Learning and Collusion in Multi-unit Auctions
Authors: Simina Branzei, Mahsa Derakhshan, Negin Golrezaei, Yanjun Han
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our contribution is to analyze these auctions in both the offline and online settings, by designing efficient bidding algorithms with low regret and giving regret lower bounds. We also analyze the quality of the equilibria in two main variants of the auction, finding that one variant is susceptible to collusion among the bidders while the other is not. |
| Researcher Affiliation | Academia | Simina Brânzei Purdue University Mahsa Derakhshan Northeastern University Negin Golrezaei MIT Yanjun Han New York University |
| Pseudocode | Yes | algorithm 1: Optimum Strategy for the Offline Problem. [...] algorithm 2: Selecting bids using the weight pushing algorithm in [TW03]. |
| Open Source Code | No | No explicit statement providing concrete access to source code for the methodology described in this paper was found. |
| Open Datasets | No | The paper presents theoretical research and does not use or refer to any publicly available or open datasets for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, thus no dataset split information (train/validation/test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details or hyperparameters. |