Support Sufficiency as Action-Sufficient Compression: A Rate-Regret Framework for Decision-Making
Researchers formalize 'support sufficiency' as a compression principle for decision-making systems, showing that robust decisions require retaining only information relevant to actions under a given consequence structure. The framework applies rate-distortion theory to support states, with regret as the distortion metric, distinguishing action adequacy from reconstruction fidelity. This theoretical contribution clarifies why certain arbitration methods fail and provides a principled approach to information compression in single-cycle decision problems.
This arXiv paper presents a formal theory of how decision-making systems should compress information. The authors define support sufficiency as the minimal information needed to select optimal actions given a consequence geometry (payoff structure). Using rate-distortion theory, they show that two support states can be merged exactly when they lead to identical optimal actions, and that approximate sufficiency is characterized by bounded expected policy regret. The optimal stochastic action channel follows a Gibbs distribution applied to support states with regret-based distortion. The work distinguishes action adequacy—preserving distinctions that matter for decisions—from other compression objectives like reconstruction fidelity or information-bottleneck prediction. The authors argue that robust single-cycle arbitration requires preserving action-relevant distinctions but not all information in the full support state.
What's missing
The paper does not discuss computational complexity of finding optimal action-sufficient compressions, applicability to multi-cycle or sequential decision problems, or empirical validation on real decision-making tasks.
What different sources said
- arXiv cs.AICenter
Support sufficiency as action-sufficient compression: a single-cycle rate-regret formulation
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