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Common Voting Rules as Maximum Likelihood Estimators. Vincent Conitzer, Tuomas Sandholm Presented by Matthew Kay. Outline. Introduction Noise Models Terminology Voting Rules Results Positive Results Lemma 1 Negative Results Conclusion Summary of Results Conclusions and Contributions - PowerPoint PPT Presentation
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11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
1
Common Voting Rules as Maximum Likelihood
Estimators
Vincent Conitzer, Tuomas Sandholm
Presented by Matthew Kay
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
2
Outline Introduction
Noise Models Terminology
Voting Rules Results
Positive Results Lemma 1 Negative Results
Conclusion Summary of Results Conclusions and Contributions Future Work
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Introduction
Two views of voting:1. Voters are idiosyncratic; the best we can do is try
to maximize social welfare using a compromise
2. There is some prior “absolute” way that we can say one candidate is better than another, and votes represent the agents’ noisy perception of this
We consider the second case only
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Introduction
Under these assumptions, voting becomes a way to infer the “absolute” or “objective” goodness of the candidates
One way to do this is a maximum likelihood estimate, or MLE
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Noise Models
Paper assumes the votes are independent and identically distributed (i.i.d.)
Conditionally independent given the outcome
Each voter has the same conditional distribution
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Noise Models
Without these restrictions, any rule is an MLE
Simply let the probability on all vote vectors that produce the correct outcome be positive, and all other probabilities be 0
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Terminology
Set of agents (voters), N = {1, 2, …, n} Set of candidates, C Set of outcomes, O:
A winner: O is the set of single candidates, C A ranking: O is the set of weak total orders of C
A set of strict total orders of C, L A voting rule p : Ln → O
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Terminology
MLEWIV: maximum likelihood estimator for winner under i.i.d. votes
MLERIV: maximum likelihood estimator for ranking under i.i.d. votes
I will shorten these to MLEW and MLER
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Outline Introduction
Noise Models Terminology
Voting Rules Results
Positive Results Lemma 1 Negative Results
Conclusion Summary of Results Conclusions and Contributions Future Work
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Scoring Rules
Let α = (α1,…, αm) s.t. α1 ≥ α2 … ≥ αm
For each voter, a candidate receives αi points if the voter ranked them at position i
The candidate with the highest score wins Examples:
Plurality: α = (1, 0, …, 0) Veto: α = (1, …, 1, 0) Borda: α = (m – 1, m – 2, …, 1, 0)
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Single Transferable Vote (STV)
Series of m – 1 plurality votes In each round, the lowest-ranked candidate is
eliminated The last remaining candidate wins
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Bucklin
Approval: a voter “approves” their top l candidates For candidate c, let B(c,l) be the number of voters
with c in their top l candidates Bucklin score: min{l : B(c,l) > n/2} To calculate: increase l until a candidate is
“approved” by > n/2 voters; this is their score For ties, consider B(c,l) - n/2
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Pairwise Rules
For these rules, we consider pairwise election graphs instead of the rankings themselves
Example: 2 voters Votes:
a > b > c b > a > c
Note (Lemma 2): for any pairwise election graph with even weights, there is a set of rankings that produces that graph
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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maximin
Candidate’s rank = their worst score in a pairwise election a: 6 b: 8 c: 10 d: 12
Outcome: a > b > c > d
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Copeland
Candidate’s rank = wins – losses(outgoing edges - incoming edges) a: 2 b: 1 c: 0 d: -1 e: -2
Outcome: a > b > c > d > e
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Ranked Pairs
Sort pairs of candidates by edge weights Start with the highest-weighted pair and “lock
in” that order “lock in” the next-highest pair, etc If an ordering cannot be “locked in”, skip it
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Ranked Pairs
Sort pairs by weight: (b,d), (a,b), (d,a), (b,c), (c,d)
“Lock in”: (b,d) : b > d (a,b) : a > b > d (d,a) : skipped (b,c) : a > b > c , a > b > d (c,d) : a > b > c > d
Outcome: a > b > c > d
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Outline Introduction
Noise Models Terminology
Voting Rules Results
Positive Results Lemma 1 Negative Results
Conclusion Summary of Results Conclusions and Contributions Future Work
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Positive Results
Basic outline for proving a rule is an MLEW or MLER Contrive a distribution (noise model) that in some
way “mimics” the behaviour of the voting rule, so that finding the maximum likelihood estimate is done either by choosing the winning candidate (MLEW) or the winning ranking (MLER)
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Positive Results
Able to show: Any scoring rule is MLEW, MLER STV is MLER
(see paper for details)
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative Results
Some voting rules are not MLER or MLEW We can prove this using Lemma 1 (page 4):
For a given type of outcome (e.g. winner or ranking), if there exist vectors of votes V1, V2 such that rule p produces the same outcome on V1 and V2, but a different outcome on V1+V2 (the votes in V1 and V2 taken together, then p is not a maximum likelihood estimator for that type of outcome under i.i.d. votes.
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Proof of Lemma 1
Consider a rule, p, that produces the same outcome, s, on V1 and V2, but a different outcome on V1+V2
)'|(maxarg
)'|()'|(maxarg
)'|(maxarg
)'|(maxarg
21'
21'
2'
1'
sSVVPs
sSVPsSVPs
sSVPs
sSVPs
s
s
s
s
MLE for V1
MLE for V2
MLE for V1 +V2
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Proof of Lemma 1
But s is not the outcome produced by p on V1+V2
So p is not an MLE for this distribution
)'|(maxarg
)'|()'|(maxarg
)'|(maxarg
)'|(maxarg
21'
21'
2'
1'
sSVVPs
sSVPsSVPs
sSVPs
sSVPs
s
s
s
s
MLE for V1
MLE for V2
MLE for V1 +V2
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Lemma 1 Implications
In order to prove a rule is not an MLEW or MLER, we need to find two sets of votes that produce the same outcome, but when combined produce a different outcome.
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : 3 votes of c > a > b 4 votes of a > b > c 6 votes of b > a > c
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : 3 votes of c > a > b 4 votes of a > b > c 6 votes of b > a > c
Round 1, c eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : 7 votes of a > b 6 votes of b > a
Round 1, c eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : 7 votes of a > b 6 votes of b > a
Round 2, b eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : a wins
V2 : 3 votes of b > a > c 4 votes of a > c > b 6 votes of c > a > b
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : a wins
V2 : 3 votes of b > a > c 4 votes of a > c > b 6 votes of c > a > b
Round 1, b eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : a wins
V2 : 7 votes of a > c 6 votes of c > a
Round 1, b eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : a wins
V2 : 7 votes of a > c 6 votes of c > a
Round 2, c eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 : a wins
V2 : a wins
a won V1 and V2, so a must win V1 + V2 for STV to be an MLEW
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 + V2:
4 votes of a > c > b 4 votes of a > b > c 9 votes of c > a > b 9 votes of b > a > c
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 + V2:
4 votes of a > c > b 4 votes of a > b > c 9 votes of c > a > b 9 votes of b > a > c
Round 1, a eliminated
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative results:STV is not MLEW
V1 + V2:
4 votes of a > c > b 4 votes of a > b > c 9 votes of c > a > b 9 votes of b > a > c
STV is not an MLEW
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative Results:Ranked Pairs
From introduction:
a > b > c > d
V1
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative Results:Ranked Pairs
From introduction:
a > b > c > d a > b > c > d
V1 V2
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative Results:Ranked Pairs
b > c > d > a
V1 + V2
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Negative Results:Ranked Pairs
Result: the ranked pairs rule is not an MLEW or MLER
Proofs for other pairwise election results are similar (see paper): Copeland is not MLEW or MLER maximin is not MLEW or MLER
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Relationship Between MLEW and MLER
From STV, we note that MLER does not imply MLEW
In addition, MLEW does not imply MLER: consider a hybrid rule that chooses the winner according to an MLEW rule and the remaining candidates from a rule which is not MLER:
a > b > c > d > …MLEW
Not MLER
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Outline Introduction
Noise Models Terminology
Voting Rules Results
Positive Results Lemma 1 Negative Results
Conclusion Summary of Results Conclusions and Contributions Future Work
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Summary of Results
Rule MLEW? MLER?
Scoring Yes Yes
STV No Yes
Bucklin No No
Copeland No No
Maximin No No
Ranked Pairs No No
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Conclusion
Paper considers applications in which there is some prior, “objective” sense in which some candidates are better than others
Contributions: Without any restrictions on the noise model, any voting rule
is an MLE. Noise models for scoring rules (showing it is an
MLEW/MLER) and STV (showing it is an MLER) Method (Lemma 1) for generating impossibility results, and
shows that various voting methods are not MLEW/MLERs
11/24/2008 CS 886 - Common Voting Rules as Maximum Likelihood Estimators - Matthew Kay
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Issues and Future Work
The distributions used to prove the positive results were somewhat contrived Need to evaluate how reasonable they are If they are unreasonable, can they be refined?
Can we build new voting rules to match an observed noise model?
Are there rules which Lemma 1 cannot prove are not MLEW/MLER but which nevertheless are not MLEW/MLER (i.e. can Lemma 1 be used to show that a rule is an MLEW/MLER?)