issues on the border of economics and computation נושאים בגבול כלכלה וחישוב
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Issues on the border of economics and computation נושאים בגבול כלכלה וחישוב. Speaker: Dr. Michael Schapira Topic: Combinatorial Auctions III. Combinatorial Auctions. Set M of m indivisible items Set N of n bidders Preferences are on subsets S – bundles – of items - PowerPoint PPT PresentationTRANSCRIPT
Issues on the border of economics and computation
נושאים בגבול כלכלה וחישוב
Speaker: Dr. Michael SchapiraTopic: Combinatorial Auctions III
Combinatorial Auctions• Set M of m indivisible items• Set N of n bidders• Preferences are on subsets S – bundles – of
items • Valuation function vi: 2M R
– vi(S) – bidder i’s value for bundle S
– monotone: vi(S) not decreasing in S
– normalized: vi() = 0
Allocation: mutually-disjoint subsets S1, S2, … Sn
Social welfare of allocation: i vi(Si)
What Do We Want?
1. “Good” (w.r.t. efficiency) outcomes (preferably optimal)
2. Incentive compatibility (preferably in dominant strategies)
3. Low running time (in the “natural parameters”: n and m)
Cannot Simply Use VCG!
• Finding optimal allocation is computationally (=NP) hard!
• Cannot compute “approximate” VCG payments.
• The “clash” between Econ and CS. What can we do?
Natural Restrictions on Bidders
• Defn: A valuation v is subadditive (complement-free) if for all S,TM, v(ST) ≤ v(S) + v(T).
• Defn: A valuation v is submodular if for all S,TM, v(ST) + v(ST) ≤ v(S) + v(T).
• Equivalent definition of submodularity: for all STM, and j not in T,
v(T{j})-v(T) ≤ v(S{j})-v(S)
(decreasing marginal utilites)
• Fact: Submodularity implies subadditivity.
Computational Perspective
• Thm: Finding an optimal allocation in combinatorial auctions with submodular bidders is NP-hard.
• Thm: A 2-approximation to the optimal allocation in combinatorial auctions with submodular bidders can be computed in a computationally-efficient manner.
• The 2-approximation algorithm is not truthful. What’s next?
Computational Perspective• Thm: There exists a computationally-
efficient and incentive compatible 2m½-approximation mechanism for auctions with subadditive bidders.
• Thm: No computationally-efficient and incentive compatible mechanism can obtain an approximation ratio of m½-e for auctions with submodular bidders.
• An inherent clash between efficient computation and incentive compatibility.
Incentive Compatibility via VCG?
• We want an algorithm that is incentive compatible in dominant strategies.
• VCG is the only general technique known for making auctions incentive compatible
– each bidder i pays: Sk≠ivk(O-i) - Sk≠ivk(Oi)
– Oi is the optimal allocation, O-i the optimal allocation of the auction without the i’th bidder.
• Problem: VCG requires finding optimal allocations!
• This is computationally intractable.
• Approximations do not suffice…
• But, that does not mean we cannot use VCG in a more creative way…
Incentive Compatibility via VCG?
• A mechanism M is MIR (= VCG-based) if:– There’s a fixed subset RM of the possible
outcomes (allocations of the m items between the n bidders) = “M’s range”.
– For every valuation profile (v1,…vn) M outputs the optimal partition in RM.
• Fact: MIR mechanisms are truthful (Why?).
RM
allpartitions
Maximal-In-Range Mechanisms
MIR for Subadditive Auctions
• Key idea: limit the set of possible allocations.– either each bidder gets at most one item– or all items are allocated to a single bidder.
• Optimal solution in the set can be found in a computationally efficient manner VCG prices can be computed incentive compatibility.
• We still need to prove that we achieve an approximation.
The Algorithm• Ask each bidder i for vi(M), and for vi(j), for
each item j.
• Construct a bipartite graph and find the maximum weighted matching P.
• can be done in polynomial time.
1
2
3
A
B
ItemsBidders
v1(A)
v3(B)
The Algorithm (Cont.)
• Let i be the bidder that maximizes vi(M).
• If vi(M)>Val(P)– Allocate all items to i.
• else– Allocate according to P.
• Let each bidder pay his VCG price (in respect to the restricted set).
Proof of Approximation Ratio
Theorem: The algorithm provides an(2m1/2)-approximation for subadditive bidders.
Proof: Let OPT=(T1,..,Tk,Q1,...,Ql), where for each Ti, |Ti|>m1/2, and for each Qi, |Qi|≤m1/2. |OPT|= Sivi(Ti) + Sivi(Qi)
Case 1: Sivi(Ti) > Sivi(Qi)(“large” bundles contribute most of the social welfare)
Sivi(Ti) > |OPT|/2At most m1/2 bidders
get at least m1/2 items in OPT.
For the bidder i the bidder i that
maximizes vi(M), vi(M) > |OPT|/2m1/2.
Case 2: Sivi(Qi) ≥ Sivi(Ti)(“small” bundles contribute most of the
social welfare)
Sivi(Qi) ≥ |OPT|/2For each bidder i, there is an
item ci, such that: vi(ci) > vi(Qi) / m1/2.
(The CF property ensures that the sum of the values is larger than the value of
the whole bundle)
{ci}i is an allocation which assigns at most one item to
each bidder: |P| ≥ Sivi(ci) ≥ |OPT|/2m1/2.