Maximizing Revenue under Market Shrinkage and Market Uncertainty
Authors: Maria-Florina F. Balcan, Siddharth Prasad, Tuomas Sandholm
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This material is based on work supported by the National Science Foundation under grants CCF1733556, CCF-1910321, IIS-1901403, and SES-1919453, the Defense Advanced Research Projects Agency under cooperative agreement HR00112020003, an AWS Machine Learning Research Award, an Amazon Research Award, a Bloomberg Research Grant, and a Microsoft Research Faculty Fellowship. S. Prasad thanks Morgan Mc Carthy for interesting discussions about real-world shrinking (combinatorial) markets.1. For all authors...(a) Do the main claims made in the abstract and introduction accurately reflect the paper s contributions and scope? [Yes](b) Did you describe the limitations of your work? [Yes] We described the limitations of our work in the Conclusions and future research section(c) Did you discuss any potential negative societal impacts of your work? [N/A](d) Have you read the ethics review guidelines and ensured that your paper conforms to them? [Yes]2. If you are including theoretical results...(a) Did you state the full set of assumptions of all theoretical results? [Yes](b) Did you include complete proofs of all theoretical results? [Yes]3. If you ran experiments...(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A](b) Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [N/A](c) Did you report error bars (e.g., with respect to the random seed after running experiments multiple times)? [N/A](d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [N/A]4. If you are using existing assets (e.g., code, data, models) or curating/releasing new assets...(a) If your work uses existing assets, did you cite the creators? [N/A](b) Did you mention the license of the assets? [N/A](c) Did you include any new assets either in the supplemental material or as a URL? [N/A](d) Did you discuss whether and how consent was obtained from people whose data you re using/curating? [N/A](e) Did you discuss whether the data you are using/curating contains personally identifiable information or offensive content? [N/A]5. If you used crowdsourcing or conducted research with human subjects...(a) Did you include the full text of instructions given to participants and screenshots, if applicable? [N/A](b) Did you describe any potential participant risks, with links to Institutional Review Board (IRB) approvals, if applicable? [N/A](c) Did you include the estimated hourly wage paid to participants and the total amount spent on participant compensation? [N/A] |
| Researcher Affiliation | Collaboration | Maria-Florina Balcan School of Computer Science Carnegie Mellon University ninamf@cs.cmu.edu Siddharth Prasad Computer Science Department Carnegie Mellon University sprasad2@cs.cmu.edu Tuomas Sandholm Computer Science Department Carnegie Mellon University Optimized Markets, Inc. Strategic Machine, Inc. Strategy Robot, Inc. sandholm@cs.cmu.edu |
| Pseudocode | No | The paper describes 'Mechanism A' and a 'learning algorithm' in text, but it does not use structured pseudocode blocks or algorithm figures with formal labels. |
| Open Source Code | No | The ethics review section explicitly states '[N/A]' for question 3a: 'Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?'. |
| Open Datasets | No | The paper is theoretical and does not describe empirical training on a dataset. The ethics review explicitly states '[N/A]' for questions related to experimental data and reproducibility. |
| Dataset Splits | No | The paper is theoretical and does not discuss empirical training/validation/test splits. The ethics review explicitly states '[N/A]' for questions related to experimental data splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental hardware setup. The ethics review explicitly states '[N/A]' for questions related to compute resources and hardware. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. The ethics review explicitly states '[N/A]' for questions related to training details, which would include software. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or training configurations. The ethics review explicitly states '[N/A]' for questions related to training details and experimental setup. |