Adaptive Conditional Forest Sampling for Spectral Risk Optimisation under Decision-Dependent Uncertainty
arXiv:2603.12507v2 Announce Type: replace Abstract: Minimising a spectral risk objective, defined as a weighted combination of expected cost and Conditional Value-at-Risk (CVaR), is challenging when the uncertainty distribution is decision-dependent, making both surrogate modelling and simulation-based ranking sensitive to tail estimation error. We propose Adaptive Conditional Forest Sampling (ACFS), a four-phase simulation-optimisation framework that integrates Generalised Random Forests for decision-conditional distribution approximation, CEM-guided global exploration, rank-weighted focused
This research addresses a critical challenge in AI optimization, particularly relevant as decision-dependent uncertainties become more prevalent in complex systems, pushing the boundaries of existing methods.
Adaptive Conditional Forest Sampling offers a more robust and accurate approach to risk optimization in AI, especially for applications where tail uncertainties are significant, which is key for reliability and safety in AI agent deployments.
The ability to more accurately estimate and manage tail risks in decision-dependent environments will improve the trustworthiness and deployability of advanced AI systems in high-stakes applications.
- · AI developers
- · Risk management firms
- · Sectors using AI for complex optimization (e.g., finance, logistics)
- · Traditional simulation-optimization methods without robust tail estimation
Improved reliability and safety in AI systems handling complex, uncertain environments.
Accelerated adoption of AI agents in critical infrastructure and financial sectors due to enhanced risk management capabilities.
Increased regulatory confidence in AI systems, potentially leading to broader deployment and new applications.
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