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Success to the Successful

July 15th, 2009 No comments

fifth_disciplineMy first introduction to the Systems Archetypes was years ago when I read Peter Senge’s book, The Fifth Discipline.  I remember relating these classic Systems Thinking stories to my own experience in business and thinking how useful it was to understand some of the problems we faced and why solutions didn’t always work out as intended.

What I’ve come to appreciate since then is how the characteristic themes of the Systems Archetypes transform across all sorts of different fields and situations — even our personal lives!

Take, for example, the basic story line of the “Success to the Successful” archetype:

When given the choice, we invest our resources where we expect them to deliver the best results.  By giving more resources to one option over another, we create a self-fulfilling prophesy whereby the favored option is perpetually more successful.

The story of the Success to the Successful archetype applies to all sorts of  situations leading to well-known patterns of behavior:

Exploring the Model Structure

We recently published a model of the Success to the Successful archetype to the web using isee NetSim. Exploring the model is a great way to understand the underlying structure of the Causal Loop Diagramsystem and think about ways to avoid the problems it creates.

You’ll also get an appreciation of how the decision policy for allocating resources can determine success rather than competence.

Running the Simulation

After you’ve explored the model, try running a simulation.  The base case scenario assumes no one has an advantage over the other.  As you can imagine, everyone is equally successful and it’s a win-win situation.  Try turning on the “Advantage A Switch”  to see how even a modest advantage for A can snowball into a disadvantage for B.  It’s surprising how quickly the gap can widen.

Using Modules to Create Models

In STELLA and iThink version 9.1, we added the ability to build models by linking together modules.  The Success to the Successful model is an example of how you can use modules to create a higher level map of your model.  This map can easily be presented as a causal loop diagram.

The beauty of modules is they simplify the process of transitioning from a CLD to a model that actually simulates.  If you’ve ever tried to convert a causal loop diagram into a stock and flow model, you can appreciate what I’m talking about!  By architecting your model into modules, you’ve got a built-in mechanism for developing your model in manageable chunks and communicating the high level causal relationships.

Version 9.1.3 Updates Key Features

June 16th, 2009 1 comment

We just released another update to STELLA and iThink — Version 9.1.3.  This particular release has a number of updates specific to key features.  For example, if you’re a Macintosh user and rely on the data import/export functionality, you’ll definitely appreciate the updates we made to make sure the software is compatible with the latest versions of Excel for Mac.

Updates to the ARRAYRANK builtin include an optional parameter to specify a secondary sort field for variables with the same value.  You can now also use the ARRAYRANK builtin  in a non-arrayed variable.

My personal favorite in this release, though, is an enhancement we made to the Spatial Map utility. In addition to color configurations, you can now assign an image to a range of values.  This really expands your ability to create  interesting visual representations of spatial data.

forest and modelUsing images to visualize simulations

This simple model of trees burning in a forest illustrates how images can be used in a spatial map configuration.

The model is set up so that initially there is one tree in the middle of the forest that is burning.  All of the other trees in the forest are healthy living trees.

When you run the simulation, the images in Spatial Map allow you to see the fire spreading to adjacent trees. It really adds to the visual effect of what’s happening in the model. Eventually the burning trees die out and you’re left with a forest mostly full of dead trees — now that’s a visual!

If you want to learn more about Spatial Map, check out Karim Chichakly’s series on spatial modeling in iThink and STELLA .

A full list of the features and fixes in Version 9.1.3 is available on our web site.  If your Technical Support Contract is current, you can go ahead and download the update now from your My Software page.

Hope you have as much fun with the spatial mapping as I did!

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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A Better Chained Slider

February 18th, 2009 No comments
Chained slider in iThink 9.1.2

Chained slider in iThink 9.1.2

The chained slider has always been a useful interface object in iThink and STELLA for allocating 100% of something to different variables within a model.  For example, you may want to build a dashboard where someone could play around with allocating funds to various programs within the Obama Administration’s economic stimulus package.

In iThink and STELLA version 9.1.2, we decided to improve the behavior of the chained slider in response to feedback we’ve received from customers.  We also wanted to nail down the behavior before we added support for it in models published to the web with isee NetSim.

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New ARRAYRANK Builtin Sorts Array Values

February 17th, 2009 4 comments

Version 9.1.2 will introduce the ARRAYRANK feature.  Here is a preview of its capabilities.

The ARRAYRANK built-in can be very powerful for some applications.  It allows you to order the values in an array from smallest to largest or vice-versa.  In this sense, it expands upon what ARRAYMINIDX (find the index of the smallest value) and ARRAYMAXIDX (find the index of the largest value) already do.

To introduce ARRAYRANK, let’s look at a simple model that uses it.  In this model, we explore the efficacy of applying tiered commissions for salespeople in an effort to encourage them to sell more.  Each tier is designed as a reward so the top seller in any given week gets the highest percentage, while those who do not perform will get the lowest.  The tiers are:

Top seller:                    20%
2nd best seller:            15%
3rd best seller:             10%
Everyone else:               5%

Note that the non-tiered system gave everyone 10%.  The model is very simple:

arrayrank-model

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