  The conventional approach to Bayesian decision-theoretic experiment design
involves searching over possible experiments to select a design that maximizes
the expected value of a specified utility function. The expectation is over the
joint distribution of all unknown variables implied by the statistical model
that will be used to analyze the collected data. The utility function defines
the objective of the experiment where a common utility function is the
information gain. This article introduces an expanded framework for this
process, where we go beyond the traditional Expected Information Gain criteria
and introduce the Expected General Information Gain which measures robustness
to the model discrepancy and Expected Discriminatory Information as a criterion
to quantify how well an experiment can detect model discrepancy. The
functionality of the framework is showcased through its application to a
scenario involving a linearized spring mass damper system and an F-16 model
where the model discrepancy is taken into account while doing Bayesian optimal
experiment design.
