An Axiomatic View of the Parimutuel Consensus Mechanism
Authors: Rupert Freeman, David M. Pennock
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Via simulations on real contest data, we show that violations of incentive compatibility are both rare and only minimally beneficial for the participants. This suggests that the parimutuel consensus mechanism is a reasonable mechanism for eliciting information in practice. |
| Researcher Affiliation | Collaboration | Rupert Freeman Duke University rupert@cs.duke.edu David M. Pennock Microsoft Research dpennock@microsoft.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | We tested the incentive compatibility of the PCM on a data set consisting of probability reports gathered from an online prediction contest called Probability Sports [Galebach, 2004]. We thank Brian Galebach for providing us with this data. The citation [Galebach, 2004] points to http://probabilitysports.com/. While a reference is given, the website is for a contest and not explicitly a public dataset repository, and the text indicates the data was provided to the authors, not publicly available. |
| Dataset Splits | No | The paper describes simulation setups including random generation of wagers and subsampling of agents, but it does not specify traditional train/validation/test dataset splits with percentages or sample counts for model training or evaluation, as the experiments analyze the mechanism itself rather than training a machine learning model. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or cloud instance types used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as libraries or solvers with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | For each of the 1643 matches and each wager distribution, we randomly generated a set of wagers drawn from that distribution. For each set of wagers we chose 50 random agents and simulated 101 reports for them in the range {0, 0.01, . . . , 0.99, 1}. ... We generated wagers from a variety of Pareto distributions. A Pareto distribution is defined by two parameters: a scale parameter k, which has the effect of multiplicatively scaling the distribution, and a shape parameter α, which affects the size of the distribution s tail. ... The first Pareto distribution we use for wager generation has α = 1.16, ... Second, we use α = 3, ... Finally, we consider a uniform distribution of wagers (that is, wi = 1 for all agents). |