This course introduces the student to computational techniques used for modeling and applications of complex real-world systems, and studies their temporal and spatial evolution. This course includes: complex systems; autonomous components; agent based modeling; stochastic simulation; species/activity modeling; use of system investigation tools.
Intended learning outcomes
Knowledge &understand
Explore how to use agent-based modeling to understand and examine a widely diverse and disparate set of complex problems
Explore why agent-based modeling is a powerful new way to understand complex systems
Understand what kinds of systems are amenable to complex systems analysis
Know how agent-based modeling has been used to study phenomena from economics to biology to political science to business and management
mental skills
Discuss how to build sound and understandable models
Test models useful paste usable in different fields
Practical & professional skills
The ability to understand the behavior of a phenomenon
The ability to link between the nature of the system and its model
Design a complete model of a composite system
Test a system model
Test data extracted from a composite system model
General and transferable skills
The possibility of imagining systematic behavior
The ability to turn an idea into a model
Ability to write a report on a model of a complex system
Teaching and learning methods
Lectures
Tutorials
Problem-based learning
Mini-projects
Methods of assessments
Midterm Exam = 20
Practical Project = 30
Final Exam = 50
Course contents
Introduction to Modelling
History of ABM and Classic Models
What is Agent-Based Modeling and Why Should You Use It?
