David T. Lee
Stanford University
18 Papers
118 Citations
David T. Lee is an academic researcher from Stanford University. The author has contributed to research in topics: Probability distribution & Voting. The author has an hindex of 7, co-authored 18 publications. Previous affiliations of David T. Lee include University of California, Santa Cruz.
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Papers
Biased assimilation, homophily, and the dynamics of polarization
Pranav Dandekar,Ashish Goel,David T. Lee +2 more
- 10 Dec 2012
TL;DR: In this article, the authors generalize DeGroot's model to account for a phenomenon well-known in social psychology as biased assimilation: when presented with mixed or inconclusive evidence on a complex issue, individuals draw undue support for their initial position thereby arriving at a more extreme opinion.
•Posted Content
Crowdsourcing for Participatory Democracies: Efficient Elicitation of Social Choice Functions
TL;DR: In this article, the Borda rule and the Condorcet winner were used to achieve a fixed ϵ-approximation to the problem of voting in participatory democracies.
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•Proceedings Article
Crowdsourcing for Participatory Democracies: Efficient Elicitation of Social Choice Functions
David T. Lee,Ashish Goel,Tanja Aitamurto,Hélène Landemore +3 more
- 05 Sep 2014
TL;DR: Algorithms are given which efficiently elicit \epsilon-approximations to two prominent voting rules: the Borda rule and the Condorcet winner, which circumvents previous prohibitive lower bounds and is surprisingly strong.
Towards Large-Scale Deliberative Decision-Making: Small Groups and the Importance of Triads
Ashish Goel,David T. Lee +1 more
- 21 Jul 2016
TL;DR: This model considers a group of participants, each having an opinion which together form a graph, and shows that for median graphs, it is possible to use a small number of three-person interactions to tightly approximate the wisdom of the crowd, defined here to be the generalized median of participant opinions, even when agents are strategic.
23
•Proceedings Article
Efficient, private, and ε-strategyproof elicitation of tournament voting rules
David T. Lee
- 25 Jul 2015
TL;DR: This paper gives algorithms which elicit approximate winners in a way which provably satisfies all three of these requirements simultaneously and significantly expand the set of voting rules for which efficient elicitation was known to be possible and improve the known approximation factors for e-strategyproof voting in the regime where the number of candidates is large.