Finalize presentation

talk/talk.tex

@@ -146,6 +146,7 @@
     Example aggregation methods: \pause
     \begin{itemize}
         \item Maximizing social welfare \pause
+        \item Maximizing voter satisfaction \pause
         \item Greedy selection \pause
         \item Fairness-based selection \pause
     \end{itemize}
@@ -189,7 +190,7 @@
     \begin{exampleblock}{$sat_\#(P_v,A)$}
         $sat_\#(P_v,A) = |A_v|$: The satisfaction of voter $v$ is the number of
         funded items that are approved.
-    \end{exampleblock} \pause
+    \end{exampleblock}
 \end{frame}
 
 \begin{frame}
@@ -264,14 +265,12 @@
                     processes \pause
                 \item however: making a series of locally optimal choices does
                     not always lead to a globally optimal choice \pause
-                \item $\mathcal{R}^g_{|A_v|}$ is similar to $k$-Approval and
-                    knapsack voting \pause
             \end{itemize}
         \item Max rules ($\mathcal{R}^m_{sat}$) are generally NP-hard \pause
             \begin{itemize}
                 \setlength{\itemsep}{.4\baselineskip}
             \item $\mathcal{R}^m_{|A_v|}$ can be solved in polynomial time
-                because one dimension is fixed \pause
+                \pause
             \item $\mathcal{R}^m_{sat_{0/1}}$: finding a bundle with at least a
                 given total satisfaction is NP-hard \pause
             \item satisfaction functions can be modeled as integer linear
@@ -400,7 +399,6 @@
 	\centering
 	\Large
 	Thank you for your attention! \\
-	Questions \& Answers
     \begin{figure}
         \centering
         \includegraphics[width=.5\textwidth]{voting_referendum.png}