Browsing by Subject "voting"

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    An Uncertainty- and Collusion-Proof Voting Consensus Mechanism in Blockchain
    (IEEE, 2023-10) Wang, Shengling; Qu, Xidi; Hu, Qin; Wang, Xia; Cheng, Xiuzhen; Computer and Information Science, Purdue School of Science
    Though voting-based consensus algorithms in blockchain outperform proof-based ones in energy- and transaction-efficiency, they are prone to incur wrong elections and bribery elections. The former originates from the uncertainties of candidates’ capability and availability, and the latter comes from the egoism of voters and candidates. Hence, in this paper, we propose an uncertainty- and collusion-proof voting consensus mechanism, including the selection pressure-based voting algorithm and the trustworthiness evaluation algorithm. The first algorithm can decrease the side effects of candidates’ uncertainties, lowering wrong elections while trading off the balance between efficiency and fairness in voting miners. The second algorithm adopts an incentive-compatible scoring rule to evaluate the trustworthiness of voting, motivating voters to report true beliefs on candidates by making egoism consistent with altruism so as to avoid bribery elections. A salient feature of our work is theoretically analyzing the proposed voting consensus mechanism by the large deviation theory. Our analysis provides not only the voting failure rate of a candidate but also its decay speed. The voting failure rate measures the incompetence of any candidate from a personal perspective by voting, based on which the concepts of the effective selection valve and the effective expectation of merit are introduced to help the system designer determine the optimal voting standard and guide a candidate to behave in an optimal way for lowering the voting failure rate.
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    Voting Characteristics of Individuals With Traumatic Brain Injury
    (Wiley, 2019-03) Hirsch, Mark A.; Kropf, Martha E.; Hammond, Flora M.; Schur, Lisa; Karlawish, Jason H.; Ball, Andrew M.; Physical Medicine and Rehabilitation, School of Medicine
    Voting is the foundation of democracy. Limited data exist about voting characteristics of individuals with neurologic impairment including those living with a traumatic brain injury (TBI). To statistically examine voting characteristics using a convenience sample of registered voters with TBI during elections held in Mecklenburg County, North Carolina—2007, 2008. Data were collected on 51 participants with TBI during May 2007, 2008 general, and 2008 Presidential Election. (i) There was a significant difference between the Competence Assessment Tool for Voting (CAT‐V) total score of participants with TBI who voted and the CAT‐V total score of participants with TBI who did not vote and the CAT‐V total score predicted voting; (ii) the age of the participants with TBI was predictive of voting; and (iii) being married was inversely related to voting. We find that there is variation in voting even among this small sample interviewed for the present study, and that the variation is predictable. Those with the highest CAT‐Vs are most likely to vote. In addition, we find that traditional predictors of voting simply are not predictors among this TBI group, and even one, whether the person is married, has a negative effect on voting.