Decision Making Algorithms

Decision making algorithms are computational methods used to model and solve problems involving choices under uncertainty, constraints, or multiple objectives. They encompass techniques from fields like operations research, artificial intelligence, and economics to optimize outcomes, such as in game theory, Markov decision processes, or multi-criteria decision analysis. These algorithms help automate or support complex decision-making in areas like robotics, finance, and logistics.

Also known as: Decision Algorithms, Decision-Making Models, Decision Theory Algorithms, Choice Algorithms, DM Algorithms

Why learn Decision Making Algorithms?

Developers should learn decision making algorithms when building systems that require automated planning, resource allocation, or strategic behavior, such as in autonomous vehicles, recommendation engines, or supply chain optimization. They are crucial for applications involving uncertainty, sequential decisions, or conflicting goals, enabling more intelligent and efficient solutions in AI, data science, and operations management.

Learning Resources

• course: Decision Making Under Uncertainty: Theory and Application
• book: Algorithms for Decision Making
• tutorial: Decision Trees and Random Forests Tutorial
• book: Reinforcement Learning: An Introduction
• course: Operations Research and Decision Making