency, the subjects were more likely to accept offers made by computer agents than by human counterparts, we observed the opposite effect for low-level urgency, where they were less likely to accept the offers of computer agents. In combination, these behavioral patterns lead to the use of computer agents yielding an increase in economic efficiency. Further, the subjects exhibited less emotionally charged behavior when facing computer agents than when facing human counterparts, as the intensity of affective processes was lower and the relationship between arousal and offer acceptance was observable only when the counterparts were human. The results of our study shed light on the potential benefits and intricacies of employing computer agents in electronic negotiations.

Using Cognitive Agents to Train Negotiation Skills.

Training negotiation is difficult because it is a complex, dynamic activity that involves multiple parties. It is often not clear how to create situations in which students can practice negotiation or how to measure students' progress. Some have begun to address these issues by creating artificial software agents with which students can train. These agents have the advantage that they can be “reset,” and played against multiple times. This allows students to learn from their mistakes and try different strategies. However, these agents are often based on normative theories of how negotiators should conduct themselves, not necessarily how people actually behave in negotiations. Here, we take a step toward addressing this gap by developing an agent grounded in a cognitive architecture, ACT-R. This agent contains a model of theory-of-mind, the ability of humans to reason about the mental states of others. It uses this model to try to infer the strategy of the opponent and respond accordingly. In a series of experiments, we show that this agent replicates some aspects of human performance, is plausible to human negotiators, and can lead to learning gains in a small-scale negotiation task.

Subjective Perceptions in Wartime Negotiation

The prevalence of negotiation in social interaction has motivated researchers to develop virtual agents that can understand, facilitate, teach and even carry out negotiations. While much of this research has analyzed how to maximize the objective outcome, there is a growing body of work demonstrating that subjective perceptions of the outcome also play a critical role in human negotiation behavior. People derive subjective value from not only the outcome, but also from the process by which they achieve that outcome, from their relationship with their negotiation partner, etc. The affective responses evoked by these subjective valuations can be very different from what would be evoked by the objective outcome alone. We investigate such subjective valuations within human-agent negotiation in four variations of a wartime negotiation scenario taken from the political science literature. We observe that the objective outcomes of these negotiations are not strongly related to the human negotiators' subjective perceptions, as measured by the Subjective Value Index. We examine the game dynamics and agent behaviors to identify features that induce different subjective values in the participants, even when separated from the effect of the objective outcomes. We thus are able to identify characteristics of the interaction between negotiation and battlefield processes that most impact people's subjective valuations.

Designing Heuristics: Hybrid Computational Models for Teaching the Negotiation of Complex Contracts

The negotiation of even the most straightforward real-world contracts tends to be quite complex. A contract with only 25 distinct issues with two alternatives each presents the parties with more than 33 million possible contracts, far too many to be evaluated exhaustively within feasible time constraints. Furthermore, contract issues that exhibit high levels of interdependence result in highly nonlinear utility functions with the possibility of many local optima. This paper employs hybrid computational models, integrating both simulated annealing and tabu list optimization, to aid in the design of social heuristics and institutional mechanisms that may serve to improve the effectiveness of human negotiators.

Effects of Communicating Issue Priority for Preference Tradeoffs in Agent-Human Negotiations

This study explores the consequences of a software agent volunteering one of its issue priorities and inviting its human counterpart to reciprocate in order to obtain preference tradeoffs in a multi-issue e-commerce negotiation scenario. Results indicated that while the agent followed the same negotiation strategy algorithm, agent-human dyads achieved better agreement rate and joint outcomes, and the human negotiators experienced more positive post-negotiation affect when the agent volunteered to communicate an issue priority than when it didn’t. The findings have implications for automated negotiation research and practice.

Automated formal analysis of human multi-issue negotiation processes

This paper reports on experiments in (human) multi-issue negotiation and their analysis, and presents a generic software environment supporting such an analysis. First, the paper presents a System for Analysis of Multi-Issue Negotiation (SAMIN). SAMIN was designed to analyse multi-issue negotiation processes between human negotiators, between human and software agents, and between software agents. The agents conduct one-to-one negotiations, in which the values across multiple issues are negotiated on simultaneously. To analyse such negotiation processes, the user can enter any formal property deemed useful into the system and use the system to automatically check this property in given negotiation traces. The paper presents the results of applying SAMIN in the analysis of empirical traces obtained from an experiment in multi-issue negotiation about second hand cars. In the experiment the efforts of 74 humans negotiating against each other have been analysed using SAMIN.

Multi-Issue Automated Negotiations Using Agents

Software agents can perform effectively as negotiators in automated negotiation settings. We present a model for software agents that can automate negotiations by implementing a multi-issue learning heuristic that allows agents to learn from the bidding behavior of opponents. The performance of agents is evaluated using an experimental study involving human subjects. The results indicate that software agents can act as effective surrogates of human negotiators under some circumstances.

Negotiation Support and E-negotiation Systems: An Overview

With negotiation being an often difficult process involving complex problems, computer-based support has been employed in its various phases and tasks. This article provides a historical overview of software used to support negotiations, aid negotiators, and automate one or more negotiation activities. First, it presents several system classifications, including implemented models, system architectures, and configurations of various systems interacting with human negotiators. Then, it focuses on NSSs (negotiation support systems) and related systems introduced in the early 1980s and on ENSs (e-negotiation systems), which are deployed on the web. These broad categories are discussed from four perspectives: real-life applications, systems used in research and training, research results, and research frameworks.

Automated Negotiation in Agent Based Commercial Grids

Negotiation is a joint decision making process among actors with competing goals. by automating negotiation processes using agent technology, the cost of this decision making process in terms of time and manpower can be effectively reduced. Competitive negotiation between decision makers is characterized by limited availability of relevant information. To succeed in negotiations, human negotiators use their intuitive and interpretational skills to leverage incomplete knowledge and identify mutually beneficial contract modifications. A major problem in the domain of automated decision making in competitive negotiations is that intuitive human decision making under incomplete information must be replaced by formal decision functions. The work presented here aims at providing formal strategies for multiple-issue negotiations that model knowledge-aware decision making processes, allowing an artificial agent to gain advantage over its counterpart. Apart from this purely competitive take, an additional negotiation protocol is presented that allows finding mutually beneficial contract modifications although preference information is withheld by the negotiating parties due to their competitive stance. The commercial exchange of distributed high-performance computational services (grid services) serves as an example area of application.

Formula omitted]egoisst: a negotiation support system for electronic business-to-business negotiations in e-commerce

Electronic negotiations can range from simple offer exchanges to complex communicative acts concerning packages of products and services. In contrast to dominant approaches aiming at automating the negotiation process (e.g. auction models), we introduce the notion of negotiation support for human negotiators conducting complex electronic negotiations. The negotiation support system N egoisst for business-to-business electronic commerce is presented that is based on theories of communication and information systems and that combines communication and document management. N egoisst has been successfully validated for e-negotiations in the construction industry.

DOC.COM: a framework for effective negotiation support in electronic marketplaces

Today, research in electronic negotiations focuses on negotiation protocols that support the negotiation of only a few properties. These protocols are mainly designed for automated negotiations between software agents. However, for peer-to-peer negotiations in business-to-business electronic marketplaces, such protocols are less appropriate. For example, negotiations about multiple attributes, complex attribute combinations, or frame contracts require more sophisticated support of the involved interactions and communication acts. In this paper we present a novel approach to the effective support of electronic negotiations among human negotiators. Our approach is based on the following observation. The outcome of a successful negotiation process is a business contract. The contract evolves during the negotiation, coordinated through the exchange of structured messages such as offers, requests, quotations, counteroffers, and acceptances. Therefore, each negotiation between business partners involves the exchanges of documents and messages. Furthermore, documents and messages are interrelated in that a message leads to a new contract version which itself is the medium for a new negotiation step and thus initiates a new message. In contrast to the current practice of separate management of messages and documents, we create a powerful framework for effective negotiation support in electronic marketplaces by combining communication and document management. Our approach enables monitoring of contractual obligations and traceability of both documents and messages and their interrelations.

Persuasive argumentation in negotiation

This paper presents Persuasive Argumentation as a means of guiding the negotiation process to a settlement. Decision theoretic approaches construct prescriptive models of the negotiation process that make various assumptions about the behavior of the negotiation participants but do not model changes in behavior. On the other hand, models for decision support leave the actual decisions to human negotiators, again not modeling or automating the negotiating process. In contrast to both approaches, our work deals with automating the negotiation process. This paper focuses on modeling the process by which the beliefs and behavior of negotiators are changed via persuasive argumentation. We claim that persuasive argumentation lies at the heart of negotiation and embodies the dynamics of negotiation. We present a model of persuasive argumentation that integrates Artificial Intelligence and decision theoretic methods. The model has been implemented as part of the PERSUADER, a multi-agent computer program that operates in the domain of labor negotiations.