junta distributions and the average-case complexity of manipulating elections a presentation by...
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Junta Distributions and the Average-Case Complexity of Manipulating Elections
A presentation by Jeremy Clark
Ariel D. ProcacciaJeffrey S. Rosenschein
Outline
Introduction• Manipulability • Design GoalsPaper Theorems• Preliminaries• Junta Distribution• Proof of TheoremsConcluding Remarks
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Introduction
This paper considers the computational complexity of manipulating an election outcome
A manipulatable election is one where the addition of a set number of votes will change the election outcome to a preferred outcome
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Manipulability
The ability to manipulate an election depends on the current results (whether exactly known or not) and the weight of the votes at the manipulator’s disposal
Given these, we can form a decisional problem
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Manipulation can be constructive or destructive
Constructive: make a candidate win
Destructive: make a candidate lose
Constructive is equivalent to multiple destructive manipulations: one for each candidate ahead of your preferred candidate
In real elections
Strategic voting (destructive)
You are a Liberal and a federalist in a Quebec riding. Current polls have the Bloc in first, Conservatives in second, and the Liberals trailing far behind.
A manipulative vote: vote Conservative to prevent the Bloc from winning
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In real (US) elections
Gerrymandering (Constructive)
You are a Democrat in charge of election zoning. The Republicans beat you marginally in two neighbouring districts. You restructure the districts by packing Democratic voters in one of the regions.
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Goal
Design a voting system such that manipulability is impossible
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Goal
Design a voting system such that manipulability is impossible
Gibbard-Satterthwaite Theorem: Any deterministic, non-dictatorial voting system contain manipulatable instances
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Goal
Design a voting system such that manipulability is intractable
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Goal
Design a voting system such that manipulability is intractable
Lots of interesting systems where manipulability is NP-Hard
However is worst-time complexity the right metric?
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Goal
Design a voting system such that manipulability is average-case intractable
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Goal
Design a voting system such that manipulability is average-case intractable
This paper examines average-case complexity on manipulation problems
It proves that general classes of NP-hard manipulation problems are polynomial in the average-case
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Outline
Introduction• Manipulability • Design GoalsPaper Theorems• Preliminaries• Junta Distribution• Proof of TheoremsConcluding Remarks
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Preliminaries
Election has m candidates
Election has n+N voters: n manipulatable voters and N non-manipulatable voters
Voters can have different weights (reduces to a voter having multiple votes)
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Preliminaries
A vote is an ordered list of candidates that gives i points to the ith candidate.
A scoring protocol, = <1, …, m>, is a vector of scores for each position where i ≥ i+1.
• Plurality: <1, 0, … , 0, 0>• Veto: <1, 1, … , 1, 0>• Borda: <m-1, m-2, … , 2, 1, 0>
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Preliminaries
A voting protocol uses multiple contests, each decided with a scoring protocol
For example, Exhaustive Ballot is an iterated plurality protocol where a candidate with over 50% of the vote wins. If no candidate wins, then the last place candidate is eliminated and the election is rerun.
Others include Copeland, Maximin, and STV
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Sensitive Scoring Protocol
In sensitive scoring protocols, m=0 and m-1 > m
<3,2,1,0><1,0,0,0><3,3,3,3> → <0,0,0,0><4,3,2,1> → <3,2,1,0>
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Manipulation Problems
Individual Manipulation (IM): Given knowledge of all other votes, can I cast my vote for my preferred candidate such that she wins? Note: ties are considered losses
P-Time in most scoring protocols (can be hard in voting protocols with unbounded candidates)
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Manipulation Problems
Coalitional-Weighted-Manipulations (CWM): Given knowledge of all other votes, can I cast a set of votes for my preferred candidate such that she wins?
NP-Hard in sensitive scoring protocols with just 3 candidates. Why? You are increasing the score of more than one candidate.
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Manipulation Problems
Score-CWM (SCWM): Given the tally of all other candidates, can I cast a set of votes for my preferred candidate such that she wins?
Assumptions:Weights are linear in precisionOutput is a linear (decisional)Score determination is linear/P-time
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Junta Distribution
Hardness: instances are full-sized and hard
Balance: both yes and no instances exist
Dichotomy: instances can be impossible or have non-negligible probability. Ignore negligible cases
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Junta Distribution
Symmetry: instance is unbiased toward any candidate
Refinement: Manipulation fails if all manipulative votes are identical
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Theorem
Let P be a sensitive scoring protocol. If m=O(1) then P, with candidates C={p,c1,c2,…,cm-1}, is susceptible to SCWM.
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Theorem
Let P be a sensitive scoring protocol. If m=O(1) then P, with candidates C={p,c1,c2,…,cm-1}, is susceptible to SCWM.
m-1>m=0 such as Borda but not Plurality
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Theorem
Let P be a sensitive scoring protocol. If m=O(1) then P, with candidates C={p,c1,c2,…,cm-1}, is susceptible to SCWM.
Fixed number of candidates
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Theorem
Let P be a sensitive scoring protocol. If m=O(1) then P, with candidates C={p,c1,c2,…,cm-1}, is susceptible to SCWM.
p is candidate to manipulate, ci are others
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Theorem
Let P be a sensitive scoring protocol. If m=O(1) then P, with candidates C={p,c1,c2,…,cm-1}, is susceptible to SCWM.
There exists a heuristic polynomial time algorithm A to solve decisional problem M with a junta distribution over set of inputs to M
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Proposition 1
Let P be a sensitive scoring protocol. Then CWM in P is NP-Hard (with m3)
Sketch of proof:CWM P Partition
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Proposition 1
Partition: given a set of integers that sum to 2K, does there exist a subset that sums to K?
Let m=3. Set n~2K. Structure N such that CWM is true iff exactly K vote p>a>b and K vote p>b>a. If, say, K+1 vote p>a>b and K-1 vote p>b>a, then CWM is false.
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Corollary
Let P be a sensitive scoring protocol. Then SCWM in P is NP-Hard (with m3)
Sketch:If CWM is NP-Hard, then SCWM is as well as
partitioning does not depend on generating tally from votes
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Proposition 2
Let P be a sensitive scoring protocol. Then * is a junta distribution for SCWM in P with C={p,c1,c2,…,cm-1} and m=O(1).
Where * is the following distribution:• Independently randomly choose w(v) from
[0,1] (with discrete precision).• Independently randomly choose S[ci] from
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Is this Junta?
Hard? YesBalance? Authors calculate bounds using
Chernoff’s bounds Dichotomy? First discrete step is non-negligibleSymmetry? Invariant to candidatesRefinement? 2nd ranked candidate will at least
tie p
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Greedy Algorithm
1. Sort candidates from lowest score to highest2. Choose p as first choice, and rest in sorted
order3. Recalculate scores and repeat for each vote4. When finished, return true iff p has highest
score
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Example
Borda: <3,2,1,0>, n=5S[Con] = 20S[Lib] = 19S[NDP] = 17S[Gre] = 10 p
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Example
S[Con] = 20S[Lib] = 19S[NDP] = 17S[Gre] = 10
t1 : Gre<NDP<Lib<Con
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Example
S[Con] = 20 + 0 = 20S[Lib] = 19 + 1 = 20S[NDP] = 17 + 2 = 18S[Gre] = 10 + 3 = 13
t1 : Gre<NDP<Lib<Con
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Example
S[Con] = 20S[Lib] = 20S[NDP] = 18S[Gre] = 13
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ExampleS[Con] = 20, 20 , 20 , 21 , 23 , 23 S[Lib] = 19, 20 , 21 , 21 , 22 , 24S[NDP] = 17, 18 , 20 , 22 , 22 , 23S[Gre] = 10, 13 , 16 , 19 , 22 , 25
t1 : Gre<NDP<Lib<Cont2 : Gre<NDP<Lib<Cont3 : Gre<NDP<Con<Libt4 : Gre<Con<Lib<NDPt5 : Gre<Lib<NDP<Con
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Greedy Properties
Greedy is P-time
Greedy never issues false positives
Greedy does issue false negatives, however these are bounded to Pr[err]1/p(n)
Therefore Greedy is deterministic heuristic polynomial time
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Theorem
Let P be a sensitive scoring protocol. If m=O(1) then P, with candidates C={p,c1,c2,…,cm-1}, is susceptible to SCWM.
There exists a heuristic polynomial time algorithm A to solve decisional problem M with a junta distribution over set of inputs to M
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Theorem 2
The paper contains a second theorem, related to the first, regarding uncertainty about the other votes
We are allowed to sample the distribution of the other votes
Essentially, we try every (m+1)! orders of candidates and sample the distribution
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Outline
Introduction• Manipulability • Design GoalsPaper Theorems• Preliminaries• Junta Distribution• Proof of TheoremsConcluding Remarks
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Conclusions
Complexity is best considered in the average-case, not worst-case
Manipulation problems have been demonstrated to be worst-case intractable and average-case tractable
This is bad news if it generalizes to any NP-Hard manipulation problem
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There is still hope
These results are for scoring protocols. Voting protocols may offer intractable manipulation.
Large number of candidates may increase average case complexity (intuitively seems the case with Theorem 2: (m+1)! grows very fast)
Junta distributions may be too permissible to easy instances
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Questions?
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Discussion
What if we make manipulability as easy as possible and let voters adapt to voting strategically?
What happens with (non-sensitive) cardinal voting schemes instead of ordinal ones, such as range voting?
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