Online Mechanism Design (Randomized Rounding on the Fly)

Online Mechanism Design (Randomized Rounding on the Fly)Piotr Krysta, University of Liverpool, UKBerthold Vcking, RWTH Aachen University, GermanyCombinatorial Auctionsm indivisible items (goods) given for sale

n potential buyers (bidders), each with a valuation function v(.) for subsets (bundles) of goods

v(.) may express complex preferences, e.g.:complements: v(camera + battery) > v(camera) + v(battery)substitutes: v(Apple iPhone + Samsung Galaxy) < v(Apple iPhone) + v(Samsung Galaxy)

Goal: Partition m goods among n bidders to maximize the social welfare (SW)

Example: m=2 bidders {A,B} n=2 goods {x,y}

v( {x} ) v( {y} ) v( {x,y} ) A 1 5 7 B 3 5 6Opt SW = 8Combinatorial Auctions: ApplicationsCombinatorial auctions have many important applications:

* Government Spectrum Auctions (UK, Germany, Sweden, USA, )

* Allocation of Airspace System Resources

* Auctions for Truckload Transportation

* Auctioning Bus Routes (London)

Combinatorial Auctions: Problem definition Combinatorial Auction (CA): n biddersU = set of m items (goods)Each e U available in b 1 copies (supply)Bidder i has valuation f-n:

Meaning: money i is willing to pay for S

Allocations:

Problem: compute allocation maximizing social welfare:

vvvvvvvvvv

b=218324567|U|= m = 8How are bidders represented ? (Demand oracles)Problem: The length of bidders valuation v(.) is exponential in m.

v(.) given by demand oracles Di(Ui, p):

Given item prices what is utility maximizing subset Si Ui and its valuation v(Si) ?

Utility of bidder i for set Si:

Demand oracle is:restricted if unrestricted if

vvvvvvvvvv

18324567

Truthful mechanisms -- deterministic mechanism for CA:

A mechanism is truthful if for each bidder i, all vi , vi and all declarations v-I of the other bidders except bidder i:

Randomized mechanism = prob. distribution over deterministic mechanisms. It is universally truthful if each of these mechanisms is truthful.

Truthfulness via direct characterization & on-line algsAchieve truthfulness by serving bidders one by one in a given order, say i=1,2,,n, and offering items at fixed (posted) prices:

If set of items offered to bidder i, define prices (indep. from i) and compute: * bundle Si := Di(Ui , pi) * payment

(without knowing the valuations of bidders i+1,,n)

Arrival models: * random order of arrivals (secretary model) * arbitrary (adversarial) order of arrivals.

Goal: find alloc. S in A maximizing the social welfare. We use standard on-line competitive analysis (CR = competitive ratio)

On-line models: standard definition & some aspectsCompetitive ratio CR (of a randomized online algo.): = set of all arrival sequences of n bidders with valuations for m itemsFor : S() = alloc. computed by algo., opt() = opt offline alloc.

OBSERVE: Adversarial arrival model: If valuations v() are unbounded, then R cannot be bounded.

REASON: The b bidders arriving last might have huge v()s, s.t. copies cannot be given to any bidders that arrive before them.Thus: assume 1 vi(S) for every bidder i, S subset U.

Random arrival model: We assume unbounded valuations.

NOTE: Random arrivals used only to extract estimate of the bids range.

Our contributions: CAs + Random arrivals model1. General v(): for any b 1 we obtain CR (the first online result with )

2. General v(), bundles size d: for any b 1 we obtain CR (previous O(d2) only for b=1)

3. XOS v() and b = 1: we obtain a CR (the first online result for submodular/XOS v(.))

Previous results: -comp. l.b. -best known u.b.XOS v(): O(log (m) log log (m))-apx univ. truthful offline mech. [Dobzinski 09]

General v(): -apx truthful in exp. offline mech. [Lavi, Swamy 05] -apx (b=1) univ. truthful [Dobzinski, Nisan, Schapira 05] -apx (deterministically) truthful [Bartal, Gonen, Nisan 05]

Our contributions: CAs + Adversarial arrivals model1. General v(): for any b 1 we obtain CR (the first online result with )

2. General v(), bundles size d: for any b 1 we obtain CR (previous O(d2) only for b=1)

3. XOS v() and b = 1: we obtain a CR (the first online result for submodular/XOS v(.))

Previous results: -comp. l.b. -best known u.b.XOS v(): O(log (m) log log (m))-apx univ. truthful offline mech. [Dobzinski 09]

General v(): -apx truthful in exp. offline mech. [Lavi, Swamy 05] -apx (b=1) univ. truthful [Dobzinski, Nisan, Schapira 05] -apx (deterministically) truthful [Bartal, Gonen, Nisan 05]

Overselling Multiplicative Price Update (MPU) Algorithm [inspired by BGN 05, ]Bidders:vvvvvvvvvv

Bidder 1

Your most profitable bundle ?

b=2

Overselling Multiplicative Price Update (MPU) Algorithm [inspired by BGN 05, ]Bidders:vvvvvvvvvv

Bidder 2

Your most profitable bundle ?

b=2

b=2Overselling Multiplicative Price Update (MPU) Algorithm [inspired by BGN 05, ]Bidders:vvvvvvvvvv

Bidder 3

Your most profitable bundle ?

Warm-up: Overselling Multiplicative Price Update (MPU) Algorithm [inspired by BGN 05, ]Order of bidders 1,2,,n is arbitrary (adversarial).

1. For each good2. For each bidder

3. Set 4. Update for each good

NOTE: Bidder i gets set

Overselling MPU Algorithm: AnalysisLEMMA 1. For any : * S assigns sb copies of each item, *

LEMMA 2. For :

THEOREM 1. S infeasible alloc. if :

infeasibleoversellingfactor

Overselling MPU Algorithm: AnalysisLEMMA 1 (Part II). For any ,where

LEMMA 2. For ,

THEOREM 1. The algorithm with outputs an infeasible alloc. S:where copies of each item is assigned;if , then

PROOF: (1) is by LEMMA 1 (P. I). By L. 1 (P. II):which with LEMMA 2 gives:

By v(opt) L, we have the following and this implies claim (2):

Overselling MPU Algorithm: AnalysisLEMMA 1 (Part I). For any , alloc. S assigns sb copies of each item, where

PROOF: Consider e U. Suppose, after some step, copies of e assigned to bidders.

price of e

After this step, the algorithm might give further copies of e to bidders whose maximum valuation exceeds L.

By definition of , L there is 1 such bidder that receives 1 copy of e.

Hence, at most copies of e assigned.

Overselling MPU Algorithm: AnalysisLEMMA 2. For ,

PROOF: = feasible allocation (allocates b of each item)

Algo. uses demand oracle: , so

By using and summing (*) for all bidders we obtain

(last follows because T allocates b copies of each item)Taking T = opt implies the claim.

Overselling MPU Algorithm with Oblivious RRLarger price update factor more feasible solution + worse approximation

Smaller helps learn correct prices, but, produces in-feasible solution.

Idea: Achieve feasibility and good approximation by defining appropriate sets Ui for demand oracles, and using RR.

Idea: Provisionally assign bundles Si of virtual copies to bidders following MPU algorithm learn correct prices

Number of virtual copies b log(bm) (LEMMA 1)

Oblivious randomized rounding (RR) used to decide (with small Pr = q) which provisional bundles Si become final bundles.

Overselling MPU Algo. with Oblivious Randomized RoundingBidders:vvvvvvvvvv

Bidder 1

Your most profitable bundle ?

b=2

YES! (Pr=q)

Bidders:vvvvvvvvvv

Bidder 2

Your most profitable bundle ?

b=2

Overselling MPU Algo. with Oblivious Randomized Rounding

YES! (Pr=q)

b=2Bidders:vvvvvvvv

Bidder 3

Your most profitable bundle ?

Overselling MPU Algo. with Oblivious Randomized Rounding

NO! (Pr=1-q)

MPU Algorithm with Oblivious RROrder of bidders 1,2,,n is arbitrary (adversarial).

1. For each good2. For each bidder

3. Set

4. Update for each good

5. With prob.

6. Update for each good

MPU Algorithm with Oblivious RR: remarks1. For each good2. For each bidder3. Set 4. Update for each good5. With prob.6. Update for each good

The algorithm outputs allocation R; payment for Ri isDef. of Ui in line 3. ensures that R is feasible!If q=0, then the provisional alloc. S is same as MPU algo. with Ui=U.If q=0, then the output alloc. R is empty.With prob. 1-q the algo. increases prices of e in Si but does not sell Si (and thus learns the correct prices).If q>0, then LEMMA 1 holds, but LEMMA 2 doesnt!We will show a stochastic version of LEMMA 2 to imply O(1/q)-apx.

Overselling MPU Algo. with RR: Analysis

Recall the previous analysis:LEMMA 1. For any : * S assigns sb copies of each item, *

LEMMA 2. For :

Always holdsNot always holds!!!

opt bundle for i

A stochastic LEMMA 2 for CAs with d-bundles LEMMA 2. Consider CA with |bundles| d, and let

Then for any and any bundle :

and

THEOREM 2. The MPU algorithm with oblivious RR and q as above is

for CA with |bundles| d and multiplicity b.

Summary and further questions?We design the first online (universally truthful) mechanisms achieving competitiveness for any supply b 1.

New technique: we combine the online allocation of bidders with the concept of oblivious randomized rounding.

Our mechanisms are simple and intuitive: each bidders demand oracle is queried only once,

We achieve competitive ratios close to or even beating the best known approx. factors for the corresponding offline setting.

Question: The main open problem is to design similar deterministic mechanisms.

Thanks! Questions ?Problem definition: Submodular and XOS valuationsWe also consider special valuations

Submodular (decreasing marginal utilities):

XOS (fractionally subadditive):

FACT: If v() if submodular then it is XOS.

PROOFSOverselling MPU Algorithm: AnalysisLEMMA 1 (Part II). For any ,

where and

PROOF: Let and

As bidders are individually rational: , hence:

Now and imply the claim.

MPU Algo. with RR: stochastic LEMMA 2LEMMA 2. If for any and any

then

PROOF: Fix bidder i and feasible opt alloc. By for any coin flips of the algorithm

by (**)MPU Algo. with RR: stochastic LEMMA 2LEMMA 2. If for any and any

then

PROOF: Sum (***) for all bidders:

MPU Algo. with RR: stochastic LEMMA 2 LEMMA 2. If for any and any

then

PROOF: By LEMMA 1:

Now: E[v(Ri)]=qE[v(Si)] as Pr[Ri=Si]=q, so E[v(R)]=qE[v(S)], and finally E[v(R)] q v(opt)/8.

Proving (**) for d-bundles LEMMA. Consider CA with |bundles| d 1, and let

Then for any and any bundle of at most d items:

PROOF: Fix bidder i. By LEMMA 1, is inof the provisional bundles , and each of them becomes final with prob. .Consider and note that if was sold times, i.e., at most b-1 of its provisional bundles became final.Thus the prob. that is: By and union bound

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