Online Information Acquisition: Hiring Multiple Agents
Authors: Federico Cacciamani, Matteo Castiglioni, Nicola Gatti
ICLR 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | First, we design a polynomial-time algorithm to find an optimal incentive compatible mechanism. Then, we study an online problem, where the principal repeatedly interacts with a group of unknown agents. We design a no-regret algorithm that provides e O(T 2/3) regret with respect to an optimal mechanism, matching the state-of-the-art bound for single-agent settings. |
| Researcher Affiliation | Academia | Federico Cacciamani Politecnico di Milano federico.cacciamani@polimi.it Matteo Castiglioni Politecnico di Milano matteo.castiglioni@polimi.it Nicola Gatti Politecnico di Milano nicola.gatti@polimi.it |
| Pseudocode | Yes | Algorithm 1 Online Information Acquisition; Algorithm 2 ESTIMATEPROB; Algorithm 3 COMMIT; Algorithm 4 ESTIMATECOSTS; Algorithm 5 Binary Search (BS) |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any datasets for training or experimentation, hence no information about public availability of a dataset is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical validation with datasets, so it does not mention training, validation, or test splits. |
| Hardware Specification | No | The paper is purely theoretical and does not describe any experimental setup or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not detail any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training settings. |