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Graduate School Course Description

Course Title: Systems Thinking and Complex Adaptive Systems Prerequisites: Multivariable Calculus, Linear Algebra, Statistics, Python Programming, GitHub Duration: 15 weeks

Course Description

This graduate course introduces students to systems thinking methodologies and their applications in understanding complex adaptive systems across diverse domains including business, ecology, urban planning, healthcare, and technology. Students will develop competency in modeling dynamic systems using causal loop diagrams, stock and flow models, and computational simulations. The course emphasizes both theoretical foundations and practical applications, enabling students to analyze real-world systems and design effective interventions using systems thinking principles.

Through hands-on modeling exercises, case studies, and simulation projects, students will learn to identify leverage points, understand feedback loops, recognize systems archetypes, and predict emergent behaviors in complex systems. The mathematical rigor builds upon students' calculus background to explore differential equations, network theory, and agent-based modeling approaches.

Learning Objectives

Upon completion of this course, students will be able to:

  1. Analyze Complex Systems: Apply systems thinking principles to decompose complex problems and identify key system structures, feedback loops, and leverage points
  2. Create Dynamic Models: Construct and validate causal loop diagrams, stock and flow models, and system dynamics simulations using professional modeling software
  3. Understand System Archetypes: Recognize and apply classical systems archetypes such as "Success to the Successful," "Fixes that Fail," and "Tragedy of the Commons" to real-world scenarios
  4. Design Interventions: Identify high-leverage intervention points using Meadows' hierarchy and design system-level solutions that address root causes rather than symptoms
  5. Simulate System Behavior: Develop and analyze computational models including agent-based simulations and network models to predict system behavior over time
  6. Apply Network Effects: Understand and model network effects, including Metcalfe's Law and scale-free networks, in technological and social systems

Course Topics

Module 1: Foundations of Systems Thinking (Weeks 1-3)

  • Introduction to systems theory and complexity science
  • Systems vs. reductionist thinking paradigms
  • Feedback and Causal Loop Diagrams (CDLs)
  • Stocks, flows, and system structure
  • Causality, correlation, and feedback loops
  • Mental models and systems maps
  • Using generative AI to create causal loop diagrams

Module 2: Causal Loop Diagrams and System Archetypes (Weeks 4-6)

  • Construction and validation of causal loop diagrams
  • Reinforcing and balancing loops
  • Delays, non-linearities, and system behavior
  • Top Classical systems archetypes:
  • Tragedy of the Commons
  • Fixes that Fail
  • Success to the Successful
  • Limits to Growth
  • Shifting the Burden
  • Drifting Goals
  • Escalation
  • Growth and Investment
  • Accidental Adversaries
  • Case studies in organizational learning and policy design

Module 3: Stock and Flow Models (Weeks 7-9)

  • Mathematical foundations: differential equations in system dynamics
  • Stock and flow modeling methodology
  • Aging chains, co-flows, and conveyor models
  • Model testing, sensitivity analysis, and validation
  • Applications in population dynamics, resource management, and supply chains

Module 4: Network Theory and Effects (Weeks 10-11)

  • Graph theory foundations for complex networks
  • Network topology: scale-free, small-world, and random networks
  • Metcalfe's Law and network externalities
  • Diffusion processes and epidemic models on networks
  • Social network analysis and organizational networks

Module 5: Agent-Based Modeling and Emergence (Weeks 12-13)

  • Introduction to agent-based computational modeling
  • Emergence and self-organization in complex systems
  • Cellular automata and multi-agent systems
  • Modeling social phenomena: segregation, cooperation, and market dynamics
  • Validation and verification of agent-based models

Module 6: Leverage Points and System Design (Weeks 14-15)

  • Meadows' hierarchy of leverage points
  • Policy resistance and unintended consequences
  • System design principles and intervention strategies
  • Adaptive management and learning organizations
  • Capstone project presentations

Major Assignments

Causal Loop Diagram Portfolio (25%)

Students create a portfolio of 5 causal loop diagrams analyzing systems from different domains (business, environmental, social, technological, personal). Each diagram must identify key feedback loops, system archetypes, and potential leverage points with supporting analysis.

System Dynamics Modeling Project (30%)

Individual project developing a quantitative stock and flow model using Vensim or similar software. Students choose a real-world system, build and validate their model, conduct sensitivity analysis, and propose policy interventions based on model insights. Topics may include urban growth, epidemic spread, organizational learning, or resource depletion.

Agent-Based Simulation (25%)

Team-based project creating an agent-based model using NetLogo or Python to explore emergent behavior in complex adaptive systems. Examples include market dynamics, ecosystem interactions, or social network phenomena. Teams present findings on how micro-level rules create macro-level patterns.

Systems Analysis Case Study (20%)

Written analysis of a contemporary complex system challenge (e.g., climate change, healthcare systems, technological disruption) using systems thinking tools and methodologies learned in the course. Students must identify system structure, predict future behavior, and recommend high-leverage interventions.

Software and Tools

  • Python - Network analysis and data visualization
  • JavaScript - CDL drawing libraries
  • Insight Maker - Web-based system dynamics modeling

Textbooks and Resources

Supplementary Reading:

  • Senge, P. M. (2006). The Fifth Discipline: The Art & Practice of The Learning Organization
  • Mitchell, M. (2009). Complexity: A Guided Tour
  • Barabási, A. L. (2016). Network Science
  • Selected academic papers and case studies

Assessment Criteria

Students will be evaluated on:

  • Technical Proficiency: Accurate construction and analysis of systems models
  • Systems Insight: Depth of understanding of system structure and behavior
  • Application: Ability to apply systems thinking to real-world problems
  • Communication: Clear presentation of systems analysis and recommendations
  • Mathematical Rigor: Appropriate use of quantitative methods and validation techniques

Prerequisites Background

This course assumes students have strong mathematical foundations including:

  • Multivariable Calculus: For understanding system dynamics differential equations
  • Linear Algebra: For network analysis and matrix operations
  • Statistics: For model validation, sensitivity analysis, and uncertainty quantification
  • Basic Programming: Helpful for simulation projects (Python or R experience preferred)

Students should be comfortable with mathematical modeling concepts and quantitative analysis. The course builds upon these foundations to explore the mathematical structures underlying complex systems behavior.