Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Randomized Wagering Mechanisms
Authors: Yiling Chen, Yang Liu, Juntao Wang1845-1852
AAAI 2019 | Venue PDF | 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 ef๏ฌcient 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 EMAIL, EMAIL, EMAIL |
| 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. |