From Conceptual Frameworks to Computational Thinking
Quick Overview
| Detail | Description |
| Course Duration | 8 weeks (April 11 – August 5, 2024) |
| Delivery Format | In-person lectures and workshops |
| Level | Beginner |
| Start Date | April 11, 2024 |
| Effort | 4–6 hours/week |
| Language | Vietnamese |
| Certificate | Yes (upon 75% attendance and final reflection submission) |
| Participants | 15–20 undergraduate students, junior researchers, and early-career professionals |
Course Description
This foundational course introduces the core principles of modeling complex systems—across environmental, social, and engineered domains. You will explore how abstract representations help us understand, explain, and predict system behavior. Through conceptual modeling exercises, hands-on tools, and peer discussions, participants will:
- Build and evaluate models from real-world scenarios.
- Explore the differences between deterministic and stochastic modeling.
- Work with agent-based, equation-based, and system dynamics approaches.
- Understand the philosophy and logic behind simplification and abstraction.
Learning Objectives
Modeling Concepts & Frameworks:
Define what models are and why they matter.
- Differentiate types of models: descriptive, predictive, prescriptive.
Model Construction Process:
- Identify entities, interactions, and rules.
- Translate conceptual systems into computational structures.
Cross-Paradigm Exposure:
- Learn fundamentals of Agent-Based Modeling (ABM), Equation-Based Modeling (EBM), and Dynamical Systems.
- Understand when and why to use each paradigm.
Evaluation and Communication:
- Use the ODD protocol to document and critique models.
- Present model logic clearly to interdisciplinary audiences.
Key Topics
- Week 1: What is a Model? (Concepts & Classifications)
- Week 2: System Representation and Mental Models
- Week 3: EBM vs ABM – Comparative Modeling Approaches
- Week 4: Introduction to Dynamical Systems
- Week 5: Schelling’s Segregation Model (Concepts & Hands-On)
- Week 6: Introduction to the GAMA Platform via Segregation Modeling
- Week 7: Heterogeneity in Environments – The ChouChevLoup Model
- Week 8: Disease & Ecosystem Dynamics + ODD Protocol Reflection
Tools & Technologies
- Modeling Environments: GAMA Platform, Vensim (optional), basic Python for structure illustration.
- Scripting: GAML basics for model building in later weeks.
- Visualization: GAMA’s 2D/3D interfaces; diagrams created in draw.io or Miro.
Target Audience
- Students: Undergraduates exploring computational methods for the first time.
- Researchers: Early-stage scientists looking to incorporate modeling into their work.
- Professionals: Those working in interdisciplinary fields involving systems thinking.
- Prerequisites: None required; logical thinking and curiosity encouraged.
Instructor(s)
- Doanh Nguyen: Course lead and systems modeling researcher; specializes in model design logic and education.
Learning Format
- Lectures: Conceptual foundations and live demonstrations.
- Workshops: Sketching models on paper, logic trees, and beginner-level simulation exercises.
- Peer Critique Sessions: Evaluate and reflect on others’ models.
- Group Mini-Project: Collaboratively present a model based on a real-world phenomenon.
Assessment & Certification
Grading:
- Participation and reflection activities (40%)
- Final week mini-project + ODD documentation (60%)
Certificate: Awarded digitally; includes summary of learning outcomes and instructor endorsement.
Contact/Get Involved
- Email: ummisco.sea@gmail.com
- Course Coordinator: Do Bui Khanh Linh (linh.dbk@vinuni.edu.vn)
Tags:
#ModelingBasics #SystemsThinking #AgentBasedModeling #Education #ComplexSystems #GAMA #Simulation
