Adaptive AI Delegation under Uncertainty: A Bayesian Governance Policy for Sequential Decision Authority

Battle Count: 68

The paper is highly relevant to quantitative trading as a governance and risk management framework rather than a direct trading strategy. It uses market data (prices, returns, volatility, macroeconomic variables), evaluates governed Sharpe ratios and drawdowns, and benchmarks against investment performance. The historical market replay section directly applies the governance policy to market data. However, the primary contribution is the governance/delegation mechanism itself, not alpha generation. The framework could be directly applied to determine how much authority to give AI-generated trading signals versus validated quantitative models. The Belief-at-Risk concept and adaptive delegation weight (alpha) are directly applicable to AI-assisted portfolio management and algorithmic trading oversight.

Implementation requires: (1) Bayesian filtering with Markov transition matrices over latent states, (2) computation of Belief-at-Risk from entropy, KL divergence, and consequence terms, (3) approximate Bellman recursion over a finite governance action space, (4) integration with AI/LLM outputs for probabilistic evidence and recommendations, (5) maintenance of validation history and reliability diagnostics, (6) calibration of multiple governance parameters (lambda_BaR, lambda_risk, lambda_dev, lambda_int). The computational complexity per step is manageable (dominated by filtering over K states and evaluating a small action set), but the full system architecture with multiple interacting components requires significant engineering effort. The paper provides Algorithm 1 as a clear implementation guide.

The paper provides a detailed algorithm (Algorithm 1) with step-by-step governance procedure, explicit mathematical formulations for Bayesian filtering, Belief-at-Risk, and Bellman recursion. Synthetic experiments use 500 Monte Carlo runs per scenario with bootstrap confidence intervals. However, no GitHub repository or code link is provided. The synthetic data-generating process parameters are described but not fully specified. Historical replay uses 'cached AI outputs' whose provenance is not detailed. The finite governance action space and specific parameter values (lambda_BaR, lambda_risk, lambda_dev, lambda_int) are partially specified in tables but not exhaustively documented.