This paper introduces Automated Institutional Discovery (AID), a novel computational framework that conceptualizes economic institutional design as a high-dimensional combinatorial search problem. Traditional institutional design relies heavily on human intuition, historical evolution, or analytically constrained mechanism design, which often fails in complex, adaptive multi-agent environments. AID transcends these limitations by framing institutions as tuples i = (r_1, r_2, ..., r_K) within an expansive institutional space and utilizing advanced search and optimization algorithms to discover configurations that maximize global objective functions F(i). By combining multi-agent simulation modeling with metaheuristic search strategies, AID evaluates allocative efficiency, incentive compatibility, resilience, and distributional equity without requiring empirical laboratory experiments. The framework establishes a paradigm shift from manual rule-making to automated machine discovery, offering robust applications for digital economies, decentralized finance, and economic governance.
This paper develops a Quantum-Institutional Automated Negotiation (QIAN) algorithm as an intelligent decision support system for carbon credit markets, contributing to quantum game theory applications in automated negotiation and institutional decision-making. We extend the Eisert–Wilkens–Lewenstein (EWL) framework by introducing an Institutional Filter Function Φ_C that maps continuous quantum strategies—phase shifts and superpositions—onto finite, legally viable contract archetypes. This filter models regulatory, political, and organizational constraints that collapse the infinite quantum strategy space into a tractable finite set, enabling computationally efficient decision support. We prove convergence of the automated negotiation algorithm to a Pareto-superior Nash Equilibrium and demonstrate, through Monte Carlo simulation with literature-calibrated parameters, that the collapsed quantum equilibrium yields a mean joint utility uplift of 13.5% over classical cooperation (95% CI: 9.8%–17.3%, p < 0.001), with the upper bound reaching 17.3% and 26.8% of simulations achieving uplifts in the 15–30% range. The framework maps directly to blockchain-based smart contracts, providing a deployable mechanism for sustainable carbon markets that aligns with SDG 13 (Climate Action) and SDG 17 (Partnerships). This work advances quantum game theory from abstract formalism to computational institutional design, offering a novel decision support approach for negotiation analysis under real-world constraints.