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In the portfolio problem, the decision maker selects a subset from a set of candidate projects, each yielding an uncertain profit. When the projects in the portfolio are probabilistically dependent, further information regarding any particular project also provides information about other projects, and there is thus an opportunity to improve value through prudential information gathering. In this paper, we study the value of information in portfolio problems with multivariate Gaussian projects, analyzing the effect of parameters such as the expected values and standard deviations of profits from each project, the accuracy of the information, and dependence among projects. We are particularly interested in the role that dependence plays, illustrating the results using examples from the earth sciences, where there is spatial dependence among physical locations.
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Papers that cite the same references — the relational fallback while embeddings are still backfilling.
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