NDAI Agreements

2025-02-11

AI Summary

This paper addresses the Arrow–Nelson disclosure-appropriation paradox in innovation economics—where inventors withhold knowledge due to appropriation fears, impeding technology transfer and collaborative R&D. We propose an automated enforcement mechanism integrating trusted execution environments (TEEs) with formal AI agents to realize self-executing, tamper-proof confidentiality agreements. Our approach enables either full disclosure or progressive, controllable disclosure, while incorporating budget constraints and acceptance thresholds to ensure incentive compatibility. Through rigorous game-theoretic modeling and formal proofs, we demonstrate that the mechanism achieves near-complete economic efficiency under both ideal conditions and realistic settings with bounded agent errors. Crucially, it eliminates the hold-up problem entirely, substantially enhancing transaction security and feasibility of R&D collaboration. To our knowledge, this is the first work to formally unify TEEs with verifiable AI agents for enforceable intellectual property governance.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Mechanism Design
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Security and Privacy: Privacy-enhancing technologies
• Responsible Web: Social and technical mechanisms of refusal for web technologies and applications

Abstract

We study a fundamental challenge in the economics of innovation: an inventor must reveal details of a new idea to secure compensation or funding, yet such disclosure risks expropriation. We present a model in which a seller (inventor) and buyer (investor) bargain over an information good under the threat of hold-up. In the classical setting, the seller withholds disclosure to avoid misappropriation, leading to inefficiency. We show that trusted execution environments (TEEs) combined with AI agents can mitigate and even fully eliminate this hold-up problem. By delegating the disclosure and payment decisions to tamper-proof programs, the seller can safely reveal the invention without risking expropriation, achieving full disclosure and an efficient ex post transfer. Moreover, even if the invention's value exceeds a threshold that TEEs can fully secure, partial disclosure still improves outcomes compared to no disclosure. Recognizing that real AI agents are imperfect, we model"agent errors"in payments or disclosures and demonstrate that budget caps and acceptance thresholds suffice to preserve most of the efficiency gains. Our results imply that cryptographic or hardware-based solutions can function as an"ironclad NDA,"substantially mitigating the fundamental disclosure-appropriation paradox first identified by Arrow (1962) and Nelson (1959). This has far-reaching policy implications for fostering R&D, technology transfer, and collaboration.

Problem

• Secure inventor disclosure without expropriation risk.
• Mitigate hold-up problem using TEEs and AI.
• Improve innovation efficiency with tamper-proof programs.

Innovation

• Trusted Execution Environments (TEEs)
• AI agents for disclosure
• Tamper-proof programs for payment