We revisit the well-studied problem of designing mechanisms for one-sided matching markets, where a set of n agents needs to be matched to a set of n heterogeneous items. Each agent i has a value vij for each item j, and these values are private information that the agents may misreport if doing so leads to a preferred outcome. Ensuring that the agents
have no incentive to misreport requires a careful design of the matching mechanism, and mechanisms proposed in the literature mitigate this issue by eliciting only the ordinal
preferences of the agents, i.e., their ranking of the items from most to least preferred. However, the efficiency guarantees of these mechanisms are based only on weak measures
that are oblivious to the underlying values. In this paper we achieve stronger performance guarantees by introducing a mechanism that truthfully elicits the full cardinal preferences of the agents, i.e., all of the vij values. We evaluate the performance of this mechanism using the much more demanding Nash bargaining solution as a benchmark, and we
prove that our mechanism significantly outperforms all ordinal mechanisms (even non-truthful ones). To prove our approximation bounds, we also study the population monotonicity of the Nash bargaining solution in the context of matching markets, providing both upper and lower bounds which are of independent interest.
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Fair and Efficient Memory Sharing: Confronting Free Riders
A cache memory unit needs to be shared among n strategic agents. Each agent has different preferences over the files to be brought into memory. The goal is to design a mechanism that elicits these preferences in a truthful manner and outputs a fair and efficient memory allocation. A trivially truthful and fair solution would isolate each agent to a 1/n fraction of the memory. However, this could be very inefficient if the agents have similar preferences and, thus, there is room for cooperation. On the other hand, if the agents are not isolated, unless the mechanism is carefully designed, they have incentives to misreport their preferences and free ride on the files that others bring into memory. In this paper we explore the power and limitations of truthful mechanisms in this setting.We demonstrate that mechanisms blocking agents from accessing parts of the memory can achieve improved efficiency guarantees, despite the inherent inefficiencies of blocking.
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- Award ID(s):
- 1755955
- PAR ID:
- 10087138
- Date Published:
- Journal Name:
- Proceedings of the ... AAAI Conference on Artificial Intelligence
- ISSN:
- 2374-3468
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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