scholarly journals Reconciling Candidate Extremism and Spatial Voting

Author(s):  
Benjamin Highton ◽  
Walter J. Stone
Keyword(s):  
2012 ◽  
Author(s):  
Cheryl Boudreau ◽  
Christopher S. Elmendorf ◽  
Scott A. MacKenzie
Keyword(s):  

Author(s):  
James F. Adams

This chapter broadly surveys spatial voting models of party competition in two dimensions, where, in Western democracies, the first dimension is typically the left-right dimension pertaining to policy debates over income redistribution and government intervention in the economy. The second dimension may encompass policy debates over issues that cross-cut the left-right economic dimension, or it may encompass universally valued “valence” dimensions of party evaluation such as parties’ images for competence, integrity, and leadership ability. The chapter reviews models with office-seeking and policy-seeking parties. It also surveys both the theoretical and the empirical literatures on these topics.


1997 ◽  
Vol 55 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Ken Kollman ◽  
John H. Miller ◽  
Scott E. Page

2021 ◽  
Vol 17 (3) ◽  
pp. 1-21
Author(s):  
Boris Aronov ◽  
Mark De Berg ◽  
Joachim Gudmundsson ◽  
Michael Horton

Let V be a set of n points in mathcal R d , called voters . A point p ∈ mathcal R d is a plurality point for V when the following holds: For every q ∈ mathcal R d , the number of voters closer to p than to q is at least the number of voters closer to q than to p . Thus, in a vote where each  v ∈ V votes for the nearest proposal (and voters for which the proposals are at equal distance abstain), proposal  p will not lose against any alternative proposal  q . For most voter sets, a plurality point does not exist. We therefore introduce the concept of β-plurality points , which are defined similarly to regular plurality points, except that the distance of each voter to p (but not to  q ) is scaled by a factor  β , for some constant 0< β ⩽ 1. We investigate the existence and computation of β -plurality points and obtain the following results. • Define β * d := {β : any finite multiset V in mathcal R d admits a β-plurality point. We prove that β * d = √3/2, and that 1/√ d ⩽ β * d ⩽ √ 3/2 for all d ⩾ 3. • Define β ( p, V ) := sup {β : p is a β -plurality point for V }. Given a voter set V in mathcal R 2 , we provide an algorithm that runs in O ( n log n ) time and computes a point p such that β ( p , V ) ⩾ β * b . Moreover, for d ⩾ 2, we can compute a point  p with β ( p , V ) ⩾ 1/√ d in O ( n ) time. • Define β ( V ) := sup { β : V admits a β -plurality point}. We present an algorithm that, given a voter set V in mathcal R d , computes an ((1-ɛ)ċ β ( V ))-plurality point in time O n 2 ɛ 3d-2 ċ log n ɛ d-1 ċ log 2 1ɛ).


2018 ◽  
Vol 60 (4) ◽  
pp. 49-68 ◽  
Author(s):  
Jorge Fábrega ◽  
Jorge González ◽  
Jaime Lindh

AbstractConsensus democracy among the main Chilean political forces ended abruptly after the 2013 presidential and parliamentary elections, the most polarized elections since the return to democracy in 1990. Relying on spatial voting theory to uncover latent ideological dimensions from survey data between 1990 and 2014, this study finds patterns of gradual polarization starting at least ten years before the collapse of consensus, based on an increasing demobilization of the political center that misaligned politicians from their political platforms (particularly in the center-left parties). That phenomenon changed the political support for the two main political coalitions and the intracoalition bargaining power of their various factions. The pattern also helps to explain the process behind the 2015 reform of the electoral system.


2012 ◽  
Vol 41 (1) ◽  
pp. 43-71 ◽  
Author(s):  
Scott L. Feld ◽  
Joseph Godfrey ◽  
Bernard Grofman

2019 ◽  
Vol 47 (6) ◽  
pp. 981-996
Author(s):  
Wangshu Mu ◽  
Daoqin Tong

Incorporating big data in urban planning has great potential for better modeling of urban dynamics and more efficiently allocating limited resources. However, big data may present new challenges for problem solutions. This research focuses on the p-median problem, one of the most widely used location models in urban and regional planning. Similar to many other location models, the p-median problem is non-deterministic polynomial-time hard (NP-hard), and solving large-sized p-median problems is difficult. This research proposes a high performance computing-based algorithm, random sampling and spatial voting, to solve large-sized p-median problems. Instead of solving a large p-median problem directly, a random sampling scheme is introduced to create smaller sub- p-median problems that can be solved in parallel efficiently. A spatial voting strategy is designed to evaluate the candidate facility sites for inclusion in obtaining the final problem solution. Tests with the Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) data set show that random sampling and spatial voting provides high-quality solutions and reduces computing time significantly. Tests also demonstrate the dynamic scalability of the algorithm; it can start with a small amount of computing resources and scale up and down flexibly depending on the availability of the computing resources.


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