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We consider the electoral bribery problem in computational social choice. In this context, extensive studies have been carried out to analyze the computational vulnerability of various voting (or election) rules. However, essentially all prior studies assume a deterministic model where each voter has an associated threshold value, which is used as follows. A voter will take a bribe and vote according to the attacker's (i.e., briber's) preference when the amount of the bribe is above the threshold, and a voter will not take a bribe when the amount of the bribe is not above the threshold (in this case, the voter will vote according to its own preference, rather than the attacker's). In this paper, we initiate the study of a more realistic model where each voter is associated with a willingness function, rather than a fixed threshold value. The willingness function characterizes the likelihood a bribed voter would vote according to the attacker's preference; we call this bribe-effect uncertainty. We characterize the computational complexity of the electoral bribery problem in this new model. In particular, we discover a dichotomy result: a certain mathematical property of the willingness function dictates whether or not the computational hardness can serve as a deterrence to bribery attackers.
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The spatial and temporal dynamics of voter preference determinants in four U.S. presidential elections (2008–2020)
Abstract Political and social processes that shape people's voting preferences might be linked to geographical location, varying from place to place, and operating at local, regional, and national scales. Here, we use a local modeling technique, multiscale geographically weighted regression (MGWR), to examine spatial and temporal variations in the influences of county‐level socio‐economic factors on voter preference during the 2008–2020 U.S. presidential elections. We argue that the local intercept in the MGWR model is an indicator of the effect of spatial context on voter preference and not only can this be separated from the effect of other socio‐economic factors, but it needs to be in order to prevent misspecification bias in the indicators of these other factors. We also identify strong and consistent divisions across the country in how context shapes election results.
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- Award ID(s):
- 2117455
- PAR ID:
- 10375577
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Transactions in GIS
- Volume:
- 26
- Issue:
- 3
- ISSN:
- 1361-1682
- Page Range / eLocation ID:
- p. 1609-1628
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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