A recently proposed Bayesian BIN model disentangles the underlying processes
that enable forecasters and forecasting methods to improve, decomposing forecasting accuracy into
three components: bias, partial information, and noise. By describing the differences between two
groups of forecasters, the model allows the user to carry out useful inference, such as calculating
the posterior probabilities of the treatment reducing bias, diminishing noise, or increasing information.
It also provides insight into how much tamping down bias and noise in judgment or enhancing the efficient
extraction of valid information from the environment improves forecasting accuracy. This package provides
easy access to the BIN model. For further information refer to the paper Ville A. Satopää, Marat Salikhov,
Philip E. Tetlock, and Barbara Mellers (2021) "Bias, Information, Noise: The BIN
Model of Forecasting"
The goal of BINtools is to implement a BIN model, a Bayesian approach to decomposing forecasting accuracy into three components: bias, partial information, and noise.
You can install the released version of BINtools from CRAN with:
install.packages("BINtools")
This is a basic example which shows you how to solve a common problem:
library(BINtools)
# An example with two forecasting groups
# a) Simulate synthetic data:
synthetic_data = simulate_data(list(mu_star = -0.8,mu_0 = -0.5,mu_1 = 0.2,gamma_0 = 0.1,
gamma_1 = 0.3, rho_0 = 0.05,delta_0 = 0.1, rho_1 = 0.2, delta_1 = 0.3,rho_01 = 0.05), 300,100,100)
# b) Estimate the BIN-model on the synthetic data:
full_bayesian_fit = estimate_BIN(synthetic_data$Outcomes,synthetic_data$Control,synthetic_data$Treatment,warmup = 1000, iter = 2000)
# c) Analyze the results:
complete_summary(full_bayesian_fit)