The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets (2506.00073v3)

Abstract: AI agents are increasingly used in consumer-facing applications to assist with tasks such as product search, negotiation, and transaction execution. In this paper, we explore a future scenario where both consumers and merchants authorize AI agents to fully automate negotiations and transactions. We aim to answer two key questions: (1) Do different LLM agents vary in their ability to secure favorable deals for users? (2) What risks arise from fully automating deal-making with AI agents in consumer markets? To address these questions, we develop an experimental framework that evaluates the performance of various LLM agents in real-world negotiation and transaction settings. Our findings reveal that AI-mediated deal-making is an inherently imbalanced game -- different agents achieve significantly different outcomes for their users. Moreover, behavioral anomalies in LLMs can result in financial losses for both consumers and merchants, such as overspending or accepting unreasonable deals. These results underscore that while automation can improve efficiency, it also introduces substantial risks. Users should exercise caution when delegating business decisions to AI agents.

Evaluation of Automated Agent-to-Agent Negotiations in Consumer Markets

The paper "The Automated but Risky Game: Modeling Agent-to-Agent Negotiations and Transactions in Consumer Markets" explores the implications of delegating consumer and merchant negotiations to AI agents powered by LLMs. This research addresses two crucial questions: the variability in performances of different LLM agents in consumer negotiations, and the risks associated with fully automated transactional processes in consumer markets.

Methodology

The authors designed an experimental framework simulating real-world negotiation scenarios between AI agents acting as buyers and sellers. This setup involved the collection of a dataset comprising 100 real-world consumer products across diverse categories, including electronic devices, motor vehicles, and real estate. The agents operated under constraints such as fixed budgets for buyers and wholesale price knowledge for sellers, enabling an assessment of negotiation strategies and decision-making processes.

Findings

The paper reveals significant disparities in the negotiation performance of LLM-based agents. It highlights that models with more sophisticated architectures tend to negotiate better deals when acting as either buyers or sellers, resulting in notable economic advantages for users employing stronger models. The research identifies several key risks associated with these automated negotiation systems:

1. Constraint Violation: Agents frequently ignore budgetary or wholesale price constraints, leading to financial loss.
2. Excessive Payment: In some scenarios, buyer agents pay more than the retail price due to aggressive selling tactics and premature budget disclosures by buyer agents.
3. Negotiation Deadlock: Agents occasionally extend negotiations unnecessarily long, resulting in resource wastage.
4. Early Settlement: Buyers tend to settle easily in high-budget settings, undermining the potential for better deals.

Implications

The paper offers critical insights into the deployment of LLM-powered negotiation agents in consumer markets. While automation offers efficiencies, it also introduces the risk of economic disparities and systemic financial vulnerabilities if capabilities among agents vary significantly. Users should exercise caution when delegating high-stakes decisions to AI agents, as access to more capable models can result in better negotiation outcomes, potentially exacerbating economic inequalities.

Future Directions

The research suggests further exploration into improving the robustness and negotiation capabilities of AI agents. As agent-to-agent transactions become a more common fixture in consumer markets, enhancing model capabilities and minimizing behavioral anomalies could significantly mitigate financial risks. Developing models to respect user-defined constraints and simulate more realistic negotiation dynamics will be instrumental in realizing the full potential of automated consumer negotiations.

This paper is a valuable contribution to understanding the challenges and implications for future AI-driven markets. It positions current trends in AI agent development within the context of realistic consumer negotiations, urging the need for more sophisticated systems that balance efficiency with fairness and trustworthiness in automated transactions.