matching markets with ordinal preferences tifr, may 2013
TRANSCRIPT
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Matching Markets with Ordinal Preferences
TIFR, May 2013
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Matching Markets
• N agents, N items, N complete preferences.
• Outcome: Agent-Item Matching
𝜋 1
𝜋 2
𝜋 3
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Outline of Talk
• Mechanisms – Random Serial Dictatorship (RSD)– Rank Maximal Matching (RMM)
• Welfare – Ordinal Welfare Factor– Rank Approximation
• Truthfulness– Dealing with randomness.
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• Agents arrive in a random permutation and pick their best unallocated item.
Random Serial Dictatorship
Choice 1Choice 2Choice 3
(2,1,3)
…
(3,2,1)
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• Maximize #(top choice), then Maximize #(top 2),...
• Polytime computable.
Rank Maximal Matching
Irving, 2003Irving, Kavitha, Melhorn, Michail, Paluch, 2004.
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Social Welfare
• Pareto Optimality. No other outcome makes everyone happier.
• RMM leads to a Pareto Optimal outcome.• RSD leads to ex-post Pareto Optimal outcome.
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Social Welfare
• Cardinal WelfareEach pair associated with cardinal number.
Social welfare = Sum of utilities.
• What to do when no numbers are known?
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• Outcome is -efficient, if for any ,
• Problem: Everyone has same ordering.
#agents with
Ordinal Welfare Factor (OWF)
(1, 1)(2, 2)(3, 3)…(N,N)
M =
(1, N)(2, 1)(3, 2)…(N,N-1)
M’ = 𝛼<1/𝑁
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Ordinal Welfare Factor (OWF)
• Randomization. A distribution is -efficient, if for any other distribution ,
• Mechanism has OWF if it returns an -efficient distribution.
agents with
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Symmetric “Bad” Example
• Every agent has same preference order.• is uniform over all matchings.• Fix matching ,
• is -efficient.
∀ 𝑖 ,𝐏𝐫 {𝑀←𝑴 } [𝑀 ≥𝑖𝑀′ ]≥ 𝑖
𝑁
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Performance of Mechanisms
• Theorem. RSD has OWF 1/2
• RMM is deterministic.
Many agents can be made better off at the expense of one agent.
Bhalgat, C, Khanna 2011.
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Strengths and Weaknesses
• Comparative Measure.• Notion of “approximation”.
Quantify mechanisms.
• Not good for deterministic mechanisms.• No notion of “how much better off”.
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Rank Approximation
• Let maximize #(agents getting top i)
• is -rank approximate if #(agents getting top in ) .
• Mechanism has -rank approximation if it returns an -rank approximate matching.
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Connection to Cardinal Welfare
• Homogenous agents: Each agent has same cardinal profile
• is -rank approximate implies -approximation for homogenous agents.
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Performance of Mechanisms
• Theorem. RMM has ½-rank approximation.- Maximal/Maximum
- Optimal.
• RSD is not -approximate for any constant .
Choice 1≈√𝑁
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Strengths and Weaknesses
• Deterministic mechanisms can have good rank approximation.
• Cardinal welfare for homogenous agents.
• Could improve many while hurting only a few.• No good rank appx known in non-matching
setting.
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Truthfulness
• If an agent lies, he gets a worse item.If an agent lies, he doesn’t get a better item.
• Issues with randomized mechanisms.What are worse and better distributions?
• Hierarchy of truthfulness.
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Randomization vs Truthfulness
Universally Truthful. Distribution over deterministic mechanisms
Strongly Truthful. (Gibbard, 77)
Lying gives a stochastically dominated allocation.
Weakly Truthful. (Bogomolnaia-Moulin, 01)
Lying can’t give stochastically dominating allocation.
Lex Truthful. (?)
Lying gives a lexicographically dominated allocation.
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Lex Truthful Implementation
• A deterministic algorithm A can be -lex-truthful implemented if there is a randomized mechanism M such that
– M is Lex Truthful.– With probability > (1-), outcome of M is same as
that of A
Theorem. Any pseudomonotone algorithm Ais -lex-implementable, for any
C, Swamy 2013
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Pseudomonotonicity
A A
M(i) M’(i)𝜋 𝑖 b M’(i) M(i)𝜋 𝑖
b 𝜋 ′ 𝑖M’(i) is below M(i) in
or there’s b above M’(i) in which has been demoted.
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Performance of Mechanisms
• RSD is Universally Truthful.Under certain conditions, it is the only strongly truthful mechanism. (Larsson, 94)
• RMM satisfies pseudomonotonicity.Therefore, it can be -LT implemeneted.
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Summary
• Welfare definitions unclear in ordinal settings.Saw two notions.Generalizes to Social choice settings.
• Truthfulness of randomized mechanisms also tricky. Hierarchy of truthfulness.
• Can results be extended to general settings?