English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Predicting the winner of an election is a favorite problem both for newsmedia pundits and computational social choice theorists. Since it is ofteninfeasible to elicit the preferences of all the voters in a typical predictionscenario, a common algorithm used for winner prediction is to run the electionon a small sample of randomly chosen votes and output the winner as theprediction. We analyze the performance of this algorithm for many common votingrules.  More formally, we introduce the $(\epsilon, \delta)$-winner determinationproblem, where given an election on $n$ voters and $m$ candidates in which themargin of victory is at least $\epsilon n$ votes, the goal is to determine thewinner with probability at least $1-\delta$. The margin of victory of anelection is the smallest number of votes that need to be modified in order tochange the election winner. We show interesting lower and upper bounds on thenumber of samples needed to solve the $(\epsilon, \delta)$-winner determinationproblem for many common voting rules, including scoring rules, approval,maximin, Copeland, Bucklin, plurality with runoff, and single transferablevote. Moreover, the lower and upper bounds match for many common voting rulesin a wide range of practically appealing scenarios.

Sample Complexity for Winner Prediction in Elections