Recursive Deal Origination Engine (RDOE): A Trust-Weighted Framework for Autonomous M&A Discovery and Market Readiness Detection

The Recursive Deal Origination Engine (RDOE) introduces a new paradigm for M&A discovery — one that replaces intent-based prospecting with recursive alignment intelligence. Instead of relying on human networks, timing, or disclosure signals, RDOE interprets static company data as epistemic structures that reveal latent readiness for transaction. Through trust-weighted recursion, RDOE continuously learns from both deal motion and non-motion, refining its internal weighting of financial stability, synergy elasticity, narrative inertia, and ownership entropy. The resulting composite metric, the Deal Origination Index (DOI), quantifies alignment potential as a function of belief coherence rather than declared intent. This paper formalizes the mathematics, architecture, and simulation protocols of RDOE, showing how autonomous deal discovery can emerge from recursive feedback loops within static datasets. It demonstrates how readiness capital — the latent value embedded in a firm’s trust structure — can be measured, visualized, and acted upon. By redefining capital flow as an epistemic process rather than a transactional event, RDOE marks the transition from human prospecting to recursive financial cognition — an era where markets learn to recognize themselves through trust resonance, alignment symmetry, and self-adjusting equilibrium. -JSR