Trellison Institute — Research Publication

Markov Chain Monte Carlo as the Computational Engine for Basel SCO60 Group 1a Tokenized Physical Asset Verification

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Abel Gutu & Robert Stillwell — LedgerWell Corporation / DaedArch Corporation
CVR Protocol Mathematical Framework Series — Paper 3
Ethereum Research SSRN — Abstract 6499138 March 18, 2026
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Abstract

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This paper introduces Markov Chain Monte Carlo (MCMC) methods as the computational mechanism enabling the Continuous Verifiable Reality (CVR) Protocol’s reputation-weighted Bayesian oracle consensus to operate at institutional scale for Basel SCO60 Group 1a tokenized physical asset verification.

We model the oracle network as a Hidden Markov Model (HMM) over continuous physical asset states, where individual oracle reports constitute noisy observations of the true underlying asset condition. The MCMC sampling engine — specifically Metropolis-Hastings with oracle-specific proposal distributions calibrated by historical accuracy — enables the network to efficiently explore high-dimensional asset state spaces while naturally incorporating oracle reputation weights as stationary distribution parameters.

We derive a formal Verification Discount methodology from MCMC posterior credible intervals, establishing that when the network posterior credible interval width falls below Basel-defined thresholds, the corresponding risk weight reduction can be precisely quantified. This bridges the gap between Papers 1–2’s theoretical framework and practical regulatory implementation.

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Key Contributions

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Hidden Markov Model formulation

Models the oracle network as an HMM where the true physical asset state evolves according to a continuous-state Markov process and oracle reports are noisy observations conditioned on both the true state and individual oracle reliability parameters.

MCMC sampling for oracle consensus

Metropolis-Hastings with oracle-specific proposal distributions enables scalable posterior inference over asset states. Oracle reputation weights enter as stationary distribution parameters, naturally down-weighting unreliable participants.

Verification Discount from posterior credible intervals

Formally derives the relationship between MCMC posterior interval width and Basel SCO60 risk weight reduction. When posterior uncertainty falls below regulatory thresholds, capital requirements decrease proportionally.

Ethiopian cooperative carbon farming case study

Empirical demonstration using Ethiopian coffee cooperative data. Shade-tree agroforestry provides both carbon sequestration and economic benefits. The case study validates MCMC convergence properties and verification discount calculations against real-world agricultural conditions.

MCMC Bayesian fusion oracle consensus Basel SCO60 Group 1a RWA tokenization CVR Protocol Hidden Markov Model carbon verification Ethiopian agriculture
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Series Context

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This is Paper 3 in the CVR Protocol Mathematical Framework Series. It builds on the theoretical foundations established in Paper 1 (CVR Framework) and Paper 2 (ProofLedger Protocol), and provides the computational engine that is generalized in Paper 4 (Threshold-Convergent Systems).

Read on Ethereum Research → SSRN Abstract 6499138 →

Publication Status

Published independently on Ethereum Research (March 18, 2026). SSRN submission (Abstract ID 6499138) publicly available and under review by SSRN staff. Carbon verification methodology validated against standards published by Dr. Barbara Haya (UC Berkeley Carbon Trading Project). Trellison Institute does not claim endorsement.

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Public comments

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