Making Connections

The Shifting the Burden Systems Archetype shows how attacking symptoms, rather than identifying and fixing fundamental problems, can lead to a further dependence on symptomatic solutions.  This Systems Archetype was formally identified in Appendix 2 of The Fifth Discipline by Peter Senge (1990).  The Causal Loop Diagram (CLD) is shown below.

When a problem symptom appears, two options present themselves:  1) apply a short-term fix to the symptom, or 2) identify and apply a longer-term fix to the fundamental issue.  The second option is less attractive because it involves a greater time delay and probably additional cost before the problem symptom is relieved.  However, applying a short-term fix, as a result of relieving the problem symptoms sooner, reduces the desire to identify and apply a more permanent fix.  Often the short-term fix also induces a secondary unintended side-effect that further undermines any efforts to apply a long-term fix.  Note that the short-term fix only relieves the symptoms, it does not fix the problem.  Thus, the symptoms will eventually re-appear and have to be addressed again.

Classic examples of shifting the burden include:

• Making up lost time for homework by not sleeping (and then controlling lack of sleep with stimulants)
• Borrowing money to cover uncontrolled spending
• Feeling better through the use of drugs (dependency is the unintended side-effect)
• Taking pain relievers to address chronic pain rather than visiting your doctor to try to address the underlying problem
• Improving current sales by focusing on selling more product to existing customers rather than expanding the customer base
• Improving current sales by cannibalizing future sales through deep discounts
• Firefighting to solve business problems, e.g., slapping a low-quality – and untested – fix onto a product and shipping it out the door to placate a customer
• Repeatedly fixing new problems yourself rather than properly training your staff to fix the problems – this is a special form known as “shifting the burden to the intervener” where you are the intervener who is inadvertently eroding the capabilities and confidence of your staff (the unintended side-effect)
• Outsourcing core business competencies rather than building internal capacity (also shifting the burden to the intervener, in this case, to the outsource provider)
• Implementing government programs that increase the recipient’s dependency on the government, e.g., welfare programs that do not attempt to simultaneously address low unemployment or low wages (also shifting the burden to the intervener, in this case, to the government)

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Editor’s Note: This is a guest post from isee’s training and consulting partner, Corey Peck of Lexidyne LLC.

The mid-term elections are now a month behind us and the political airwaves are still abuzz with commentary about the results.  Exit polls showed that unemployment was at the top of most voters’ list of issues, and that concerns about the federal government’s financial condition (record deficits and debt levels) were a hot topic as well.  Voters appeared to be asking “How can the federal government spend so much money and have so little positive impact on the nation’s economy?” 

The responses by politicians to such an important question are all over the map.  Democrats are claiming that economic conditions would have been much worse if not for massive federal bailouts and stimulus spending.  Republicans are touting the situation as a death knoll for the Obama platform in an effort to position themselves for 2012.  And the Tea Party movement has emerged to push for a roll-back of what they see as an intrusive and ineffective “Big Government”.

But, this political posturing reminds me that one of the true strengths of Systems Thinking is to force people to think very clearly and very operationally about the structure/behavior link embedded in such cases.  A little over a year ago, we sat down with Dr. Mark Paich, who used some very simple stock/flow language and some well-established principles of macroeconomics to lay out some relevant dynamics about the economic crisis and its aftermath:

• Why the collapse of the housing market made consumers re-evaluate their net asset position and hence started saving more of their incomes to pay off high interest credit card debt.
• How such actions on the part of consumers, in aggregate, kicked off a vicious cycle of decreased spending and contracting national output.
• Why government stimulus spending could close some, but not all, of the gap left by suddenly thrifty consumers, and that the recovery was likely to be a long, slow one.

We certainly don’t know how the future will play out, but the data suggest that consumers are indeed cutting back spending, and paying off debt.  (The Bureau of Economic Analysis has terrific historical data on household balance sheets and income.)  The unemployment numbers remain stubbornly high (around 9.5%), and although the recession is technically over, few economists are predicting rapid post-crisis economic expansion.

For a bit of clarity amidst all the rhetoric, you may want to check out Mark’s video offering.  His model and associated explanation do not provide a “magic bullet” of a solution, but they do provide some substance (and perhaps insight) to this vexing situation.  Now if only the politicians could follow suit!

To read a previous blog post about Modeling the Economic Crisis or view a 5-minute video trailer, click here.

Editor’s note:  This post is part two of a two part series on mental models.  You can read the first post by clicking here.

In part one of this series I stated “A mental model is a model that is constructed and simulated within a conscious mind.”  A key part of this definition is that mental models are not static; they can be played forward or backward in your mind like a video player playing a movie.  But even better than a video player, a mental model can be simulated to various outcomes, many times over, by changing the assumptions.

Mental Simulation

Remember the example from part one of the child reaching for the hot stove?  One possible outcome we can simulate is that the child does not get burned.  We can simulate this outcome by altering our assumptions. We could include a parent in the room who rescues the child in the nick of time.  Or, we could simulate the child slipping just before reaching the stovetop because the hardwood floor appears slippery.  This kind of mental simulation allows us to evaluate what may happen, given different conditions, and inform our decision making.  We don’t have to make any decisions while looking at the picture, but imagine what actions you might take if the scene above was actually unfolding in front of you.

It seems effortless to mentally simulate these types of mental models.  Most of the time we are not even aware that we are doing it.  But other times, it becomes very obvious that our brain is working rather hard.  For example, looking at the chess board below, can you determine if the configuration is a checkmate?

It is indeed.  But I’ll bet it took noticeably more effort for you to mentally simulate the chess game than it did with the child near the stove scenarios.  Think about the mental effort that the players make trying to simulate the positions on the board just a few moves ahead in the game.

The paper “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information” by G.A. Miller (1956) established that people can generally hold seven objects (numbers, letters, words, etc.) simultaneously within their working memory.  Think of “working memory” as you would think of memory in a computer.  It’s like the amount of RAM we have available to perform computations within our mind.  And it’s not very much.  This means if people want to do any really complex information processing, they’ll need some help.  Over the last 50 years or so, the help has come from computers.  (In fact, IBM designed a computer specifically for playing chess, dubbed ‘Deep Blue’).

Digital computers have catapulted humankind’s ability to design, test, and build new technology to unbelievable levels in a relatively short period of time.  Space exploration, global telecommunication, and modern health care technology would not have been possible without the aid of computers.  We are able to perform the computation required to simulate complex systems using a computer instead of our minds.  Running simulations with a computer is faster and more reliable.

What makes a model useful?

Models that we can simulate using computers come in many forms.  For example, a model could be a financial model in a spreadsheet, an engineering design rendered with a CAD program, or a population dynamics model created with STELLA.  But what makes any of these models useful?  Is it the model’s results?  Its predictions?  I think the ability to explain the results is what makes a model truly useful.

Models are tools that can contribute to our understanding and decision making processes.  To make decisions, a person needs to have some understanding of the system the model represents.  A business finance model, for example, can be a useful tool if you understand how the business works.

Consider a model that does not provide any explanatory content, only results.  This type of model is often referred to as a black box.  It gives you all the answers, but you have no idea how it works.  People rarely trust these types of models and they are often not very useful for generating understanding.

The most useful models are structured so that the model itself will provide an explanatory framework that enables someone to ask useful questions of it.  Those questions may be answered by experimenting with the model (simulating) which, in turn, can help deepen a person’s understanding of the system.

This is an important feedback loop in a person’s learning process.  This feedback loop can be accelerated if the model provides explanations and can be simulated with a computer.

Transforming your mental models into visual models that are easier to understand and experiment with, will deepen your understanding, and help you communicate your models more effectively.

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Editor’s note: This is a guest post by isee’s consulting & training partner Chris Soderquist

It’s been a little over 10 weeks since the Deepwater Horizon oil rig explosion that has resulted in a constant flow of oil into the Gulf of Mexico.  Oil is now beginning to impact the economy of the Florida coast.  Some estimate that the disaster could cost nearly 200,000 tourism jobs.   Efforts  to remove the impacts on the environment, including a massive rescue of manatees could cost billions.  The ability to truly restore the environment to pre-April 20 conditions is beyond that of mere mortals. It is an event truly unprecedented.

Who’s to Blame?

The current media focus has centered on an activity I refer to as the Find the Knucklehead Game. The idea is that there must be someone out there to blame, tar and feather, and that if we just remove the idiot from the system we’ll never have this problem again. Finé. Complete. Case closed! (And there was great rejoicing…)

However, if we begin to apply the systems thinking paradigm, there’s another analysis that might suggest we are in a long-term trend that is still just ramping up – and that if we don’t take action soon the impact on the economy and environment may be much worse.

A Systems Thinking Perspective

Two of the skills required to practice systems thinking are Dynamic Thinking and 10,000 Meter Thinking. If we look at the history of oil extraction from the balcony, as a long-term trend over time, we see something that may be quite useful. Let’s apply a concept developed by Charles Hall (SUNY-College of Environmental Science and Forestry) called EROI (“energy return on investment”).  EROI is the ratio between the energy we receive – to run our transportation system, heat our buildings, run electricity generators – and the amount of energy required to get the raw material out of the ground and process it into usable form. In the early 20th Century, oil was easy to extract. In many places it was just below the surface in the wide open fields. The EROI was 100:1 in 1930 – 100 units of energy received for 1 unit of energy extracting/processing. Since then there has been a marked decline, and as the United States passed peak oil production (the maximum production rate) in the early 1970s, and as we’ve begun importing most of our oil, the EROI for oil in the US is approximately 20:1.

Charles Hall's Energy Return on Investment Graph

Why the marked decline? Because the purest, easiest to extract/process oil has likely been found and burned (leading to too much in our atmosphere, but that’s another point). We now need to go to more distant places (no longer fields) – offshore, into the Arctic – to find oil. Often the oil is no longer in purest form; converting the “oil goo” from the tar sands in Alberta, Canada is a prime example.

Continuation of a Trend

Using the systems thinking perspective, we may conclude that the “event” of Deepwater Horizon is an inevitable continuation of a trend (that includes the Exxon Valdez). And that the trend is likely to get worse as our need (thirst) for oil increases – which will occur as developing nations continue trying to catch up to the developed world, and as we hope for “economic recovery.”

This leads to several questions in my mind.

1. If it’s true that the easiest to extract and process oil has been found and used, what’s this indicate about the risks we’ll incur as it requires increasing effort to get oil in the future? More deepwater drilling? Environmental degradation/damage to get oil like tar sands extracted?
2. How much oil really is left – how long will we continue to have this cheap resource?
3. Further, if we are facing increasing risks and costs for every unit of oil we acquire, when will that begin to have severe impacts on our economy (as the per unit cost of the stuff we love – iPods, cars, flying to Europe – increases, the profit margins of our globalized corporations will decrease)?
4. And when will we decide that we need to develop a way of living that is independent of this resource? To recover from our “addiction to oil.”

How Much Oil Is Left?

I’ve developed and published an isee NetSim model that you may use to explore questions regarding how long the resource will last and the implications of the economy on that length. You may explore it here.

Ultimately, we need to stop the Find the Knucklehead Game and instead recognize that it is we – in the collective sense – that are responsible. Not just for the recent disaster, but the long-term trend and its consequences (including the unintended consequences of climate change). The system is behaving as it is designed. It is up to us to design a different system!

Editor’s note: This is a guest post by isee’s consulting & training partner Chris Soderquist

Background

The April 26, 2010 article in the New York Times titled “We have met the enemy and he is PowerPoint” has created quite a stir. It is particularly telling that three days after its publication, it is the most emailed article on their website! The most interesting aspect of the article to me, as a system dynamics practitioner, is the publication of a system dynamics map on the US Counter-insurgency strategy as the example (i.e. visual sound bite) demonstrating why PowerPoint is so problematic. This is actually a poor example of the author’s point, since it is not PowerPoint, and because the map was shown out of context.

Although the diagram doesn’t portray how or why PowerPoint is misused, it does demonstrate some reasons why system dynamics maps and models are not more broadly used to communicate systemic issues. In this post, I will describe what issues a PowerPoint paradigm creates and how system dynamics can address those issues; more importantly, I will show why the STELLA and iThink software have features such as storytelling and web publishing in order to help people develop deeper, more systemic understanding of the complex problems humanity must address.

The Problem with PowerPoint

I don’t have anything against using PowerPoint. Those of you who have taken one of my webinars for isee systems know that I rely heavily on the software in my instruction and facilitation. I think there are inherent software limitations that combine with a cultural paradigm, that lead to its misuse. Currently, I see it promotes the following approaches to problem solving:

1. Narrow focus in space and time – due to limited screen real estate
2. Passive absorption of information of data – lazy learning, not experiential
3. Simplistic bullet point thinking – linear thinking, focusing on factors in a non-operational way

This all creates confusion between reducing complication and simplifying complexity. The world is a dynamically complex place, and thank goodness for that! Picture the blandness of a world that is simple, where everyone thought and acted the same, where you always knew exactly what would happen because it was so simple. Boring! On the other hand, dynamic complexity makes it difficult to resolve what currently appear to be intractable problems, such as environmental degradation, poverty, global economic turmoil. Living in a dynamically complex world necessitates finding ways to simplify complexity to its essence, making manageable and useful mental models.

That’s why people are drawn to lists (e.g., bullet point and linear thinking), believing it simplifies complexity; just give me a list of what’s wrong or what to do! What lists do well is remove complication, but they also remove the dynamic essence of reality, often making mental models that are less than useful.

System dynamics

System dynamics is an approach to building understanding that expands boundaries, looks at the world as comprised of feedback loops, uses a visual language that promotes operational thinking, and creates active learning. It’s a terrific approach to counter the many problems inherent in applying PowerPoint paradigm!

All of the above helps develop useful mental models that are both simplified and still capture the essence of reality. However, taking a map out of context – even one much simpler than shown in the article – and including it in PowerPoint will not create understanding, only confusion! When I’m in front of a group and have enough time, I will always draw it up on a flipchart or board, to bring the group along with its unfolding. The rapid feedback creates an engaged group capable of learning. But in the absence of time, or if you need to communicate to people “on their own time” you will find features in STELLA and iThink invaluable!

I’ve published a map to the web with the isee NetSim software to demonstrate how you can use system dynamics to create online experiential learning labs. Take a tour of the map below to see how the stock/flow language and Storytelling can overcome the passive absorption of bulletized information that PowerPoint facilitates.

Click on the image below to make sense of the map

Another interesting perspective from Linda Booth Sweeney on the New York Times article can be found on her blog.

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.

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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I’m often asked by customers that are new to Systems Thinking, “How can this approach add value to conceptualizing and understanding common, everyday issues?”  The issues range from business design to environmental concerns to macroeconomic dynamics.  In response to this question, I can tell you from my personal experience, nothing beats seeing a skilled practitioner use our software tools and the Systems Thinking methodology to make sense out of a complex problem.

With this in mind, we collaborated with our consulting and training partner, Lexidyne LLC, to create a new series of video-based presentations focused on common but often misunderstood problems that can be conceptualized, expanded, and then explored using Systems Thinking.  We recently released the first video in this series — Understanding the Economic Crisis presented by Dr. Mark Paich.

Judging from its title, you might think Understanding the Economic Crisis presents a huge complex model of the macro economy.  To the contrary, dynamic modeling expert, Mark Paich, begins with a very simple model of something we all can relate to — the individual consumer.

Stock and flow map of an individual consumer's balance sheet

Mark expands upon the model and shows how a sudden drop in housing prices affects individual consumption.   As you might expect, when Total Net Worth falls, the individual responds by spending less.  When housing prices fall, home equity loans no longer provide the purchasing power for big ticket items like cars, vacation homes and big screen TVs.

The real surprise however, comes when Mark further expands the individual consumer model to include the economy as a whole.  When everyone’s net worth decreases at the same time a phenomena known as the “Paradox of Thrift” occurs. The paradox states that if everyone tries to save money during times of recession, total savings for the overall economy may fall.  The dynamics generated by adding elements of the macro economy to the model are indeed surprising.

Mark’s easily understood model leads to some real insights concerning the policy implications for an economic recovery.   It also provides a great example of how Systems Thinking can be used to deepen your understanding of a complex issue in order to make better decisions.  If you haven’t seen the video, I highly recommend it.  The following trailer highlights some of the key points in Mark’s presentation and will give you a taste of the full presentation.

(If you cannot see the video below in your RSS reader, please visit the post page)

For more information or to purchase Understanding the Economic crisis, click here.