Project description
A closer look at election manipulation
It is widely theorised that large electorates can make accurate decisions by collectively considering information from numerous voters. However, this theory overlooks the presence of biased organisers who seek to manipulate the outcomes. These organisers can include CEOs conducting shareholder votes, regional governments organising referendums and political parties during general elections. The ERC-funded InfoAggregation project aims to investigate how biased and privately informed entities – referred to as election organisers – impact the information aggregation capacity of elections. The project will develop and analyse new voting models while examining the effectiveness of institutional safeguards designed to prevent such manipulation. InfoAggregation will also apply the theory to analyse shareholder voting and corporate control, exploring ways to mitigate bias and ensure fair outcomes.
Objective
Elections are the foundation for democratic decision making. This research program will examine the effects of biased and privately informed entities—election organizers—on the ability of elections to aggregate information: Existing theory demonstrates that large electorates can reach correct decisions by aggregating information dispersed among many voters. However, existing theory does not account for the ubiquitous presence of biased organizers who intend to affect the election outcome. Examples of biased organizers may include a CEO holding a shareholder vote, a regional government holding a referendum, and political parties in general elections.
This project will develop and analyze new models of voting that account for the effects of biased organizers on information aggregation. One of the examples I consider is an election organizer who can increase voter participation at some cost (e.g. through advertising). Preliminary work suggests that the presence of biased organizers has significant impact. As increasing participation becomes cheap, equilibria exist where the election organizer recruits a large number voters and yet the majority votes almost surely for the organizer’s favorite policy. This failure of information aggregation contrasts starkly with existing results for elections in which the number of voters is exogenously large.
I will study the effectiveness of institutional safeguards against such manipulation, including supermajority rules, publicity requirements, and the regulation of communication to voters, and I will apply the theory in the context of shareholder voting and corporate control. Thus, this research program has important implications for the design of elections in realistic voting scenarios.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
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Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.1. - EXCELLENT SCIENCE - European Research Council (ERC)
MAIN PROGRAMME
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
ERC-STG - Starting Grant
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Call for proposal
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(opens in new window) ERC-2014-STG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
53113 BONN
Germany
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