GLOSSARY

Markov Decision Process (MDP)

Away to model decision-making where an agent chooses actions that affect what happens next, with outcomes depending only on the current situation and not the past.

What is Markov Decision Process (MDP)?

A Markov Decision Process (MDP) is a mathematical framework used to model decision-making in situations where outcomes are partly random and partly under the control of a decision-maker. It provides a structured way to define states, actions, rewards, and probabilities for predicting and optimizing sequential decisions.

How Markov Decision Process (MDP) works

MDPs operate on four main components:

States: The different possible situations an agent can be in.
Actions: The set of choices available to the agent at each state.
Transition Probabilities: The likelihood of moving from one state to another after an action.
Rewards: The value (positive or negative) received after taking an action.

By repeatedly interacting with its environment, an agent evaluates the expected long-term rewards of different strategies and identifies the optimal policy—the best action to take in each state to maximize outcomes.

Benefits and drawbacks of using Markov Decision Process (MDP)

Benefits:

Provides a rigorous way to handle uncertainty in decision-making.
Enables optimization of long-term outcomes, not just immediate results.
Widely applicable across industries, from AI to logistics and finance.

Drawbacks:

Computationally expensive for large state or action spaces.
Requires accurate definition of transition probabilities and rewards, which may be difficult in real-world scenarios.
Assumes the “Markov property”—future states depend only on the present state, which may oversimplify complex environments.

Use case applications for Markov Decision Process (MDP)

Supply Chain Optimization: Choosing inventory and distribution strategies under uncertain demand.
Customer Experience Management: Recommending next-best actions in personalization engines.
Healthcare: Modeling treatment plans where patient outcomes depend on sequential interventions.
Finance: Portfolio management under uncertain market conditions.
Robotics & AI: Training autonomous systems to navigate and act optimally in dynamic environments.

Best practices of using Markov Decision Process (MDP)

Start with a simplified state and action space before scaling to complex environments.
Ensure reliable data collection to accurately estimate transition probabilities and rewards.
Use approximation methods (e.g., reinforcement learning) for large-scale problems where exact solutions are not feasible.
Continuously update the model as new data or business conditions evolve.
Align the definition of rewards with business objectives to ensure meaningful optimization.

Recap

A Markov Decision Process (MDP) is a powerful tool for modeling sequential decision-making under uncertainty. While it offers structured optimization and broad applicability, it also comes with complexity challenges. Businesses that carefully design their state, action, and reward definitions—and combine MDP with scalable computational methods—can unlock significant value in operations, customer engagement, and strategic planning.