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.
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p<0.0005. The remaining comparisons are ties. Against the effective rank and the Vendi score the index ties in every scheme and both samples, and the margin over the Absorption Ratio narrows under the strictest labelling. On real matrices the far simpler node degree reproduces the attribution. A lead-lag analysis puts the peak cross-correlation at zero lag: this is a coincident state index, not a forecast.