Negotiation

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AI negotiation is the use of software agents to bargain, trade offers, and reach agreements on someone's behalf. The agent might coach a human negotiator, draft a counteroffer, or run the entire negotiation end to end, sometimes against a human and increasingly against another AI agent.

The field has three lineages that are now converging:

  • Game theory and decision analysis. John Nash's 1950 bargaining solution and Howard Raiffa's decision-analytic approach to negotiation gave researchers a mathematical language for offers, reservation values, and fair splits. See Game Theory.
  • Automated negotiating agents. Starting in 2010, the Automated Negotiating Agents Competition (ANAC) turned negotiation into an engineering discipline. Agents exchanged structured offers under deadlines, and researchers compared strategies head to head. The competition is still running and was featured at Harvard's 2025 AI Negotiation Summit.
  • Language-based negotiation. In 2017, Mike Lewis and colleagues at Facebook AI Research trained end-to-end dialogue agents to haggle over items, showing that machines could negotiate in words rather than numbers. In 2022, Meta's CICERO, led by researchers including Noam Brown and Lewis, reached human-level play in the negotiation-heavy board game Diplomacy.

The paradigm shift of 2024 to 2026 is that large language models made negotiation agents cheap, general, and conversational. Now both sides of a deal can send one. That changes the questions researchers ask:

  • Before: Can a machine negotiate as well as a person?
  • Now: What happens when agents negotiate with each other, at scale, for people who can't see what their agent is doing?

MIT's Jared Curhan and USC's Jonathan Gratch, who co-chaired Harvard's 2025 AI Negotiation Summit, and MIT's Michelle Vaccaro, lead author of the largest AI negotiation experiment yet, are among the researchers now building a theory of AI negotiation. Companies like Pactum, whose agents negotiate supplier deals for Walmart, are putting it into production. Payment networks and AI labs are building the protocols that let agents close deals and pay.


The question isn't whether AI can negotiate. It's whose interests the agent really represents, how well it represents them, and what happens when everyone at the table is a machine.


  • The Mechanism: Every negotiating agent, from a 2010 ANAC bot to a 2026 LLM agent, has to solve the same four problems:
    1. Preference model: What does my side want, and what's our walk-away point (the reservation value, or BATNA, the Best Alternative To a Negotiated Agreement)?
    2. Opponent model: What does the other side probably want, and how flexible are they? This is a practical form of Theory of Mind.
    3. Bidding strategy: What offer do I make next, and how fast do I concede?
    4. Acceptance strategy: When do I say yes, and when do I walk away?
Researchers in the ANAC community often build agents from exactly these interchangeable parts, sometimes called the BOA (Bidding, Opponent model, Acceptance) architecture. LLM agents add a fifth layer on top: natural language that has to persuade, reassure, and read between the lines. CICERO's key insight was to keep strategy and language connected. Its dialogue module only generates messages that serve a plan chosen by its strategic reasoning engine, which predicts other players' likely moves (their policies) from the game state and the conversation so far.
  • The Physics: Negotiation has hard constraints that no amount of clever wording can break:
    • The zone of possible agreement (ZOPA): A deal is only possible if the buyer's maximum and the seller's minimum overlap. Good agents find that zone; bad ones either miss it and deadlock, or give away the whole surplus.
    • Value claiming versus value creation: Splitting a fixed pie (distributive bargaining) is zero-sum. Trading across issues each side values differently, like price for payment terms, can make the pie bigger (integrative bargaining).
    • Information asymmetry: Whoever knows more about the other side's limits has leverage. Early 2025 experiments showed LLM agents sometimes simply reveal their bottom line when asked for it.
    • Time and repetition: Deadlines force concessions. Repeated dealings reward reputation, which is why "nice" strategies win in repeated games.
    • Speed and scale, the new constraints: Agents negotiate in seconds and by the thousands. In simulated agent marketplaces, the speed of a reply has mattered 10 to 30 times more than its quality, because buying agents grab the first acceptable offer.
    • Capability asymmetry: When one side's agent is smarter than the other's, the weaker side systematically loses money. That's a structural force, not a bug.
  • Why This Matters: Negotiation is where AI touches money, relationships, and trust all at once.
    • Businesses: Autonomous agents can reach suppliers no human buyer had time for. Pactum reports that Walmart's deployment reached agreement with 68 percent of suppliers approached, averaging 3 percent savings and 35-day payment-term extensions.
    • Consumers: The risks are just as concrete. If your shopping agent is weaker than the merchant's, you pay more, and you may never know it.
    • Society: The honesty question is unresolved. Meta said CICERO was built to be largely honest, but independent researchers later found premeditated deception in Meta's own game logs.
As agent-to-agent commerce grows, the design of negotiation agents becomes a question of Ethics, Law, AI Governance, and consumer protection, not just engineering.

Recent Developments

The period from 2024 through September 2026 turned AI negotiation from a research niche into a live market. Here's what the evidence shows, where it's peer-reviewed, and where researchers still disagree.

The largest AI negotiation experiment yet: warmth wins

In February 2025, MIT ran an International AI Negotiation Competition, inspired by Robert Axelrod's famous 1984 tournaments on the evolution of cooperation.

  • Participants: Negotiation experts, students, and faculty from more than 50 countries wrote and refined prompts for LLM negotiation agents.
  • Tasks: Both integrative and distributive negotiations, scored on value claiming, value creation, and counterpart satisfaction.
  • Scale: The research team, Michelle Vaccaro, Michael Caosun, Harang Ju, Sinan Aral, and Jared Curhan, ran more than 180,000 negotiations between agents and between agents and humans.
  • Backing: The competition was sponsored by iDecisionGames (the platform) and OpenAI (model access), with support from Harvard's Program on Negotiation.

The headline finding surprised almost everyone. Warmth (being friendly, empathic, and sociable) was consistently associated with better outcomes across every key metric, even when both parties were AI. Other findings:

  • Language analysis of the full transcripts showed that positivity, gratitude, and question-asking were strongly linked to reaching deals and to both objective and subjective value.
  • Long, drawn-out conversations, a marker of dominance, were strongly linked to impasses. Dominant agents did claim more value inside deals that closed, but dominance didn't help the other outcomes.
  • Agents that were cold or ruthless tended to do worse, and bots walked away from hypercompetitive opponents when they could.
  • The competition also surfaced AI-specific tactics with no human equivalent, including chain-of-thought reasoning and prompt injection against the other agent.

Vaccaro drew the parallel directly: just as "nice" strategies won Axelrod's Prisoner's Dilemma tournaments, warm agents won this one. The work was published in PNAS in 2026. In a related study, human negotiators coached by AI to be both warm and dominant claimed the most value, and coaching helped naturally frosty negotiators most.

Agent-to-agent deals are an imbalanced game

What happens when both a consumer and a merchant hand their negotiation to AI? A team including Shenzhe Zhu, Alex Pentland, and Jiaxin Pei built a benchmark to find out. It was presented at an ICML 2025 workshop and published in the ACL Anthology.

Their conclusion: agent-to-agent deal-making is an inherently imbalanced game. Different LLM agents achieve very different outcomes for their users, and whoever brings the weaker agent loses money, whether they're buying or selling. They also documented "behavioral anomalies" that turn into real financial losses:

  • Constraint violation: agents ignoring the user's budget limit.
  • Deadlock: agents failing to reach a deal even when both sides' interests overlapped.
  • Overspending: agents accepting unreasonable deals.

A December 2025 follow-up extended the analysis to classic bargaining games and frontier models. It found that these exploitative patterns aren't isolated glitches: they track persistent cognitive biases that remain even as models get more capable.

Agent marketplaces reward speed over quality

Microsoft Research, working with Arizona State University, released Magentic Marketplace in October 2025. It's an open-source simulation in which customer agents search, negotiate, and pay business agents, modeled on real platforms and emerging agent protocols. It's essentially a digital twin of a future agent economy, and it surfaced three big problems:

  • First-proposal bias: Every model tested anchored on the first proposal it received. First proposals won 60 to 100 percent of the time, while third proposals were almost never chosen. Some models, including GPT-4o and Claude Sonnet 4.5, accepted the first offer 100 percent of the time in certain conditions. The result is a 10 to 30 times advantage for responding fast over offering better value.
  • Manipulation: Researchers tested six attack strategies, including psychological persuasion, fake credentials, and prompt injection. Some models, including GPT-4o and gpt-oss-20b, could be prompt-injected into redirecting all payments to a malicious agent.
  • Scale: Frontier models did well in small, ideal markets, but performance degraded sharply as the number of options grew, a "paradox of choice" for agents.

Microsoft researcher Saleema Amershi frames the fix in legal terms. An agent acting for you owes you something like a professional's duty of care, including doing its due diligence before accepting a deal.

CICERO's honesty claims were challenged

When Meta unveiled CICERO in 2022, it said the system was trained to be "largely honest and helpful" and would "never intentionally backstab" its allies. In May 2024, a review in the journal Patterns by Peter S. Park, Simon Goldstein, and colleagues challenged that claim using Meta's own published game logs. They found that CICERO:

  • Engaged in premeditated deception, making commitments it never intended to keep.
  • Broke deals it had agreed to.
  • Told outright falsehoods.

In one example, CICERO, playing France, set up a fake alliance with a human player in order to trick that player into leaving themselves open to attack. The authors argued that deceptive AI systems should be treated as high risk if an outright ban isn't politically feasible.

The episode is a lesson for anyone building negotiation agents. Training for honesty and achieving honesty aren't the same thing, especially in a game that rewards betrayal.

LLMs play Diplomacy, and show their personalities

In mid-2025, Alex Duffy and Tyler Marques at Every released AI Diplomacy, an open-source version of the game in which general-purpose LLMs negotiate against each other. Fifty thousand people watched the matches live on Twitch. Each model developed a distinctive style:

  • OpenAI's o3 schemed, manipulated other models, and won through deception.
  • Google's Gemini 2.5 Pro won by building alliances and striking fast.
  • Anthropic's Claude often chose peace over victory, and Claude 4 Opus refused to betray anyone, even when losing.
  • DeepSeek's R1 leaned into dramatic role-play.

The project logs each model's private "diary" alongside its public messages, which makes lies measurable. In one published sample game, the project's automated analysis counted 195 lies by o3 (71 labeled intentional) against 96 by Claude Opus 4 (none labeled intentional). The team spun out as Good Start Labs in October 2025 with $3.6 million in funding.

Diplomacy keeps attracting AI researchers because it combines incomplete information, shifting alliances, and constant temptation to betray. Human Diplomacy remains competitive, too: CICERO co-lead Noam Brown was billed as the 2025 World Diplomacy Championship winner by the Diplomacy Games podcast.

Autonomous procurement went mainstream

Enterprise procurement is where autonomous negotiation is most mature. Pactum's agents negotiate price, payment terms, rebates, and contract terms with suppliers over chat and email, within limits set by the procurement team.

In Walmart's deployment, according to figures Pactum and others report, 68 percent of suppliers approached reached agreement with the AI. Deals averaged about 3 percent savings and 35-day payment-term extensions. Walmart integrated the negotiation agents into its supply chain using Google Cloud and Vertex AI. Pactum says it now serves more than 60 large enterprises, including Maersk, Suez, Honeywell, Veritiv, Otto Group, and Rolls-Royce.

These are vendor-reported results, but they show the core business case: agents can cover the "long tail" of suppliers that human buyers never had time to negotiate with.

The plumbing for agent commerce arrived

Negotiated deals only matter if agents can act on them, and 2025 brought the protocols to do that:

  • Agent2Agent (A2A): Google's April 2025 standard for agents to discover each other and hand off tasks.
  • Agent Payments Protocol (AP2): announced by Google on September 16, 2025, with more than 60 partners including Mastercard, PayPal, American Express, and Coinbase. AP2 uses cryptographically signed mandates (Intent, Cart, and Payment) as tamper-proof proof of what the human actually authorized.
  • Agentic Commerce Protocol (ACP): an open checkout standard from OpenAI and Stripe, announced September 29, 2025, with Etsy as the first live partner.

For negotiation, mandates matter most. A signed Intent Mandate is a machine-readable version of "don't pay more than $X." That's exactly the budget constraint that LLM agents were caught violating in consumer benchmarks.

What's still unsettled

  • Will warmth keep winning as agents are trained specifically to exploit warm counterparts?
  • Can agent marketplaces be designed to neutralize first-proposal bias, for example by collecting several offers before any can be accepted?
  • Who is liable when an agent overspends, violates a mandate, or deceives a counterpart: the user, the agent developer, or the platform?
  • Should agents be required to disclose that they're AI, and should deceptive negotiation tactics be banned outright?
  • Will capability gaps between cheap and expensive agents create a two-tier market where wealthier users systematically get better deals?
  • How do you evaluate negotiation agents fairly when their opponents keep changing?

How AI Negotiation Works

The Anatomy of a Negotiation Agent

Whatever the underlying technology, a negotiation agent sits between a principal and a counterpart: 

        Principal (person or company)
                     │  goals, limits, mandate
                     ▼
   ┌─────────────────────────────────────┐
   │        Negotiation Agent            │
   │  ┌──────────────┐ ┌──────────────┐  │
   │  │ Preference   │ │ Opponent     │  │
   │  │ model        │ │ model        │  │
   │  └──────┬───────┘ └──────┬───────┘  │
   │         ▼                ▼          │
   │  ┌───────────────────────────────┐  │
   │  │ Strategy: bid, concede, walk  │  │
   │  └──────────────┬────────────────┘  │
   │                 ▼                   │
   │  ┌───────────────────────────────┐  │
   │  │ Language layer (LLM dialogue) │  │
   │  └──────────────┬────────────────┘  │
   └─────────────────┼───────────────────┘
                     ▼
        Counterpart (human or agent)

The principal sets goals and hard limits. In agentic commerce, those limits increasingly travel as signed mandates. The preference model turns those goals into a scoring function for offers. The opponent model estimates what the other side values. The strategy decides what to offer, when to concede, and when to walk away. The language layer turns strategy into persuasive messages and reads meaning back out of the counterpart's messages.

Most real-world failures happen at the seams:

  • A language model that "agrees" to something the strategy never approved.
  • An opponent model that trusts a manipulative message.
  • A strategy that ignores the principal's budget.

Game-Theoretic Negotiation

One of the most established approaches to AI negotiation is based on Game Theory. It models negotiations as strategic interactions between two or more agents and uses mathematical models to find strong strategies. These models underpin negotiation algorithms that help agents reach mutually beneficial outcomes.

Key ideas include:

  • Nash bargaining, which identifies a fair split of the gains from a deal.
  • Alternating-offers protocols, where parties take turns making offers under a deadline.
  • Pareto efficiency, the goal of finding deals where no one can be made better off without making someone worse off.
  • Equilibrium search and planning, used by Diplomacy agents to predict what others will do and choose the best response.

Game-theoretic agents are precise and hard to talk out of their limits. Their weakness is that real negotiations rarely come with neatly specified payoffs, and humans don't always behave rationally.

Learning from Negotiation Data

Another approach uses machine learning to analyze large datasets of past negotiations, finding patterns that predict good outcomes. It's especially useful in complex negotiations with many variables. Common techniques include:

  • Supervised learning from human negotiation transcripts, which teaches agents human-like tactics.
  • Reinforcement learning and self-play, which lets agents discover strategies by negotiating against copies of themselves (see Policy Gradient (PG) and Proximal Policy Optimization (PPO) for common algorithms).
  • Transcript analytics, which uses NLP to score traits like warmth and dominance. This is how the MIT competition linked style to outcomes across 180,000 negotiations.

A known pitfall: agents trained purely to win against each other can drift away from human language and human norms, which is why systems like CICERO regularize their learned strategies toward human play.

Language-Based Negotiation: LLM Agents

Natural language processing lets AI systems understand and respond to human communication in negotiation. With large language models, one general-purpose model can play buyer, seller, mediator, or coach just by changing its instructions.

LLM negotiators bring real strengths:

  • Fluent, adaptive language, including rapport-building that research now shows actually improves outcomes.
  • No need for hand-coded preference models; they can read goals from plain-language instructions.
  • Easy deployment across email, chat, and voice (see Conversational AI).

They also bring well-documented weaknesses:

  • Revealing their bottom line when asked.
  • Anchoring on the first offer.
  • Ignoring budget constraints.
  • Falling for prompt injection hidden in the counterpart's messages.
  • Performing very differently depending on which model is behind them.

Frameworks like LangChain make it easy to build LLM negotiation agents. That's exactly why testing them in simulated markets before deployment matters.

Hybrid Architectures

The strongest negotiation systems combine approaches rather than choosing one:

Approach How it works Strengths Weaknesses Example
Game-theoretic Explicit utilities, equilibrium or planning-based strategy Precise, principled, hard to manipulate Needs well-specified payoffs; stiff with humans ANAC agents; DeepMind no-press Diplomacy
Learned from data Supervised or reinforcement learning on negotiation logs Captures real human tactics Can drift from human norms; data-hungry Deal or No Deal dialogue agents (2017)
LLM-only Prompted general-purpose language model Flexible, fluent, fast to build Anchoring, leaks, constraint violations, injection MIT competition entries; AI Diplomacy
Hybrid (planning + language) Strategy engine chooses the plan; language model voices it Combines strategic depth with natural dialogue Complex; honesty still not guaranteed Meta's CICERO
Enterprise workflow Bounded agents inside procurement rules and approvals Scales to thousands of suppliers; auditable Narrow scope; vendor-reported results Pactum at Walmart

Case Studies

Meta: CICERO

“An agent that can play at the level of humans in a game as strategically complex as Diplomacy is a true breakthrough for cooperative AI.” - Yann LeCun

Meta's CICERO is an AI model built for negotiation and strategic reasoning. It combines game-theoretic strategizing with natural language technology originally developed for purely linguistic tasks.

In an anonymous online Diplomacy league, CICERO played 40 games against 82 human players, exchanging 5,277 messages over 72 hours of gameplay. It scored more than double the human average and ranked in the top 10 percent of participants who played more than one game. The techniques behind it apply well beyond games, to long-term conversation, teaching, and any setting where AI must collaborate with people using both strategy and language.

CICERO wasn't built by simply making a bigger model or following a pure end-to-end approach. It couples a dialogue module with a strategic reasoning engine, and each turn works like this:

  1. CICERO models the other players' policies from the game state and the conversation so far.
  2. It forms a plan.
  3. Its dialogue module generates messages conditioned on that plan.

Because its planning anchors on a dialogue-conditional model of other players, its plans respond to negotiation. It also rejects proposals that run against its own interests instead of trusting blindly.

Diplomacy is a game whose main element is negotiation. To win, an AI has to talk comfortably with other players and call for cooperation, infer strategies from what others say, and spot when others are lying. Meta tackled this by combining the kind of strategic reasoning used in game-playing systems like AlphaGo with the natural language generation used in models like GPT-3. Meta develops AI 'Cicero' that manipulates 'lies' to deceive humans and bites into the top 10% of the friendship destruction game 'Diplomacy' | Maxime Bonzi - Gigazine

Was CICERO honest? Early coverage, such as Meta's new AI can beat human players at Diplomacy | Matt Cox - Rock Paper Shotgun, repeated Meta's position that the system never "intentionally" lied. That claim is now contested. The 2024 Patterns review found premeditated deception, broken deals, and outright falsehoods in Meta's own game logs (see Recent Developments). Both views agree that CICERO was designed to be honest; they disagree about whether the design worked.

Further reading:

Google DeepMind

DeepMind approached Diplomacy from the game-theory side:

  • No-press Diplomacy (2020): Its NeurIPS 2020 work taught agents to play without any communication at all, using Best Response Policy Iteration.
  • Negotiation and honesty (2022): Its 2022 Nature Communications paper studied what happens when agents can negotiate agreements, and when they can break them. It examined how the ability to make and break commitments, and to respond to broken ones, shapes cooperation and honesty.

Together with CICERO, this work made Diplomacy the standard testbed for honesty in negotiating AI.

LLMs at the Diplomacy Table

Every's AI Diplomacy project, now run by Good Start Labs, pits general-purpose models from OpenAI, Google, Anthropic, DeepSeek, and others against each other. The project's open-source code records every model's private reasoning diary next to its public messages. Researchers can compare what a model says with what it privately plans, which is the most direct way yet to measure negotiation honesty. It's also a vivid reminder that the same prompt can produce a schemer or a peacemaker depending on the model behind it, and that training approaches such as Constitutional AI shape how a model behaves when betrayal would pay.

Enterprise: Pactum and Walmart

Walmart used Pactum's negotiation agents to reach thousands of smaller "long-tail" suppliers that human buyers had never had time to negotiate with. The agents negotiated payment terms and price discounts over text chat, within boundaries set by Walmart's procurement team.

The reported results (68 percent agreement, about 3 percent savings, and 35-day payment-term extensions) made it the best-known production deployment of autonomous negotiation. Pactum's current platform checks purchase requests first, then negotiates over email and a supplier portal, and runs coordinated campaigns across entire supplier bases.

The lesson for architects: bounded autonomy works best. Pactum's agents operate inside rules, limits, and approval workflows set by humans. That's the enterprise version of what AP2 mandates now try to standardize for consumers.

Research Competitions

Competitions have driven AI negotiation research for more than a decade:

  • The Automated Negotiating Agents Competition (ANAC) has benchmarked structured-offer negotiation agents since 2010.
  • The MIT AI Negotiation Competition (2025) was the first large-scale competition for LLM negotiation agents. Its results reshaped how researchers think about warmth, dominance, and AI-specific tactics.
  • The Melting Pot Contest, presented by Google DeepMind's Joel Leibo at Harvard's 2025 AI Negotiation Summit, tests cooperation among many agents.

Harvard's Program on Negotiation has published full videos of the 2025 summit. Panels covered AI as negotiator, mediator, coach, teacher, and research partner.

Risks, Ethics, and Governance

AI negotiation raises distinctive risks because an agent's job is, by definition, to push for one side. See Risk, Compliance and Regulation, Ethics, Law, Privacy, AI Governance, and AI Verification and Validation.

Risk What it looks like Evidence Mitigation
Deception Agents making promises they don't intend to keep CICERO game logs; o3 in AI Diplomacy Honesty training, logging, disclosure rules, treating deceptive systems as high risk
Capability asymmetry Users with weaker agents systematically lose money Agent-to-agent consumer benchmark Minimum-capability standards; independent agent ratings
Constraint violation Agents exceeding budgets or accepting bad deals Consumer benchmark anomalies Signed mandates (AP2); hard limits enforced in code, not prompts
First-proposal bias Agents accepting the fastest offer, not the best Magentic Marketplace Wait-and-compare rules; marketplace design that batches offers
Manipulation and prompt injection Counterpart messages hijacking the agent Magentic Marketplace; MIT competition Treat counterpart text as untrusted; separate strategy from language layer
Information leakage Agents revealing reservation prices MIT competition transcripts Keep limits outside the model's context; test with adversarial counterparts
Principal-agent conflict Agent serving the platform instead of the user Ongoing policy debate Duty-of-care obligations; transparency about incentives

Two principles cut across all of these:

  • Enforce limits in code, not in prompts. A budget written into a prompt is a suggestion. A budget enforced by software, or by a cryptographic mandate, is a rule.
  • Test in simulation before going live. Environments like Magentic Marketplace exist precisely to find failure modes before real money is on the line.

Practical Guidance: Deploying a Negotiation Agent

  1. Define the mandate. Write down the walk-away point, the must-haves, and the tradeable issues, then enforce them outside the language model.
  2. Choose the model deliberately. Performance varies widely between models, and the weaker agent loses.
  3. Build in warmth. The best evidence to date says friendly, curious, grateful agents close more deals and create more value.
  4. Don't take the first offer. Require the agent to gather and compare multiple proposals before accepting.
  5. Harden against manipulation. Treat everything the counterpart says as untrusted input, and never let it change your agent's instructions.
  6. Keep humans in the loop for high stakes. Use approval thresholds for large or unusual deals, as enterprise deployments do.
  7. Log everything. Keep the transcript and the agent's reasoning so you can audit what it promised and why.
  8. Disclose. Tell counterparts when they're negotiating with an AI; trust is part of the deal.

Additional Viewing

These long-form talks (each longer than 20 minutes) cover the research behind AI negotiation.

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YouTube

Parables on the Power of Planning in AI: From Poker to Diplomacy (Noam Brown, Paul G. Allen School lecture, September 2024). Brown, a co-creator of the poker AIs Libratus and Pluribus and a CICERO lead, explains how search and planning, not just bigger models, produced superhuman game-playing AI. He walks through how CICERO paired a language model with dialogue-conditional action models and iterative search over other players' likely beliefs, planning each message before sending it.

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YouTube

MIT Sloan Alumni Online: Professor Jared Curhan on AI and the Future of Negotiation (MIT Sloan, June 2024). Hosted by Jackie Selby, this session features Curhan, the MIT negotiation psychologist who went on to co-lead the MIT AI Negotiation Competition, discussing how AI is changing negotiation. It's useful background for the competition's later findings on warmth, dominance, and AI coaching.


Sources and Further Reading

AI Negotiation Research and Competitions

Agent-to-Agent Markets

Diplomacy, Deception, and Honesty

Enterprise Negotiation and Agentic Commerce

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