An optimal decision, following the logic of the theory, is one that maximizes the expected utility.
Robust optimization maximizes the expected NPV over an ensemble of permeability fields to overcome this shortcoming of certainty equivalence optimization.
Given heterogeneity, I identify the mechanism that maximizes the expected revenue among all incentive-compatible mechanisms of equity auctions.
If the distribution function is log-concave, then this mechanism also maximizes the expected surplus from any given coalition.
From a Bayesian decision theoretical perspective, the optimal design is the design that maximizes the expected utility.
In the case of pairwise sequence alignment, it is possible to find an alignment that maximizes the expected AMA value using posterior decoding.
As in POMDPs, the criterion is to regard a policy, i.e., an action-selection rule, as optimal if it maximizes the expected total reward.
A design of an experiment is called optimal within this set of designs if it maximizes the expected gain of knowledge about the parameters.
This is the allocation that maximizes the expected social surplus subject to the biddersʼ incentive compatible constraints when the first best is not implementable.
We propose a strategy that allows the valuation of flexibility under uncertainty, seeking the flow control strategy that maximizes the expected net present value, dynamically reacting to new information as it is acquired.
Assuming a linear paired comparison model for outcomes, we develop a pairing method that maximizes the expected gain in Kullback Leibler information from the prior to the posterior distribution.
