Elements of information theory
Elements of information theory
An application of copulas to accident precursor analysis
Management Science
Correlations and Copulas for Decision and Risk Analysis
Management Science
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Introduction to Stochastic Dynamic Programming: Probability and Mathematical
Neuro-Dynamic Programming
Options in the Real World: Lessons Learned in Evaluating Oil and Gas Investments
Operations Research
Assessing Dependence: Some Experimental Results
Management Science
An adaptive automated method for identity verification with performance guarantees
Electronic Commerce Research
Decision Analysis
Decision Analysis
Decision Analysis
Paradoxes in Learning and the Marginal Value of Information
Decision Analysis
Consistency of Sequential Bayesian Sampling Policies
SIAM Journal on Control and Optimization
A Copulas-Based Approach to Modeling Dependence in Decision Trees
Operations Research
A Simulation-Based Approach to Decision Making with Partial Information
Decision Analysis
Approximating Joint Probability Distributions Given Partial Information
Decision Analysis
Decreasing Marginal Value of Information Under Symmetric Loss
Decision Analysis
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In this paper, we develop a practical and flexible framework for evaluating sequential exploration strategies in the case where the exploration prospects are dependent. Our interest in this problem was motivated by an oil exploration problem, and our approach begins with marginal assessments for each prospect (e.g., what is the probability that the well is wet?) and pairwise assessments of the dependence between prospects (e.g., what is the probability that both wells i and j are wet?). We then use information-theoretic methods to construct a full joint distribution for all outcomes from these marginal and pairwise assessments. This joint distribution is straightforward to calculate, has many nice properties, and appears to provide an accurate approximation for distributions likely to be encountered in practice. Given this joint probability distribution, we determine an optimal drilling strategy using an efficient dynamic programming model. We illustrate these techniques with an oil exploration example and study how dependence and risk aversion affect the optimal drilling strategies. The information-theory-based techniques for constructing joint distributions and dynamic programming model for determining optimal exploration strategies could be used together or separately in many other applications.