International Journal of Science, Engineering and TechnologyAuthors: Vaivaw Kumar Singh
Abstract: Decision making under uncertainty is one of the main difficulties not only in economics, finance, operations research, engineering, and artificial intelligence but also pretty much anywhere. Most of the traditional optimization techniques work on the assumption that the underlying models are a perfect reflection of the real-world systems, but, the reality is that model misspecification is an everyday event due to incomplete information, structural changes, and uncertain environments (Hansen & Sargent, 2008). These types of errors might dramatically lower the quality of decisions and the performance of the system. This paper starts from the existing optimization methods' problems and derives a joint structure that combines robust optimization and learning-based optimization for handling model misspecification. Besides reviewing the literature that supports the four types of decision-making methods, i.e. robust optimization, reinforcement learning, adaptive decision-making, and distributionally robust optimization, the study also presents a balanced performance analysis of these methods at various stages of development and takes into account different modes of fuzziness and uncertainty. The results show that robust optimization can effectively deal with uncertainty and result in conservative decisions; in contrast, learning-based methods improve adaptability but are exposed to shifts in the distributions and the presence of structural model errors (Sutton & Barto, 2018). Our setup implements both awareness of uncertainty and the ability to learn new things in decision making process and results in solutions that have adequate levels of both robustness and flexibility. Our research offers to the body of optimization knowledge a system upon which conceptual support can be built for reliable and adaptive decision making in a constantly changing environment. The structure may find its facets in the areas of finance healthcare supply chain management, autonomous systems, and artificial intelligence.
