Approximately Strategy-Proof Voting Eleanor BirrellRafael Pass Cornell University.

Slides:



Advertisements
Similar presentations
Optimal Space Lower Bounds for All Frequency Moments David Woodruff MIT
Advertisements

Ulams Game and Universal Communications Using Feedback Ofer Shayevitz June 2006.
Sep 16, 2013 Lirong Xia Computational social choice The easy-to-compute axiom.
THE CENTRAL LIMIT THEOREM
Sep 15, 2014 Lirong Xia Computational social choice The easy-to-compute axiom.
1 AI and Economics: The Dynamic Duo Ariel Procaccia Center for Research on Computation and Society Harvard SEAS AI AND ECONOMICS DYNAMIC DUO THE.
On the Robustness of Preference Aggregation in Noisy Environments Ariel D. Procaccia, Jeffrey S. Rosenschein and Gal A. Kaminka.
Ioannis Caragiannis, Jason A. Covey, Michal Feldman, Christopher M. Homan, Christos Kaklamanis, Nikos Karanikolask, Ariel D. Procaccia, Je ff rey S. Rosenschein.
Voting and social choice Vincent Conitzer
How Hard Is It To Manipulate Voting? Edith Elkind, U. of Warwick Helger Lipmaa, Tartu U.
Algorithmic Game Theory Uri Feige Robi Krauthgamer Moni Naor Lecture 9: Social Choice Lecturer: Moni Naor.
How “impossible” is it to design a Voting rule? Angelina Vidali University of Athens.
Complexity of manipulating elections with few candidates Vincent Conitzer and Tuomas Sandholm Carnegie Mellon University Computer Science Department.
Arrow’s impossibility theorem EC-CS reading group Kenneth Arrow Journal of Political Economy, 1950.
Manipulation Toby Walsh NICTA and UNSW. Manipulation Constructive  Can we change result so a given candidate wins Destructive  Can we change result.
+ Random Tie Breaking Toby Walsh NICTA and UNSW. + Random Tie Breaking Haris Aziz, Serge Gaspers, Nick Mattei, Nina Narodytska, Toby Walsh NICTA and UNSW.
1 Truthful Mechanism for Facility Allocation: A Characterization and Improvement of Approximation Ratio Pinyan Lu, MSR Asia Yajun Wang, MSR Asia Yuan Zhou,
Sep. 5, 2013 Lirong Xia Introduction to Game Theory.
Using computational hardness as a barrier against manipulation Vincent Conitzer
Using computational hardness as a barrier against manipulation Vincent Conitzer
On the Limits of Dictatorial Classification Reshef Meir School of Computer Science and Engineering, Hebrew University Joint work with Shaull Almagor, Assaf.
Reshef Meir, Ariel D. Procaccia, and Jeffrey S. Rosenschein.
COLOR TEST COLOR TEST. Dueling Algorithms N ICOLE I MMORLICA, N ORTHWESTERN U NIVERSITY WITH A. T AUMAN K ALAI, B. L UCIER, A. M OITRA, A. P OSTLEWAITE,
Shahar Dobzinski (Hebrew U) Ariel D. Procaccia (MS Israel R&D Center)
Ariel D. Procaccia (Microsoft)  Best advisor award goes to...  Thesis is about computational social choice Approximation Learning Manipulation BEST.
Speaker: Ariel Procaccia Joint work with: Michael Zuckerman, Jeff Rosenschein Hebrew University of Jerusalem.
Ties Matter: Complexity of Voting Manipulation Revisited based on joint work with Svetlana Obraztsova (NTU/PDMI) and Noam Hazon (CMU) Edith Elkind (Nanyang.
Rank Aggregation Methods for the Web CS728 Lecture 11.
Strategy-Proof Classification Reshef Meir School of Computer Science and Engineering, Hebrew University A joint work with Ariel. D. Procaccia and Jeffrey.
CPS Voting and social choice
Social choice theory = preference aggregation = voting assuming agents tell the truth about their preferences Tuomas Sandholm Professor Computer Science.
How Hard Is It To Manipulate Voting? Edith Elkind, Princeton Helger Lipmaa, HUT.
Approximation algorithms and mechanism design for minimax approval voting Ioannis Caragiannis Dimitris Kalaitzis University of Patras Vangelis Markakis.
Junta Distributions and the Average Case Complexity of Manipulating Elections A. D. Procaccia & J. S. Rosenschein.
Reshef Meir Jeff Rosenschein Hebrew University of Jerusalem, Israel Maria Polukarov Nick Jennings University of Southampton, United Kingdom COMSOC 2010,
Automated Design of Voting Rules by Learning From Examples Ariel D. Procaccia, Aviv Zohar, Jeffrey S. Rosenschein.
1 Manipulation of Voting Schemes: A General Result By Allan Gibbard Presented by Rishi Kant.
Complexity of Mechanism Design Vincent Conitzer and Tuomas Sandholm Carnegie Mellon University Computer Science Department.
On Stochastic Minimum Spanning Trees Kedar Dhamdhere Computer Science Department Joint work with: Mohit Singh, R. Ravi (IPCO 05)
Social choice theory = preference aggregation = truthful voting Tuomas Sandholm Professor Computer Science Department Carnegie Mellon University.
Strategic Behavior in Multi-Winner Elections A follow-up on previous work by Ariel Procaccia, Aviv Zohar and Jeffrey S. Rosenschein Reshef Meir The School.
Strategy-Proof Classification Reshef Meir School of Computer Science and Engineering, Hebrew University A joint work with Ariel. D. Procaccia and Jeffrey.
Arrow’s Theorem The search for the perfect election decision procedure.
APPROXIMATION ALGORITHMS VERTEX COVER – MAX CUT PROBLEMS
1 Introduction to Quantum Information Processing CS 667 / PH 767 / CO 681 / AM 871 Richard Cleve DC 2117 Lecture 19 (2009)
Elections and Strategic Voting: Condorcet and Borda E. Maskin Harvard University.
Mechanism design. Goal of mechanism design Implementing a social choice function f(u 1, …, u |A| ) using a game Center = “auctioneer” does not know the.
1 Elections and Manipulations: Ehud Friedgut, Gil Kalai, and Noam Nisan Hebrew University of Jerusalem and EF: U. of Toronto, GK: Yale University, NN:
Computing the Degree of the Manipulability in the Case of Multiple Choice Fuad Aleskerov (SU-HSE) Daniel Karabekyan (SU-HSE) Remzi M. Sanver (Istanbul.
Optimal Manipulation of Voting Rules Edith Elkind Nanyang Technological University, Singapore (based on joint work with Svetlana Obraztsova)
Mechanism Design on Discrete Lines and Cycles Elad Dokow, Michal Feldman, Reshef Meir and Ilan Nehama.
Automated Mechanism Design Tuomas Sandholm Presented by Dimitri Mostinski November 17, 2004.
Great Theoretical Ideas in Computer Science.
The Price of Uncertainty in Communication Brendan Juba (Washington U., St. Louis) with Mark Braverman (Princeton)
Umans Complexity Theory Lectures Lecture 7b: Randomization in Communication Complexity.
Social choice theory = preference aggregation = voting assuming agents tell the truth about their preferences Tuomas Sandholm Professor Computer Science.
0 Fall, 2016 Lirong Xia Computational social choice The easy-to-compute axiom.
Sampling Distribution Models
Information Complexity Lower Bounds
Applied Mechanism Design For Social Good
Chaitanya Swamy University of Waterloo
James Zou1 , Sujit Gujar2, David Parkes1
شاخصهای عملکردی بیمارستان
Lecture 24 NP-Complete Problems
فرق بین خوب وعالی فقط اندکی تلاش بیشتر است
Manipulation Lirong Xia Fall, Manipulation Lirong Xia Fall, 2016.
Function Notation “f of x” Input = x Output = f(x) = y.
CPS 173 Voting and social choice
No Guarantee Unless P equals NP
Computational social choice
Presentation transcript:

Approximately Strategy-Proof Voting Eleanor BirrellRafael Pass Cornell University

u Charlie (A) = 1 u Charlie (B) =.9 u Charlie (C) =.2 The Model … σ Alice = {A,B,C}σ Bob = {C, A, B}σ Charlie = {A,C,B}σ Zelda = {C,B,A} ABC σ Charlie (A) > σ Charlie (B) σ Charlie (B) > σ Charlie (C) Goal: Voters honestly report their preference σ f Goal: f is strategy-proof Bad News: Only if f is dictatorial or binary. [Gibb73, Gibb77, Satt75] Goal: f is strategy-proof Bad News: Only if f is dictatorial or binary. [Gibb73, Gibb77, Satt75] u i (j) Є [0,1] Goal: f is strategy-proof Bad News: Only if f is trivial. [Gibb73, Gibb77, Satt75] Goal: f is strategy-proof Bad News: Only if f is trivial. [Gibb73, Gibb77, Satt75]

Circumventing Gibbard-Satterthwaite Hard to manipulate? – BTT89, FKN09, IKM10 Randomized Approximations? – CS06, Gibb77, Proc10 Restricted preferences? – Moul80 Relaxed Problem? ε - Strategy Proof: By lying, no voter can improve their utility very much δ - Approximations: f’ returns an outcome that is close to f(σ) ε - Strategy Proof: By lying, no voter can improve their utility very much δ - Approximations: f’ returns an outcome that is close to f(σ)

σ Alice σ Bob σ Charlie σ Zelda AB f C u Charlie (A) = 1 u Charlie (B) =.9 u Charlie (C) =.2 Strategy Proof: By lying (mis-reporting their preference σ i ), no voter can improve their utility u i. ε-Strategy Proof: By lying (mis-reporting their preference σ i ), no voter can improve their utility u i by more than ε. ɛ - Strategy-Proof Voting Strategy Proof: ε-Strategy Proof:

δ - Approximations Defining “Close”Defining Approximation f’ is a δ-approx. of f if the outcome of f’ is always close to that of f. Distance depends on both input and output: f’(x) = f(y) s.t. Δ(x,y) < δ σ Alice σ Bob σ Charlie σ Zelda … σ' Bob σ‘ Zelda 5 2 4

Is ε-Strategy Proof Voting Possible? ε = o (1/n)ε = ω (1/n) δ = βnNoYes Theorem 1: Theorem 2:

ε-Strategy Proof Voting: A Construction Deterministic Rule ( f ):Approximation ( f’ ): d = 5 d = 2 d = 1 d = 3 d = 4 d = 1 d = 2 d = 3 d = 4 d = 5

f ε-Strategy Proof Voting: A Construction AB C {A, B, C} {A, C, B} {C, A, B} {C, B, A} Distance: d f ( f(σ), j) Proportional Probability: Pr [ f’ ( σ ) = j ] ξ ACB 1 ε/3 Note: Only works for

How Good is This? Every voting rule has a.05-strategy-proof 650-approx. And a. 01-strategy-proof 3,250-approx. And a.005-strategy-proof 6,500-approx. And a.001-strategy-proof 32,500-approx. And a.0005-strategy-proof 65,000-approx. CandidateVotes Obama69,498,215 McCain59,948,240 Nader738,720 Baldwin199,437 McKinney161,680 CandidateVotes Carpenter6,582 Fishpaw5,865 Cole4,500 Sweeney1,988 Carlson1,837

This is Asymptotically Optimal h(σ):= i=1 i=n … …… j=1 j=k j=1j=k Return g(σ) Select player i: Select rank j: Prob: kε(k-1)kε(k-k) kε(k-1) kε(k-k) 1 - n∑kε(k-j) j Punish Deviating 0-strategy proof trivial trivial 0-strategy proof prob. dist. over trivial rules. [Gibb77] ε-strategy proof prob. dist. over trivial rules (ε = o(1/n)). ε = o(1/n) no good ε-strategy proof approx of Plurality. trival no good approx. Reduction: ε-SP to 0-SP p p 1 - np

Summary ε = o (1/n)ε = ω (1/n) δ = βn Thank you! A new technique for circumventing Gibbard-Satterthwaite Extensions Small elections? Uncertainty in inputs? YesNo