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The Degree of (Extended) Justified Representation and Its Optimization

Biaoshuai Tao, Chengkai Zhang, Houyu Zhou

TL;DR

This paper introduces the degree of (Extended) Justified Representation ((E)JR degree) to quantify how many voters are represented within each cohesive group in multiwinner approval voting, and defines optimization problems MDJR and MDEJR to maximize JR and EJR degrees, respectively, noting the maximum possible degree is $\lceil n/k \rceil$. It develops both positive and negative results: simple polynomial-time rules yield $1/k$- and $1/(k+1)$-approximations for MDJR and MDEJR, respectively, while strong inapproximability bounds of $(k/n)^{1-\varepsilon}$ and $(1/k)^{1-\varepsilon}$ hold; the problems are W[2]-hard when parameterized by $k$ but become fixed-parameter-tractable when also parameterized by the maximum achievable degree $c_{\max}$. The authors provide MDJR and MDEJR algorithms with runtimes of the form $f(k,c_{\max})\cdot\mathrm{poly}(m,n)$, bridging combinatorial optimization with parameterized complexity in the JR/EJR setting. Overall, the work maps the computational landscape of maximizing (E)JR degree, highlighting trade-offs between representing more voters and tractability, and points to future directions such as restricted preference domains and ratio-based measures.

Abstract

Justified Representation (JR)/Extended Justified Representation (EJR) is a desirable axiom in multiwinner approval voting. In contrast to that (E)JR only requires at least \emph{one} voter to be represented in every cohesive group, we study its optimization version that maximizes the \emph{number} of represented voters in each group. Given an instance, we say a winning committee provides a JR degree (EJR degree, resp.) of $c$ if at least $c$ voters in each $\ell$-cohesive group ($1$-cohesive group, resp.) have approved $\ell$ ($1$, resp.) winning candidates. Hence, every (E)JR committee provides the (E)JR degree of at least $1$. Besides proposing this new property, we propose the optimization problem of finding a winning committee that achieves the maximum possible (E)JR degree, called \MDJR and \MDEJR, corresponding to JR and EJR respectively. We study the computational complexity and approximability of \MDJR of \MDEJR. An (E)JR committee, which can be found in polynomial time, straightforwardly gives a $(k/n)$-approximation. We also show that the original algorithms for finding a JR and an EJR winner committee are also $1/k$ and $1/(k+1)$ approximation algorithms for \MDJR and \MDEJR respectively. On the other hand, we show that it is NP-hard to approximate \MDJR and \MDEJR to within a factor of $\left(k/n\right)^{1-ε}$ and to within a factor of $(1/k)^{1-\varepsilon}$, for any $ε>0$, which complements the positive results. Next, we study the fixed-parameter-tractability of this problem. We show that both problems are W[2]-hard if $k$, the size of the winning committee, is specified as the parameter. However, when $c_{\text{max}}$, the maximum value of $c$ such that a committee that provides an (E)JR degree of $c$ exists, is additionally given as a parameter, we show that both \MDJR and \MDEJR are fixed-parameter-tractable.

The Degree of (Extended) Justified Representation and Its Optimization

TL;DR

This paper introduces the degree of (Extended) Justified Representation ((E)JR degree) to quantify how many voters are represented within each cohesive group in multiwinner approval voting, and defines optimization problems MDJR and MDEJR to maximize JR and EJR degrees, respectively, noting the maximum possible degree is . It develops both positive and negative results: simple polynomial-time rules yield - and -approximations for MDJR and MDEJR, respectively, while strong inapproximability bounds of and hold; the problems are W[2]-hard when parameterized by but become fixed-parameter-tractable when also parameterized by the maximum achievable degree . The authors provide MDJR and MDEJR algorithms with runtimes of the form , bridging combinatorial optimization with parameterized complexity in the JR/EJR setting. Overall, the work maps the computational landscape of maximizing (E)JR degree, highlighting trade-offs between representing more voters and tractability, and points to future directions such as restricted preference domains and ratio-based measures.

Abstract

Justified Representation (JR)/Extended Justified Representation (EJR) is a desirable axiom in multiwinner approval voting. In contrast to that (E)JR only requires at least \emph{one} voter to be represented in every cohesive group, we study its optimization version that maximizes the \emph{number} of represented voters in each group. Given an instance, we say a winning committee provides a JR degree (EJR degree, resp.) of if at least voters in each -cohesive group (-cohesive group, resp.) have approved (, resp.) winning candidates. Hence, every (E)JR committee provides the (E)JR degree of at least . Besides proposing this new property, we propose the optimization problem of finding a winning committee that achieves the maximum possible (E)JR degree, called \MDJR and \MDEJR, corresponding to JR and EJR respectively. We study the computational complexity and approximability of \MDJR of \MDEJR. An (E)JR committee, which can be found in polynomial time, straightforwardly gives a -approximation. We also show that the original algorithms for finding a JR and an EJR winner committee are also and approximation algorithms for \MDJR and \MDEJR respectively. On the other hand, we show that it is NP-hard to approximate \MDJR and \MDEJR to within a factor of and to within a factor of , for any , which complements the positive results. Next, we study the fixed-parameter-tractability of this problem. We show that both problems are W[2]-hard if , the size of the winning committee, is specified as the parameter. However, when , the maximum value of such that a committee that provides an (E)JR degree of exists, is additionally given as a parameter, we show that both \MDJR and \MDEJR are fixed-parameter-tractable.
Paper Structure (22 sections, 15 theorems, 20 equations, 1 figure, 4 algorithms)

This paper contains 22 sections, 15 theorems, 20 equations, 1 figure, 4 algorithms.

Key Result

Proposition 1

For any $\gamma>0$, there is a ballot instance with maximum JR degree $c^\ast_{\text{JR}}$ and maximum EJR degree $c^\ast_{\text{EJR}}$ such that

Figures (1)

  • Figure 1: An example of the construction with $\phi=(x_1\vee\neg x_3)\wedge(\neg x_1\vee x_2)$. Edges in the graph represent approvals. An edge connecting between a group of voters and a group of candidates indicates that every voter in the voter group approves every candidate in the candidate group.

Theorems & Definitions (34)

  • Definition 1: Justified representation (JR)
  • Definition 2: Extended Justified Representation
  • Example 1
  • Definition 3: Proportionality degree
  • Example 2
  • Definition 4: JR Degree
  • Definition 5: EJR degree
  • Proposition 1
  • Definition 6: Maximum (E)JR degree, MDJR ( MDEJR)
  • Proposition 2
  • ...and 24 more