TY - GEN
T1 - Quantized VCG mechanisms for polymatroid environments ∗
AU - Ge, Hao
AU - Berry, Randall A.
N1 - Funding Information:
∗This work was supported in part by the National Science Foundation grant TWC-1314620.
Publisher Copyright:
© 2019 Association for Computing Machinery.
PY - 2019/7/2
Y1 - 2019/7/2
N2 - Many network resource allocation problems can be viewed as allocating a divisible resource, where the allocations are constrained to lie in a polymatroid. We consider market-based mechanisms for such problems. Though the Vickrey-Clarke-Groves (VCG) mechanism can provide the efficient allocation with strong incentive properties (namely dominant strategy incentive compatibility), its well-known high communication requirements can prevent it from being used. There have been a number of approaches for reducing the communication costs of VCG by weakening its incentive properties. Here, instead we take a different approach of reducing communication costs via quantization while maintaining VCG’s dominant strategy incentive properties. The cost for this approach is a loss in efficiency which we characterize. We first consider quantizing the resource allocations so that agents need only submit a finite number of bids instead of full utility function. We subsequently consider quantizing the agent’s bids.
AB - Many network resource allocation problems can be viewed as allocating a divisible resource, where the allocations are constrained to lie in a polymatroid. We consider market-based mechanisms for such problems. Though the Vickrey-Clarke-Groves (VCG) mechanism can provide the efficient allocation with strong incentive properties (namely dominant strategy incentive compatibility), its well-known high communication requirements can prevent it from being used. There have been a number of approaches for reducing the communication costs of VCG by weakening its incentive properties. Here, instead we take a different approach of reducing communication costs via quantization while maintaining VCG’s dominant strategy incentive properties. The cost for this approach is a loss in efficiency which we characterize. We first consider quantizing the resource allocations so that agents need only submit a finite number of bids instead of full utility function. We subsequently consider quantizing the agent’s bids.
KW - Mechanism design
KW - Quantization
KW - Worst-case efficiency
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U2 - 10.1145/3323679.3326524
DO - 10.1145/3323679.3326524
M3 - Conference contribution
AN - SCOPUS:85069789548
T3 - Proceedings of the International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc)
SP - 261
EP - 270
BT - Mobihoc 2019 - Proceedings of the 2019 20th ACM International Symposium on Mobile Ad Hoc Networking and Computing
PB - Association for Computing Machinery
T2 - 20th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 2019
Y2 - 2 July 2019 through 5 July 2019
ER -