Add proportional greedy rule
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@ -295,6 +295,36 @@ voters that are represented by the subset.
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voters $v_1$ and $v_5$ and project $p_4$ voters $v_2$, $v_3$ and $v_4$.
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\end{example}
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The third rule, which places a heavy emphasis on cost versus benefit, is similar
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to the greedy rule but instead of disregarding the satisfaction per cost that a
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project provides, it seeks to maximize the sum of satisfaction divided by cost
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for a project $p\in P$:
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\begin{equation}
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\frac{\sum_{v\in V}sat(P_v,A\cup\{p\}) - \sum_{v\in V}sat(P_v,A)}{c(p)}
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\end{equation}
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\textcite{talmonFrameworkApprovalBasedBudgeting2019} call this type of
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aggregation rule \emph{proportional greedy rule}. Example~\ref{ex:prop greedy}
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shows how the outcome of a budgeting scenario might look like compared to using
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a simple greedy rule or a max rule. Since the proportional greedy rule is a
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variation of the simple greedy rule, it is therefore also solvable in polynomial
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time. The variation of computing the satisfaction per unit of cost does not
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change the complexity since it only adds an additional step which can be done in
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constant time.
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\begin{example}\label{ex:prop greedy}
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We again have the same set of projects $P = \{ p_2,p_3,p_4,p_5,p_6 \}$, the
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same budget limit of $B = 10$ and the five voters: $v_1 = \{ p_2,p_5,p_6
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\}$, $v_2 = \{ p_2, p_3,p_4,p_5 \}$, $v_3 = \{ p_3,p_4,p_5 \}$, $v_4 = \{
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p_4,p_5 \}$ and $v_5 = \{ p_6 \}$. If we combine the satisfaction function
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$sat_\#$ from equation~\ref{eq:3} with the proportional greedy rule, we get
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the same result as with the simple greedy rule of $\{ p_4,p_5 \}$. While the
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simple greedy rule selects first $p_5$ and then $p_4$, the proportional
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greedy rule first selects $p_4$ and then $p_5$. The rule
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$\mathcal{R}_{sat_\$}^p$ yields the same result as $\mathcal{R}_{sat_\$}^g$
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and $\mathcal{R}_{sat_\$}^m$ of $\{ p_4,p_5 \}$. $\mathcal{R}_{sat_{0/1}}^p$
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however, gives $\{ p_2,p_3,p_4 \}$.
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\end{example}
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\printbibliography
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\end{document}
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