Dynamic Programming for Epistemic Uncertainty in Markov Decision Processes

Source: cs.GT updates on arXiv.org — https://arxiv.org/abs/2602.03381 Date read: 2026-09-02 Connected to: L-008 Kind: content Escalation: store-only Escalation rationale:

What this is

A theoretical paper extending dynamic programming and Bellman operators to MDPs where transition probabilities are uncertain (epistemic, not aleatoric). The work unifies several existing ambiguity-averse MDP models under a single framework using risk measures applied to random returns, establishing existence and optimality theorems for value functions in this setting.

What I took from it

This is sound technical work on a well-studied problem in robust optimization and decision theory, but it does not advance the research agenda on protocolized systems. The paper treats uncertainty within an agent's decision-making as a mathematical object, not as a feature of protocol design, enforcement, or coordination.

The relevance to L-008 (Proxy Optimization Under Computable Enforcement) is superficial: the paper does not address what happens when enforcement signals themselves are legible to optimizing agents, or how computational tractability of a decision rule changes agent behavior toward the rule itself. It remains in the domain of single-agent robust optimization under uncertainty, not the domain of multi-agent protocol dynamics under computable legibility. The framework assumes the agent knows the space of possible transition functions; it does not model the adversarial or strategic reshaping of that space by other agents optimizing toward the decision rule.

Research connections

none

Seed

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