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@@ -93,15 +93,13 @@ Each experiment is associated with a cost $c_i\in\reals_+$. Moreover, the experi
The cost $c_i$ can capture, \emph{e.g.}, the amount the subject $i$ deems sufficient to
incentivize her participation in the experiment.
In the full-information case, the experiment costs are common knowledge; as such, the optimization problem that the experimenter wishes to solve is:
-\begin{center}
-\textsc{ExperimentalDesignProblem} (EDP)
+\medskip\\\hspace*{\stretch{1}}\textsc{ExperimentalDesignProblem} (\EDP)\hspace*{\stretch{1}}
\begin{subequations}
\begin{align}
\text{Maximize}\quad V(S) &= \log\det(I_d+\T{X_S}X_S) \label{modified} \\
\text{subject to}\quad \sum_{i\in S} c_i&\leq B
\end{align}\label{edp}
\end{subequations}
-\end{center}
We denote by
\begin{equation}\label{eq:non-strategic}
OPT = \max_{S\subseteq\mathcal{N}} \Big\{V(S) \;\Big| \;