How much Data do We Need? Sequential Data Collection for Stochastic Programming
Data-driven optimization often requires collecting data to estimate uncertain model parameters before solving the underlying decision problem. In practice, however, data acquisition may incur non-negligible costs, making it critical to determine when to stop additional data collection. In this paper, we study an optimal stopping problem for sequential data collection in stochastic optimization under parameter uncertainty. We propose a benefit-driven stopping framework that balances information gain and sampling cost. We model the unknown distribution parameter within a Bayesian learning framework and update beliefs sequentially as new observations are collected. At each iteration, the dec...
| Authors | Xin Li, Juergen Branke, Xuan Vinh Doan |
|---|---|
| Primary category | math.OC |
| Published | 11 July 2026 |
| Last updated at source | 11 July 2026 |
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