Beyond Regularity: Simple versus Optimal Mechanisms, Revisited

A large proportion of the Bayesian mechanism design literature is restricted to the family of regular distributions $\mathbb{F}_{\text {reg }}$ [Mye81] or the family of monotone hazard rate (MHR) distributions $\mathbb{F}_{M H R}$ [BMP63], which has overshadowed this rich and well-developed theory. We (re-)introduce two generalized families: quasi-regular distributions $\mathbb{F}_{Q-r e g}$ and quasi-MHR distributions $\mathbb{F}_{Q-M H R}$. Altogether, these four families form the following hierarchy: $\mathbb{F}_{\mathrm{MHR}} \subsetneq\left(\mathbb{F}_{\mathrm{reg}} \cap \mathbb{F}_{Q-\mathrm{MHR}}\right) \subsetneq \mathbb{F}_{\mathrm{reg}}, \mathbb{F}_{Q-\mathrm{MHR}} \subsetneq\left(\mathbb{F}_{\mathrm{reg}} \cup \mathbb{F}_{Q-\mathrm{MHR}}\right) \subsetneq \mathbb{F}_{Q-\mathrm{reg}}$ Likewise, the parameterized families of $\lambda$-regular (a.k.a. $\alpha$ strongly regular) distributions [CR14], [SS19], which smoothly interpolate $\mathbb{F}_{\text {reg }}$ and $\mathbb{F}_{\text {MHR }}$, generalize to $\lambda$-quasi-regular distributions. The significance of our new families is manifold. Firstly, their defining conditions are immediate “economic” relaxations of the original defining conditions (e.g., regularity as monotonicity of the virtual value functions), capturing key economic intuitions. Secondly, they satisfy natural mathematical properties (about order statistics) failed for the original families, thus technically more tractable. Thirdly, numerous results (by [BK96], [HR09a], [CD15], [DRY15], [HR14], [AHN ${ }^{+}$19], [JLTX20], [JLQ ${ }^{+}$19b], [FLR19], [GHZ19b], [JLX23], [LM24] etc) known merely for the original families now can extend to our new families. Many of these extensions incur no quantitative loss, or even improve the state of the art for the original families. Finally, beyond the third point, our new families guide us to entirely new perspectives and thus entirely unknown results. For example, regarding revenue maximization for symmetric versus asymmetric regular buyers, we acquire $\frac{1}{2}$ - versus 0.1908 -approximations for the (less-than-)one-sample prophet inequalities, respectively. To the best of our knowledge, such results are blank in the literature, despite their widely-studied welfare maximization counterparts [CDFS22], [RWW20], [CCES20], [CDF ${ }^{+}$21], [CCES24].

Institutions

Publication Details

Published
2025-12-14
DOI
https://doi.org/10.1109/focs63196.2025.00020
Primary Topic
Auction Theory and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond Regularity: Simple versus Optimal Mechanisms, Revisited

Auction Theory and Applications
article

Beyond Regularity: Simple versus Optimal Mechanisms, Revisited

article en

Abstract

A large proportion of the Bayesian mechanism design literature is restricted to the family of regular distributions $\mathbb{F}_{\text {reg }}$ [Mye81] or the family of monotone hazard rate (MHR) distributions $\mathbb{F}_{M H R}$ [BMP63], which has overshadowed this rich and well-developed theory. We (re-)introduce two generalized families: quasi-regular distributions $\mathbb{F}_{Q-r e g}$ and quasi-MHR distributions $\mathbb{F}_{Q-M H R}$. Altogether, these four families form the following hierarchy: $\mathbb{F}_{\mathrm{MHR}} \subsetneq\left(\mathbb{F}_{\mathrm{reg}} \cap \mathbb{F}_{Q-\mathrm{MHR}}\right) \subsetneq \mathbb{F}_{\mathrm{reg}}, \mathbb{F}_{Q-\mathrm{MHR}} \subsetneq\left(\mathbb{F}_{\mathrm{reg}} \cup \mathbb{F}_{Q-\mathrm{MHR}}\right) \subsetneq \mathbb{F}_{Q-\mathrm{reg}}$ Likewise, the parameterized families of $\lambda$-regular (a.k.a. $\alpha$ strongly regular) distributions [CR14], [SS19], which smoothly interpolate $\mathbb{F}_{\text {reg }}$ and $\mathbb{F}_{\text {MHR }}$, generalize to $\lambda$-quasi-regular distributions. The significance of our new families is manifold. Firstly, their defining conditions are immediate “economic” relaxations of the original defining conditions (e.g., regularity as monotonicity of the virtual value functions), capturing key economic intuitions. Secondly, they satisfy natural mathematical properties (about order statistics) failed for the original families, thus technically more tractable. Thirdly, numerous results (by [BK96], [HR09a], [CD15], [DRY15], [HR14], [AHN ${ }^{+}$19], [JLTX20], [JLQ ${ }^{+}$19b], [FLR19], [GHZ19b], [JLX23], [LM24] etc) known merely for the original families now can extend to our new families. Many of these extensions incur no quantitative loss, or even improve the state of the art for the original families. Finally, beyond the third point, our new families guide us to entirely new perspectives and thus entirely unknown results. For example, regarding revenue maximization for symmetric versus asymmetric regular buyers, we acquire $\frac{1}{2}$ - versus 0.1908 -approximations for the (less-than-)one-sample prophet inequalities, respectively. To the best of our knowledge, such results are blank in the literature, despite their widely-studied welfare maximization counterparts [CDFS22], [RWW20], [CCES20], [CDF ${ }^{+}$21], [CCES24].

Hong Kong University of Science and Technology (HK), Huawei Technologies (China) (CN), Decision Sciences (United States) (US), University of Hong Kong (HK)
Openalex Percentile: Top 21%
Auction Theory and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.