Foundations of a Time-Consistent Counterfactual Actuarial Runtime for Autonomous AI Agents

Battle Count: 55

The paper is primarily about AI agent risk management and actuarial insurance rather than direct quantitative trading. However, it has meaningful relevance: (1) trade execution is explicitly listed as a side-effect-bearing action example; (2) the dynamic risk measure framework (CVaR, entropic risk) is directly applicable to trading risk management; (3) the budget guarantee and conservative gating concepts parallel position sizing and risk limits in trading; (4) the ambiguity-set robust capital concept connects to model uncertainty in trading strategies. The connection is indirect - it provides a governance layer for AI agents that might execute trades, rather than a trading strategy itself.

High complexity due to: (1) requires well-defined interventional causal models or simulators for counterfactual loss computation; (2) dynamic risk measure recursion must be implemented correctly for time consistency; (3) boundary design requires domain expertise and careful calibration of exposure states; (4) conservative envelope construction via conformal prediction must match deployment adaptivity; (5) the safe-default mapping is contractually fixed and non-trivial to design; (6) ambiguity-set optimization for robust capital is computationally demanding; (7) the framework is theoretical - practical implementation requires the companion empirical paper's runtime infrastructure.

The paper is a theoretical working paper with complete mathematical proofs and well-defined assumptions. All four theorems have proof sketches provided. However, as a foundational framework paper, full reproducibility depends on the companion empirical paper (arXiv:2605.25632) which implements the runtime. The mathematical objects are precisely defined but require domain-specific calibration (safe-default mappings, boundary designs, risk measure choices) for practical reproduction.