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..
Online Market Equilibrium with Application to Fair Division
Authors: Yuan Gao, Alex Peysakhovich, Christian Kroer
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, numerical experiments on real and synthetic datasets show that the dynamics converges quickly under various metrics. |
| Researcher Affiliation | Collaboration | Yuan Gao Columbia University EMAIL Alex Peysakhovich Facebook AI Research EMAIL Christian Kroer Columbia University EMAIL |
| Pseudocode | Yes | In the PACE dynamics, each buyer maintains a pacing multiplier βt i, starting from an initial value β1 i = 1 + δ0 for some small δ0 > 0 (e.g., δ0 = 0.05). At time step t, the following events take place. (a) An item θt appears and each buyer i sees a value vi(θt). (b) Each buyer i bids their paced value βt ivi(θt) for the item. (c) The item is allocated to the highest bidder (the winner at t): it = arg maxi βt ivi(θt), with ties broken arbitrarily. For concreteness, we always choose the lowest winning index, i.e., it = min arg max i βt ivi(θt). Then, the price of θt is set by the first-price rule pt(θt) = max i βt ivi(θt) = βt itvi(θt) and the winner it pays this price pt(θt) for the item θt. (d) Each buyer i gets a utility ut i = vi(θt)I{i = it}. In other words, the winner it gets vit(θt) and other buyers get zero. (e) Each buyer i updates its cumulative average utility ut i: ut i = t 1 t ut 1 i + 1 t vi(θt)I{i = it}. (f) Each buyer i updates their pacing multiplier βt+1 i as follows: βt+1 i = Π[li,hi](Bi/ ut i) := min{max{li, Bi/ ut i}, hi}. where li = Bi/(1 + δ0) and hi = 1 + δ0 for some fixed δ0 > 0 (e.g., δ0 = 0.05). |
| Open Source Code | Yes | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? Yes. |
| Open Datasets | Yes | We evaluate the PACE dynamics in several real and synthetic datasets, namely, Movie Lens, Household Items and an infinite-dimensional market instance with item space Θ = [0, 1] and vi being linear functions on [0, 1]. For the first two datasets, see [31] for more information and exploratory data analysis. |
| Dataset Splits | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? N.A. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? No. (N.A.) |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? N.A. |