Preventing Arbitrage from Collusion When Eliciting Probabilities
Authors: Rupert Freeman, David M. Pennock, Dominik Peters, Bo Waggoner1958-1965
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |