Investigating the characteristics of one-sided matching mechanisms under various preferences and risk attitudes

Hosseini, Hadi; Larson, Kate; Cohen, Robin

doi:10.1007/s10458-018-9387-y

Cited by 8 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The empirical work by Hosseini, Larson, and Cohen (2018) disclosed some results on the manipulability of the Probabilistic Serial mechanism. Their experiments show that the mechanism is almost always manipulable for various combination of agents and items, and the fraction of strongly manipulable profiles goes to one as the ratio of items to agents increases.…”

Section: Related Workmentioning

confidence: 99%

Bounded Incentives in Manipulating the Probabilistic Serial Rule

Wang

Wei

Zhang

2020

AAAI

View full text Add to dashboard Cite

The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is 3/2. That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.

show abstract

Section: Related Workmentioning

confidence: 99%

Bounded Incentives in Manipulating the Probabilistic Serial Rule

Wang

Wei

Zhang

2020

AAAI

View full text Add to dashboard Cite

show abstract

“…The empirical work by Hosseini et al (2018) disclosed some results on the manipulability of the Probabilistic Serial mechanism. Their experiments show that the mechanism is almost always manipulable for various combination of agents and items, and the fraction of strongly manipulable profiles goes to one as the ratio of items to agents increases.…”

Section: Related Workmentioning

confidence: 99%

Bounded Incentives in Manipulating the Probabilistic Serial Rule

Wang

Wei

Zhang

2020

Preprint

View full text Add to dashboard Cite

The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is 3 2 . That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.

show abstract

“…Nonetheless, under a large market assumption PS and RSD converge and the desirable properties of both are attained (Kojima and Manea, 2010;Che and Kojima, 2010). More recent work has also further studied and compared the performance of these two mechanisms with respect to different metrics both theoretically and experimentally (e.g., Aziz et al (2016); Hosseini et al (2018)).…”

Section: Approximate Utility Monotonicitymentioning

confidence: 99%

A Truthful Cardinal Mechanism for One-Sided Matching

Abebe¹,

Cole²,

Gkatzelis³

et al. 2020

Proceedings of the Fourteenth Annual ACM-SIAM Symposium on Discrete Algorithms

View full text Add to dashboard Cite

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 vi,j 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 vi,j 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. * Rediet Abebe was supported in part by a Facebook scholarship.

show abstract

Investigating the characteristics of one-sided matching mechanisms under various preferences and risk attitudes

Cited by 8 publications

References 48 publications

Bounded Incentives in Manipulating the Probabilistic Serial Rule

Bounded Incentives in Manipulating the Probabilistic Serial Rule

Bounded Incentives in Manipulating the Probabilistic Serial Rule

A Truthful Cardinal Mechanism for One-Sided Matching

Contact Info

Product

Resources

About