Summary of "Modeling for Systems Thinking Video 2 Mental Modeler"
Summary of "Modeling for Systems Thinking Video 2 Mental Modeler"
This video, presented by Steven Gray and Alison from Michigan State University, explores the concept of mental models within the context of Systems Thinking and introduces how these relate to formal scientific models, particularly computational models. The video also categorizes different modeling approaches used in Systems Thinking and provides examples from food systems to illustrate these concepts.
Main Ideas and Concepts
- Two Broad Categories of Models:
- Mental Models:
- Informal, internal models in our minds.
- Represent associations and relationships we perceive in the world.
- Used constantly for decision-making.
- Can evolve as we interact with formal models.
- Rooted in cognitive science history (Kenneth Craik, 1943; Philip Johnson-Laird).
- Scientific Models:
- Formal, external representations.
- Often computational or physical.
- Built based on mental models and empirical data.
- Can be manipulated to analyze systems and run scenarios.
- Mental Models:
- Mental Models in Everyday Decision-Making:
- Example: Combining knowledge of pickles and ice cream to imagine "pickled ice cream."
- Mental models enable us to make decisions even without direct experience.
- Communication of mental models often happens implicitly without explicit discussion of assumptions.
- Categories of Systems Thinking Models (Based on research from SynC, Annapolis):
- Qualitative Approaches:
- No computer needed.
- Includes scenarios, visioning exercises, concept mapping, rich pictures.
- Useful for problem identification and framing.
- Semi-Quantitative Approaches:
- Include Causal Loop Diagrams, Fuzzy Cognitive Mapping, and playing games.
- Useful for data collection and parameter estimation.
- Mental Modeler software is based on Fuzzy Cognitive Mapping.
- Quantitative (Computational) Approaches:
- Require computers.
- Include agent-based modeling, system dynamics, Bayesian belief networks.
- Useful for analyzing complex system interactions and running “what-if” scenarios.
- Require more training and data but offer higher analytical power.
- Qualitative Approaches:
- Progression from Qualitative to Quantitative Modeling:
- Models move from informal, easy-to-use tools with lower analytical capacity to formal, complex tools requiring expertise but offering deeper insights.
- Mental Modeler fits in the middle, bridging qualitative and quantitative methods.
- Examples Using Food Systems:
- Rich Pictures (Qualitative):
- Visual, flexible drawings representing concepts and their relationships.
- Used to organize ideas and identify connections.
- Causal Loop Diagrams (Semi-Quantitative):
- Use words and arrows to show positive or negative relationships.
- More structured than rich pictures, with constraints on types of relationships.
- Can incorporate some numeric values.
- System Dynamics Models (Quantitative):
- Include complex relationships with feedback loops and differential equations.
- Can simulate outcomes over time under various scenarios.
- Provide detailed predictions and system behavior analysis.
- Rich Pictures (Qualitative):
- Modeling as a Learning Process:
- Creating and refining formal models helps clarify and sometimes change our mental models.
- Offloading complexity to computational tools allows better understanding and communication of systems.
Methodology / List of Instructions (Implied)
- Understanding Mental Models:
- Recognize mental models as informal internal representations.
- Reflect on how these models influence decision-making.
- Communicate mental models explicitly when possible to clarify assumptions.
- Using Systems Thinking Models:
- Start with qualitative approaches to identify and frame problems.
- Use semi-quantitative methods to gather data and parameterize relationships.
- Employ quantitative computational models to analyze system dynamics and test scenarios.
- Model Development Process:
- Begin with mental models.
- Translate to informal external representations (e.g., rich pictures).
- Progress to structured semi-quantitative models (Causal Loop Diagrams, fuzzy cognitive maps).
- Develop formal computational models (system dynamics, agent-based).
- Use empirical data to refine models.
- Iterate to improve understanding and update mental models.
Speakers / Sources Featured
- Steven Gray – Michigan State University
- Alison – Michigan State University (last name not provided)
- Historical references:
- Kenneth Craik (1943) – Originator of mental model concept
- Philip Johnson-Laird – Cognitive scientist who expanded on mental models
- Research institution:
- SynC, Annapolis, Maryland – Provided categories of Systems Thinking models
This summary captures the core lessons about mental models, their relationship to scientific models, and the spectrum of Systems Thinking modeling techniques, illustrated with practical examples from food systems.
Category
Educational
