Contract Design Under Approximate Best Responses
Authors: Francesco Bacchiocchi, Jiarui Gan, Matteo Castiglioni, Alberto Marchesi, Nicola Gatti
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main result is a polynomial-time algorithm to compute an optimal contract under approximate best responses. We also investigate the learnability of contracts under approximate best responses, by providing a no-regret learning algorithm for a natural application scenario where the principal has no prior knowledge about the environment. This paper presents theoretical results and has the goal of advancing the field of Machine Learning. |
| Researcher Affiliation | Academia | 1Politecnico di Milano 2University of Oxford. Correspondence to: Francesco Bacchiocchi <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Compute an optimal δ-robust contract Algorithm 2 Regret minimizer for δ-robust contracts |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code or a link to a repository. |
| Open Datasets | No | We consider an online learning framework similar to the one studied by Zhu et al. (2023), in which the features of agent s actions, i.e., costs and probabilities over outcomes, depend on an agent s type that is sampled at each round from some (fixed) unknown probability distribution. |
| Dataset Splits | No | The paper does not conduct experiments on specific datasets, so there is no mention of dataset splits. |
| Hardware Specification | No | The paper focuses on theoretical analysis and algorithm design, and does not describe any experimental setup or specific hardware used. |
| Software Dependencies | No | The paper does not mention any specific software or libraries with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental parameters like hyperparameters or training configurations. |