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..
Preventing Arbitrage from Collusion When Eliciting Probabilities
Authors: Rupert Freeman, David M. Pennock, Dominik Peters, Bo Waggoner1958-1965
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider the design of mechanisms to elicit probabilistic forecasts when agents are strategic and may collude with one another. First, we present a novel strictly proper mechanism that does not admit arbitrage provided that the reports of the agents are bounded away from 0 and 1, a common assumption in many settings. Second, we discover strictly arbitrage-free mechanisms that satisfy an intermediate guarantee between weak and strict properness. |
| Researcher Affiliation | Collaboration | 1Microsoft Research, 2Carnegie Mellon University, 3CU Boulder |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention or provide access to any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no information on public datasets for training is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with datasets, thus no information on training/validation/test splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe any experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not involve empirical experiments, thus no experimental setup details like hyperparameters or training settings are provided. |