Efficient Voting via the Top-k Elicitation Scheme:
Description: Efficient Voting via the Top-k Elicitation Scheme: A Probabilistic Approach Joel Oren, University of Toronto Joint work with Yuval Filmus, Institute of Advanced Study 1 Motivation Common theme: communication-efficient group decision-making.
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slide1. Efficient Voting via the Top-k Elicitation Scheme: A Probabilistic Approach Joel Oren, University of Toronto
Joint work with Yuval Filmus, Institute of Advanced Study 1<br>
slide2. Motivation Common theme: communication-efficient group decision-making.
How to pick the single “best” item/candidate, without extracting too much information from the customers/committee members.
Hiring committees: members can’t rank all of the candidates.
Too many candidates.
Committee members may only be familiar with just subsets of candidates in their own fields. 2<br>
slide3. The Basic Setting 3 A canonical task: select the “best” outcome.
What is the information need for doing so. Social choice: methods for aggregating preferences.
Efficient preference elicitation.<br>
slide4. Basic Definitions 4 Borda Harmonic Geometric<br>
slide5. 5<br>
slide6. Previous Work 6<br>
slide7. 7<br>
slide8. Three Distributional Models (and high-level Results) 8<br>
slide9. 9<br>
slide10. Empirical Results for the three algorithms 10 FairCutoff overlaps with Opt FairCutoff overlaps with Opt<br>
slide11. A Threshold Theorem for PSRs 11<br>
slide12. Applications 12<br>
slide13. Proof Sketch – Order Statistics of Correlated RVs 13<br>
slide14. Proof Sketch (continued) 14<br>
slide15. 15<br>
slide16. The Adversarial Model 16<br>
slide17. 17<br>
slide18. Conclusions & Future Directions 18<br>
slide19. Contact: oren@cs.toronto.edu Homepage: www.cs.toronto.edu/~oren Thank you! 19<br>
Joint work with Yuval Filmus, Institute of Advanced Study 1<br>
slide2. Motivation Common theme: communication-efficient group decision-making.
How to pick the single “best” item/candidate, without extracting too much information from the customers/committee members.
Hiring committees: members can’t rank all of the candidates.
Too many candidates.
Committee members may only be familiar with just subsets of candidates in their own fields. 2<br>
slide3. The Basic Setting 3 A canonical task: select the “best” outcome.
What is the information need for doing so. Social choice: methods for aggregating preferences.
Efficient preference elicitation.<br>
slide4. Basic Definitions 4 Borda Harmonic Geometric<br>
slide5. 5<br>
slide6. Previous Work 6<br>
slide7. 7<br>
slide8. Three Distributional Models (and high-level Results) 8<br>
slide9. 9<br>
slide10. Empirical Results for the three algorithms 10 FairCutoff overlaps with Opt FairCutoff overlaps with Opt<br>
slide11. A Threshold Theorem for PSRs 11<br>
slide12. Applications 12<br>
slide13. Proof Sketch – Order Statistics of Correlated RVs 13<br>
slide14. Proof Sketch (continued) 14<br>
slide15. 15<br>
slide16. The Adversarial Model 16<br>
slide17. 17<br>
slide18. Conclusions & Future Directions 18<br>
slide19. Contact: oren@cs.toronto.edu Homepage: www.cs.toronto.edu/~oren Thank you! 19<br>