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Management Sciences and Quantitative Methods Commons™
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Full-Text Articles in Management Sciences and Quantitative Methods
Connecting Big Data With Big Decisions: Ideas For Synthesizing Analytics And Decision Analysis, Jeffrey Keisler
Connecting Big Data With Big Decisions: Ideas For Synthesizing Analytics And Decision Analysis, Jeffrey Keisler
Management Science and Information Systems Faculty Publication Series
This paper describes an approach to connect decision analysis models with outputs of analytic methods applied to various types of big data. Decision analysis models focus on issues of concern to a decision maker and incorporate use of a range of methods and axioms to develop insights about what the decision maker should do. In particular, decision analysis models typically use subjective judgments from the decision maker to describe beliefs about the likelihood of events and the desirability of outcomes. In order for human judgments to be improved by the availability of large amounts of data and processing power, it …
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
Management Science and Information Systems Faculty Publication Series
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
Management Science and Information Systems Faculty Publication Series
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.