During this period, Bayesian decision theory emerges as the unifying framework for reasoning under uncertainty in attribution, forecasting, and decision design. Researchers emphasize how belief updates with evidence, the use of state-space and dynamic models, and explicit treatment of model uncertainty shape decision making in cognitive judgments and beyond. Cross-period works illustrate Bayesian analysis of attribution processes, Bayesian forecasting with dynamic recursive methods such as Kalman-like recursions, and the management of prior information through robust design choices and uncertainty quantification. The emphasis on integrating beliefs, evidence, and decision criteria helps solidify Bayesian methods as central to approach uncertainty with probabilistic reasoning.