Information Collection on a Graph

Ilya O. Ryzhov () and Warren B. Powell ()
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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

Operations Research, 2011, vol. 59, issue 1, 188-201

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.

Keywords: optimal learning; knowledge gradient; Bayesian learning; stochastic shortest paths; ranking and selection (search for similar items in EconPapers)
Date: 2011
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Citations: View citations in EconPapers (6)

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