An optimal control model for adaptive auto-insurance pricing learns claim risks from telematics, captures multi-period driver responses to discounts, and applies Lagrangian relaxation to achieve asymptotically optimal portfolio-wide discount allocation.
Stable function approximation in dynamic programming
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Soft Bellman residual minimization with weighted Lp-norm aligns the objective with Bellman contraction as p increases and yields performance error bounds.
citing papers explorer
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Prescriptive Optimization for Adaptive Auto-insurance Pricing with Telematics Data
An optimal control model for adaptive auto-insurance pricing learns claim risks from telematics, captures multi-period driver responses to discounts, and applies Lagrangian relaxation to achieve asymptotically optimal portfolio-wide discount allocation.
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Contraction-Aligned Analysis of Soft Bellman Residual Minimization with Weighted Lp-Norm for Markov Decision Problem
Soft Bellman residual minimization with weighted Lp-norm aligns the objective with Bellman contraction as p increases and yields performance error bounds.