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..
Dynamic Contracting under Positive Commitment
Authors: Ilan Lobel, Renato Paes Leme2101-2108
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We model this problem as a dynamic game where the seller chooses a mechanism at each period subject to a sequential rationality constraint, and characterize the perfect Bayesian equilibrium of this dynamic game. We prove the equilibrium is efficient and that the seller s revenue is a function of the buyer s ex ante utility under a no commitment model. |
| Researcher Affiliation | Collaboration | Ilan Lobel,1 Renato Paes Leme2 1NYU Stern, 2Google Research EMAIL, EMAIL |
| Pseudocode | No | The paper is theoretical and does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | A complete version of our paper containing the missing proofs as well as a longer discussion of our results and extensions is available online at (Lobel and Paes Leme 2018). (This link points to an online appendix, not a code repository, and no other statement of code release is found). |
| Open Datasets | No | The paper focuses on theoretical modeling and does not use or refer to any publicly available datasets for training, only abstract valuation distributions F(). |
| Dataset Splits | No | The paper is theoretical and does not specify any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications used for running experiments. |
| Software Dependencies | No | The paper describes a theoretical model and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or system-level training settings. |