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Information Collection on a Graph

Author

Listed:
  • Ilya O. Ryzhov

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08540)

  • Warren B. Powell

    (Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08540)

Abstract

We derive a knowledge gradient policy for an optimal learning problem on a graph, in which we use sequential measurements to refine Bayesian estimates of individual edge values in order to learn about the best path. This problem differs from traditional ranking and selection in that the implementation decision (the path we choose) is distinct from the measurement decision (the edge we measure). Our decision rule is easy to compute and performs competitively against other learning policies, including a Monte Carlo adaptation of the knowledge gradient policy for ranking and selection.

Suggested Citation

  • Ilya O. Ryzhov & Warren B. Powell, 2011. "Information Collection on a Graph," Operations Research, INFORMS, vol. 59(1), pages 188-201, February.
    • Handle: RePEc:inm:oropre:v:59:y:2011:i:1:p:188-201
    DOI: 10.1287/opre.1100.0873
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    References listed on IDEAS

    as
    1. Peter Frazier & Warren Powell & Savas Dayanik, 2009. "The Knowledge-Gradient Policy for Correlated Normal Beliefs," INFORMS Journal on Computing, INFORMS, vol. 21(4), pages 599-613, November.
    2. Stephen E. Chick & Noah Gans, 2009. "Economic Analysis of Simulation Selection Problems," Management Science, INFORMS, vol. 55(3), pages 421-437, March.
    3. Stephen E. Chick & Koichiro Inoue, 2001. "New Procedures to Select the Best Simulated System Using Common Random Numbers," Management Science, INFORMS, vol. 47(8), pages 1133-1149, August.
    4. Stephen E. Chick & Jürgen Branke & Christian Schmidt, 2010. "Sequential Sampling to Myopically Maximize the Expected Value of Information," INFORMS Journal on Computing, INFORMS, vol. 22(1), pages 71-80, February.
    5. Bruce Schmeiser, 1982. "Batch Size Effects in the Analysis of Simulation Output," Operations Research, INFORMS, vol. 30(3), pages 556-568, June.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Daniel R. Jiang & Warren B. Powell, 2018. "Risk-Averse Approximate Dynamic Programming with Quantile-Based Risk Measures," Mathematics of Operations Research, INFORMS, vol. 43(2), pages 554-579, May.
    2. Susan Jia Xu & Mehdi Nourinejad & Xuebo Lai & Joseph Y. J. Chow, 2018. "Network Learning via Multiagent Inverse Transportation Problems," Service Science, INFORMS, vol. 52(6), pages 1347-1364, December.
    3. Ilya O. Ryzhov, 2016. "On the Convergence Rates of Expected Improvement Methods," Operations Research, INFORMS, vol. 64(6), pages 1515-1528, December.
    4. Melih Çelik & Özlem Ergun & Pınar Keskinocak, 2015. "The Post-Disaster Debris Clearance Problem Under Incomplete Information," Operations Research, INFORMS, vol. 63(1), pages 65-85, February.
    5. Ilya O. Ryzhov & Warren B. Powell & Peter I. Frazier, 2012. "The Knowledge Gradient Algorithm for a General Class of Online Learning Problems," Operations Research, INFORMS, vol. 60(1), pages 180-195, February.
    6. Boris Defourny & Ilya O. Ryzhov & Warren B. Powell, 2015. "Optimal Information Blending with Measurements in the L 2 Sphere," Mathematics of Operations Research, INFORMS, vol. 40(4), pages 1060-1088, October.

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