The Value of Information in Portfolio Problems with Dependent Projects

Bhattacharjya, Debarun; Eidsvik, Jo; Mukerji, Tapan

doi:10.1287/deca.2013.0277

Cited by 25 publications

(18 citation statements)

References 34 publications

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“…Bhattacharjya et al (2013) demonstrate how a particular portfolio problem can be viewed as an extension of TALL; here, we show how a particular stopping problem can also be viewed as an extension of TALL-it is an extension in the "sequential direction. "…”

Section: Results For Nosi-nmentioning

confidence: 98%

The Value of Information in Some Variations of the Stopping Problem

Bhattacharjya

Deleris

2014

Decision Analysis

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I n stopping problems, the decision maker receives a sequence of candidates and decides whether to select the current candidate and thereby stop the search or whether to continue surveying. Some examples include searching for an apartment, selecting an employee for a job, or choosing a new product or service from a sequence of vendors. Most of the literature on stopping problems assumes that the decision maker can perfectly evaluate the candidate when surveyed. We consider practical variations where surveying a candidate may provide no further information or imperfect information about its value. We show how the stopping problem can be considered an extension of the two-action (go/no-go) problem and present threshold optimal policies for the general problem. For the case where surveying candidates provides no further information and the uncertain values of candidates are normally distributed, we present analytical results that highlight how the value of information is affected by the parameters. With the help of an illustrative example, we demonstrate how a model that ignores sequential decisions could potentially severely underestimate the value of information. We also present a data-driven application of our results by studying the value of reports to a potential used car buyer.

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Section: Results For Nosi-nmentioning

confidence: 98%

The Value of Information in Some Variations of the Stopping Problem

Bhattacharjya

Deleris

2014

Decision Analysis

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“…Bhattacharjya et al (2013) extended this result for multivariate models and spatial decision situations with two alternatives at every spatial location. To illustrate, assume a multivariate Gaussian prior distribution for the values, say,…”

Section: Review Of Methodsmentioning

confidence: 88%

“…While similar methods have been applied in medical applications (Strong et al, 2014), here the spatial aspects that are typical in decision situations in the earth sciences are stressed, illustrating how the approximation equations work in this setting. Results obtained by simulation and linear regression are compared with fully analytical solutions wherever possible, such as for linearized Gaussian models along with some working assumptions pertaining to the underlying decision situation (Bhattacharjya et al, 2013).…”

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confidence: 99%

Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data

Eidsvik¹,

Dutta

Mukerji

et al. 2017

Math Geosci

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Value of information analysis is useful for helping a decision maker evaluate the benefits of acquiring or processing additional data. Such analysis is particularly beneficial in the petroleum industry, where information gathering is costly and time-consuming. Furthermore, there are often abundant opportunities for discovering creative information gathering schemes, involving the type and location of geophysical measurements. A consistent evaluation of such data requires spatial modeling that realistically captures the various aspects of the decision situation: the uncertain reservoir variables, the alternatives and the geophysical data under consideration. The computational tasks of value of information analysis can be daunting in such spatial decision situations; in this paper, a regression-based approximation approach is presented. The approach involves Monte Carlo simulation of data followed by linear regression to fit the conditional expectation expression that is needed for value of information analysis. Efficient approximations allow practical value of information analysis for the spatial decision situations that are typically encountered in petroleum reservoir evaluation. Applications are presented for seismic amplitude data and electromagnetic resistivity data, where one example includes multi-phase fluid flow simulations.

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“…In simulations we also ran tests with a criterion aiming to classify significantly large temperature gradients in the main current direction, that is,

E false[\nabla_{θ} x_{t} false| \cdot false] - \sqrt{V a r [\nabla_{θ} x_{t} | \cdot]}

, where the conditioning represents currently available data, and θ is a predefined direction. One could also go further to account for the uncertainty in future data along the next sample line that entails an integral over the data (Bhattacharjya, Eidsvik, & Mukerji, ; Eidsvik, Martinelli, & Bhattacharjya, ), or even use the expectation over future lines, with additional computational complexity for nonmyopic approaches. But the simple weighting in Equation is a practical solution which gave reasonable results for our field tests, and we leave more complex objective functions for future work.…”

Section: Methodsmentioning

confidence: 99%

Information‐driven robotic sampling in the coastal ocean

Fossum

Eidsvik

Ellingsen

et al. 2018

Journal of Field Robotics

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Efficient sampling of coastal ocean processes, especially mechanisms such as upwelling and internal waves and their influence on primary production, is critical for understanding our changing oceans. Coupling robotic sampling with ocean models provides an effective approach to adaptively sample such features. We present methods that capitalize on information from ocean models and in situ measurements, using Gaussian process modeling and objective functions, allowing sampling efforts to be concentrated to regions with high scientific interest. We demonstrate how to combine and correlate marine data from autonomous underwater vehicles, model forecasts, remote sensing satellite, buoy, and ship‐based measurements, as a means to cross‐validate and improve ocean model accuracy, in addition to resolving upper water‐column interactions. Our work is focused on the west coast of Mid‐Norway where significant influx of Atlantic Water produces a rich and complex physical–biological coupling, which is hard to measure and characterize due to the harsh environmental conditions. Results from both simulation and full‐scale sea trials are presented.

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The Value of Information in Portfolio Problems with Dependent Projects

Cited by 25 publications

References 34 publications

The Value of Information in Some Variations of the Stopping Problem

The Value of Information in Some Variations of the Stopping Problem

Simulation–Regression Approximations for Value of Information Analysis of Geophysical Data

Information‐driven robotic sampling in the coastal ocean

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