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 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. |