  This study investigates Bayesian ensemble learning for improving the quality
of decision-making. We consider a decision-maker who selects an action from a
set of candidates based on a policy trained using observations. In our setting,
we assume the existence of experts who provide predictive distributions based
on their own policies. Our goal is to integrate these predictive distributions
within the Bayesian framework. Our proposed method, which we refer to as
General Bayesian Predictive Synthesis (GBPS), is characterized by a loss
minimization framework and does not rely on parameter estimation, unlike
existing studies. Inspired by Bayesian predictive synthesis and general Bayes
frameworks, we evaluate the performance of our proposed method through
simulation studies.
