Computational Social Choice

1. Computational Social Choice Lirong Xia IJCAI-13 Tutorial Aug 4, 2013

2. A shameless advertisement… • About RPI • The first technological institutes in English-speaking countries • Graduate school rankings in US • 47thComputer Science • 28th Computer Engineering • 17th Applied Math • I am generally interested in both theory and application of economics and computation. • Hiring highly motivated Ph.D. students • If interested, please send me an email (xial@cs.rpi.edu)

3. 2011 UK Referendum • The second nationwide referendum in UK • 1stwas in 1975 • Member of Parliament election: Plurality rule  Alternative vote rule? • 68% No vs. 32% Yes

4. Ordinal Preference Aggregation: Social Choice A profile > > A B C Alice social choice mechanism > > A C A B Bob > > C B A Carol

5. Ranking pictures [PGM+ AAAI-12] . . . . . . . . . . . . . . . . . > > . . . . . . . . . . . A C B C C B A B > > A B > > … Turker1 Turker2 Turkern

6. Social choice Profile R1 R1* social choice mechanism R2 R2* Outcome … … Rn Rn* Ri, Ri*: full rankings over a set A of alternatives

7. Social Choice and Computer Science Computational thinking + optimization algorithms CS Social Choice 21th Century Strategic thinking + methods/principles of aggregation PLATO 4thC. B.C. LULL 13thC. BORDA 18thC. CONDORCET 18thC. ARROW 20thC. TURING et al. 20thC. PLATO et al. 4thC. B.C.---20thC.

8. Applications: real world • People/agents often have conflicting preferences, yet they have to make a joint decision

9. Applications: academic world • Multi-agent systems [Ephrati and Rosenschein91] • Recommendation systems [Ghoshet al. 99] • Meta-search engines [Dwork et al. 01] • Belief merging [Everaereet al. 07] • Human computation (crowdsourcing) [Mao et al. AAAI-13] • etc.

10. A burgeoning area • Recently has been drawing a lot of attention • IJCAI-11: 15 papers, best paper • AAAI-11: 6 papers, best paper • AAMAS-11: 10 full papers, best paper runner-up • AAMAS-12 9 full papers, best student paper • EC-12: 3 papers • Workshop: COMSOC Workshop 06, 08, 10, 12, 14 • Courses: • Technical University Munich (Felix Brandt) • Harvard (Yiling Chen) • U. of Amsterdam (UlleEndriss) • RPI (2013 fall Lirong Xia) • Book in progress: Handbook of Computational Social Choice

11. Flavor of this tutorial • High-level objectives for • design • evaluation • logic flow among research topics “Give a man a fish and you feed him for a day. Teach a man to fish and you feed him for a lifetime.” -----Chinese proverb • Plus some concrete results

12. How to design a good social choice mechanism? What is being “good”?

13. Two goals for social choice mechanisms GOAL1: democracy GOAL2: truth 1. Classical Social Choice 3. Statistical approaches 2. Computational aspects

14. Outline 1. Classical Social Choice 45 min 5 min 2.1 Computational aspects Part 1 55 min NP- Hard 15 min 2.2 Computational aspects Part 2 30 min NP- Hard 5 min 3. Statistical approaches 75 min NP- Hard

15. Common voting rules(what has been done in the past two centuries) • Mathematically, a social choice mechanism (voting rule) is a mapping from {All profiles} to {outcomes} • an outcome is usually a winner, a set of winners, or a ranking • m : number of alternatives (candidates) • n : number of agents (voters) • D=(P1,…,Pn) a profile • Positional scoring rules • A score vectors1,...,sm • For each vote V, the alternative ranked in the i-th position gets si points • The alternative with the most total points is the winner • Special cases • Borda, with score vector (m-1, m-2, …,0) • Plurality, with score vector (1,0,…,0) [Used in the US]

16. An example • Three alternatives {c1, c2, c3} • Score vector(2,1,0) (=Borda) • 3 votes, • c1 gets 2+1+1=4, c2 gets 1+2+0=3, c3 gets 0+0+2=2 • The winner is c1 2 1 0 2 1 0 2 1 0

17. Plurality with runoff • The election has two rounds • In the first round, all alternatives except the two with the highest plurality score drop out • In the second round, the alternative that is preferred by more voters wins • [used in Iran, North Carolina State] a > b > c > d a > d c > d > a >b d > a d >a • d > a > b > c • d > a • b > c > d >a d

18. Single transferable vote (STV) • Also called instant run-off voting or alternative vote • The election has m-1rounds, in each round, • The alternative with the lowest plurality score drops out, and is removed from all votes • The last-remaining alternative is the winner • [used in Australia and Ireland] a > b > c > d a > c > d a > c a > c c > d > a c > d > a >b c > a c > a • d > a > b > c • d > a > c • b > c > d >a • c > d >a a

19. The Kemeny rule • Kendall tau distance • K(V,W)= # {different pairwise comparisons} • Kemeny(D)=argminW K(D,W)=argminWΣP∈DK(D,W) • For single winner, choose the top-ranked alternative in Kemeny(D) • [Has a statistical interpretation] K( b ≻c≻a,a≻b≻c ) = 2 1 1

20. …and many others • Approval, Baldwin, Black, Bucklin, Coombs, Copeland, Dodgson, maximin, Nanson, Range voting, Schulze, Slater, ranked pairs, etc…

21. Q: How to evaluate rules in terms of achieving democracy? • A: Axiomatic approach

22. Axiomatic approach(what has been done in the past 50 years) • Anonymity: names of the voters do not matter • Fairness for the voters • Non-dictatorship: there is no dictator, whose top-ranked alternative is always the winner • Fairness for the voters • Neutrality: names of the alternatives do not matter • Fairness for the alternatives • Consistency: if r(D1)∩r(D2)≠ϕ, then r(D1∪D2)=r(D1)∩r(D2) • Condorcet consistency: if there exists a Condorcet winner, then it must win • A Condorcet winner beats all other alternatives in pairwise elections • Easy to compute: winner determination is in P • Computational efficiency of preference aggregation • Hard to manipulate: computing a beneficial false vote is hard

23. Which axiom is more important? • Some of these axiomatic properties are not compatible with others • Food for thought: how to evaluate partial satisfaction of axioms?

24. An easy fact • Theorem. For voting rules that selects a single winner, anonymity is not compatible with neutrality • proof: > > Alice > > Bob ≠ W.O.L.G. Anonymity Neutrality

25. Another easy fact [Fishburn APSR-74] • Thm. No positional scoring rule is Condorcet consistent: • suppose s1 >s2 >s3 is the Condorcet winner > > 3 Voters CONTRADICTION > > 2 Voters : 3s1 + 2s2+ 2s3 < > > 1Voter : 3s1 + 3s2+ 1s3 > > 1Voter

26. Not-So-Easy facts • Arrow’s impossibility theorem • Google it! • Gibbard-Satterthwaite theorem • Next section • Axiomatic characterization • Template: A voting rule satisfies axioms A1, A2, A2  if it is rule X • If you believe in A1 A2 A3 are the most desirable properties then X is optimal • (anonymity+neutrality+consistency+continuity) positional scoring rules[Young SIAMAM-75] • (neutrality+consistency+Condorcet consistency) Kemeny[Young&LevenglickSIAMAM-78]

27. Food for thought • Can we quantify a voting rule’s satisfiability of these axiomatic properties? • Tradeoffs between satisfiability of axioms • Use computational techniques to design new voting rules • use AI techniques to automatically prove or discover new impossibility theorems [Tang&Lin AIJ-09]

28. Outline 1. Classical Social Choice 45 min 5 min 2.1 Computational aspects Part 1 55 min 15 min 2.2 Computational aspects Part 2 30 min 5 min 3. Statistical approaches 75 min

29. Computational axioms • Easy to compute: • the winner can be computed in polynomial time • Hard to manipulate: • computing a beneficial false vote is hard

30. Computational axioms • Easy to compute: • the winner can be computed in polynomial time • Hard to manipulate: • computing a beneficial false vote is hard

31. Which rule is easy to compute? • Almost all common voting rules, except • Kemeny: NP-hard [Bartholdi et al. 89], Θ2p-complete [Hemaspaandra et al. TCS-05] • Young: Θ2p-complete [Rothe et al. TCS-03] • Dodgson: Θ2p-complete [Hemaspaandra et al. JACM-97] • Slater: NP-complete [Hurdy EJOR-10] • Practical algorithms for Kemeny (also for others) • ILP [Conitzer, Davenport, & Kalagnanam AAAI-06] • Approximation [Ailon, Charikar, & Newman STOC-05] • PTAS [Kenyon-Mathieu and W. SchudySTOC-07] • Fixed-parameter analysis[Betzler et al. TCS-09]

32. Really easy to compute? • Easy to compute axiom: computing the winner takes polynomial time in the input size • input size: nmlogm • What if m is extremely large?

33. Combinatorial domains(Multi-issue domains) • The set of alternatives can be uniquely characterized by multiple issues • Let I={x1,...,xp} be the set of p issues • Let Di be the set of values that the i-th issue can take, then A=D1×... ×Dp • Example: • Issues={ Main course, Wine } • Alternatives={} ×{ }

34. Multiple referenda • In California, voters voted on 11 binary issues ( / ) • 211=2048 combinations in total • 5/11 are about budget and taxes • Prop.30 Increase sales and some income tax for education • Prop.38 Increase income tax on almost everyone for education

35. Overview Combinatorial voting • Preference • representation • New voting rule • Evaluation

36. Preference representation: CP-nets[Boutilier et al. JAIR-04] Variables:x,y,z. Graph CPTs This CP-net encodes the following partial order: x y z

37. Sequential voting rules [Lang IJCAI-07] • Issues: main course, wine • Order: main course > wine • Local rules are majority rules • V1: > , : > , : > • V2: > , : > , : > • V3: > , : > , : > • Step 1: • Step 2: given , is the winner for wine • Winner: ( , )

38. Research topics • How can we say that sequential voting is good? • computationally efficient • satisfies good axioms [Lang and Xia MSS-09] • need to worry about manipulation in the worst case [Xia, Conitzer &Lang EC-11] • Other compact languages • GAI network [Gonzales et al. AIJ-11] • TCP-net [Li et al. AAMAS-11] • Soft constraints [Pozza et al. IJCAI-11]

39. Other combinatorial domains • Belief merging [Gabbay et al. JLC-09] • Judgment aggregation [List and Pettit EP-02] merging operator K1 K2 … Kn

40. Computational axioms • Easy to compute: • the winner can be computed in polynomial time • Hard to manipulate: • computing a beneficial false vote is hard

41. Strategic behavior (of the agents) • Manipulation: an agent (manipulator) casts a vote that does not represent her true preferences, to make herself better off • A voting rule is strategy-proof if there is never a (beneficial) manipulation under this rule • How important strategy-proofness is as an desired axiomatic property? • compared to other axiomatic properties

42. Manipulation under plurality rule (ties are broken in favor of ) > > > > Alice Plurality rule > > Bob > > Carol

43. Any strategy-proof voting rule? • No reasonable voting rule is strategyproof • Gibbard-Satterthwaite Theorem [Gibbard Econometrica-73, Satterthwaite JET-75]:When there are at least three alternatives, no voting rules except dictatorships satisfy • non-imposition: every alternative wins for some profile • unrestricted domain: voters can use any linear order as their votes • strategy-proofness • Axiomatic characterization for dictatorships!

44. A few ways out • Relax non-dictatorship: use a dictatorship • Restrict the number of alternatives to 2 • Relax unrestricted domain: mainly pursued by economists • Single-peaked preferences: • Range voting: A voter submit any natural number between 0 and 10 for each alternative • Approval voting: A voter submit 0 or 1 for each alternative

45. Computational thinking • Use a voting rule that is too complicated so that nobody can easily predict the winner • Dodgson • Kemeny • The randomized voting rule used in Venice Republic for more than 500 years [Walsh&Xia AAMAS-12] • We want a voting rule where • Winner determination is easy • Manipulation is hard

46. Overview Manipulation is inevitable (Gibbard-Satterthwaite Theorem) Can we use computational complexity as a barrier? Why prevent manipulation? • Yes • May lead to very • undesirable outcomes Is it a strong barrier? • No How often? Other barriers? • Seems not very often • Limited information • Limited communication

47. Manipulation: A computational complexity perspective If it is computationallytoo hard for a manipulator to compute a manipulation, she is best off voting truthfully • Similar as in cryptography For which common voting rules manipulation is computationally hard? NP- Hard

48. Computing a manipulation • Initiated by [Bartholdi, Tovey, &Trick SCW-89b] • Votes are weighted or unweighted • Bounded number of alternatives [Conitzer, Sandholm, &Lang JACM-07] • Unweightedmanipulation: easy for most common rules • Weighted manipulation: depends on the number of manipulators • Unbounded number of alternatives (next few slides) • Assuming the manipulators have complete information!

49. Unweightedcoalitional manipulation (UCM) problem • Given • The voting rule r • The non-manipulators’ profile PNM • The number of manipulators n’ • The alternative c preferred by the manipulators • We are asked whether or not there exists a profile PM (of the manipulators) such that c is the winner of PNM∪PM under r

50. The stunningly big table for UCM