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Value Of Information Analysis: The State Of Application. Environment Systems & Decisions, Jeffrey Keisler, Eric Chu, Zachary Collier, Nina Sinatra, Igor Linkov
Value Of Information Analysis: The State Of Application. Environment Systems & Decisions, Jeffrey Keisler, Eric Chu, Zachary Collier, Nina Sinatra, Igor Linkov
Jeffrey Keisler
The value of information (VoI) is a decision analytic method for quantifying the potential benefit of additional information in the face of uncertainty. This paper reviews the prevalence of VoI applications reported in the peer-reviewed literature from the years 1990–2011. We categorize papers’ applications across the types of uncertainties considered, modeling choices, and contexts of social importance (such as health care and environmental science). We obtain and analyze statistics on the range of applications and identify trends and patterns in them, and conclude with an interpretation of what these mean for researchers and practitioners as they pursue new efforts. Key …
Additivity Of Information Value In Two-Act Linear Loss Decisions With Normal Priors, Jeffrey Keisler
Additivity Of Information Value In Two-Act Linear Loss Decisions With Normal Priors, Jeffrey Keisler
Jeffrey Keisler
For the two-act linear loss decision problem with normal priors, conditions are derived for which the expected value of perfect information about two independent risks is super-additive in value. Several applications show how a variety of decision problems can reduce to the canonical problem, and how the general results obtained here can be translated simply to prescriptions for specific situations.
Technical Note: Comparative Static Analysis Of Information Value In A Canonical Decision Problem, Jeffrey Keisler
Technical Note: Comparative Static Analysis Of Information Value In A Canonical Decision Problem, Jeffrey Keisler
Jeffrey Keisler
To gain insight into the behavior of the value of information, this paper identifies specific rules for a canonical decision problem: the two-act linear loss decision with normal prior probability distributions. Conditions are derived for which the expected value of perfect information increases when mean and standard deviation are both linear functions of an exogenous variable. A variety of richer decision problems can be adapted to the problem, so that the general results obtained here can be immediately applied to understand drivers of information value.