Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multidimensional Bayesian Utility Maximization: Tight Approximations to Welfare
Authors: Kira Goldner, Taylor Lundy
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Justification: This paper does not include experiments. It is in the area of theory (algorithmic game theory) from the call for papers. Our abstract and introduction discuss only our results, which are backed by theoretical proof. |
| Researcher Affiliation | Academia | Boston University; EMAIL. Supported by NSF Award CNS-2228610, NSF CAREER Award CCF-2441071, and a Shibulal Family Career Development Professorship. University of British Columbia; EMAIL. Supported by an NSERC Discovery Grant, and a CIFAR Canada AI Research Chair (Alberta Machine Intelligence Institute). |
| Pseudocode | Yes | Definition 2 (Single-Item Prior-Free Mechanism). The mechanism of Hartline and Roughgarden [2008] used as a subroutine in our multidimensional Prior-Free-Favorites mechanism is as follows: Rename the bids participating in this single-dimensional mechanism v1 > . . . > vn . Choose j uniformly at random from {0, . . . , log(n )}. Run a v2j+1-lottery (with vn +1 := 0): choose a bidder uniformly at random from those who report at least v2j+1, then allocate to this bidder and charge price v2j+1. That is, the bidders are ordered by report, a power-of-two bidder is chosen uniformly at random, and then this bidder is used as a price, and the mechanism allocates to a bidder chosen uniformly at random from those who reported above this price. |
| Open Source Code | No | Justification: This paper does not include experiments requiring code. |
| Open Datasets | No | Justification: This paper does not include experiments. |
| Dataset Splits | No | Justification: This paper does not include experiments. |
| Hardware Specification | No | Justification: This paper does not include experiments. |
| Software Dependencies | No | Justification: This paper does not include experiments. |
| Experiment Setup | No | Justification: This paper does not include experiments. |