Solving Cross-Boundary Coordination Through Intelligent Digital Negotiation
Digital twins and multi-agent generative systems (MAGS) securely automate negotiate agreements across organizational boundaries.
The automated negotiation testbed explores how digital twins and multi-agent systems can automate and optimize complex agreements between organizations, particularly in sectors like supply chain, logistics, and manufacturing. Traditional manual negotiation is often slow, subjective, and constrained by incomplete information. This testbed demonstrates that digital twins—representing assets, systems, and processes—can be used to define utility functions and simulate negotiation outcomes in real time.
Built using NEC’s automated negotiation platform and XMPro’s MAGS system, the automated negotiation testbed enables agents to autonomously engage in cross-boundary negotiations, factoring in institutional policies, privacy constraints, and shared operational goals. It compares this digital-first approach to conventional negotiation methods, using expert evaluation and metrics such as closing rate, utility optimization, and Pareto efficiency.
By layering digital twin intelligence with generative agent behavior, the testbed shows how parties can arrive at high-quality agreements faster, with better alignment to planning constraints and downstream resource allocation. This fusion of real-time simulation and autonomous reasoning opens the door to more flexible and scalable coordination frameworks across industries.
Key contributions include a reference architecture for automated negotiation, integration strategies for legacy systems, and a pathway to securely embedding decision-making into distributed environments. This approach demonstrates how automated negotiation can streamline collaboration across competing organizations. It provides a scalable foundation for decision automation in complex, multi-stakeholder environments.
The testbed contributes to industry advancement in the following ways:
Coordination-Within-Competition: Facilitating effective agreements across organizational boundaries by enabling autonomous systems to negotiate in dynamic, multi-stakeholder environments.
Utility Evaluation: Using digital twins to accurately represent assets, constraints, and performance factors, resulting in more informed and optimized negotiation outcomes.
Decision-Making Flexibility: Enhancing interactivity, reasoning, and autonomy by integrating automated negotiation systems with MAGS that adapt to complex negotiation scenarios.
Resource Allocation: Improving planning constraints and resource distribution by aligning negotiation strategies with real-time operational data and organizational objectives.
Institutional Policy Adherence: Ensuring security, compliance, and trust during automated negotiations by maintaining strict adherence to role-based access and cross-domain data-sharing policies.
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