Proof of principle for a self-governing prediction and forecasting reward algorithm: Modeling consensus of a group of experts in adversarial collaboration. (1.0.0)
Package for simulating the behavior of experts in a scientific-forecasting competition, where the outcome of experiments itself depends on expert consensus. We pay special attention to the interplay between expert bias and trust in the reward algorithm. The package allows the user to reproduce results presented in arXiv:2305.04814, as well as testing of other different scenarios.
Release Notes
Package for simulating the behavior of experts in a scientific-forecasting competition, where the outcome of experiments itself depends on expert consensus. We play special attention to the interplay between expert bias and trust in the reward algorithm itself.
The package allows the user to reproduce results presented in arXiv:2305.04814, as well as other different scenarios.
Associated Publications
Proof of principle for a self-governing prediction and forecasting reward algorithm: Modeling consensus of a group of experts in adversarial collaboration. 1.0.0
Package for simulating the behavior of experts in a scientific-forecasting competition, where the outcome of experiments itself depends on expert consensus. We pay special attention to the interplay between expert bias and trust in the reward algorithm. The package allows the user to reproduce results presented in arXiv:2305.04814, as well as testing of other different scenarios.
Release Notes
Package for simulating the behavior of experts in a scientific-forecasting competition, where the outcome of experiments itself depends on expert consensus. We play special attention to the interplay between expert bias and trust in the reward algorithm itself.
The package allows the user to reproduce results presented in arXiv:2305.04814, as well as other different scenarios.