Another Look at the Radner–Stiglitz Nonconcavity in the Value of Information

Chade, Héctor; Schlee, Edward E.

doi:10.1006/jeth.2001.2960

Cited by 56 publications

(39 citation statements)

References 20 publications

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“…Our model differs because instead of facing a binary decision about whether or not to learn (they either enter an occupation or they do not), workers can 3 The fact that patterns of experimentation are often non-monotonic and complex is a manifestation of the of the famous Radner and Stiglitz (1984) result that the value of information is non-concave. This result is generalized by Chade and Schlee (2002).…”

mentioning

confidence: 55%

“…For the rest of the N − 2 jobs, the higher is α j , the more output depends on θ. 6 Information in the model is symmetric; firms and workers have common priors on θ, k is known to everyone and output is commonly observed. Workers and firms acquire additional information about a worker's unknown skill through successive observations of output.…”

Section: Environment Technology and Informationmentioning

confidence: 99%

“…5 Here we assume that skills are perfect substitutes in production. However, the solution to this problem is equivalent to the solution of one in which there is a Cobb-Douglas production function, where yt = lnỹ, k = lnk, θ = lnθ, = ln˜ and lnỹt = ln[˜ θ α tk 1−α t ] = αt lnθ + (1 − αt) lnk + ln˜ t. 6 We assume that set of jobs is exogenously given. Our results below, however, suggest that if firms were costlessly able to choose as many jobs as they want, they would choose a continuum of jobs so that…”

Section: Environment Technology and Informationmentioning

confidence: 99%

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Experimentation and Job Choice

Antonovics

Golan

2012

Journal of Labor Economics

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confidence: 55%

Section: Environment Technology and Informationmentioning

confidence: 99%

Section: Environment Technology and Informationmentioning

confidence: 99%

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Experimentation and Job Choice

Antonovics

Golan

2012

Journal of Labor Economics

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“…Radner and Stiglitz [25] discusses the problem in considerably greater depth, giving general conditions under which the value of information is not concave. This research has been extended more recently by Chade and Schlee [26] and Delara and Gilotte [27] to more general settings.…”

Section: The S-curve Problemmentioning

confidence: 96%

The Knowledge Gradient for Optimal Learning

Powell

2011

Wiley Encyclopedia of Operations Research and Management Science

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Optimal learning addresses the problem of how to collect information so that it benefits future decisions. For off‐line problems, we have to make a series of measurements or observations before choosing a final design or set of parameters; for on‐line problems, we learn from rewards we are receiving, and we want to strike a balance between rewards earned now and better decisions in the future. This article reviews these problems, describes optimal and heuristic policies, and shows how to compare competing policies. Then, the presentation focuses on the concept of the knowledge gradient, which guides information collection by maximizing the marginal value of information. We show how this idea can be applied to both on‐line and off‐line problems, as well as a broad range of other applications which have not previously yielded to formal techniques.

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“…Before selecting a project, the decision maker may acquire information about one or more projects. 1 See also Chade and Schlee (2002).…”

Section: Introductionmentioning

confidence: 99%

Decreasing Marginal Value of Information Under Symmetric Loss

Bikhchandani

Mamer

2013

Decision Analysis

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W e investigate conditions under which the value of additional information is diminishing in a setting where the decision maker has access to multiple, identically-distributed, information signals. The signals are assumed to be independent conditional on an unknown payoff-relevant parameter. The decision maker minimizes a quadratic loss function. Quadratic losses arise in quality control, scoring rules, and other applications. We characterize two concepts of diminishing marginal value of information. The first is an ex ante concept, before any information is observed, and the second is an ex post concept, after observation of previous information signals. The former concept is useful for ex ante information acquisition decisions and the latter for sequential information acquisition.Key words: value of information; concavity of value of information; quadratic loss function; Bayesian models History:

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Another Look at the Radner–Stiglitz Nonconcavity in the Value of Information

Cited by 56 publications

References 20 publications

Experimentation and Job Choice

Experimentation and Job Choice

The Knowledge Gradient for Optimal Learning

Decreasing Marginal Value of Information Under Symmetric Loss

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