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 [1].

Dynamic Auctions with Bank Accounts

Authors: Vahab Mirrokni, Renato Paes Leme, Pingzhong Tang, Song Zuo

IJCAI 2016 | Venue PDF | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental Finally, we empirically evaluate the revenue performance of a heuristically constructed double-reserve auction and the optimal one on an in๏ฌnite sequence of i.i.d. items.
Researcher Affiliation Collaboration Google Research, NY, USA EMAIL Pingzhong Tang and Song Zuo Institute for Interdisciplinary Information Sciences, Tsinghua University, Beijing, China EMAIL
Pseudocode No The paper does not contain any structured pseudocode or algorithm blocks.
Open Source Code No The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available.
Open Datasets No The paper uses mathematical distributions (exponential, lognormal, uniform) for empirical evaluation, which are not datasets with public access information in the typical sense (e.g., links, DOIs, specific repositories, or formal citations to pre-existing data collections). They describe theoretical models evaluated on simulated data derived from these distributions.
Dataset Splits No The paper does not provide specific details on dataset splits (training, validation, test) or cross-validation methodology.
Hardware Specification No The paper does not provide any specific hardware details used for running the experiments.
Software Dependencies No The paper does not provide specific software dependencies with version numbers.
Experiment Setup No The paper describes the distributions used for evaluation but lacks specific details such as hyperparameters, numerical settings for the dynamic programming algorithm, or simulation parameters to reproduce the experimental setup.