Learnable Predictions Need Not Be Actionable: Proper Repair Dimension for Online Buying

Abstract

Learnability and actionability are different requirements for online predictions. Littlestone dimension controls ordinary online learnability, but proper repair dimension controls actionable online buying. A concept class can be easy to learn in the standard mistake-bound sense and still be hard to maintain as a live actionable prediction once the algorithm is required to stay proper and to buy the realized action online. For a finite binary concept class, this paper defines the proper repair dimension PRD(H) by a dynamic program on version spaces. The value PRD(H) is exactly the optimal deterministic worst-case number of repairs for a proper learner that must keep a live hypothesis through every realizable labeled sequence. The paper proves Ldim(H) <= PRD(H) <= |H|-1, with tight examples: the full class on d coordinates has Ldim = PRD = d, while the universal coordinate class U_n has Ldim(U_n) = floor(log_2 n) and PRD(U_n) = n-1. This gap transfers directly to online buying. The paper builds a unit-cost actionable buying instance from every proper class H and proves that the terminal benchmark is uOPT = 1 while every deterministic proper actionable algorithm pays exactly 1 + PRD(H) in the worst case. For U_{2^d}, this gives deterministic cost 2^d despite Littlestone dimension d. The paper also gives a positive transfer theorem for componentized actionable prediction classes, showing that bounded repair dimension together with bounded PRD-load congestion yields controlled actual buying cost.