Randomized Wagering Mechanisms
Authors: Yiling Chen, Yang Liu, Juntao Wang1845-1852
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct simulations to evaluate the average-case performance of wagering mechanisms. We show that LWS and RP-SWME are more efficient than existing deterministic (weakly) incentive compatible mechanisms WSWM, NAWM and DCA. Meanwhile, we show that RP-SWME has smaller payoff variance and higher probability of winning money than LWS. In the simulations, we generate the predictions of agents using two models: i). the logit model (Satop a a et al. 2014), ii). the synthetic model (Ranjan and Gneiting 2010; Allard, Comunian, and Renard 2012; Satop a a et al. 2014). |
| Researcher Affiliation | Academia | Yiling Chen,1 Yang Liu,2 Juntao Wang1 1Harvard University, 2University of California, Santa Cruz yiling@seas.harvard.edu, yangliu@ucsc.edu, juntaowang@g.harvard.edu |
| Pseudocode | Yes | Mechanism 1 Lottery Wagering Mechanisms, Mechanism 2 Surrogate Wagering Mechanisms, Algorithm 3 Error Rate Selection Algorithm, Mechanism 4 Random Partition SWME (RP-SWME) |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes generating predictions using models like logit and synthetic, and wagers using uniform and Pareto distributions, but it does not specify or provide access information for a public dataset in the traditional sense. |
| Dataset Splits | No | The paper describes simulation setups and data generation methods but does not provide specific train/validation/test dataset splits or mention a validation set for reproducibility. |
| Hardware Specification | No | The paper describes conducting simulations but does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for these experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or version numbers needed to replicate the experiment, beyond mentioning the use of the Brier score. |
| Experiment Setup | Yes | In the simulations, we generate the predictions of agents using two models: i). the logit model (Satop a a et al. 2014), ii). the synthetic model (Ranjan and Gneiting 2010; Allard, Comunian, and Renard 2012; Satop a a et al. 2014). The details of these models can be found in Section 8.1 of the full version (Chen, Liu, and Wang 2018). We generate the wagers of agents by two distributions: i). a uniform distribu- tion over [0,1], ii). a Pareto distribution with shape parameter 1.16 and scale parameter 1, characterizing the 20% of the population has 80% of the wealth (Freeman, Pennock, and Vaughan 2017). We use Brier score as the scoring rule used in the wagering mechanisms that we evaluate. |