Efficiency of the First-Price Auction in the Autobidding World
Authors: Yuan Deng, Jieming Mao, Vahab Mirrokni, Hanrui Zhang, Song Zuo
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the price of anarchy of first-price auctions in the autobidding world, where bidders can be either utility maximizers (i.e., traditional bidders) or value maximizers (i.e., autobidders). We show that with autobidders only, the price of anarchy of first-price auctions is 1/2, and with both kinds of bidders, the price of anarchy degrades to about 0.457 (the precise number is given by an optimization). |
| Researcher Affiliation | Collaboration | Yuan Deng Google Research dengyuan@google.com Jieming Mao Google Research maojm@google.com Vahab Mirrokni Google Research mirrokni@google.com Hanrui Zhang Chinese University of Hong Kong hanrui@cse.cuhk.edu.hk Song Zuo Google Research szuo@google.com |
| Pseudocode | No | The paper does not contain any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | The paper does not provide any statement about open-sourcing code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets or data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |