R. Bellman's 1957 paper, “A Markovian Decision Process,” published in the Journal of Mathematics and Mechanics, presents the foundational framework of Markov decision processes (MDPs). Bellman details the mathematical formulation of MDPs, providing a systematic approach to modeling decision-making problems where outcomes are partly random and partly under the control of a decision-maker. This work introduces key concepts such as states, actions, transition probabilities, and rewards, along with the principle of optimality and dynamic programming. Bellman's contributions significantly advanced the field of operations research and laid the groundwork for future developments in decision theory, reinforcement learning, and artificial intelligence.