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What are “Mental Models”?

March 12th, 2010 14 comments

Editor’s note: This is part one of a two part series on Systems Thinking and mental models

In writing and teaching people about Systems Thinking, we often refer to “mental models”.  For some people, this comes as a bit of a surprise, because the context usually involves building models with the iThink or STELLA software.  They don’t expect us to start talking metaphysically about thinking.  “Is this about philosophy or modeling software?” they may wonder.  The software is actually a tool to help construct, simulate and communicate mental models.

Let’s define the term model: A model is an abstraction or simplification of a system.  Models can assume many different forms – from a model volcano in a high school science fair to a sophisticated astrophysical model simulated using a supercomputer.  Models are simplified representations of a part of reality that we want to learn more about.  George Box stated: “Essentially, all models are wrong, but some are useful”.  They are wrong because they are simplifications and they can be useful because we can learn from them.

So, what is a “mental model”?  A mental model is a model that is constructed and simulated within a conscious mind.  To be “conscious” is to be aware of the world around you and yourself in relation to the world.  Let’s take a moment to think about how this process works operationally.

Thinking about trees

Imagine that you are standing outside, looking at a tree.  What happens?  The lenses in your eyes focus light photons onto the retinas.  The photosensitive cells in your retinas respond by sending neural impulses to your brain.  Your brain processes these signals and forms an image of the tree inside your mind.

So at this point, we’ve only addressed the mechanisms by which you perceive the tree.  We have not addressed understanding what a tree is or considered changes over time.   We are dealing with visual information only.  There is nothing within this information that tells you what a tree actually is.

What makes the image of a tree in your minds click as an actual tree that exists right there in front of you?  This is where mental models kick in and you start to think about the tree.  The tree is actually a concept of something that exists in physical reality.  The “tree concept” is a model.  Understanding the concept of a tree requires more information than is available through sensory experience alone.  It’s built on past experiences and knowledge.

A tree is a plant.  It is a living thing that grows and changes appearance over time, often with the seasons.  Trees have root systems.  Trees use leaves for photosynthesis.  Wood comes from trees.  I can state these facts confidently because I have memories and knowledge of trees contained within my mental models.  Mental models contain knowledge and help us create new knowledge.

 

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Insight-based Model Investigates the Housing Crisis

May 5th, 2009 3 comments

wpi-logoFor the past few months I’ve been taking a distance learning course at WPI called “System Dynamics Foundations: Managing Complexity”. The course covers a broad range of topics about the system dynamics methodology and how it has been applied in the real world.

One of the things I really like about the course is the different perspective the instructors bring to the table (or in this case my computer screen.) Last week’s lecture focused on three different styles of system dynamics modeling – Causal Loop Diagrams (CLDs), insight-based models and calibrated models.  While both instructors agreed there is value in all three approaches to dynamic modeling, there was clearly a difference of opinion about what is required to actually DO something with a proposed solution to a problem.

The topic got me to thinking about the types of STELLA and iThink models that are being built and how they are being used to DO something about real world problems. I would guess that the majority of the models fall into the insight-based category.   One of the reasons we put so much effort into creating communication features in our software is so that those insights can be shared and discovered by others.  The “ah-ha” moments that come from experimenting with simulations are often a great vehicle for getting conversations going about a particular issue and discussing possible solutions.

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