### Abstract

The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in microeconomics. In the classical auction theory for agents with quasi-linear utility and single-dimensional preferences, Bulow and Roberts [1] show that the optimal auction of Myerson [2] is in fact optimizing marginal revenue. In particular Myerson's virtual values are exactly the derivative of an appropriate revenue curve. This paper considers mechanism design in environments where the agents have multi-dimensional and non-linear preferences. Understanding good auctions for these environments is considered to be the main challenge in Bayesian optimal mechanism design. In these environments maximizing marginal revenue may not be optimal, and furthermore, there is sometimes no direct way to implement the marginal revenue maximization mechanism. Our contributions are three fold: we characterize the settings for which marginal revenue maximization is optimal (by identifying an important condition that we call revenue linearity), we give simple procedures for implementing marginal revenue maximization in general, and we show that marginal revenue maximization is approximately optimal. Our approximation factor smoothly degrades in a term that quantifies how far the environment is from an ideal one (i.e., where marginal revenue maximization is optimal). Because the marginal revenue mechanism is optimal for well-studied single-dimensional agents, our generalization immediately extends many approximation results for singledimensional agents to more general preferences. Finally, one of the biggest open questions in Bayesian algorithmic mechanism design is in developing methodologies that are not brute-force in size of the agent type space (usually exponential in the dimension for multi-dimensional agents). Our methods identify a subproblem that, e.g., for unitdemand agents with values drawn from product distributions, enables approximation mechanisms that are polynomial in the dimension.

Original language | English (US) |
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Title of host publication | Proceedings - 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013 |

Pages | 628-637 |

Number of pages | 10 |

DOIs | |

State | Published - Dec 1 2013 |

Event | 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013 - Berkeley, CA, United States Duration: Oct 27 2013 → Oct 29 2013 |

### Publication series

Name | Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS |
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ISSN (Print) | 0272-5428 |

### Other

Other | 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013 |
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Country | United States |

City | Berkeley, CA |

Period | 10/27/13 → 10/29/13 |

### Fingerprint

### All Science Journal Classification (ASJC) codes

- Computer Science(all)

### Cite this

*Proceedings - 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013*(pp. 628-637). [6686199] (Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS). https://doi.org/10.1109/FOCS.2013.73

}

*Proceedings - 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013.*, 6686199, Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS, pp. 628-637, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013, Berkeley, CA, United States, 10/27/13. https://doi.org/10.1109/FOCS.2013.73

**The simple economics of approximately optimal auctions.** / Alaei, Saeed; Fu, Hu; Haghpanah, Nima; Hartline, Jason.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - The simple economics of approximately optimal auctions

AU - Alaei, Saeed

AU - Fu, Hu

AU - Haghpanah, Nima

AU - Hartline, Jason

PY - 2013/12/1

Y1 - 2013/12/1

N2 - The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in microeconomics. In the classical auction theory for agents with quasi-linear utility and single-dimensional preferences, Bulow and Roberts [1] show that the optimal auction of Myerson [2] is in fact optimizing marginal revenue. In particular Myerson's virtual values are exactly the derivative of an appropriate revenue curve. This paper considers mechanism design in environments where the agents have multi-dimensional and non-linear preferences. Understanding good auctions for these environments is considered to be the main challenge in Bayesian optimal mechanism design. In these environments maximizing marginal revenue may not be optimal, and furthermore, there is sometimes no direct way to implement the marginal revenue maximization mechanism. Our contributions are three fold: we characterize the settings for which marginal revenue maximization is optimal (by identifying an important condition that we call revenue linearity), we give simple procedures for implementing marginal revenue maximization in general, and we show that marginal revenue maximization is approximately optimal. Our approximation factor smoothly degrades in a term that quantifies how far the environment is from an ideal one (i.e., where marginal revenue maximization is optimal). Because the marginal revenue mechanism is optimal for well-studied single-dimensional agents, our generalization immediately extends many approximation results for singledimensional agents to more general preferences. Finally, one of the biggest open questions in Bayesian algorithmic mechanism design is in developing methodologies that are not brute-force in size of the agent type space (usually exponential in the dimension for multi-dimensional agents). Our methods identify a subproblem that, e.g., for unitdemand agents with values drawn from product distributions, enables approximation mechanisms that are polynomial in the dimension.

AB - The intuition that profit is optimized by maximizing marginal revenue is a guiding principle in microeconomics. In the classical auction theory for agents with quasi-linear utility and single-dimensional preferences, Bulow and Roberts [1] show that the optimal auction of Myerson [2] is in fact optimizing marginal revenue. In particular Myerson's virtual values are exactly the derivative of an appropriate revenue curve. This paper considers mechanism design in environments where the agents have multi-dimensional and non-linear preferences. Understanding good auctions for these environments is considered to be the main challenge in Bayesian optimal mechanism design. In these environments maximizing marginal revenue may not be optimal, and furthermore, there is sometimes no direct way to implement the marginal revenue maximization mechanism. Our contributions are three fold: we characterize the settings for which marginal revenue maximization is optimal (by identifying an important condition that we call revenue linearity), we give simple procedures for implementing marginal revenue maximization in general, and we show that marginal revenue maximization is approximately optimal. Our approximation factor smoothly degrades in a term that quantifies how far the environment is from an ideal one (i.e., where marginal revenue maximization is optimal). Because the marginal revenue mechanism is optimal for well-studied single-dimensional agents, our generalization immediately extends many approximation results for singledimensional agents to more general preferences. Finally, one of the biggest open questions in Bayesian algorithmic mechanism design is in developing methodologies that are not brute-force in size of the agent type space (usually exponential in the dimension for multi-dimensional agents). Our methods identify a subproblem that, e.g., for unitdemand agents with values drawn from product distributions, enables approximation mechanisms that are polynomial in the dimension.

UR - http://www.scopus.com/inward/record.url?scp=84893444810&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84893444810&partnerID=8YFLogxK

U2 - 10.1109/FOCS.2013.73

DO - 10.1109/FOCS.2013.73

M3 - Conference contribution

AN - SCOPUS:84893444810

SN - 9780769551357

T3 - Proceedings - Annual IEEE Symposium on Foundations of Computer Science, FOCS

SP - 628

EP - 637

BT - Proceedings - 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, FOCS 2013

ER -