<p>Decision making has a broad range of applications from everyday mundane choices to life-or-death medical decisions. How to make the best decision depends on the magnitude and uncertainty of the quantity of interest. For example, one might decide which car to buy based on consumer reports or which medical treatment to take based on the estimated treatment effectiveness derived from a meta-analysis of randomized clinical trials. This paper compares two commonly applied decision making strategies—deterministic decision and randomized decision—by varying the magnitude and uncertainty of the quantity of interest to determine which strategy performs better. These strategies are investigated under the framework of Bayesian hierarchical models (BHM), which provide a coherent framework for modeling uncertainty and handling heterogeneity. We present both theoretical results and simulation studies. Additionally, simulated real-world case studies are given to illustrate how the two decision strategies are applied and to explore their implications. Generally, when evaluation is based on the population mean, the randomized decision tends to perform better. When evaluation is based on the sub-population mean, the randomized decision is preferable if the population variance is smaller than the sub-population variance and the population means are more different. Conversely, the deterministic decision is better when the sub-population variance is larger than the population variance or the sub-population means are closer to each other. Understanding the interplay between decision-making and BHM can deepen insights into both fields and offer a powerful tool for addressing real-world problems.</p>