Pay to (Not) Play: Monetizing Impatience in Mobile Games
Authors: Taylor Lundy, Narun Raman, Hu Fu, Kevin Leyton-Brown
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We illustrate these theoretical results in several examples and show that they also hold empirically in the more complicated fully-sensitive setting. We then test the simple pricing scheme with simulations, showing that in a variety of different settings, this scheme is competitive with other, natural pricing schemes. |
| Researcher Affiliation | Academia | 1 University of British Columbia 2 Shanghai University of Finance and Economics, Key Laboratory of Interdisciplinary Research of Computation and Economics |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not explicitly state that source code for its methodology is available or provide a link to a code repository. It links to an extended version of the paper on arXiv, but this is not a code repository. |
| Open Datasets | No | The paper conducts simulations based on theoretical distributions of player types ('populations of players') rather than using an external, publicly available dataset with concrete access information or formal citations. |
| Dataset Splits | No | The paper simulates player populations and does not describe specific training, validation, or testing dataset splits in the conventional sense for empirical data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run its simulations or experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | Full details of the parameters used in the simulations and the formal descriptions of these new ingredients can be found in Appendix G in the extended version. |