SURPRISE! and When to Schedule It.
Authors: Zhihuan Huang, Shengwei Xu, You Shan, Yuxuan Lu, Yuqing Kong, Tracy Xiao Liu, Grant Schoenebeck
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To quantify the relationship between information flow and audiences perceived quality, we conduct a case study where subjects watch one of the world s biggest esports events, LOL S10. In addition to eliciting information flow, we also ask subjects to report their rating for each game. We find that the amount of surprise in the end of the game plays a dominant role in predicting the rating. |
| Researcher Affiliation | Academia | Department of Computer Science, Peking University Center on Frontiers of Computing Studies, Peking University School of Economics and Management, Tsinghua University School of Information, University of Michigan |
| Pseudocode | No | The paper defines concepts and formulas (e.g., for belief curves and surprise amount) but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about making the source code for its methodology or platform publicly available, nor does it include links to a code repository. |
| Open Datasets | No | The paper states: "We use our platform to conduct a study for LOL S10 which consisted of 76 individual games." This indicates the authors collected their own dataset but do not provide any information or links for public access to this dataset. |
| Dataset Splits | No | The paper describes data collection and analysis but does not mention any training, validation, or test dataset splits for model development or evaluation. |
| Hardware Specification | No | The paper describes the data collection platform and the analysis performed but does not provide any specific details about the hardware (e.g., CPU, GPU models, memory) used for these processes. |
| Software Dependencies | No | The paper mentions: "Some of the figures are generated with Python Matplotlib [Hunter, 2007]." While it names Matplotlib, it does not provide specific version numbers for Matplotlib, Python, or any other relevant software libraries or tools, which is required for reproducibility. |
| Experiment Setup | No | The paper describes the setup for data collection (e.g., incentive mechanisms for participants) but does not provide details specific to computational experimental setups, such as hyperparameters, optimization settings, or training configurations for any machine learning models. |