Agent-Based and Monte Carlo Simulation

About this course

Design agent-based models with autonomous agents, interaction rules, and emergent behavior for complex adaptive systems. Apply Monte Carlo simulation to risk analysis, financial option pricing, and project scheduling under uncertainty.

• 20 lessons
• 2 h
• advanced

What you'll learn

• Define complex adaptive systems and their key characteristics
• Explain when simulation is preferred over analytical or closed-form solutions
• Identify the essential components of an agent-based model
• Describe the steps in the ABM development lifecycle
• Design agent attributes and state variables for a given problem
• Implement decision rules that reflect bounded rationality and heuristics

Finishing the course

• 80 knowledge-check questions along the way, so you know the ideas stuck before you move on.
• Watching is free and needs no account. Sign in to save your progress. Pass each lesson's quiz (60%+) to earn the certificate. Each certificate has its own public verification link to share, marked quiz-verified.

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