The Secretary Problem with Competing Employers on Random Edge Arrivals
Authors: Xiaohui Bei, Shengyu Zhang4818-4825
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we analyze the Nash equilibria of the online secretary game with competing employers in the edge arriving model. We focus on a continuous-time setting that captures the essence of the game but avoids some messy integrality issue in computations. Similar to many models from previous works, in these games, each player s strategy at any given time can be described by a threshold, which specifies the time until when the player is not making any offers to any candidates, regardless of their ranks, and after the threshold time, the player will hire the first candidate that is better than all candidates arrived so far. We consider two models in which the employers can be either adaptive or non-adaptive. When employers are adaptive, they are allowed to update their threshold during the hiring process based on other employers actions. On the other hand, non-adaptive employers can only stick to one threshold time throughout the process. |
| Researcher Affiliation | Collaboration | Xiaohui Bei1, Shengyu Zhang2 1 Nanyang Technological University 2 Tencent |
| Pseudocode | Yes | Algorithm 1: Algorithm for computing continuous winning probability in a k-player hiring process |
| Open Source Code | No | The paper does not mention any open-source code release for the methodology described. |
| Open Datasets | No | The paper uses a 'continuous-time model' with candidates forming 'the set of natural numbers N with the usual ordering,' which is a mathematical abstraction, not a real-world public dataset. Therefore, no information on data availability is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and mathematical, describing an algorithm for computation. It does not mention any specific hardware used for experiments or computations. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical derivations and algorithm design. It does not mention any specific software dependencies or version numbers required to replicate any computational aspects. |
| Experiment Setup | No | The paper is theoretical, focusing on mathematical modeling and analysis. It does not describe any empirical experimental setup details, hyperparameters, or training configurations. |