Frontline Negotiations

Resources Negotiation case studies

Follow our real-life negotiation case studies and learn how to prepare a humanitarian negotiation step by step.

Understand how to apply the Naivasha Grid , a conceptual framework that supports humanitarian workers to prepare for and manage field negotiations more systematically.

For a more detailed explanation of our negotiation tools, check the  CCHN Field Manual on Frontline Humanitarian Negotiation .

Negotiate a vaccination campaign in a conflict area

Negotiate access and assistance in an idp camp, negotiate a vaccination campaign in a conflict area fr, negotiate access and assistance in an idp camp es.

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The CCHN supports humanitarian agencies in expanding their internal negotiation capacity by providing bespoke learning and development support for all staff members.

This activity is for you if...

…you are looking to strengthen staff members’ negotiation skills within your organisation. …you would like your internal negotiation training to be informed by recent research and by the practice of hundreds of humanitarian professionals working around the globe.

What will you learn?

The CCHN can support your internal Learning department in the design of a specific curriculum (either ad-hoc or permanent), based on our methodology and in line with your agency’s current challenges and learning strategy.

The curriculum may take a peer-to-peer online/onsite format, or it may be an individual experience through e-learning materials and self-learning.

We provide the requesting agency with access to the complete CCHN learning methodology. Alternatively, we embed learning sessions based on our methodology in an existing learning programme delivered by the agency.

We also support your agency in responding to specific challenges through tailored learning content with a thematic or regional focus.

Who can sign up?

Any humanitarian agency or learning institution.

What language will we speak?

English, French, Spanish and Arabic.

How to sign up?

Please contact us to obtain more information and a tailored proposal.

We support humanitarian agencies or field teams by advising them on negotiating access and protection based on our analytical tools and policy work.

…you are looking for tailored guidance navigating a complex humanitarian scenario. …you wish to be supported in the application of CCHN’s strategic tools to your local challenges.

The CCHN provides different levels of advisory support. Level 1  –  Bilateral technical support . We provide guidance to community members and partner organisations through the expertise of CCHN staff and specialised consultants. Level 2  –  Specialised research and policy response . The CCHN’s Research and Development team will provide in-depth analysis and guidance, in collaboration with the Operations team and selected members of our community of practice. Level 3  –  Direct advisory support . You will be supported by a CCHN Mobile Advisory Team comprising our internal operational capacity as well as external resources.

Discover state-of-the-art negotiation tools, apply them to your own negotiations, and critically reflect with peers while contributing to the advancement of research.

… you would like to “deep dive” into CCHN negotiation tools learned during Peer Workshops, Advanced Humanitarian Negotiation Workshops or Thematic Sessions. … you are testing and practising these tools in your daily professional life and engaging in critical reflection about the tools and your practice with peers and with potential support of CCHN mentors. …you wish to be among the first to test and validate innovative negotiation tools that have been recently developed by CCHN researchers and community members. …you are available and committed to joining at least three Negotiation Lab sessions.

Negotiation Labs are critical discussions and exchanges among field practitioners around existing or pilot tools and models that have been recently elaborated by CCHN researchers or by community members in the context of Thematic Groups. Labs may be co-facilitated by CCHN mentors and other selected members of the CCHN community with extensive humanitarian experience and excellent knowledge of CCHN tools. You will have the opportunity to test the tools, apply them in your negotiations and provide feedback for further development, while also supporting ongoing research efforts.

You should have previously attended a CCHN Peer Workshop or Executive Programme.

What is the event format?

Negotiation Labs are organised in small groups, based on demand, over three to five sessions. They take place online and include three to five participants.

Will you receive a certificate?

You will not receive a certificate for this activity.

Negotiation Labs are organised on demand.

Join other humanitarian practitioners and mental health experts over the course of a few days and strengthen your capacity to prepare, manage and recover from high-pressure negotiations.

…you are seeking a safe and confidential space to discuss with other humanitarian professionals about the personal challenges and dilemmas of negotiating under pressure or in high-risk contexts. …you wish to explore the latest tools and methods to identify, manage and cope with stress in complex environments. …(for retreats aimed at training facilitators) you are willing to facilitate similar activities in the future and to organise additional ones in your region.

Retreats will provide you with a safe environment to exchange and new techniques to better prepare, manage, and recover from high-pressure situations. You will learn how to build your resilience and support colleagues facing personal, ethical, and professional dilemmas.

Among the topics tackled during retreats are the mental, emotional and physical dimensions of pressure management and self-care as well as the “before”, “during” and “after” of negotiating under pressure.

You should have previously attended a CCHN Peer Workshop or Executive Programme. Retreats are particularly suitable for community members with a strong interest in the CCHN’s mental health and pressure management activities.

Some retreats aim to train new facilitators, providing them with the tools to organise their own workshops. In this case, you should have completed a Training of Facilitators and have a strong interest in the topic of resilience in negotiation under pressure. You should have the commitment and resources to organise at least two sessions / series of sessions / a retreat in your local context within the 12 months following the training.

English, French or Spanish; additional languages may be available for self-organised workshops, depending on the context where the retreat takes place.

Retreats take place in person. They typically last five days (some parallel activities may take place online for the wider community). They feature group discussions and exercises.

Retreats include a maximum of 25 participants. In-person participants should be able to cover flight expenses and visa fees.

Yes. Those who attend the event in its entirety receive a Certificate of Completion.

Visit the special events calendar reserved for CCHN community members and sign up online for the next event.

Learn helpful techniques to become more resilient to pressure during high-stakes negotiations and provide similar support to the members of your team.

…you wish to learn techniques to better prepare, manage, and cope with high-pressure negotiations. …you feel a need to share and exchange confidentially about issues relating to mental health and self-care during negotiation processes. …you would like to become more resilient and prevent the negative impact of pressure in the future. …you are planning to use self-management tools to support your team members through complex negotiations.

The “Prepare for Pressure” programme will help you better understand your behaviour, master your emotions, and learn methods to reduce the impact of pressure during high-stakes negotiations.

The workshop is provided regularly in English, French and Spanish.

The workshops, facilitated by expert pressure management consultants, take place online and are based on the sharing of the participants’ experiences. They include breathing and other body exercises.

The programme is composed of four modules lasting 10 hours in total. Workshops are normally organised every two months. Each event features a maximum of 25 participants.

Create a one-on-one relationship with another humanitarian professional within the CCHN community. Learn from and with a colleague who understands your negotiation context in a safe space for exchange and reflection.

… you are currently negotiating at the frontlines of humanitarian action. … you are looking for ways to strengthen your negotiation skills while discussing your own experience. … you would like to connect with other professionals while stimulating reflection, critical thinking, exchange of ideas, and brainstorming.

Mentors expand their negotiation expertise while developing soft skills like active listening, critical thinking, and providing feedback.

Mentees gain access to a safe, confidential space of reflection and exchange with an experienced peer negotiator.

Both mentors and mentees should have previously attended a CCHN Peer Workshop or Executive Programme. Mentors join the programme upon invitation, depending on the skills and expertise they demonstrate. They attend an onboarding workshop before becoming listed in the CCHN mentors’ database. Mentees can join the programme by submitting an online application. The CCHN team provides them with guidance so they can fully take advantage of the mentoring relationship.

Training and onboarding materials are available in English, French and Spanish; however, the mentoring exchanges can take place in any language shared by the mentor and mentee. The mentors currently available in our database collectively speak more than 80 languages.

Before entering a mentoring relationship, mentors attend an onboarding workshop where they learn about mentoring practices and skills including structuring a mentoring relationship, active listening and providing feedback. They later practice these skills during role-play simulations. Mentees who apply gain access to the CCHN mentoring database, where they can autonomously select and contact the mentor(s) whose profile or expertise best matches their needs. The one-on-one relationship between a mentee and a mentor takes place privately and confidentially according to the participants’ preferences.

The CCHN organises “Mentoring Coffee” events twice per month. All participants are welcome to attend and discuss mentoring practices as a group.

The mentors who attend an onboarding workshop in its entirety receive a Certificate of Completion.

Mentees do not receive a certificate.

Contact us if you are interested in becoming a mentee.

Join an informal, regular gathering of humanitarian professionals to discuss a specific negotiation challenge and produce practical guidance for humanitarian colleagues.

…you’re looking to receive practical guidance from other frontline negotiators on your operational challenges. … you are committed to working with other community members towards developing concrete tools, guidelines, or frameworks that can support humanitarian practitioners. …you wish to discuss with experts and researchers, engage yourself in operational research, create space for discussion, and think outside the box to find creative solutions to shared challenges. … you can commit to attending periodic discussions around the group’s topic.

You should have previously attended a CCHN Peer Workshop or Executive Programme. You should be committed to developing a particular topic related to humanitarian negotiation.

English or any other language depending on the preference of the group.

Thematic group meetings take place online at regular intervals (typically every 4-6 weeks).

Thematic groups are informal exchanges, and you will not receive a certificate for this activity.

Informal but structured group discussions around a specific negotiation angle or context, either online or in person.

…you’re looking for an informal group exchange on a certain challenge relating to humanitarian negotiations. …you wish to rely on the support of a global network to help you plan and carry out future humanitarian negotiations.

Anyone who has previously attended a CCHN Peer Workshop or Executive Programme.

Arabic, English, French, or Spanish depending on the geographical focus of each event.

Peer circles may take place online or in connection with in-person events. Their length varies depending on the theme; online events typically last one to two hours. Each event has an average of 20 participants.

Peer circles are informal exchanges; you will not receive a certificate for this activity.

Test your negotiation skills in a realistic scenario and put your knowledge of the CCHN tools into practice.

…you would like to test your understanding of the negotiation tools and methods learned during previous workshops. …you are looking to strengthen your problem-solving skills through roleplay and better prepare for your next negotiation.

Arabic, English, French, or Spanish.

Simulations can take place either in person or online, with the use of virtual interactive boards. They are usually organised as a complement to a Peer Workshop or another learning activity.

A simulation lasts between two and four hours and features a maximum of 30 participants.

Become a CCHN workshop facilitator and help other humanitarian professionals strengthen their negotiation skills while benefitting from their collective expertise. Take your engagement in the CCHN Community of Practice to the next level and lead Peer Workshops for your team or for the wider humanitarian community, with support from the CCHN.

…you are interested in learning facilitation techniques that are applicable across different domains. …you would like to benefit from the expertise of frontline negotiators sharing their own experience and practice. …you wish to share your learning on humanitarian negotiation with members of your organisation or other professionals across the sector. … you are available to facilitate CCHN Peer Workshops both online and onsite.

You will learn facilitation techniques to guide other humanitarian professionals in applying the negotiation methodology developed by the CCHN.

The topics discussed include active listening, effective communication, storytelling and delivering presentations without making use of PowerPoint.

At the end of the training, you will be able to facilitate CCHN Peer Workshops, including by using case studies and simulation exercises.

Active CCHN facilitators gain access to dedicated learning and sharing opportunities, including the Facilitators Annual Meeting.

You should have previously attended a CCHN Peer Workshop as an engaged participant. You should demonstrate a very good understanding of the CCHN negotiation tools and commitment to share your learning with other professionals.

Trainings of Facilitators are available both online and in person. Online workshops include four sessions lasting two hours each and welcome a maximum of 25 participants; they focus on building facilitation skills for online events.

In-person workshops last four full days and welcome a maximum of 15 participants. They are aimed at building skills to facilitate in-person events.

Once you complete the training, you will be invited to join Peer Workshops as a facilitator.

Yes. Those who attend the workshop in its entirety and consequently facilitate at least one Peer Workshop will receive a Certificate of Completion.

Advanced Humanitarian Negotiation Workshops offer participants an opportunity to consolidate their previous learning while acquiring advanced skills and tools to plan, manage or evaluate humanitarian negotiations. You will dive deeper into the behavioral aspects of negotiation through CCHN tools, putting them into practice in context-specific scenarios.

…you took stock of the negotiation tools and strategies discovered during a Peer Workshop and feel the need of more solid or in-depth grounding. … you are interested in advanced and more complex tools to plan and evaluate your negotiations and critically reflect about your current practice. … you wish to improve your negotiations and communication skills, experimenting and learning from mistakes. …you are a mid- or senior-level humanitarian professional carrying out regular negotiations at the frontlines.

Advanced Humanitarian Negotiation Workshops tackle different topics over four days:

  • Day 1: Designing and understanding the mandate of the negotiation.
  • Day 2: Understanding your counterpart.
  • Day 3: Building trust and crafting an argument.
  • Day 4: Designing a negotiation strategy (optional).

A negotiation simulation completes the workshop on the fourth day. The Advanced Humanitarian Negotiation Workshop is based on the sharing of the participants’ negotiation experience and simulations. You will be asked to (confidentially) share your negotiation stories with the group as a basis for joint discussion and exercises.

You should have previously attended a CCHN Peer Workshop or Executive Programme. You should also have several years of experience negotiating in the field.

English, Spanish and French – with the possibility of live interpretation into other languages.

Advanced Humanitarian Negotiation Workshops may take place online or in person. In-person workshops last for three or four full days, welcoming 16-20 participants. Online workshops can be organised on demand.

Applied Negotiation Workshops help humanitarian professionals develop additional skills to plan and carry out negotiations in specific contexts or around particularly challenging operational topics. Participants are introduced to context-tailored methods, tools and case-studies based on the latest CCHN research and on humanitarian practice.

… you’re seeking to consolidate your previous learning from attending a Peer Workshop. …you wish to acquire advanced skills and tools to plan and evaluate humanitarian negotiation and issue a mandate. …you are a mid- or senior-level humanitarian professional carrying out regular negotiations at the frontlines.

Applied Negotiation Workshops tackle different topics over three days:

  • Day 1: Humanitarian negotiation as a personal endeavour and institutional process.
  • Day 2: Humanitarian negotiation as a professional relationship: managing and leveraging risks.
  • Day 3: Building trust and fostering legitimacy and strategic planning in complex environments.

In-person workshops last for three full days. They feature 16- 20 participants.

A successful negotiation does not only rely on the tools and strategies applied; it also depends on how the negotiator interacts with the counterpart. Learn how to develop negotiation skills including communication, self-awareness, emotional intelligence, and conflict management.

… you wish to become more aware of how your behaviour and body cues may affect the outcome of a negotiation. …you’d like to discover additional approaches helping to build a relationship of trust with a counterpart.

Each workshop is divided into four sessions, respectively focusing on:

  • Self-awareness (social and emotional intelligence, microexpression and emotional triggers, conflict handling styles).
  • Leading the team into the negotiation process (decision making, delegation and empowerment, making appropriate decisions).
  • Communicating and transaction (local codes, influencing, listening skills, linguistics, creating trust, intercultural communication).
  • Roleplay and the behavioural aspects of a negotiation.

Soft skills workshops are usually delivered online over the course of two days; they include roleplay and simulations. They are often organised in connection with another in-person workshop. Each event welcomes an average of 20 participants.

A first step into your CCHN learning pathway and an opportunity to join a global community of humanitarian negotiators. Peer Workshops provide you with knowledge of fundamental negotiation tools which are essential to plan, carry out and evaluate field negotiations. This knowledge will come in handy as you expand your negotiation expertise and prepare for more advanced workshops. Completing a Peer Workshops is a pre-requisite to join the CCHN community of practice and to attend other CCHN learning activities.

…you want to gain a fundamental understanding of negotiation tools and methods, share your negotiation experience and learn from others, connect with frontline negotiators in your region or around a specific topic, and set the foundation to attend more advanced workshops in the future.

  • Carrying out a context analysis to understand the environment in which the negotiation takes place.
  • Developing a tactical plan and assembling the right negotiation team.
  • Critically reflecting on your role in the negotiation and how your counterpart may perceive you.
  • Identifying the actors that may influence your counterpart.
  • Understanding your counterpart’s position, reasoning and values.
  • Defining your own position, your institutional limitations and bottom lines.

You will also discover some basic techniques to de-escalate a high-tension situation. You will then put your new learning into practice during a simulation exercise at the end of the workshop.

You should be a humanitarian professional with a minimum of three years of negotiation experience in a field context. Peer Workshops are open to both national and international staff of humanitarian organisations.

Arabic, English, French, Spanish or Portuguese, depending on the regional focus of each workshop. Learning materials can be translated into additional languages.

Peer Workshops are based on the sharing of the participants’ negotiation experiences. You will be invited to (confidentially) share your own stories with the group as a basis for joint discussion and learning.

Online workshops include six sessions, each lasting two hours (10 hours in total), taking place over the course of either three or five days.

In-person workshops are held over three full days and may feature additional thematic sessions.

The CCHN will accept a maximum of 30 people for in-person workshops and a maximum of 50 people for online workshops.

Visit our public events calendar to discover which of our upcoming workshops is most relevant for you, then submit your application online.

An interactive and confidential safe space for humanitarian decision-makers and senior management to share complex negotiation experiences and better lead negotiation teams as they navigate relationships with difficult counterparts. Completing the Executive Programme allows access into the CCHN community of practice as well as other advanced learning opportunities.

…you are a decision-maker within a humanitarian agency (Country Representative, Country Director, Deputy Director or equivalent level) and act as the mandator in frontline negotiation processes. …you wish to strengthen your leadership in guiding your agency’s negotiation teams. …you wish to build advanced negotiation skills in complex environments while becoming part of a professional network of senior managers.

The Executive Programme makes use of practical exercises, peer exchanges and simulations to encourage learning around the following topics:

  • Designing adequate strategies for complex humanitarian negotiations.
  • Sorting information and coping with disinformation in complex environments.
  • Leading high-stakes negotiations while managing competing agendas.
  • Managing and leveraging risks in frontline negotiations.
  • Facing difficult counterparts and regaining trust.
  • Constructing a positive dialogue on controversial issues.
  • Developing a collaborative approach and professional culture in complex environments.

Seasoned humanitarian managers currently covering a Country Director, Deputy Director, or equivalent role.

Executive Programme workshops are usually held in person over the course of three days. However, different formats may be available upon request. Each event welcomes an average of 30 participants.

…you want to take a closer look at a specific topic or challenge you face as part of your negotiation processes and receive practical guidance from other professionals.

Thematic sessions are based on CCHN research and on the sharing of the participants’ negotiation experiences. We select operational themes or contexts and tailor the session around them.

Some of the topics we tackled in previous thematic sessions include: negotiating humanitarian access and corridors in sensitive contexts, negotiating with the help of interpreters, managing mis- and disinformation in humanitarian contexts, negotiating with armed groups, negotiating in the context of protection or healthcare operations.

Arabic, English, French, or Spanish depending on the geographical focus or topic of each session.

Thematic sessions may take place either online or in person. Length varies depending on the theme discussed. Each session has an average of 30 participants.

No, you will not receive a certificate for this activity.

…you want to gain a fundamental understanding of negotiation tools and methods, share your negotiation experience and learn from others, connect with frontline negotiators in your region or around a specific topic, and set the foundation to attend more advanced workshops in the future.

Each event welcomes an average of 30 participants.

Visit our public events calendar to discover which of our upcoming workshops is most relevant for you, then submit your application online. [email protected]

HKS Case Program


The teaching cases in this section introduce students to the theory and practice of negotiation by emphasizing both analytical and interpersonal skills. Several lessons can be found, including how to trade on differences to create value, overcome a status and power imbalance, build a multi-party coalition, and balance the demands of internal vs. external negotiations.

Evelyn Diop

Evelyn Diop

Publication Date: May 30, 2023

 Evelyn is a seasoned nonprofit fundraising professional with roots in the corporate world, who thrives when faced with a strategic challenge. While she had been successfully leading change as a chief development officer (CDO) at...

case studies on negotiation

Leadership and Negotiation: Ending the Western Hemisphere’s Longest Running Border Conflict

Publication Date: October 4, 2022

For centuries, Ecuador and Peru each claimed sovereignty over a historically significant, but sparsely inhabited tract of borderland in the Amazonian highlands. The heavily disputed area had led the two nations to war—or the brink of...

Simulation - Galvis City

Galvis City Schools Collective Bargaining Simulation

Publication Date: June 8, 2022

This is a seven-party exercise, with six negotiators and one facilitator. Representatives from a large school district and its affiliated teachers’ union must negotiate for three rounds. The Mayor serves as a facilitator and convening...

Issue Brief - “Be SURE” You Are Prepared to Negotiate WELL

Briefing Sheet: “Be SURE” You Are Prepared to Negotiate WELL

Publication Date: May 6, 2022

This briefing sheet reviews a four-step “Be SURE” negotiation preparation framework. It was developed to complement educational and resource materials accessible through the HKS SLATE Negotiate WELL (Work, Education, Life, and...

When Gender Matters in Organizational Negotiations

When Gender Matters in Organizational Negotiations

Publication Date: March 18, 2022

Learning Objective:The overarching learning objective is to help students recognize the situational circumstances that moderate gender effects in negotiation. Core lessons include: (a) A person’s gender is not a reliable predictor of their...

Teaching Case - Priya Iman

Negotiate WELL (Work, Education, Life, & Leadership): A Strategic Preparation Workbook

Publication Date: October 5, 2021

The Strategic Preparation Workbook guides students in preparing for a work or life negotiation so that they are more likely to succeed in negotiations. The Workbook outlines a four-part process in preparing for a negotiation and finishes with...

Teaching Case - Maryam Hassan

Maryam Hassan

Publication Date: March 8, 2021

Maryam and Sameer, brother and sister, were searching for an apartment in Hitech City, Hyderabad. Recent college graduates who were now starting jobs with high-profile technology firms, they wanted to lease an apartment together. The case...

Teaching Case - Shahana Patel

Shahana Patel

Shahana had just received a job offer from a trendy global startup in India, but she was getting married in five months and wanted to negotiate for a short leave for the wedding and a transfer to the company’s Hong Kong office to be with...

Teaching Case - Priya Iman

Priya, a graduate student of public policy, was offered internships from two units within the International Development Fund. One offered a good salary, took advantage of her past work experience but was longer than she wanted; the other,...

Teaching Case - Angel Torres Sequel

Angel Torres Sequel

The Angel case was developed from accounts by students and managers about challenges and opportunities in early career employment opportunities for students in technology and engineering jobs, particularly those from less privileged and...

Teaching Case - Angel Torres

Angel Torres

Teaching Case - “I-We” Self-Advocacy: Negotiating in Early Career

Self-Advocating in Early Career

Many people early in their careers find self-advocacy awkward or may even perceive it as impossible. They don’t know what is reasonable for them to request or propose, they are unsure how to go about it, who their...

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Negotiation Experts

Home » Resources » Case Studies » Negotiating with WalMart Buyers

Negotiating with WalMart Buyers

Walmart buyers are trained to treat their vendors in a variety of ways, depending on where you fit into their plan. This case shares a story of a vendor called Sarah who negotiated a win-win outcome with Walmart.

WalMart, the world’s largest retailer, sold $514.4 billion worth of goods in 2019. With its single-minded focus on “EDLP” (everyday low prices) and the power to make or break; suppliers, a partnership with Walmart is either the Holy Grail or the kiss of death, depending on one’s perspective.

There are numerous media accounts of the corporate monolith riding its suppliers into the ground. But what about those who manage to survive, and thrive, while dealing with the classic hardball negotiator?

In “Sarah Talley and Frey Farms Produce: Negotiating with Walmart” and “Tom Muccio: Negotiating the P&G Relationship with Walmart,” HBS professor Jim Sebenius and Research Associate Ellen Knebel show two very different organisations doing just that. The cases are part of a series that involve hard bargaining situations.

“The concept of win-win bargaining is a good and powerful message,” Sebenius says, “but a lot of our students and executives face negotiation counterparts who aren’t interested in playing by those rules. So what happens when you encounter someone with a great deal of power, like Walmart, who is also the ultimate non-negotiable partner?”

The case details how P&G executive Tom Muccio pioneers a new supplier-retailer partnership between P&G and Walmart. Built on proximity (Muccio relocated to Walmart’s turf in Arkansas) and growing trust (both sides eventually eliminated elaborate legal contracts in favor of Letters of Intent), the new relationship focused on establishing a joint vision and problem-solving process, information sharing, and generally moving away from the “lowest common denominator” pricing issues that had defined their interactions previously. From 1987, when Muccio initiated the changes, to 2003, shortly before his retirement, P&G’s sales to Walmart grew from $350 million to $7.8 billion.

“There are obvious differences between P&G and a much smaller entity like Frey Farms,” Sebenius notes. “Walmart could clearly live without Frey Farms, but it’s pretty hard to live without Tide and Pampers.”

Sarah meets Goliath

Sarah Talley was 19 in 1997, when she first began negotiating with Walmart’s buyers for her family farm’s pumpkins and watermelons. Like Muccio, Talley confronted some of the same hardball price challenges, and like Muccio, she acquired a deep understanding of the Walmart culture while finding “new money” in the supply chain through innovative tactics.

For example, Frey Farms used school busses ($1,500 each) instead of tractors ($12,000 each) as a cheaper and faster way to transport melons to the warehouse.

Talley also was skillful at negotiating a coveted co-management supplier agreement with Walmart, showing how Frey Farms could share the responsibility of managing inventory levels and sales and ultimately save customers money while improving their own margins.

“Two sides in this sort of negotiation will always differ on price,” Sebenius observes. “However, if that conflict is the centerpiece of their interaction, then it’s a bad situation. If they’re trying to develop the customer, the relationship, and sales, the price piece will be one of many points, most of which they’re aligned on.”

Research Associate Knebel points out that while Tom Muccio’s approach to Walmart was pioneering for its time, many other companies have since followed P&G’s lead and enjoyed their own versions of success with the mega-retailer. Getting a ground-level view of how two companies achieved those positive outcomes illustrates the story-within-a-story of implementing corporate change.

“Achieving that is where macro concepts, micro imperatives, and managerial skill really come together,” says Sebenius. And the payoffs—as Muccio and Talley discover—are well worth the effort.

Sarah Talley’s Key Negotiation Principles

  • When you have a problem, when there’s something you engage in with Walmart that requires agreement so that it becomes a negotiation, the first advice is to think in partnership terms, really focus on a common goal, for example of getting costs out, and ask questions. Don’t make demands or statements. Rather ask if you can do this better. If the relationship with Walmart is truly a partnership, negotiating to resolve differences should focus on long term mutual partnership gains.
  • Don’t spend time griping. Be problem solvers instead. Approach Walmart by saying, “Let’s work together and drive costs down and produce it so much cheaper you don’t have to replace me, because if you work with me I could do it better.”
  • Learn from and lobby with people and their partners who have credibility, and with people having problems in the field.
  • Don’t ignore small issues or let things fester.
  • Try not to let Walmart become more than 20% of your company’s business.
  • It’s hard to negotiate with well trained buyers who know that their company could put your company out of business.
  • Never go into a meeting without a clear negotiation agenda . Make good use of the buyers’ face time. Leave with answers. Don’t make small talk. Get to the point; their time is valuable. Bring underlying issues to the surface. Attack them head on and find resolution face to face.
  • Trying to bluff Walmart buyers is never a good idea. There is usually someone willing to do it cheaper to gain the business. You have to treat the relationship as a marriage. Communication and negotiated compromises are key.
  • Don’t take for granted that just because the buyer is young they don’t know what they are talking about or that it will be an easy sell. Most young buyers are very ambitious to move up within the company and can be some of the toughest, most educated buyers you will encounter. Know your product all the way from the production standpoint to the end use. Chances are your buyer does, and will expect you to be even more knowledgeable.

My top 3 favorites are don’t ignore small issues, be a problem solver and hold on to a high percentage of your business. You should always communicate when something comes up instead of letting it fester because it could develop into something big that would have never happened if discussed in the first place. When you develop your own business you should never let someone take over more percentage than you have because then you will lose control over what you started. Never gripe and be a problem solver. Larger companies don’t want to hear complaining they want to see action and larger profits

I have negotiated with Walmart for large and small business and I don’t recall any subjects of the conversations that were valued more or equal to price and their margin protection. Logistics or supply it was still a an unyielding stand of profit. Kroger,Publix, Winn Dixie, would &will negotiate for volume -promotions -discounting. Your article is not specific enough for analysis nor to draw the conclusions you present.

The two cases, one with a large vendor and the other with a small one, both working with Wal-Mart reframes some of the classic views of negotiating in a practical way.

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I played the role of Sally’s agent and recent partner at the firm that manages acts for celebrities. The other part of Lyric’s business manager during the negotiations was played by JInwook Bae, with whom we met and arrived at a possible deal to have Sally Soprano perform the role of Norma.

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Be SPECIFIC with your answers, citing precise examples from the case. Use more space if necessary. IMPORTANT! 10% of your grade on this assignment will be based on the visual quality of your written work. This includes (but is not limited to) providing all requested information, proofreading your document, running a grammar and spell check, and insuring that your document is properly formatted and aligned. It is fully expected that you will submit “professional quality” work to your instructor, both in content and in presentation.


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Following between the co – owners of the Hackerstar Company, I was appointed the chief attorney representing one of the owners by the name Alan Hacker. This negotiation is taking place so that the two can come to an agreement that will see Litigation ruled out, Hackerstar receive royalties that Star is claiming and Hacker gets to move on with his innovations with another group of investors.

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As of date, the National Football League is running its 2012 draft, which is a far cry from what happened just a year ago. When the labor talks between the NFL and the union broke down, the Union decertified, allowing individual players to file lawsuits against the NFL. It took a little over a year before the lockout came to an end and only because the owners agreed to the recommended settlement of the NFL Players’ Association.

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Why did Google and China choose to negotiate a partnership, bearing in mind what was at stake and the difference in their individual objectives? Google would risk going against one of its principles (do not be evil), whereas China would need to rethink its censorship policy.

Recommended Course of Action:

Business institutions and states should look at the best option (negotiation) to ensure that they fulfill their needs as much as possible. Therefore, both parties should be ready to make compromises, but ensure that they do not end up on the losing end.

Basis for Recommendation:

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Liu, Y., Tang, W., He, J., Liu, Y., Ai, T., & Liu, D. (2015). A land-use spatial optimization model based on genetic optimization and game theory. Computers, Environment, and Urban Systems, 49, 1-14.


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Organizations mostly find it difficult managing their finances especially when some employees stay dormant because their services are not required. Medical Care centers have different types of staffs who have specified duties. As a manager in one of the leading healthcare center, I wished to cut down the overhead costs while maintaining the revenue cycle. The process was critical, but I managed to resolve some of the key issues. The following is a negotiation plan and the resolving steps.

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Over the past 100 years, more than 60 percent of world’s wetlands have been destroyed as people search for land to settle on, farm, and establish other types of business investments. Currently, wetlands cover 6 percent of the world’s surface providing a wide array of environmental services, including soil erosion control, water storage and filtration, a buffer against flooding, biodiversity maintenance, nutrient recycling, nursery for fisheries, and carbon storage among others.

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Auctions: The various mechanisms of auctions researchers have designed including English, Vickery and Dutch. The English auction is the one whereby the highest bidder is the one who wins the price offered. The Vickery auction adopts an approach whereby the one who wins is the highest bidder and instead pays second highest offer price, whereas in the Dutch the person who auctions starts with a very high price and gradually lowers it up to the point where acceptance is made by the first bidder.

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case studies on negotiation

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Teaching Negotiation: The Art of Case Study Writing

By PON Staff — on September 26th, 2017 / Teaching Negotiation

case studies on negotiation

Little has been written on what it takes to create a great case study to use for teaching negotiation.

What needs to be taken into account in deciding whether a particular negotiation merits a written case study?  What are the guidelines for writing negotiation cases?  Do the traditional guidelines for preparing case studies in other fields apply?

Jim Sebenius , the Gordon Donaldson Professor of Business Administration at Harvard Business School, and Director of the Harvard Negotiation Project , addressed these questions in his presentation at the NP@PON Faculty Dinner Seminar on October 7, 2010. His article, “ Developing Negotiation Case Studies ,” began as a memo to a novice case writer about how to write an effective negotiation case. Now it is a full-length article that will appear in a forthcoming issue of Negotiation Journal .

Teaching Negotiation

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Sebenius offers three kinds of advice to anyone thinking about writing a case as the basis for teaching negotiation to students or other interested audiences :

  • How to decide whether a case lead is worth pursuing;
  • How to select a perspective suited to your teaching objectives; and
  • Ten nuts and bolts suggestions for structuring and producing an excellent case study.

Begin by asking,  “Will this case contribute to better theory and yield prescriptive implications that can be generalized across diverse contexts?”

Sebenius also asks, “Will the case foster learning by allowing the reader to grapple with the experiences of others, and will it reveal the power and limits of received theory?” He urges case writers to clarify their goals and answer the question, a case for what? Knowing the purpose of the case will make it easier to decide the level of detail at which the writing should proceed.

Sebenius encourages case writers to rigorously investigate the worthiness of their protagonists in order to help answer the question, a case of what?

A compelling narrative will likely emerge if protagonists have attempted to overcome high barriers. This will increase the odds that the case reader will “relish the challenge” of wrestling with the same decisions that faced the protagonists. On the other hand, Sebenius is quick to caution against being taken in by the lure of a writing a case mainly on the basis of high profile protagonists or well-known settings when the negotiation itself may not be that interesting.

Ultimately, a good case prospect will permit the case writer to fill in two key blanks: “This appears to be an intriguing case of ___, and thus, worth delving into more deeply, in order to use for the following reason(s)___.”

Once it is clear what the case is for and what it is a case of, the case writer must choose a case type and perspective.

Protagonist vs. Situation:

Protagonist-centered cases require the reader to grapple with difficult decisions and often to understand the challenge from one or more specific perspectives, placing the reader in the shoes of the person(s) facing specific negotiation challenges . Situation-centered cases are written from a non-specific perspective, allowing the reader to understand all the details of what occurred.

Decision vs. Autopsy:

Decision cases are experiential, putting the reader in the shoes of the protagonist, requiring them to grapple with tough decisions. They are often written in “real time,” so students must make decisions without knowing the outcome. “Autopsy” cases give the reader all the information from beginning to end, inviting discussants to analyze what did happen and why; they normally lack the tension of decision cases.

Library vs. Field:

Library cases rely on secondary materials while field cases are based on interviews and access to non-public information. Though field cases are generally preferable, Sebenius acknowledges the challenge of getting protagonists to approve of what’s included in the case.

Actual or Disguised:

Actual cases use accurate names and facts while disguised cases preserve the essential negotiation issues while hiding these identifying characteristics. Though actual are preferable, some situations may be too sensitive to include real names.

John Hammond, an experienced Harvard case writer, points out that disguising cases can invite protagonists to be more forthcoming.

For teaching negotiation purposes, Sebenius has found protagonist-centered, actual, decision cases that are field-based are the most effective. They require students to face tough decisions based on imperfect information and uncertain circumstances-exactly as is the case in reality.

The last section of the report gives nuts and bolts advice on how to structure and produce cases. They should have multiple parts that advance the case, highlighting multiple decision points. Part A might present the reader with background information and indicate the specific challenges facing the protagonist. Part B could then explain what the protagonist did to address these challenges. Cases do not have to be limited by only two parts, of course, and they can be enhanced by video supplements of various kinds.

Sebenius urges case writers to begin by getting down the critical elements of the story.  Structuring the material under various analytical headings is (initially) secondary to capturing the raw material for the ultimate case. The narrative should include enough information for readers to identify barriers to agreement. Case writers should pursue interviewees from all sides of the negotiation or dispute and seek feedback from outside readers to help identify gaps and biases. Although it is challenging, Sebenius urges cases writers to limit themselves 4-6 pages (ideally) to ten printed pages (maximum) for each part (“A”, “B”, etc.). He points out that there are even 2-4 page cases that contain all the classic tensions needed to support rich discussions yielding powerful lessons. He suggests that case authors present sufficient raw material to enable readers and discussants to draw their own conclusions, and save any editorializing and analysis for an accompanying teaching note.

Reactions to Sebenius’ Negotiation Pedagogy at the Program on Negotiation (NP@PON) Presentation

One participant asked to what degree Sebenius’ criteria for writing negotiation cases might apply to preparing other kinds of teaching cases as well.

Does teaching negotiation present specific challenges or opportunities with regard to the cases we develop or how we teach them?

Sebenius acknowledged that there are a lot of articles on how to write great case studies.  Harvard Business School, which played a key role in developing the case method of instruction, has produced many works on this topic. For the purposes of teaching negotiation cases, though, one must be sure that certain key elements are covered including multiple perspectives, the interests of the parties, their BATNAs, what was and wasn’t communicated, the role of “second tables,” etc.

In terms of case type and perspective, John Hammond (Harvard Business School) suggested that many cases are hybrids and cautioned against such either/or choices. For example, if you disguise a case, people may be more likely to be open about what really happened. Sometimes it is possible later on to turn a disguised case into an attributed case. Lisle Baker (Suffolk University Law School) challenged Sebenius’ assertion that open cases are always preferable in a classroom setting.

Baker suggested that there might be value in a disguised case rather than an open case when dealing with a generalist audience with a particular bias.

Mike Wheeler ( Harvard Business School ) asked, “Does a case have to be real to be an effective teaching piece?” A number of participants highlighted situations in which fictional cases were effective teaching tools. Several participants even suggested potential ways to evaluate the effectiveness of fictional versus true cases.

Dan Shapiro ( Harvard Medical School ) suggested comparing the effectiveness of two classes that emphasize the same core concepts, one using traditional cases and the other using the Iliad or Shakespeare.

Susan Podziba (a Boston area mediation practitioner) said that fiction often “gives us something that resonates with our humanness and with our experience outside an analytic negotiation.” The audience agreed that there are many lessons that can be drawn from a close analysis of fiction writing, including how to keep a reader engaged and the undeniable power of a compelling narrative.

Wheeler commented further on case writing experiences he has had in which a case develops meaning that he could not have anticipated from the start. There was agreement that case writing is usually an iterative process and that a case continues to reveal itself and develop as it is being written and taught. Susan Podziba asked, “How do you capture this understanding for new case writers and help them remain open to surprises as they are writing a case?”

In light of the Sebenius challenge regarding keeping cases succinct, Mike Wheeler asked whether it might be useful to present cases in graphic form.

Gordon Kaufman (MIT’s Sloan School of Management) suggested we should take a successful case and present it in classic and graphic form and teach it to multiple classes, so that we could have a basis for judging the incremental effectiveness of one modality over another.

Panelists elaborated on ways of evaluating the effectiveness of cases and case instruction in the teaching negotiation field. Larry Susskind (MIT’s Department of Urban Studies and Planning) asked if there is an empirical basis for Harvard Business School’s commitment to the case teaching method. He suggested asking alumni to identify specific cases that enhanced their ability to handle a negotiation in practice. Mike Wheeler pointed out that because cases are part of a larger curriculum, it might not be possible to figure out the impact of particular cases on a student’s subsequent negotiating capabilities.

Florrie Darwin (Harvard Law School) emphasized that there is a difference between what people learn and what people remember. Sebenius summarized the ongoing challenge in the field “to explore more systematically the various aspects of cases and how they map onto student learning.”

Written by Carrie O’Neil, edited by Warren Dent, taken from the bi-annual e-newsletter Negotiation Pedagogy at the Program on Negotiation E-Newsletter (NP@PON), which can be found here .

Related Article: New Program on Negotiation Teaching Materials About Great Negotiator Martti Ahtisaari

Originally published in 2010.

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Preparing for negotiation.

Understanding how to arrange the meeting space is a key aspect of preparing for negotiation. In this video, Professor Guhan Subramanian discusses a real world example of how seating arrangements can influence a negotiator’s success. This discussion was held at the 3 day executive education workshop for senior executives at the Program on Negotiation at Harvard Law School.

Guhan Subramanian is the Professor of Law and Business at the Harvard Law School and Professor of Business Law at the Harvard Business School.

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Guhan Subramanian and Nithyasri Sharma

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Case Studies

Since ENS was established in 1978, it has become the trusted partner of global brands and large corporations. To understand the pivotal role ENS has played in various industries, here are a few stories of how our programs have transformed the way people and organisations view negotiations and the successful outcomes it has helped achieve.

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Restoring Predictability in a World-wide Commodity Agreement

The possibility of a second five-year marketing agreement between the world's largest producer of a certain commodity and a major trading house looked to be destroyed. Our advisers were called in by the producer company to give process advice, check the quality of preparation and to rehearse the team. The second agreement was signed to the benefit of both parties.

Achieving Cultural Shift in Workplace Agreement Negotiations

A client in the energy industry was faced with complex workplace negotiations and impending hostile strike action. ENS was called in to intervene and facilitate peaceful negotiations between the two parties.

Humanising the Process of Hostile EBA Negotiations

A client in the printing industry needed to conduct three separate negotiations quickly to avoid threatened strike action. We helped all sides to focus on relationship aspects and 'humanise' the process. Negotiations were concluded quickly, industrial action was avoided and the level of hostility significantly reduced.

Strategic Response to a Price Increase Demand

A client received a demand for a cost increase of over 20%. ENS trained the negotiating team and developed a negotiating strategy that focused on identifying and managing risks via structured questioning. After the negotiation, costs were reduced by more than 20% without straining the business relationship.

Empowering Key Staff to Become Effective Negotiators

The client was renegotiating an annual supply contract with a large supplier that set a contract price based on faulty assumptions on upward annual price reviews.

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Rethinking Negotiation

  • Barry Nalebuff
  • Adam Brandenburger

case studies on negotiation

For decades, negotiators have been working out agreements by focusing on interests, not positions. But the messy problem of how to share the gains created by deals has remained unresolved—until now. The answer, argue Yale’s Nalebuff and NYU’s Brandenburger, lies in accurately identifying and sizing the negotiation “pie,” which they define as the additional value produced by an agreement to work together. It’s the value over and above the sum of the two sides’ best alternatives to a negotiated agreement, or BATNAs.

The pie most people have in their heads, however, is the total value available to be split. Because of this, they argue over the wrong numbers and issues, taking positions that they think are reasonable but that are in fact self-interested.

Once the pie is properly understood, the allocation rule is simple: The parties in a negotiation have an equal claim on the pie, so it should be divided evenly. This is true regardless of what they can accomplish on their own, because both are equally needed to create the gains. This principle can be applied in a variety of increasingly complicated real-world scenarios, which the authors walk readers through in this article.

A smarter way to split the pie

Idea in Brief

The problem.

People don’t understand what’s really at stake in a negotiation. Their misconceptions make it much harder to reach an agreement.

Why It Happens

Negotiators focus on the total amount to be divided, not on the value created by an agreement. That leads to conflicting views on power and fairness.

The Solution

Recognize that the gains to be shared are the additional value the agreement creates over and above the sum of the two sides’ best alternatives. This negotiation pie should be divided equally, because both sides are equally essential to creating it.

Negotiation is stressful. A great deal is at stake: money, opportunity, time, relationships, reputations. Often that brings out the worst in people as they attempt to take advantage of the other side or try to look tough. So wouldn’t we all be better off if there was a way to treat people fairly in a negotiation and get treated fairly in return? In the following pages we’ll offer a simple, practical, field-tested approach that enables you to do just that.

  • Barry Nalebuff is the Milton Steinbach Professor at Yale School of Management and a cofounder of Honest Tea.
  • Adam Brandenburger is the J.P. Valles Professor at the Stern School of Business at New York University, distinguished professor at NYU Tandon School of Engineering, and faculty director of the Program on Creativity and Innovation at NYU Shanghai.

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case studies on negotiation

Our Top 10 Practice Negotiation Exercises and Activities

If you are looking to take your negotiation skills to the next level from the comfort of your own home, check out our online negotiation course. everyone knows that practice makes perfect....

If you are looking to take your negotiation skills to the next level from the comfort of your own home, check out our  online negotiation course .

Everyone knows that practice makes perfect. Similarly, if you want to get better at negotiation, you need to practice negotiating.

Tips for Practicing Negotiating Exercises

Negotiating exercises are a valuable opportunity to recognize your weaknesses and improve upon what you bring to the table. Like all things, some people have a particular talent for negotiation, but they require nurturing to help them to reach their potential.

So, whether you’re an experienced negotiator or a total novice, what does it take to get ready to bolster your negotiation skills?

Get in the Right Mindset

The first step is to establish the correct mindset for negotiation. It requires you to open your mind and be willing to accept criticism under the guidance of a mentor.

In other words, avoid trying these exercises while stressed, frustrated, or angry.

Change Up the Exercises

Every exercise on this list has a specific principle behind it. However, there’s little benefit in repeating the same exercise in precisely the same way.

All negotiation scenarios are different, and you must become comfortable deploying the fundamentals against whatever you encounter. That means switching up how you deploy negotiation exercises.

Work Together with Your Colleagues

The obvious benefit of working with multiple partners is that you can try your skills on multiple people and personalities. It’s a challenge that will make you more comfortable walking into the unknown.

However, another advantage to working with your colleagues on this is that everybody learns more about themselves, including their strengths and weaknesses. It’s a chance for each person to involve themselves in a journey of self-discovery.

Training received by a single person can be used to boost the power of the collective.

Minimize Distractions

Ensure that you set aside a dedicated period during the day to participate in these exercises. Negotiations are serious scenarios with little room for error.

Treat your exercises with the same seriousness as a formal business meeting.

Engage with a Mentor

How do you know if you’re progressing in your exercises? A mentor will tell you. Mentors are experienced operators with the skills to recognize your flaws and provide tailored recommendations for addressing them.

This is precisely what KARRASS seminars provide you with access to. Whether in-person or virtually, negotiation practice sessions allow you to receive feedback from a real expert.

Our 14 Favorite Negotiation Practice Exercises

Regular practice is essential for any master negotiator in the making. You must avoid entering your first negotiation without the proper training. Even trying this puts your company at risk because if you fail at negotiating, it could have genuine implications for your organization's future.

Exercises aim to replicate the skills and scenarios you will likely encounter in the field. By having a range of exercises at your disposal, you will be poised to excel.

Below is a list of 10 Negotiating Exercises and Activities that will help you improve your skillset. 

1. Learning Both-Win® Strategy: Practice Bartering in a Market

We are conditioned to believe that when a price is printed on a tag, it’s no longer up for negotiation. But what that tag is actually telling us is that this is what the store owner has decided is the best match between market pressures on the seller’s side and price incentives on the buyer’s side. When you find a product at a low price, you feel like you’ve won the price comparison game, and when you buy a product, the seller feels they have won your business.

But that scenario just proves how limited our imaginations are when we approach the buyer/seller relationship. If you as the buyer can think outside the box, you may find that you can create an opportunity for both of you to get what you want: a Both-Win ® outcome.

Both-Win® is possible when you can create new value where none was seemingly available before.

2. Practice Reading the Room

Too often we focus on information and forget that we are negotiating with fellow humans. As any great poker player will tell you, all humans have tells of one sort or another. And as any stand-up comic will tell you, the better you can read the room, the better you’ll be able to appeal to the specific people in front of you. Every audience is different, and every negotiating table is different.

When we negotiate, oftentimes our words say one thing while our demeanor says something else. Practice paying attention to how others communicate more than the information they are saying by the way they sit, whether they are looking at you or looking away, and whether their faces are open and interested or closed and distracted. If you practice this with people you know well, then you can start to learn how certain tells indicate a willingness to talk further, a fear that they won’t get what they want, or (hopefully) a distinct interest in what you have to offer and motivation to learn more! This will help you build your mental encyclopedia of body language so that when you’re at a negotiating table, you have some tools to draw on every time to make a decision about whether to go in one direction or another.

But there is more to reading the room than body language. You also want to read the table, so to speak, even before you’re sitting down to talk. Your relationship with your own negotiation team and the other side starts before you arrive. As you work with buyers or sellers by phone and email, practice picking up cues. Which issues seem to make the other side tense? Which issues does the other side keep returning to? Make a note of these. And while you’re at it, see if you can figure out some background information.

Try to figure out who at the institution drives decisions, and of course make a note of this information, too.

You can practice all of this when you’re making those everyday decisions with friends, family, and the person trying to sell you a dishwasher or car (see number 5 below).

3. Role-Playing Scenarios

There is nothing better for honing your Effective Negotiatin g® skills than role-playing with a friend or associate. We hear from our seminar participants that the case studies are especially effective because there’s nothing like adding some pressure to help us see where we make mistakes. Role-playing also helps point out how effective certain tactics can be in a pressured situation.

Find a friend or associate to practice with to help you focus on where your strengths and weaknesses are, and be sure to incorporate some of the elements that make a real negotiation complicated and unpredictable to get the most out of it.

4. Practice Negotiating from the Worst Position Possible

Think about something practical that you’ve been working toward getting for a long time without success. It’s helpful to practice starting from a position of wanting something -- like a new dishwasher or car -- from a position of having limited funds. This is where you can practice all the techniques of negotiation that move you from an impossible position (the dishwasher you want costs $X but you only have $X-20% to spend) to a handshake and a good deal.

Start by preparing to walk into a negotiation with this kind of serious limit. Think about what kinds of research could help you figure out how to defend against nibbles, for example. This advance research will give you a larger playing field for your negotiation, setting you up to get what you want from the negotiation!

As you negotiate, notice which techniques work in a personal negotiation that wouldn’t work as well in a team negotiation between much larger entities. For example, an interaction between a single buyer and a single seller can rely a lot more on building personal satisfaction for the seller (building satisfaction), possibly by building the authority of the seller to their own organization. Reflect on how a team negotiation requires some different tactics.

5. Know the Power of the “No!”

We are conditioned to negotiate toward a “yes-yes” situation. Anthropologists tell us it’s in our DNA to want to reach consensus and find harmony, after all we are social animals. But one of the most powerful tools in a negotiator’s box is the willingness to say a firm “no” and stand by it. There are several important reasons why you should keep this tactic in mind every time you come to the table.

Negotiations can easily become all-encompassing, and we might feel as though a perceived win or loss in this negotiation is a measure of something larger. The danger there, however, is that each negotiation is actually just one along a long string of negotiations we will take on -- often with the same entities -- over a long period of time. When appropriate, and when it’s used well, saying “no” this time can set an anchor point for future negotiations, giving you more power the next time you’re at the table. This could even push the other side toward new concessions in advance of your next meeting, thereby doing some of your negotiating for you.

Practice using the strategic “no” and, under the right circumstances, you may well find that concessions that the other side was holding in their back pocket come up more quickly, improving your success at Effective Negotiating®.

6. Understanding Assumptions and Setting a Guiding Principle

We tend to assume that other people broadly share the same assumptions that we do about what is valuable and what is not. This can be dangerous, however, because while most of us share some assumptions, in practice that can sometimes mean very different things to different people. This is why it can be helpful, and sometimes even crucial, to understand how to set a guiding principle at the beginning of a negotiation. As you talk through everything from what to have for dinner to whether you should renegotiate last year’s deal with a supplier or buyer, notice that you probably see value differently from others you’re talking with.

Notice that if you set value differently even with somebody who is supposed to be on your own team, this leads to problems. If it’s getting late at dinnertime, you might suggest going out to eat as a way of decreasing stress, but your spouse might see staying home as the much less stressful option. Unless you name this up front, you might find you’re arguing for the same thing -- decreasing stress -- while effectively negotiating against each other. Similarly, when you push your organization to renegotiate a big deal your team settled a year ago, you might be thinking about the value of bringing the price down, while somebody else on your team might see value in holding off on some major demands that you managed to stave off last time. If you aren’t on the same page as your own team about what the guiding principle is and what has value, then you will be working against each other.

In those scenarios, you’re working with somebody on the same side. But this is also an important tool to use with an opposing team. For example, if you can set a guiding principle up front that as the seller you deserve a fair profit, then you have an assumption built into the negotiation that is going to benefit you.

7. Build Your Team Skills

The great Kareem Abdul-Jabbar once said: "Five guys on the court working together can achieve more than five talented individuals who come and go as individuals." When a star athlete gets onto the field or the court for a competitive sport, they need to decide how much their strategy is to be a lone superstar and how much their strategy relies on working with the team. Even if you do most of your negotiating on your own, you have some network of connections -- even if this includes your own distributors or bulk vendors -- that you have to keep in mind when you’re sitting down to negotiate or renegotiate an important contract.

One of the major pitfalls of a team negotiation can come from competitiveness or just plain disorganization within the team. The best way to head this off is to practice your team skills all the time. When you’re in the thick of a negotiation, everybody on the team needs clearly designated roles. But before you get there, you’ll need to have some open airing of ideas and approaches. Somebody on your team is going to be a great researcher while somebody else is going to be great at figuring out the margins on the fly.

Even if your next big negotiation isn’t coming up anytime soon, you can keep honing the strategizing and cooperative skills that will give you that crucial edge for Effective Negotiating® when the time comes.

8. Be Adaptable to Changing Conditions

Between the time you make your first call to a buyer or seller and the time you sit down, many of the factors will change. These will change again between the time you sit down and the time you shake hands. Every negotiation means preparing for different demands or possibilities, but inevitably unexpected issues come up. How can you prepare for this so you are in the best possible position to deal with the unexpected?

Practice being ready for the inevitable by taking a little time to do extra research ahead of time. As technologies evolve and companies change focus, their interests change. Remember that you are negotiating not only with the partner you have in front of you, but also with the future version of this partner. If a corporate parent company is merging or shifting within the market, how could this change their priorities between your last negotiation and your next one? What partnerships do you already have that could give you an advantage at the table? What do you have to offer that could be valuable now in a new way?

It also pays to think about where your organization is headed. Could you offer a concession in return for something especially valuable that the buyer or seller has newly available to them? Thinking in these terms can help you to practice reacting to the unexpected by bringing your own unanticipated options to the table.

9. Upgrade Your Skills!

At some point as you do these exercises you’ll realize that you’re very good at figuring out some of the body language, guiding principles, and research that will put you in a stronger negotiating position. But that’s no substitute for gaining the tested skills that professional training can offer you.

Karrass is on the leading edge of techniques for Effective Negotiating® in a changing world. Every day we are training your competitors in crucial tactics to get what they want from you, and your best defense is a finely tuned offense to counter these techniques and make your mark next time you sit down at the table!

10. Talk to Experts or Take a Course!

KARRASS offers Effective Negotiating® seminars across the world for individuals and teams as well as “ In-House ” tailored packages to address the specific needs of your organization. Let us show you the value of bringing real expertise into the room with you, and demonstrate why we have been an industry leader for almost half a century.

There’s Nothing Like Our Private Negotiation Training!

Ramp up your negotiation skills and learn how to be the go-to person in your company for tackling even the most formidable negotiation teams. At KARRASS, we have provided virtual and physical negotiation training sessions to more than one million business people worldwide.

Take advantage of experienced mentors, structured teaching, and value-driven activities to strengthen your strengths and weaken your weaknesses. Sign up for KARRASS negotiation training now.

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case studies on negotiation

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Breaking an Impasse By Dr. Chester Karrass You did everything right, yet you find yourself at an impasse with the other party...

Dealing with a Person Saying No to Everything & Learning How to Say No More Yourself Dealing with people who always say no can be a perplexing and challenging experience. Whether it's a colleague, boss, friend, family member, or partner, constantly facing resistance can create frustration and hinder effective communication...

Famous & Inspirational Negotiation Quotes Negotiation, as a dynamic and ever-evolving art form, has been an integral part of human interaction for centuries. To fully grasp its significance and master its intricacies, we must delve into the annals of history and explore the profound insights and perspectives of those who have come before us...

case studies on negotiation

case studies on negotiation

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Case study on negotiation

copperhead, one of the world’s major providers of global supply management software and services, helps companies reduce costs through efficient product and services sourcing. It has handled more than $50 billion worth of products and services in the oil and gas, other natural resources, retail, transport, finance, and Industrial sectors for customers Including General Motors, Nestle©, Shell, Japan Energy, Mediumistic, and Academy Cheapest. Shanghai-based JAM, one of the biggest gaming and hospitality companies in Asia, is owned by Chinese businessman Tan Www

Boo. This case study revolves around the period when KM has been a HyperCard client for six months, and the companies have signed an agreement to conduct projects.

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The first, completed in March 2005 and tremendously successful, saved JAM some $1 million, and the second one is set to start. Lamprey’s with the results, JAM wishes to explore the possibility of other Joint endeavourers with HyperCard. To this end, a meeting is arranged between Jam’s Senior Vice-President of Finance Iris Ma and Hyperspace Regionalism’s Directorate Dubious, and attended by Jam’s

Vice-President for Procurement Henry Chow and HyperCard Sales Group Director Layton Pang. Ma is keen to explore more projects with HyperCard and has tasked Chow to follow up with Hypermarket soon as possible. The managing director of HyperCard suggests that a session be arranged with key stakeholders from both companies to discuss and assess possible opportunities for other KM projects.

The Scene Ma and Chow agreed to the suggestion and asked that a proposal be submitted to JIM after the opportunity assessment meeting that was attended by Chow, his assistant Mary he, who is also the purchasing manager, and two members from

HyperCard. Both parties Identified ten possible projects. Exe asked for a proposal to be submitted to KM through her, and HyperCard provided a competitive price package that Included services over a twelve-month period. As Is to be expected from a Chinese company like KM, Exe asked for a reduction in the licensing fee, additional program management days (at no extra cost), and an extension of the software term from twelve months to twenty-four months. In reply, HyperCard put in writing its discussions to date with JAM: 1. KM had agreed that HyperCard could add value to the projects Identified.

JAM would sign for a ten-project package to get a competitive price. 3. If HyperCard could meet SMS demands, the latter would sign the contract by May-end 2005. Exe agreed to point one above, but was noncommittal on points two and three.

After much discussion, HyperCard agreed to lower its fee and provide KM additional program management days at no additional cost. However, HyperCard said it could not agree to extend the twelve month term for use of the software without charging extra. Then, to complicate matters further, Exe suggested that KM could not commit to an agreement even If all the Issues were solved.

I nee most recent negotiations were contacted quilt nasally, slice HyperCard knew that Exe was not the decision maker and approval had to come from her top management. Negotiations to Date Hyperglycemia’s concern was how likely JIM would be to enter into an agreement even if the issues were resolved, and within what time frame.

JIM argued that the proposed price was beyond what it could afford, although it recognized the need for help from HyperCard to implement the projects, and that it needed twenty-four months to implement the ten projects due to its lack of manpower.

HyperCard took the position that, while it was prepared to look into the fee structure and program management term as part of the total package, the request for twenty-four months was not reasonable. Although it reasoned that other organizations were able to implement ten projects in twelve months, to satisfy JIM, HyperCard negotiated a mid- way solution: a maximum of eighteen months. When one week passed and there was no response from JIM, HyperCard asked if it would be prepared to sign if HyperCard acceded to its three requests.

Exe replied that she would submit the proposal for approval to her superiors, Tan and Ma, but added that there was no guarantee the agreement would be signed by the end of May. From Hyperspace perspective, all the issues presented by JIM had been resolved?yet there was still no deal.

When asked about the status of the project, JIM cited staff turnover, but then mentioned another possible IT project where there was a clear need for HyperCard. The discussion ended with JIM requesting that HyperCard prepare the preliminary work and submit yet another proposal.

Based on the updated information, it appeared that the IT reject might get underway earlier than the previously proposed ten projects. Moreover, given that this project had an entirely different scope, there was a strong argument to negotiate a separate deal for it. Whichever proposal JAM wished to undertake first, HyperCard was ready to negotiate and finalize an agreement, but it could not yet tell whether the latest development was a genuine project or a further stalling tactic.

Observations 1.

Both parties acted rationally in the way the negotiations were conducted, and it helped that the relationship between them was excellent from the start. . HyperCard gave in to Jams demands in the hope of concluding the agreement quickly and starting the projects. But JIM continued to indulge in last minute wavering and only introduced a new project, which took the parties away from the initial negotiations. 3.

The way HyperCard responded to Jam’s delaying tactics, showed it’s patience and its determination to get the deal done. 4.

Meanwhile, JIM believed it was negotiating from a position of strength, having even gone so far as to assert that it had in-house a system similar to that of HyperCard that could probably fulfill its deeds, even though without the sophistication of the HyperCard product. 5. Going forward, it was critical that HyperCard engage with Ma, the senior vice-president and decision maker, since the groundwork had been laid with her staff.

But they were finding it difficult to do so due to the complicated hierarchy of the Chinese companies. . HyperCard reflected that maybe it should have asked for the agreement to be signed within a fixed time when it met Jam’s initial demands, although JIM had previously delayed decision making on other projects. Conclusions This case is typical of what vendors face in a competitive, hi-tech environment, and Illustrates ten opportunity teeny nave to reduce tenet price. Handle appropriately, a win-win outcome is not difficult to achieve. From this case study, at least two scenarios for short-term success can be derived.

First, assuming the vendor, is taking a tough stance, you can take a long-term perspective and conclude the first sale with a friendly, competitive attitude, countering the buyer’s demands with suitable offers, while never losing sight of your determination to bag the order. But, besides showing friendliness, flexibility, and determination, you must show the potential buyer that oh will be there for them over the long haul. For this you require people with leadership qualities in your team.

Should one lack high-quality leaders in your team, you have the option of a second scenario. In this case, you would show, right from the beginning,that your team comprises hard workers who will do whatever the buyer needs.

By adopting Chinese-style service orientation and dedication and making your team indispensable to the potential buyer, being available daily, and making yourself virtually a part of your opposite number’s staff?you could clinch a deal. HyperCard failed to show the requisite service orientation and commitment.

From the perspective of longer-term success, an initial achievement provides the opportunity for friendship to be cultivated with the client company’s key people, perhaps even with the CEO, the final decision maker. Should one eventually become accepted as “family,” the client will telephone you for what they want and no longer require competitive quotations. But to reach that point, you will have had to develop a genuine friendship and service orientation with those at the top of the client company.

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Conflict-based negotiation strategy for human-agent negotiation

  • Open access
  • Published: 03 November 2023

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  • Mehmet Onur Keskin 1 ,
  • Berk Buzcu 1 &
  • Reyhan Aydoğan   ORCID: 1 , 2  

Cite this article

Day by day, human-agent negotiation becomes more and more vital to reach a socially beneficial agreement when stakeholders need to make a joint decision together. Developing agents who understand not only human preferences but also attitudes is a significant prerequisite for this kind of interaction. Studies on opponent modeling are predominantly based on automated negotiation and may yield good predictions after exchanging hundreds of offers. However, this is not the case in human-agent negotiation in which the total number of rounds does not usually exceed tens. For this reason, an opponent model technique is needed to extract the maximum information gained with limited interaction. This study presents a conflict-based opponent modeling technique and compares its prediction performance with the well-known approaches in human-agent and automated negotiation experimental settings. According to the results of human-agent studies, the proposed model outpr erforms them despite the diversity of participants’ negotiation behaviors. Besides, the conflict-based opponent model estimates the entire bid space much more successfully than its competitors in automated negotiation sessions when a small portion of the outcome space was explored. This study may contribute to developing agents that can perceive their human counterparts’ preferences and behaviors more accurately, acting cooperatively and reaching an admissible settlement for joint interests.

Avoid common mistakes on your manuscript.

1 Introduction

Negotiation is an interaction among self-interested parties that have a conflict of interests and aim to achieve a joint agreement. It can occur daily basis when parties need to make decisions collectively on any matters such as personal activities (e.g., arranging holiday plans), professional procedures (e.g., job interviews, task or resource allocations), or societal matters(e.g., effective energy distribution). Depending on the complexity of the decisions, this process can be time-consuming and cumbersome for human stakeholders. Therefore, researchers in the field of Artificial Intelligence have put their effort into automating this process over the last decades [ 1 , 8 , 14 ]. Recently, there has been a high interest in human-agent negotiations in which intelligent agents negotiate with their human counterparts [ 4 , 28 ]. Creating large-scale social impact by such intelligent systems requires understanding how human decisions are made and their preferences and interests [ 26 ]. That is, agents should be capable of understanding why their opponent made such offers and what is acceptable to their opponent so that it can adapt its bidding strategy accordingly to increase the chance of reaching mutually beneficial agreements. That shows the importance of the opponent modeling during the negotiation.

There are a variety of opponent modeling techniques proposed in automated negotiation literature [ 6 ]. As far as the existing opponent models to predict the opponent’s preferences are concerned, it is observed that they attempt to learn a model from bid exchanges and mostly have some particular assumptions about both opponent’s bidding behavior and preference model (e.g., having an additive utility function and employing time-based concession strategy). Even simple heuristic models such as the frequentist approach [ 19 , 31 ] perform well in negotiation. Although there are relatively much fewer offer exchanges in human-agent negotiation in contrast to automated one (i.e., the number of offers typically does not exceed 20-30 offers in human-agent negotiation [ 22 ]), some studies adopt their variants in human-agent negotiation [ 25 ].

This study pursues an alternative way of modeling human opponents’ preferences by searching for cause-effect relationships in human negotiators’ bidding patterns. Here, the main challenge is to learn meaningful preference relations that enable our agents to generate offers that are more likely to be acceptable by their opponents despite the small number of offer exchanges. Accordingly, this study proposes a novel conflict search-based opponent modeling strategy mainly designed to learn human opponents’ preferences in multi-issue negotiations to generate well-targeted offers leading to mutually beneficial agreements (i.e., high social welfare). The proposed opponent modeling approach has been evaluated experimentally concerning different performance metrics, such as the model’s accuracy and the model’s effect on the negotiation and negotiation outcome. To show the performance of the proposed approach, we conducted two human-agent negotiation experiments involving 70 participants in total and compared our agent performance with those of the aforementioned well-known frequentist approaches [ 19 , 31 ]. Our results showed that the proposed conflict-based opponent model outperformed them dramatically in terms of their prediction accuracy. Furthermore, we studied the effect of the model on the negotiation outcome in automated negotiation by involving 15 state-of-the-art negotiation agents from the International Automated Negotiating Agents Competition (ANAC) [ 15 ] on six different negotiation scenarios. Our results showed that our agent gained the highest average individual utility and social welfare (i.e., both product of utilities and the sum of utilities) on average.

The rest of the paper is organized as follows. Section 2 reviews the related work on opponent modeling. The proposed opponent model is explained in Section 3 , and the negotiation strategy of the agent utilizing the model is defined in Section 4 . Section 5 presents our experimental setup and analysis of the results. Finally, we conclude our work with a discussion involving future work directions in Section 6 .

2 Related work

Automated negotiation has been widely studied for several decades, and a variety of negotiation frameworks have been proposed so far [ 2 , 8 ]. By their nature, automated agents try to find the most beneficial agreement for both parties by making many consecutive offers up to a particular deadline (time or round). As Hindriks, Jonker, and Tykhonov point out that agents can benefit from learning about their opponent during negotiation [ 12 ], a variety of opponent modeling approaches have been proposed in the negotiation community, such as opponent’s preferences (e.g., [ 12 , 19 , 21 , 25 , 31 , 32 ]), the acceptability of an offer (e.g., [ 20 , 26 , 29 ]) and negotiation strategy/attitude (e.g., [ 16 , 23 , 27 ]). The main opponent strategy is identifying the opponent’s preferences by analyzing offer exchanges between parties. Afterward, the agent examines the opponent’s negotiation offers with estimated opponent preferences to get an idea about its strategy/attitude. Various modeling techniques have been used in these strategies, such as kernel density, Bayesian learning, and frequentist models. While building up their model, those opponent modeling approaches rely on some assumptions such as having a predetermined deadline, capturing their preferences in the form of an additive utility function, and following a turn-taking negotiation protocols such as (Stacked) Alternating Offers Protocol [ 2 ] and conceding over time (e.g., time-based concession strategies). In the following part, we mention the most relevant works. A more detailed explanation about opponent modeling can be found in the survey [ 6 ].

Another common preference model technique in the literature is based on Bayesian learning [ 12 , 32 ]. Hindriks et al. use Bayesian learning to predict the shape of the opponent’s utility function, the corresponding rank of issue values, and issue weights [ 12 ]. As an extension of Hindrik’s work, Yu et al. incorporate regression analysis into Bayesian learning by comparing the predicted future bids and actual incoming bids. Accordingly, they update the Bayesian belief model by considering both current and expected coming bids.

Recent studies’ most common preference modeling strategies are variations of the frequentist models. The winner of the Second Automated Negotiating Agents Competition [ 15 ] called Hardheaded agent [ 19 ] uses a simple counting mechanism for each issue value and analyzes the contents of the opponent’s consequent offers. The main heuristic is that the opponent would concede less on the essential issues while using the preferred values in its offers. Therefore, while analyzing the opponent’s current and previous offer, if the value of an issue is changed, that issue’s weight is decreased by a certain amount. While such a simple approach is initially intuitive, information loss seems inevitable. Due to the nature of the negotiation, the opponent may need to concede even on important issues. Those moves may mislead the model. In addition, the concession amount may vary during the negotiation, which the frequentist approach needs to capture.

Moreover, suppose the opponent repeats the same bid multiple times. In that case, the model may overvalue those repeated issue values while underestimating the unobserved values (e.g., converging a zero utility since it is not seen). Tunalı, Aydoǧan, and Sanchez [ 31 ] aims to resolve those problems by comparing the windows of offers instead of consecutive pairs of offers and offering a more robust estimation of the opponent’s behavior. It adopts a decayed weight update to avoid incorrect updates when opponents concede on the most critical issues. Furthermore, it smoothly increases the importance of issue values to avoid unbalanced issue value distributions when the opponent offers the same offer repeatedly. Although their approach outperforms the classical frequency approach, it still suffers from only counting the issue value appearance because it ignores the varying utility patterns of the opponent’s offers.

Apart from the opponent modeling in automated negotiation, we review the opponent modeling approaches particularly designed for human-agent negotiations. Lin et al. introduce the QOAgent using kernel density estimation (KDE) for modeling opponent’s preferences [ 21 ]. According to their results, the QOAgent can reach more agreements. In most cases, it achieves better agreements than the human counterpart playing the same role in individual utility. As an extension of QOAgent, Oshrat et al. present an agent unlike most other negotiating agents in the automated negotiation, the KBAgent attempts to utilize previous negotiations with other human opponents having the same preferences to learn the current human opponent [ 26 ]. This approach requires an essential assumption that human participants will behave similarly to each other. Thus, KBAgent builds a broad knowledge base from its previous opponents and accordingly offers based on a probabilistic model constructed from the knowledge base utilizing kernel density estimation. In their experimental comparison, the KBAgent outperformed the QOAgent. In these studies, the authors focus on the overall negotiation performance rather than the performance of the proposed opponent modeling approach. However, we examine the accuracy of the opponent modeling and the performance of the whole negotiation strategy.

Furthermore, Nazari, Lucas, and Gratch follow a similar intuition with frequentist models for human-agent negotiation [ 25 ]. However, they take into account only the preference ranking of the negotiation issues instead of estimating the overall utility of each outcome. For issue values, they consider a predefined ordering. However, those assumptions may not hold in negotiations where a human participant may have a different evaluation of issue values. In their negotiation, their agent considers the importance of the issues and the expected ordering of the issue values while generating their offers. A similar heuristic with the frequentist approach holds here. That is, an issue is more important if the opponent consistently asks for more on that issue. It leads to the same intrinsic problem of the frequentist approach.

Instead of learning an explicit preference model, some studies focus on understanding what offers would be acceptable for their opponent. Sanchez et al. use Bayesian classifiers to learn the acceptability of partial offers for each team member in a negotiation team [ 29 ]. They present a model for negotiation teams that guarantees unanimous decisions consisting of predictable, compatible, and unforeseen issues. The model maximizes the probability of being accepted by both sides. While their model relies on predictable issues such as price, our model is designed to handle unpredictable discrete issues. Lastly, it is good to mention the reinforcement learning approach proposed for human-agent negotiation [ 20 ]. Lewis et al. collect a large dataset consisting of offers represented in natural language from 5808 sessions on Amazon’s Mechanical Turk. They present a reinforcement learning model to maximize the agent’s reward against human opponents. Accordingly, they aim to estimate the negotiation states acceptable for their human counterparts.

More recently, researchers have been trying to incorporate deep learning models in opponent modeling. For instance, Sengupta et al. has implemented a reinforcement learning-based agent that can adapt to unknown agents per experiences with other agents. In order to model the opponent, they have applied the Recurrent Neural Networks model, specifically LSTM, since they use time series data from the negotiation steps. However, they switched their implementation to a 1D-CNN classifier instead due to data limitations. They observe an opponent agent’s bidding strategy according to the agent’s self-utility and try to cast it into a class of known behaviors. According to this classification, the agent swaps negotiation strategy within the runtime [ 30 ].

Meanwhile, Hosokowa and Fujita expand upon the classical frequentist approach through the addition of the ratio of offers within specified slices of the negotiation timeline, and they implement a weighting function to stabilize the ratios as time passes to capture the change of an opponent’s concession toward the end of negotiation [ 13 ].

3 Proposed conflict-based opponent modelling (CBOM)

Our opponent modeling called Conflict-Based Opponent Modeling (CBOM) aims to estimate the opponent’s preferences represented utilizing an additive utility function shown in ( 1 ) where \(w_{i}\) represents the importance of the negotiation issue \(I_i\) (i.e., issue weight), \(o_{i}\) represents the value for issue i in offer o , and \(V_{i}\) is the valuation function for issue i , which returns the desirability of the issue value. Without losing generality, it is assumed that \(\sum _{i\in n} w_{i} = 1\) and the domain of \(V_{i}\) is (0,1) for any i . The higher the \(V_{i}\) is, the more preferred an issue value is.

Regarding the issue valuation/weight functions (i.e., preferences on issue values), targeting to learn these functions directly from the opponent’s offer history may not be a reliable approach since the opponent’s negotiation strategy may mislead us. Although the contents of the opponent’s offers give insight into which values are more preferred over others, depending on the employed strategy, we may end up with a different model estimation. For instance, the frequency of the issue value appearance might be a good indicator for understanding the ranking of issue values. However, it is not sufficient to deduce to what extent each issue value is preferred. Fluctuations in the opponent’s offers or repeating the exact offers often mislead the agent into accurately estimating the additive utility function. Therefore, we aim first to detect the preference ordering pattern rather than quantifying an evaluation function directly and then interpolate it.

As most of the existing opponent models in the literature do, our model assumes the opponent concedes over time. Initially, the agent does not know anything about its opponent’s preferences; therefore, it creates a template estimation model according to the given domain configuration. In other words, the agent starts with an initial belief in ranking the issue values ( \(V_i\) ) and issues ( \(W_i\) ). The agent may assume that the opponent’s value function is the opposite of its value function. For instance, If the agent prefers \(V_1>\) to \(V_2\) , it may consider that its opponent prefers \(V_2\) to \(V_1\) . Alternatively, it may consider an arbitrary ordering for the opponent. Consider that we have n issues and for each issue i there are possible issue values denoted by \({D_{i}}=\{v_{1}^{i},\dots ,v_{m}^{i}\}\) . Assuming an arbitrary preference ordering for the opponent, ( 2 ) and ( 3 ) shows how the initial valuation values and issue weight are initiated. The agent keeps the issues and issue values in order in line with the estimated opponent preferences. Meaning that \(v_{k}^{i}\) is preferred over \(v_{j}^{i}\) by the opponent where where \(k>j\) . Accordingly, ( 2 ) assigns compatible evaluation values via max normalization. Similarly, the issue weights are initialized by sum normalization, where each issue weight is in the [0, 1] range, and their sum is equal to 1. Equation ( 3 ) ensures that their sum equals one.

As the agent receives the opponent’s offers during the negotiation, it updates its belief incrementally based on the inconsistency between the current model and the opponent’s offers. To achieve this, it stores all bids made by the opponent so far, and when a new offer arrives, the current offer is compared with the previous bids with respect to any conflicting ordering. In particular, common and different values in the offer contents are detected. For different values, the system checks whether there is any conflicting situation with the current model. Recall that the current offer is expected to have the less preferred values since the model assumes that the opponent concedes over time. However, according to the learned model, the ordering may not match the expectation. In such a case, the model is updated.

Two types of conflict in the estimated model could be detected: issue value conflict and issue conflict . To illustrate those conflicts, let us examine some examples where agents negotiate over three issues (i.e., A , B , and C ) in Fig. 1 . As current belief indicates \(b_1 \succ b_2 \succ b_3\) where \(b_i\) denotes a possible issue value for the issue B , \(b_1\) is preferred \(b_2\) . In the given negotiation dialogue in Fig. 1 a, it can be seen that the opponent’s previous offer and current offers are \(O_{t}=<a_1, b_2, c_1>\) and \(O_{t+1}=<a_1, b_1, c_1>\) , respectively. Agents can examine the contents of the offers and find unique value changes to make some inferences on the preferences. For our case, the only difference in the offers is the value of the issue B . Relying on the assumption that the human negotiator leans towards concession over time, the agent could infer \(b_2 \succ b_1\) . Recall that the most preferred values would appear early. As seen clearly, this preference ordering conflicts with that of the agent’s belief. We call this type of conflict “issue value conflict” in our study.

figure 1

Preference conflict extraction example

The latter conflict type is about the importance of the issues. In the given an example in Fig. 1 b, the consecutive offers involve more than one issue value difference, particularly on issues B and C . Then, the agent can deduce \((c_1,b_2) > (c_2, b_1)\) by relying on the concession assumption mentioned above. Individually, ordering in issue C is consistent with the belief (i.e., \(c_1 \succ c_2\) ); however, the ordering on issue B is conflicting (i.e., \(b_2 > b_1\) ). Therefore, in order to have \((c_1,b_2) \succ (c_2, b_1)\) , the importance of the issue C should be higher than that of B (i.e., \(C \succ B\) ). This inference conflicts with the current belief of the agent, which says B is more important than C .

figure d

Conflict-based Opponent Model (CBOM).

Algorithm 1 shows how the ranking of the issue values is extracted. When the opponent makes an offer ( \(O_c\) ), the agent compares the content of the current offer with that of each offer in the opponent’s offer history to find the unique values and consequently extract some preferential comparisons (Lines 1–4). Afterward, the agent keeps all those comparisons in a dictionary called CM (Line 3). By reasoning on each comparison in this set by considering the current belief set, each conflict is extracted and stored in AC (Line 5-7). It is worth noting that the method can find issue value conflicts consisting of multiple issues. After keeping track of all possible conflicts, the agent must determine how to update its beliefs. Counting the number of conflicts on each issue value pair, it considers the issue value orderings having the least conflicts and updates its belief accordingly (Lines 8-16). The agent detects issue value pairs in the conflict set for each issue and compares their occurrences to determine which one to stick on. For instance, if the agent observes conflicting information, the more frequent ordering becomes more dominant, and the agent adapts its beliefs accordingly. After updating the beliefs about issue value orderings, it does the same kind of updates for the issue ordering (Lines 18-25). After finalizing the updates on the rankings, it estimates the utility space of the opponent by utilizing the update operations in ( 2 ) & ( 3 ).

figure 2

Example process of the conflict-based opponent model (CBOM)

To illustrate this, we trace the negotiation in Fig. 2 , where we can observe how this opponent model works. Following the same domain, we first arbitrarily set our initial beliefs about preference ordering (e.g., \(a_3 \succ a_2 \succ a_1\) for the values of issue A ). The agent keeps track of offers made by the opponent so far. In our example, you can see the offer history at time \(t+2\) . Following, the agent compares all previous offers with each other (i.e., pairwise comparison) and tries to extract an ordering relation. Here, \(O_{t}\) and \(O_{t+1}\) denote the first and second offer made by the opponent. Since the agent believes that the opponent’s earlier offers are more preferred over the later offers, it extract that \(a_1\) , \(b_2\) \(\succ \) \(a_2\) , \(b_1\) . This knowledge does not give any novel insight to update our beliefs, but we store this ordering for future analysis in the following rounds. When the opponent makes the offer \(O_{t+2}\) , the model compares it with all the previous offers pairwisely as well as the previously extracted information (e.g., the \(a_1\) , \(b_2\) \(\succ \) \(a_2\) , \(b_1\) relation). Starting from the first offer in the offer history ( \(O_{t}\) - \(O_{t+2}\) ), the model acquires the information of \(a_1 \succ a_2\) , since there is only one issue with a different value. When it compares \(O_{t+1}\) with \(O_{t+2}\) , it extracts ( \(b_1 \succ b_2\) ) and updates its beliefs accordingly. Similarly, the extracted information could be utilized to reason about the ordering of the negotiation issues (e.g., \(A \succ B\) ) based on the contradiction between ( \(a_1\) , \(b_2 \succ a_2, b_1\) ) and ( \(b_1 \succ b_2\) ). Consequently, the agent deduces that the importance of issue A is more than issue B considering the assumption that one-issue comparisons are more reliable than multi-issue comparisons, which conflicts with the current belief and updates its belief accordingly.

4 Proposed conflict-based negotiation strategy

This section presents our negotiation strategy employing the opponent model mentioned above. This strategy incorporates the estimated opponent modeling into the Hybrid strategy [ 16 ], which estimates the target utility of the current offer based on time and behavior-based concession strategies.

The Algorithm 2 elaborates how the agent makes its decisions during the negotiation. In each round, it calculates a target utility by employing the hybrid bidding strategy. Consequently, it generates candidate offers that were not offered by the agent (i.e., CBOM Agent). Its utility is in the range of lower and upper target utility (i.e., \(TU_{cbom}\) - \(\epsilon \) and \(TU_{cbom}\) + \(\epsilon \) ) (Lines 1–9). If there is no such an offer, the boundary is enlarged with a dynamically generated small number according to the domain size (Line 8). We define a round count n where we believe the agent has enough offers from its opponent to estimate their preferences. The value of n may vary depending on whether the agent negotiates with a human or agent negotiator. If the number of received offers from the opponent is less than n , the agent picks the offer maximizing its utility among potential offers (Lines 10–12). Shortly, the system does not engage the opponent model until there are enough offers accumulated in the history of opponent offers. Otherwise, the agent selects the offer whose estimated utility product is the maximum (Line 14). If the opponent made an offer with a utility higher than our lowest utility bid and the utility of the current candidate’s offer (Line 16), the agent accepted its opponent’s offer instead of making the offer. This acceptance condition is slightly more cooperative than the \(AC_{next}\) acceptance strategy. Otherwise, it makes the chosen offer (Line 19).

figure e

Conflict-based negotiation strategy.

Equation ( 4 ) outlines how the agent computes the target utility for its upcoming offer, according to [ 16 ]. The concession function ( \(TU_{Times}\) ), represented by ( 5 ), incorporates t , the scaled time ( \(t \in [0, 1]\) ), and \(P_0\) , \(P_1\) , \(P_2\) , which correspond to the curve’s maximum value, curvature, and minimum value, respectively. Note that the values of \(P_0\) , \(P_1\) , and \(P_2\) in our experiment are 0.9, 0.7, and 0.4, respectively. The behavioral aspect of the "Hybrid" strategy involves scaling the overall utility change by a time-varying parameter, \(\mu \) , to estimate the target utility, as demonstrated in ( 4 ). \(U(O_a^{t-1})\) signifies the agent’s utility for its preceding offer. Positive changes imply that the opponent has made concessions; hence, the agent should also make concessions. In ( 6 ), \(U(O_a^{t-1})\) again represents the agent’s utility for its prior offer. Positive changes indicate that the opponent has conceded, prompting the agent to make concessions. Considering the opponent’s previous n bids, where \(W_i\) represents the weights of each utility difference, the behavior-based approach determines overall utility changes, as demonstrated in ( 7 ). Equation ( 8 ) reveals that the value of the coefficient \(\mu \) is determined by the current time and \(P_3\) , which controls the degree of mimicry. Initially, the agent decreases or increases the target utility less than its opponent; subsequently, the degree of mimicry rises over time. Therefore, the “HybridAgent” strategy can smoothly conform with domains of varying sizes and harmonize with distinctive opponents utilizing behavior-based components of the HybridAgent. As an extension of the bidding strategy, CBOM agent also generates a target utility value by combining different p-values for various domain sizes. It cares about the social welfare score for both parties, choosing the most agreeable offer from the list of offers that the opponent model creates. Thus, it is expected that CBOM agent can achieve higher utility while maximizing social welfare and finding quicker mutual agreement.

figure 3

Offer selection example of the CBOM Agent according to \(\epsilon \) boundary

In accordance, Fig. 3 illustrates an example of the selected offer of the CBOM Agent according to its boundaries. The figure is structured with the y-axis representing the agent’s utility and the x-axis representing the opponent’s utility for the potential outcomes in the domain. Each outcome is depicted by blue dots on the graph. The red dot represents the target utility offer at a given time, indicating the preferred outcome for the agent. The red circle is drawn by adding the target utility to an epsilon value. Within this boundary, the agent selects the offer closest to the Nash offer ( \(d_N\) ), depicted in green. When the domain size is limited, only a few bids may remain within a specific utility range. Without enlarging the epsilon, the agent might end up repeating certain offers. The number of offers within the offer window should be increased in such situations. Expanding this boundary allows the agent to explore more offers, which helps avoid sending repetitive final offers to the opponent while still adhering to the target window. Examining other offers within the same window allows the agent to identify a more appropriate choice while upholding its target utility.

5 Experimental analysis

We first examine the performance of the proposed Conflict-based Opponent Model (CBOM) by conducting two different human experiments (Section 5.1 ) and extend this evaluation by considering the performance of the proposed strategy using this opponent model through agent-based negotiation simulations (Section 5.2 ).

5.1 Evaluation of opponent modeling via human-agent experiments

To show how well the proposed opponent modeling approach predicts the human opponent’s preferences, we conducted experiments where participants negotiated with our agent on a given scenario to find a consensus within limited rounds by following the Alternating Offers Protocol (Section 3 ).

figure 4

RMSE & Spearman Correlations for the experiment in Island Scenario ( \(\star \) represents p < 0.001)

We consider the performance metrics to assess the quality of the predictions: Spearman’s correlation and root-mean-square error (RMSE). The former metric indicates the accuracy of the predicted order of the outcomes according to the learned utility function, whereas the latter measures how accurate estimated utilities are. For correlation estimation, possible outcomes are sorted concerning the learned opponent model, and this ranking is compared with the actual ordering. Consequently, the Spearman correlation is calculated between the actual outcome ranking and the estimated one. The correlation would be high when both orderings are similar to each other. The correlation coefficient r ranges between -1 and 1, where the sign of the coefficient shows the direction, and the magnitude is the strength of the relationship. For RMSE, the utility of each outcome is estimated according to the learned model, and the error in the prediction is calculated (See ( 9 )). When the estimated utility values are close to the actual utility values, the RSME values would be low. In summary, low RSME and high correlation values are desired in our case.

Baarslag et al. compare the performance of the existing opponent models in automated negotiation [ 5 ]. Their results show that frequentist-based opponent modeling approaches are the most effective among the existing ones despite the approach’s simplicity. Therefore, we use a benchmark involving two different state-of-the-art frequentist opponent modeling approaches widely used in automated negotiation employed in HardHeaded [ 19 ] and Scientist [ 31 ] agents to evaluate the performance of the proposed opponent model. Frequentist opponent modeling techniques mostly rely on heuristics, assuming that the opponent would concede less on the essential issues and the preferred values appear more often than less preferred ones. Consequently, they check the frequency of each issue value’s appearance in the offers. Furthermore, they compare the content of the consecutive offers and find out the issues with changed values. In other words, if the value of an issue is changed in the opponent’s consecutive offers, the weights of those issues are decreased by a certain amount (i.e., becoming less critical). In the Scientist Agent, Tunalı et al. aims to resolve some update problems and enhance the model by comparing a group of offer exchanges instead of only consecutive pairs of offers and adopting a decayed weight update mechanism. Each opponent model is fed and updated in each round by simulating the negotiation data obtained from human-agent negotiation experiments. At the end of each negotiation, the estimated models are evaluated according to the RMSE and Spearman correlation metrics explained above.

figure 5

RMSE & Spearman Correlations for the experiment in Grocery Scenario ( \(\star \) represents p < 0.001)

5.1.1 Study 1: human-agent negotiation in deserted Island scenario

We analyzed and utilized the negotiation log data collected during the human-agent negotiation experiments in [ 4 ], where the participants negotiated on a particular scenario called “Deserted Island”. They negotiated resource allocation based on the division of eight survival products by two partners who fell on the deserted island. Each participant attended two negotiation sessions where the utility distributions of the issues were the same, but the orderings differed. Table  1 shows the preference profiles for both sessions. During the experiments, participants only know their preferences, and so does the agent. In this study, 42 participants (21 men, 21 women, median age: 23) were included and asked to negotiate with our agent on a face-to-face basis, and the agent made counteroffers. Offer exchanges in both sessions were recorded separately for each session. At the end of this data collection process, 46 sessions using the time-based stochastic bidding tactic (TSBT) and 38 sessions using the behavior-based adaptive bidding tactic (BABT) were obtained. The average negotiation rounds to reach an agreement was 14.84, with a standard deviation of 5.2.

Figure 4 shows box plots for each opponent modeling technique’s RMSE and Spearman correlation values. As far as the correlation values are concerned, it can be said that CBOM ’s ranking predictions are better than Scientist and Frequentist (See Fig. 4 b). To apply the appropriate statistical significance test, we first check the normality of the data distribution via the Kolmogorov-Smirnov normality test and then the homogeneity of variance via Levene’s Test. We applied the dependent sample t-test or the Wilcoxon Signed Rank test, depending on the results. If the data distribution passes these tests, the paired t-test is applied; otherwise, a non-parametric statistics test, namely the Wilcoxon-Signed Rank test. All statistical test results are given at the 99 \(\%\) confidence interval (i.e., \(\alpha \) = 0.01). When we apply the statistical tests, it is seen that CBOM ’s ranking performance is statistically significantly better than others (p < 0.01). Similarly, the errors on the estimated utilities via CBOM are lower than the errors via other approaches (see Fig. 4 a). Furthermore, it is seen that Scientist statistically significantly performed better than Frequentist for both metrics except when the agent employs the TSBT strategy.

5.1.2 Study 2: human-agent negotiation in grocery scenario

In this part, we analyzed and utilized the negotiation log data collected during another human-agent negotiation experiment in [ 16 ] where the participants negotiated on a particular scenario called “Grocery”. Different from the first study, the negotiation domain does not consist of binary resource items (i.e., allocate or not allocate ). Instead, the negotiation parties negotiate on the number of items to be allocated (i.e., how many items will be allocated). In this scenario, there are four types of fruits, where each participant can have up to four of each, and the opponent gets the rest. The participants aim to find an adequate division of the fruits. Table  2 shows the agent and participant’s preference profiles for both sessions. It is worth noting that each party only knows its scores. In this experiment, the participants negotiated against an agent employing the hybrid strategy where TSBT and BABT strategies are used together for bidding. 28 participants attended two negotiation sessions where all negotiation sessions ended with an agreement, thus, totaling up to 56 negotiation sessions against the agent. The average negotiation rounds to reach an agreement was 19.39, with a standard deviation of 11.82.

Similar to the previous study, we update each opponent modeling by using the offer exchanges by the human participants and calculate the error and correlation values with the final model at the end of each negotiation. Figure  5 shows box plots for RMSE and Spearman correlation values per each opponent modeling technique in this scenario. We can conclude that CBOM statistically significantly outperformed others, whereas Frequentist performs better than Scientist when we analyze the statistical test results. Those results are in line with the first study and strongly show the success of the proposed opponent modeling in human-agent negotiations. It is worth noting that the prediction error in the grocery scenario is lower than in the island scenario. Although the number of possible outcomes in these scenarios is the same (256), the number of issues in grocery scenarios is lower than in the island scenario. Therefore, one can intuitively think it is easier to predict the evaluation values in the grocery scenario compared to island scenarios. In addition, this study’s average number of rounds is higher (19.39 versus 14.84). When we receive more offers, the model’s accuracy may increase depending on the model.

5.2 Evaluation of the CBOM agent via automated negotiation experiments

In this section, we evaluate the performance of our agent employing the proposed CBOM opponent modeling by comparing its performance with that of the state-of-the-art negotiating agents available in automated negotiation literature. We built a rich benchmark of 15 successful negotiating agents who competed in the International Automated Negotiating Agents Competition ANAC [ 15 ] between 2011 and 2017. We ran negotiation tournaments in Genius, where each agent bilaterally negotiated on various negotiation scenarios. Six negotiation scenarios were used during the tournament, and the details of those scenarios are given in Table 3 . As can be seen, the size and opposition degree of preference profiles in the given scenario is different. The size of the scenarios determines the search space. The larger the search space is, the more difficult it might be to estimate an accurate model based on the opponent’s offers exchanges. Next, the opposition is valuable information regarding understanding the domain’s capacity to satisfy both parties [ 5 ]. That is, it indicates how difficult it is to find a consensus. Taking the opposition of the preference profiles into account while analyzing the negotiation results may help us get an insight into how well the proposed negotiation strategy is in terms of social welfare with varying difficulties in finding an agreement.

We formed a pool of agents involving our Conflict-based agent and the ANAC finalists in different categories. We ran a tournament in Genius where each agent bilaterally negotiated with each other on scenarios described in Table 3 . The ANAC agents used in this evaluation are listed as follows:

figure 6

Agreement rates of agents

Boulware and Conceder are baseline agents available in Genius framework.

Hardheaded [ 19 ] was the winner of individual utility category in ANAC 2011.

NiceTitForTat  [ 7 ] was the finalist of individual utility category in ANAC 2011.

CUHKAgent  [ 11 ] was the winner of individual utility category in ANAC 2012.

IAmHaggler2012  [ 15 ] was the winner of the Nash category in ANAC 2012.

Atlas3  [ 24 ] was the winner of individual utility category in ANAC2015, .

ParsAgent2  [ 17 ] was the winner of the Nash category in ANAC 2015.

AgentX  [ 9 ] was fourth of the Nash category in ANAC 2015.

Caudeceus  [ 10 ] was the winner of individual utility category in ANAC 2016.

YXAgent  [ 3 ] was the second of individual utility category in ANAC 2016.

PonPoko Agent  [ 3 ] was winner of individual utility category in ANAC 2017.

AgentKN  [ 3 ] was the second of the Nash category in ANAC 2017.

In order to study how well our opponent model performs when it negotiates with automated negotiating agents, we compare the performance of opponent models used in Conflict-based (CBOM), Scientist, and HardHeaded by integrating those opponent models into our negotiation strategy. We calculated the Spearman correlation between the actual and estimated ranks of the outcomes per each scenario and reported their averages. Note that the higher correlation is, the better the prediction is. Table 4 shows those Spearman correlations and RMSE in the utility calculations where the best scores are boldfaced. It is seen that CBOM is more successful than others in terms of Spearman correlation, except for the results obtained in the grocery and politics domains. Furthermore, RMSE results show that the CBOM is more successful in all domains.

Next, we analyze the performance of the proposed negotiation strategy relying on the CBOM opponent modeling against the ANAC finalists. The most widely used performance metric in negotiation is the final received utility, which is intuitive and in line with Kiruthika’s approach to Multi-Agent Negotiation systems [ 18 ]. There are other metrics, such as nearness to Pareto optimal solutions/Kalai point/the Nash point, the sum of both agents’ agreement utility (i.e., social welfare), and the product of those agreement utilities. Accordingly, we evaluate the performances regarding average individual received utility, Nash distance, and social welfare.

figure 7

Best six agents in all automated negotiation results

First, we analyze the average individual utilities received by each agent. Table 5 shows those utilities per each agent in each negotiation scenario where the highest scores are boldfaced. The last column shows the average scores of each agent in all domains. Our agent took in the first top three agents. We noticed that the worst performance of our agent was in the supermarket domain, where the outcome space is too large to search. Moreover, our agent performed well in the smart energy grid and grocery scenarios, whose opposition levels are high.

Table  6 shows the average Nash distance for each agent in all scenarios separately, and the final column indicates the average of all scenarios. Here, the lower the Nash distance is, the fairer the agent’s outcomes are. Our conflict-based agent outperformed the ANAC finalist agents except for Nice TitForTat, which is known for maximizing social welfare in the Politics scenario (See Table 8 in Appendix). Similar results were obtained when we analyzed the social welfare in terms of summation of both agents’ agreement utilities (See Appendix). Overall results support the success of our agent, and the reason may stem from the fact that our agent aims to learn its opponent’s preferences over time and aims to find win-win solutions for both sides.

When we investigate the overall agreement rate, it can be seen that most of the agents have a high acceptance rate, and the leading ones, like ours and Atlas3, found agreements in all negotiations, as seen in Fig. 6 . The final metric that we investigated is the average rounds to reach an agreement. In our experiments, the deadline is set to 5000 rounds per negotiation scenario. Table 7 shows the average rounds that the agent reached their agreement. It can be observed that the the size of the outcome space and the opposition level may influence the agreement round. In large and competitive scenarios, agents needed more rounds to reach an agreement. Among all agents, Agent X tended to reach a consensus sooner than all other agents. Furthermore, IAMHaggler2012 and ParsCat agents tend to explore the offered space as much as they can in the given time. Therefore, these are the agents least affected by the size of the outcome space and its competitiveness. Our conflict-based agent could reached an agreement sooner than more than half of the agents but it is worth noting that it took more time in terms of seconds due to its computational complexity similar to AgentKN (See Table 9 in Appendix).

As a result of all the automated negotiations, we determined the six most successful agents in both the individual and fairness category. Figure  7 shows clearly that our agent gains the highest individual gain while having a fairer win-win solution (i.e., minimum distance to Nash solution). It is worth noting that while having high utility, our agent lets its opponent gains relatively high utility in contrast to other top agents.

6 Conclusion and future work

In conclusion, this work presents a conflict-based opponent modeling approach and a bidding strategy employing this model for bilateral negotiations. Apart from evaluating the performance of the proposed opponent model in two different human-negotiation experiment settings, the proposed strategy was also tested against the finalist of the ANAC agents considering various performance metrics such as individual utility and distance to the Nash solution. Our results show that the proposed approach outperformed the state of the negotiating agents, and the proposed opponent model performed better than other frequency-based models. The contribution of this study is twofold: (1) introducing a novel opponent modeling approach to learn human negotiators’ preferences from limited bid exchanges and (2) presenting a suitable bidding strategy relying on the proposed opponent model for both collaborative and competitive negotiation settings.

Due to the algorithm’s complexity, the agent’s performance decreases when the outcome space becomes more extensive or the number of generated offers made by the opponent increases in automated negotiation. We are planning to reduce the computational complexity of the opponent modeling by adopting dynamic programming properties and local search. The upcoming study will focus on opponent model strategies that decrease the human-agent negotiation duration with the optimal number of rounds. It would be interesting to create stereotype profiles by mining the previous negotiation history and matching the current opponent’s profile based on their recent offer exchanges.

Understanding and discovering the opponent’s preferences over negotiation may play a key role in adopting strategic bidding strategies to find mutually beneficial agreements. However, as stated before, it is challenging to create a mental model for the opponent’s preferences based on a few bid exchanges. In contrast to automated negotiation, the number of exchanged bids is limited in human-agent negotiation. That requires a bidding strategy smartly exploring the potential bids and building upon an opponent model, capturing the critical components of the opponent’s preferences. While creating such modeling is not trivial with limited bid exchanges, the agent can exploit its previous negotiation experiences and take advantage of repeated patterns. As future work, it would be interesting to create different mental models from previous negotiation experiences by applying our model and trying to detect which mental model fits better for the current human negotiators. Consequently, instead of starting to learn from scratch, our model can enhance the chosen model by analyzing the current bid exchanges. Furthermore, the agent can exploit different types of inputs, such as the opponent’s arguments and facial expressions, to enhance opponent modeling.

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This work has been supported by a grant of The Scientific and Research Council of Turkey (TÜBİTAK) with grant number 118E197 and partially supported by the Chist-Era grant CHIST-ERA-19-XAI-005, the Scientific and Research Council of Turkey (TÜBİTAK, G.A. 120N680).

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