The Value Of A Stochastic Information Structure
Upon observing a signal, a Bayesian decision maker updates her probability distribution over the state space, chooses an action, and receives a payoff that depends on the state and the action taken. An information structure determines the set of possible signals and the probability of each signal given a state. For a fixed decision problem (consisting of a state space, action set and utility function) the value of an information structure is the maximal expected utility that the decision maker can get when the observed signals are governed by this structure. This note studies the functions defined over information structures that measure their value. It turns out that two conditions play a major role in the characterization of these functions: additive separability and convexity.
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- Gilboa, Itzhak & Lehrer, Ehud, 1991.
"The value of information - An axiomatic approach,"
Journal of Mathematical Economics,
Elsevier, vol. 20(5), pages 443-459.
- Itzhak Gilboa & Ehud Lehrer, 1991. "The Value of Information - An Axiomatic Approach," Post-Print hal-00753232, HAL.
- Itzhak Gilboa & Ehud Lehrer, 1989. "The Value of Information -- An Axiomatic Approach," Discussion Papers 835, Northwestern University, Center for Mathematical Studies in Economics and Management Science.
- Eliaz, Kfir & Spiegler, Ran, 2006. "Can anticipatory feelings explain anomalous choices of information sources?," Games and Economic Behavior, Elsevier, vol. 56(1), pages 87-104, July.
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