On a Competitive Secretary Problem with Deferred Selections
Authors: Tomer Ezra, Michal Feldman, Ron Kupfer
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the structure and performance of equilibria in this game. For random tie breaking, we characterize the equilibria of the game, and show that the expected social welfare in equilibrium is nearly optimal, despite competition among the agents. For ranked tie breaking, we give a full characterization of equilibria in the 3-agent game, and show that as the number of agents grows, the winning probability of every agent under non-immediate selections approaches her winning probability under immediate selections. |
| Researcher Affiliation | Collaboration | Tomer Ezra1 , Michal Feldman1,2 , Ron Kupfer3 1Tel Aviv University 2Microsoft Research 3The Hebrew University of Jerusalem |
| Pseudocode | No | The paper describes strategies and algorithms in natural language and bullet points (e.g., 'If t n, then select the maximal available award.'), but does not present structured pseudocode blocks or algorithms labeled as such. |
| Open Source Code | No | The paper does not provide any links to open-source code for the methodology described. It refers to a full version on arXiv, which is a preprint server, not a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets; therefore, no public dataset information for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments or datasets, so it does not discuss training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |