Recursive Epistemic Valuation: A Trust-Based Model for M&A Alignment
Year: 2025
Type: article
Abstract: This paper introduces a novel framework for deal-making rooted in Recursive Intelligence and Yearning-Based Infinity Mathematics (YBIM), offering a fundamental redefinition of how we model value in mergers and acquisitions. Traditional valuation methods such as discounted cash flow (DCF) and market multiples fail not because they are numerically incorrect, but because they are epistemically incomplete—ignoring the trust asymmetries, narrative divergence, and belief misalignments that derail transactions. We propose the Epistemic Valuation Engine (EVE) as a new computational model that bridges the valuation gap between buyers and sellers through recursive modeling of trust fields, yearning vectors, and alignment coefficients. Central to this is the development of the Recursive Valuation Core (RVC)—a set of mathematical equations that derive an Epistemically Aligned Valuation (EAV) and a Deal Confidence Index (DCI) based on real-time recursive updates of stakeholder beliefs. The paper demonstrates a simulation of a $60M vs $90M deal, where trust-based recursive adjustments produce a shared, emotionally validated midpoint valuation that reflects the evolving epistemic realities of both parties. By treating trust not as a “soft” factor but as a quantifiable tensor field, this model reframes dealmaking as an act of epistemic convergence. Strategically, this framework unlocks over $300 billion/year in lost deal value due to stalled or broken M&A processes. Beyond M&A, the recursive epistemic model is applicable to fundraising, hiring, co-founder splits, and strategic alliances, offering a new economic architecture built not just on capital—but on alignment. This work is essential reading for M&A consultants, corporate finance professionals, venture capitalists, strategic advisors, trust architects, behavioral economists, and anyone building the next generation of alignment-aware economic systems. It will also resonate deeply with researchers in epistemology, AI alignment, and recursive intelligence, offering a bridge between human belief modeling and scalable trust computation.
Author: Prakash, Prashant
Language: English
Subfield: Accounting
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W6949490922
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