An optimal algorithm for on-line bipartite matching
STOC '90 Proceedings of the twenty-second annual ACM symposium on Theory of computing
SODA '91 Proceedings of the second annual ACM-SIAM symposium on Discrete algorithms
Pricing WiFi at Starbucks: issues in online mechanism design
Proceedings of the 4th ACM conference on Electronic commerce
AdWords and generalized online matching
Journal of the ACM (JACM)
Online Stochastic Matching: Beating 1-1/e
FOCS '09 Proceedings of the 2009 50th Annual IEEE Symposium on Foundations of Computer Science
Multi-parameter mechanism design and sequential posted pricing
Proceedings of the forty-second ACM symposium on Theory of computing
Resource allocation in decentralised computational systems: an evolutionary market-based approach
Autonomous Agents and Multi-Agent Systems
Online bipartite matching with unknown distributions
Proceedings of the forty-third annual ACM symposium on Theory of computing
Online mechanism design for electric vehicle charging
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Online vertex-weighted bipartite matching and single-bid budgeted allocations
Proceedings of the twenty-second annual ACM-SIAM symposium on Discrete Algorithms
A model-based online mechanism with pre-commitment and its application to electric vehicle charging
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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We study online bipartite matching settings inspired by parking allocation problems, where rational agents arrive sequentially and select their most preferred parking slot. In contrast to standard online matching setting where edges incident to each arriving vertex are revealed upon its arrival, agents in our setting have private preferences over available slots. Our focus is on natural and simple pricing mechanisms, in the form of posted prices. On the one hand, the restriction to posted prices imposes new challenges relative to standard online matching. On the other hand, we employ specific structures on agents' preferences that are natural in many scenarios including parking. We construct optimal and approximate pricing mechanisms under various informational and structural assumptions, and provide approximation upper bounds under the same assumptions. In particular, one of our mechanisms guarantees a better approximation bound than the classical result of Karp et al.[10] for unweighted online matching, under a natural structural restriction.