President Bush and greenhouse gases

I haven't been a political blogger, and I'm not about to start now. Yet the news of the past few days does offer ways to illustrate systems concepts I've mentioned before, and so I thought I'd point out what I hope is obvious to all here.

For but one example, take US President Bush's goal of having greenhouse gas (GHG) emissions stop growing by 2025, which is stirring up comment world-wide.

In system dynamics terms, GHG emissions (largely CO₂) are a flow, and the amount of CO₂ in the atmosphere is a stock. If you recall what I've written before on stocks and flows, you'll see that stopping the increase of a flow does not mean that the stock will decrease; it simply means that it will increase less rapidly.

In other words, even if we do meet this goal, things may well continue to get worse well after 2025, but they will at least get worse less rapidly after then.

I want to show you a little model that demonstrates that behavior, but, to publish it here, I'd like to get the numbers at least close to right, and that would take a bit of research time I don't have tonight. Let me try an analogy, instead; those of you who studied and remember the calculus can probably make a more elegant argument, and those who do system dynamics models can create one on your own in a few minutes (if you have the needed parameters, let me know, or post a pointer to your model).

In the real world, we are emitting CO₂ into the atmosphere by breathing, burning fossil fuels, and the like. That stock of CO₂ in the atmosphere is growing and threatening climate havoc.

Some of that CO₂ is taken out of the atmosphere each year through the action of photosynthesis and perhaps other mechanisms.

According to the science I read, we have too much CO₂ in the atmosphere at present, and our global CO₂ emissions per year, already above what the environment can naturally purge, are increasing. If that weren't the case, there would be little reason for President Bush's call to action.

Let's look at an analogous situation. For example, let's say you have a bathtub that's three-fourths full of water. The drain is open, but it's partially clogged, and so it's draining slowly.

In addition, the faucet is turned on, putting more water in the tub. It so happens that the water is currently coming into the tub faster than the partially-stopped drain can take it out, so the water level is rising, causing fears for the well-being of the bathroom floor.

The person controlling the faucet is opening the faucet as we speak, letting water come into the tub at an ever faster rate. That person, realizing the risk to the floor, promises to stop opening the faucet anymore in about 15 minutes.

What do you think will happen to the floor?

Even with the rough data I supplied, I hope you can see that the water will rise increasingly rapidly for the next 15 minutes. If the person takes their hand off the faucet in 15 minutes, the water will continue to rise until it overflows the tub (assuming it doesn't overflow sooner). The only way to save the floor is to reduce the flow of water from the faucet to below the flow of water out of the drain before the tub overflows. Even if they started reducing the flow of water out of the faucet now, the water in the tub would still rise until the inflow was less than the outflow.

Of course, this is a silly little example; the real world of GHG emissions is much more complex. Yet the general principle of stocks and flows holds: as long as the inflow exceeds the outflow, the stock will rise.

I'm not about to use this short, informal essay to argue for or against specific GHG or climate proposals or to try to balance climate stability against economic stability. I am suggesting that we all remember the lesson of stocks and flows when we are thinking about or evaluating policies such as these.

PS: Thanks to colleague Wayne Wakeland for, in a totally different situation, reminding me of the effectiveness of simple bathtub models (and I hope it worked here!).

Cassandra's curse and LTG

Almost two years ago, I posted about Limits to Growth: The 30-Year Update. Whether you saw that posting or not, I suspect you know Limits to Growth, often referred to by its initials as LTG.

Now Ugo Bardi has written Cassandra's curse: how "The Limits to Growth" was demonized in The Oil Drum: Europe. It's his view how LTG started to stimulate true dialog about a major challenge for the planet and how it then became "everyone's laughing stock" (well, perhaps not everyone's).

That's changing. As Bargi notes,

Climate studies have also brought back the limits of resources to attention; in this case intended as the limited capability of the atmosphere to absorb the products of human activities. In this field, the LTG study can be seen as having taken the right approach from the beginning; modeling for the first time the interaction of the environment with the human industrial and agricultural system.

If you've not read LTG, I encourage you to read it now. If you'd like, you can buy Limits to Growth: The 30-Year Update online, or you can find it in your favorite library (you can change the country or specify the location more precisely).

IMT 586: Information Dynamics I

If you're in the Puget Sound area and have been thinking about enrolling in IMT 586 (Information Dynamics I, called system dynamics by most of the rest of the world) at the University of Washington, now's the time; the quarter starts next week.

For more on the course, see my two prior announcements.

System dynamics course

Have you heard of system dynamics here or in your reading elsewhere? Would you like to learn more, including how to create computer simulation models to make sense of some of the challenges and puzzles you face, be they at work or in the news?

The University of Washington Information School is offering IMT 586, a first course in system dynamics, in the winter quarter. Yes, I'll be teaching it. You can learn more about it, including tips on how to register, in my earlier posting called Information Dynamics: IMT 586. My instructor class description lists the three goals I have for this course. For anyone concerned about the level of mathematics required in this course, that page also points to a brief description by the author of our text describing the level of mathematics needed to do this work.

I look forward to meeting some of you in that class!

Information Dynamics: IMT 586

Have you ever wondered ...

• what causes some ideas, products, and companies to become fads that peak and die, while others have staying power?
  
  
• why there are business cycles?
  
  
• what causes some diseases to become epidemics and others to subside with little effect?
  
  
• why real change often takes so long?
  
  
• the role information plays in the answer to each of these questions?
  

Would you like to learn to answer these and other such questions
yourself? Are you a student at the University of Washington, or do you live within commuting distance?

Then sign up for the Information School's IMT 586, Information Dynamics I, in the Winter Quarter 2008. I look forward to seeing some of you there.

Focus on the patterns, not the events

If you've ever seen the video that accompanies The Beer Game, there's something eerily familiar in the news about real estate in the USA. (Disclaimer: The video I saw was a VHS tape with a PBS segment on a previous boom and bust cycle in real estate. I can't promise the current DVD contains the same material.) Despite 50 years of knowing that the principles of feedback control theory apply to human and organizational systems, we still create systems with poor information feedback that get us into the ecstasy of boom times followed by the despair of busts.

What does this mean to us, assuming we're not directly impacted by current real estate woes? Where do you see the potential for boom and bust in your world? How do you know? How do you test your hunches?

I can't tell you when you'll experience a bust, but I can help you discover how you can design a business or organizational system that is less likely to experience such boom and bust cycles.

Incidentally, if you'd like to play The Beer Game but don't think you have an opportunity, check out the online version.

Rehearsing

I often help people with presentations, and I've noticed that those who rehearse seem to be those who do better. Now Garr Reynolds of Presentation Zen has done an excellent job of explaining the creative process of presenting ideas to others in his Steve Jobs and the art of the swordsman.

Note the two keys to presentation success:

• Intense rehearsal in a team setting
  
• Absolutely no attention to technique or form in the actual presentation
  

Reread Garr's comments, if you need to, and note comments such as, "...once we allow our mind to drift to thoughts of success and failure or of outcomes and technique while performing our art we have at that moment begun our sure decent." [sic]

How can we possibly get through a presentation while following the second key? By following the first key until we have internalized what we want to say, how we want to say it, how others will hear it and respond, and what we can do if something goes differently than we expect. Then we have to rehearse it some more.

As someone once noted, we often rehearse something until we get it right. That means we may have done it wrong 20 times and right once; which do you think will stick with us better?

I think the same thing applies in other areas of our professional lives, and I think Dietrich Dörner and Harald Schaub might agree. That's why I wrote A somewhat unified view of decision making: to suggest the importance of spending time wrestling with what we do at a time that's apart from the actual doing. Whether we use computer simulation, scenario planning, role playing, or something else, the opportunity to rehearse what we do professionally before we do it and to learn from what we actually do afterwards to improve for next time is exceedingly valuable. And it's the cyclic action learning that helps us improve and helps keep us from getting fixated on a bad idea.

If you're still thinking of presentations, check out Garr's presentation tips.

Is business getting worse?

Bloomberg says "CEOs See `No Clear Signs' of Crisis as Woes Intensify." Are things really getting worse, even as people put smiles on their faces, as that article seems to indicate?

We obviously won't know for a while. Even if things get worse for some companies, others will likely do okay, and some will thrive (or, if things go well, others will likely do okay, and some will suffer).

To a large degree, the key is being good at responding to what happens, not simply what happens. We get good by being lucky, by thinking clearly, or by having been in this situation before and having learned (or by some combination of those). While I have no help for you in the luck category, there are myriad approaches to thinking clearly, and I've tried to touch on a few in Making Sense With Facilitated Systems.

You might say that there's no way to experience the future before you get there (the third alternative). As Dietrich Dörner and Harald Schaub point out, that's not necessarily the case. Simulation (system dynamics, usually) is a way to explore challenges we might face in the future and to learn which strategies are likely to be more successful.

How are you preparing for the challenges you might face? If you'd like to talk about some of the possibilities, drop me a line.

Systems thinking and art

Not long ago, The Diagram published one of my system dynamics diagrams because they were attracted by its design ("But is it art?"). Recently I discovered a quotation I wanted to share with you, for I think it conveys something important about about organizational or societal simulation and modeling as well as about art.

Suggestion—the part standing for the whole—is a principal means by which art communicates; this is why art often tells us so much with such economy.

Jane Jacobs, The Death and Life of Great American Cities, p. 377.

That's what system dynamics in particular and systems thinking in general is all about: economy in expressing the essence of a problem to foster economy in solving the problem and economy in creating deeper insights to be able to solve the next, similar problem.

I like that quotation.

I like the book, too; I'll probably write more about it soon.

Why are we headed there?

Yesterday I posted a rather dark story about what we could be facing. As with any system dynamics model, it's showing the likely effect of the modeled policies; it's not predicting the future. (There is a difference; call if you'd like to chat about it.)

If that message is so dark, why don't we see much action to change our course? (Admittedly, we're beginning to see more action now.) The Oil Drum published Living for the Moment while Devaluing the Future, an essay exploring just that question. For a more academic approach, see Larry Karp's Global Warming and Hyperbolic Discounting, an article referenced at the bottom of The Oil Drum article (now on my reading list—I've only had time to skim it so far).

I think these ideas are important to understand and explore as we try to craft a "soft landing" from our ecological overshoot.

I think they may also be important to us in business. We get used to constant discount rates, because that's what we use. Do our customers and our bosses (they do have similar roles in our lives) really think that way, or do they do hyperbolic discounting, too? Would it it make a difference which they use? Would that difference imply we should act differently than we do?

Where are our policies leading us?

A policy is a set of guidelines or rules by which we make decisions. We have certain policies by which we work and live, even if we don't always make those explicit. If we see a pattern of decisions, decisions that seem cut out of the same mold, that's likely evidence of a policy.

François Cellier of the Institute of Computational Science of ETH Zürich, Switzerland has published an article, Ecological Footprint, Energy Consumption, and the Looming Collapse, at The Oil Drum that examines the potential effects of our policies towards growth. It's a high-level view, to be sure, but sometimes those offer great insights. Be sure to read both the article and the accompanying slide set (the article isn't that long; it's the 333 comments that take up most of the length).

I think this is a very important discussion. That's why I think it's important for each of us to be skeptical about such claims. It's not because I think he's wrong; his analysis, at least so far, seems good. It's not a call to wait for "proof," for, as John Sterman points out, we're not really waiting; we are doing things to the environment every day. It's not a call to ignore the claims, for that's not being skeptical; it's a call to test them and then to act based on what we determine. It's not a call for depression; Cellier does show a way forward (especially in the slides).

By suggesting we be skeptical, I may give the impression I think we can ignore this for a bit. I want to re-emphasize that the IPCC and others have given some pretty clear signals that the time to act is now (actually, the time to act was some years in the past; the next available time to act is now).

What does this mean for our businesses and for business in general? I think it means figuring out what to do to ensure the sustainability of our businesses and our economic system in the face of the challenges the best science says await us. The key lesson from "Out of Gas: A Systems Perspective on Potential Petroleum-Fuel Depletion" was that we not wait too long to attend to signals we get, for our systems have inertia, and we can't, as much as we might wish, always change direction instantaneously. Pay attention to Cellier's description of easy and difficult problems starting on slide 38; the signals may not be as we'd normally expect. Sometimes we can't wait to feel the wind from an impending storm; we have to rely on forecasts from meteorologists to know when to board up windows in the face of an approaching hurricane.

We can also apply that message to more typical business decisions. Do we discover we will need to add (or remove) capacity well in advance, so we can react smoothly, or do we make such discoveries only when the market begins to complain loudly? How do we figure out whether our latest initiative is about to make real progress or it's about to fail and we should abandon it and change course?

Reacting too soon can lead to the Chicken Little trap: if we respond too quickly, we diffuse our energies by responding to simple noise; if we respond too slowly, we're trapped. One of the lessons I've learned is that feedback models (of the sort I've sometimes discussed here, also called system dynamics models) can help us find what things to monitor so that we have a clearer picture to guide our decisions.

What do you think?

IMT586: a course in system dynamics

While I have taught system dynamics and systems thinking courses and workshops for various organizations, some have asked if I was planning any public courses. I can now tell you that I'm the lecturer in spe for IMT586, a graduate course in information (system) dynamics for the Information School at the University of Washington. IMT586 will be offered starting in the winter quarter of 2008. It will be offered in the MSIM program to both day and executive students. It currently appears it will also be open to non-matriculated students, so, if you live within commuting distance of the University of Washington's main Seattle campus and are interested in learning system dynamics, check it out.

As we get closer and more information is available on the UW iSchool Web site, I'll post updates on Making Sense With Facilitated Systems.

Of course, if you want custom training tailored for your organization's needs, feel free to contact me.

An accidental experiment

I've published a number of At Any Rate™ columns through Pegasus Communications. They consist of text designed to capture people's interest up front and to remind them of what they experienced later as well as a simulation model people can download and explore. The model leads people through three stages: an initial stage-setting exercise, a more complete model to show added complexity in the problem at hand, and an exploration area where people can dig a bit deeper to try their hand at addressing the problem.

Pegasus Communications advertises each At Any Rate in their free Leverage Points newsletter that has a rather large circulation, and they set up a discussion area in their Pegasus Forums for each one.

In other words, that column seems to be planted in a fertile ground in which to talk about such things. The models are interactive. They tell a story. They are published on a high-visibility site and advertised in a high-distribution newsletter. There's a space established to enable discussion.

Yet I've gotten very few off-the-record comments (all favorable) about those columns. I've seen very few comments in the Pegasus Forum. I'm not sure anyone has contacted me about what they've seen there. I'm not complaining; I know that I don't write letters to the editor of the local newspaper, even if I strongly agree or disagree with what the newspaper has published. As a result, I don't necessarily expect (although I would welcome) lots of dialog about what I post online.

On Monday, April 9, I published a similar model (new URL) on Drew McManus's Adaptistration as part of his TAFTO 2007 (new URL) series. It was not interactive; rather, it contained diagrams, graphs, and a computer program (or a text-based model, which is the same thing). Admittedly, I tried hard to use literate programming ideas to intertwine the model and the story so that it would be more interesting and readable, and I let two others in the potential audience see an advance copy so I could find and fix any impenetrable sections.

Within a day, I had a thoughtful, lengthy comment added to that column. Two bloggers made quite favorable comments about the essay. I know of at least one person who had been telling me he'll get to the latest At Any Rate any day now who read and commented on my TAFTO column within a day.

What gives?

While I realize that the singular of data is anecdote, I think that this is showing me the barrier we erect when we ask people to download, run, and learn from an interactive model. While the barrier might be lower if I had used a simulator for At Any Rate that ran in a browser, I'm not sure; one would still need to take perhaps half an hour, perhaps more, to work through the model. It takes much less time to install the software, and i'ts a one-time action—a number of readers already have it.

The barrier may be more complex than simply the challenge of installing the isee Player required by the At Any Rate column. To explore and really learn from a simulation, someone needs to be willing to experiment. That means taking the time to understand the environment, to formulate hypotheses, to write those hypotheses down, to run various tests on the simulation model, to compare the results of the test with the hypotheses, and probably to try new experiments based on the learnings from initial experiments. That's far different than just opening the application, pressing a few buttons, and seeing what happens.

Needless to say, most of us who create such interactive simulations try hard to guide the user through the process. Most of us encourage people to form those hypotheses and to document them in writing or in graphs before starting a simulation. Yet I know (from personal experience—I'm not immune) that it's far too easy to treat a simulation as a video game: press the button, and see what happens. That's not often the path to deep learning.

With the non-interactive version, people can read just a paragraph or skim the entire article to see if it seems interesting. They can come back later to dig more deeply. They can print it out, if they wish, and read it on the bus on their commute. If a text version of the model is included, they can, if they want, copy it into the simulator and explore it themselves.

My lesson? Interactive simulation is no panacea, and it may be a disadvantage if I want to get my story told, especially if my audience consists of busy or high-level people. By telling a good story, I can help the reader learn something, most likely in less time.

Is there a role for exploration, experimentation, and interactive simulations? Certainly! But I need to be sure to consider the audience, their current interests, and what they know and want to learn.

I'd welcome others' insights and experiences. In an action research sense, I'll spend some time trying to disconfirm my conjecture; in the process, I might learn more.

TAFTO 2007

As promised, today is the day my article Is TAFTO a good idea? Really? (new URL) has been published at Adaptistration. If you're interested in classical music, I encourage you to take a look and see if you agree with my thinking. If you're more interested in making sense of tough problems, I also encourage you to take a look. I've used some graphs I don't normally see in such modeling work. In either case, I'd be interested in your reactions.

If you happen to be here because you found me on Adaptistration, welcome! You must be a lover of classical music (you may also be an orchestra administrator, a musician, a board member, or all of the above). In that case, you might also enjoy a recent conversation I had with Greg Sandow called Making musical sense by email, and you might like a short exploration I did of a statement in the recent Knight Foundation Magic Of Music Final Report called Making sense with numbers.

I'd enjoy having you as a regular reader of Making Sense With Facilitated Systems, and I'd welcome your comments either here or on the Adaptistration article.

More on factorial designs and simulation

Factorial designs and simulation: apparently Professor Barlas is teaching it in Istanbul!

For the analytically-minded among you, I'd note that MCSim, coupled with a bit of infrastructure you can develop, can make running small factorial experiments (up to a few hundred runs) a fairly quick and painless task. That includes their development, their execution, and their analysis. If a system dynamics model is deterministic, as many are, there's no need for replication.

You can see a simple example in my upcoming TAFTO contribution. I was pleased at the way in which a factorial design approach enabled me to generate and see useful results from a relatively simple model.

Making musical sense by email: the table of contents

For the past two weeks, I've been posting excerpts of an email conversation between Greg Sandow, composer, consultant to orchestras, author, professor, and music critic, and me about the future of classical music. I thought you might be interested in reading these excerpts, either because you're interested in the future of classical music or because you're interested in observing light-weight approaches to using systemic approaches as we seek to make sense of tough problems.

Because the conversation is spread across multiple postings, I'm providing this page as a table of contents for the series—a list of links to help you read the series in order from start to finish or to help you find the one section you're seeking.

1. Part 1: The introduction
   
2. Part 2: My first email to Greg
   
3. Part 3: Greg's reply
   
4. Part 4: My first email with higher-resolution graphics
   
5. Part 5: An augmented model
   
6. Part 6: What did you observe about the dialog?
   
7. Part 7: What did I observe about the dialog?
   
8. Appendix A: The first model
   
9. Appendix B: The second model
   
10. Special feature: An essay by Dr. Glenda H. Eoyang
    
    

If you're just discovering this series, welcome! It's a bit lengthy, so you may want to read it in several sittings. All of us involved in creating this series would appreciate any comments you might have.

Thanks to Greg for the dialog and for permission to post his words here and to Glenda for her contribution that indicates this approach is gathering momentum elsewhere, too.

Making musical sense by email, special feature

One of the primary reasons to publish this series was to suggest that system dynamics, one methodology in the larger systems thinking field, could be applied beneficially in a low-key way to help make sense of complex situations. Instead of making a big deal of talking about system dynamics as a useful approach, getting buy-in to try it on a particular case, and then doing it, we can just apply it naturally when and as the need arises. Sure, those of us doing the work need to educate those seeing such models for the first time so they know what they're getting and how to understand them, but I think this series has demonstrated that it's possible to do that as part of the process, not up front.

It turns out, not surprisingly, that I'm not alone in having these thoughts. Friend and colleague Dr. Glenda Eoyang has been having similar thoughts regarding a different systems thinking methodology. Glenda started exploring nonlinear dynamics and social systems in 1989, received her doctorate in Human Systems Dynamics in 2002, and founded the Human Systems Dynamics Institute in 2003. She teaches, consults, researches, and writes. She helps people see patterns that emerge from the chaos of human interactions and take adaptive action to increase coherence, health, and sustainability for individuals, teams, institutions, and communities. Her profound understanding of the many theoretical streams of complexity science and her gift for clarity make her an excellent guide into the world of human systems dynamics.

Because she's using similar approaches in a related field, I invited her to share her thoughts with us.

---

Technology matures and, thank goodness, we do, too. In Seattle there is an elegant hotel that was built by Ford Motor Company early in the last century. Gentlemen would live in the hotel while they learned the basic skills of car ownership, including driving and auto mechanics. Six months after the hotel was built, Ford found more efficient ways to meet their customers’ needs. I, too, have been seduced by the new. Fully one quarter of the first computer course I wrote covered binary arithmetic and the history of computing. Today, only the mathematicians and historians find that stuff interesting. In the dawning phases of a technology, bridges to the past are critical. As the technology emerges it integrates into our other intelligences, and we return our focus to the work at hand. The technology becomes a means rather than an end in itself. In future I hope we will be as amazed that people spent days learning “systems thinking” skills as we are with Ford’s hotel and the history of computing as a core competence for users.

When I discover a new technology it seems complicated and exotic. I want to understand its secrets and plumb its depths. For a short time, the technology itself is a preoccupation. I focus on it as if it were an end in itself. Over time, though, I become accustomed to the new ways to think and act. I absorb the new views and tools into my repertoire. They become a part of me, and I am able to see through them rather than focusing on them directly.

Today, my clients are more ready to think systemically than they are to learn about systems thinking. I believe this is the transition Bill and others are seeing in themselves and their clients. For example, a colleague who is a professional evaluator doesn’t design and implement “evaluation systems.” Rather, she works as part of the management team to generate and present meaningful data in response to specific strategic and tactical questions. Another Human Systems Dynamics Associate works in a school system, using the language of education and educational reform to spark conversation and action about complex human systems dynamics. I’m supporting a strategic planning process for a fast-growing international consulting firm. I introduce tools and techniques only in service of the conversation toward the organization’s business goals and improved performance. I add value not because I bring an exotic set of mysterious tools but because I use powerful tools to help them think and act with more insight, intention, and collaboration.

This transformation isn’t easy for me. I like binary arithmetic. I feel powerful when I hold the keys to a mysterious new discipline. On the other hand, my clients find the transition quite appealing. We work together on their concerns, leveraging their knowledge and expectations, rather than asking them to leave their world views behind and align with my arcane methods and visions of reality.

I am beginning to think of myself as a “praxis partner”—one who works with others to blend theory and practice in the service of effective action. As the technology of systems thinking matures, I hope my clients and I can, too.

Glenda H. Eoyang, Ph.D.
Executive Director, Human Systems Dynamics Institute

---

What are you seeing in your organizations? Are people doing more of this blending there, too? Is that helpful, or do you miss something in the process? Both Glenda and I would enjoy hearing your feedback. Comment here, or contact Glenda or me directly.

Making musical sense by email, appendix B

Here is the second of the two models I did in support of the dialog Greg Sandow and I had on the future of classical music. This one models the added effect that might occur if people stop attending concerts after they turn 30. Some stop forever, while others return when they turn 50. I left the model as close as I could to the previous one.

States = {
twenties, thirties, forties, fifties, sixties,
thirtiesNOGO, fortiesNOGO
 };

Inputs = {
newpa,  # Number of young people becoming concert-goers pa
};

Outputs = {
input,
avgage,
total
};

decade = 10.0;
bins = 5.0;   # no. of decades modeled
population = 1.0e6;  # total initial concertgoers

fractionNOGO = 0.50;  # fraction of twenties who leave for two decades
fractionQUIT = 0.20;  # fraction of twenties who leave forever

Initialize{
twenties = (population / bins) * (1 + fractionQUIT);
thirties = (population / bins) * (1 - fractionNOGO);
thirtiesNOGO = (population / bins) * fractionNOGO;
forties = (population / bins) * (1 - fractionNOGO);
fortiesNOGO = (population / bins) * fractionNOGO;
fifties = population / bins;
sixties = population / bins;
}

Dynamics{
from20spa = twenties / decade;
to40spa = thirties / decade;
to50spa = forties / decade;
to60spa = fifties / decade;
endingpa = sixties / decade;

quittingpa = fractionQUIT * from20spa;
hiatuspa = fractionNOGO * from20spa;
to30spa = from20spa - (quittingpa + hiatuspa);

to40NOGOpa = thirtiesNOGO / decade;

returningpa = fortiesNOGO / decade;

dt(twenties) = newpa - (to30spa + quittingpa + hiatuspa);
dt(thirties) = to30spa - to40spa;
dt(thirtiesNOGO) = (hiatuspa - to40NOGOpa);
dt(forties) = to40spa - to50spa;
dt(fortiesNOGO) = (to40NOGOpa - returningpa);
dt(fifties) = (to50spa + returningpa) - to60spa;
dt(sixties) = to60spa - endingpa;
}

CalcOutputs{
input = newpa;
total = twenties + thirties + forties + fifties + sixties;
avgage = (25.0 * twenties + 35 * thirties + 45 * forties
+ 55 * fifties + 65 * sixties) / total;
}

If the last model was quick and simple, perhaps this one is quick and dirty, for it had much less testing than the other one. I did it primarily to start a conversation we haven't finished yet, and I'll spruce up this model and test it more adequately as we go.

The reason to show this to you is to show how easy it is to change such a model to incorporate new conjectures. What's more, because these are text models, it's quite easy to use diff to see what's changed between the two. Because the entire model is in text, you can see the entire thing at one glance.

Here's the associated simulation file that describes the experiments. As you can see, it's almost like the other one; I just had to add the new parameters.

Integrate(Lsodes, 1e-6, 1e-6, 1);
OutputFile("simplerun02.out");
StartTime(0.0);
fractionNOGO = 0.50;
fractionQUIT = 0.20;

Simulation{
newpa = NDoses(2,20000.0,10000.0,0.0,10.0);
PrintStep(input,0.0,200.0,1.0);
PrintStep(total,0.0,200.0,1.0);
PrintStep(avgage,0.0,200.0,1.0);
}

Simulation{
newpa = PerExp(20000.0, 400.0, 0.0, 0.010);
PrintStep(input,0.0,200.0,1.0);
PrintStep(total,0.0,200.0,1.0);
PrintStep(avgage,0.0,200.0,1.0);
}

Simulation{
newpa = NDoses(2,20000.0,0.0,0.0,10.0);
PrintStep(input,0.0,200.0,1.0);
PrintStep(total,0.0,200.0,1.0);
PrintStep(avgage,0.0,200.0,1.0);
}
END.

For more on the syntax used in these files, see the documentation for MCSim.

Eagle-eyed readers will catch a spelling error and a mistake in the version I published previously. I've fixed those problems in the model shown here. Interestingly, the simulation outputs only change slightly, and their shape is the same, both because the dynamics are heavily controlled by the main aging chain and the cutoff of new attendees and because the mistake substituted a variable with a similar value. Thus I'm not providing new graphs.

In actually working on this model, I found it faster to print all the stocks and flows and then use the analysis program to select which variables to explore. In this appendix, I'm only showing print statements for the key variables used to produce the graphs in the report, so you have less text to read.

Making musical sense by email, appendix A

Because I believe in transparency as far as possible, here is the first of two models I did in support of the dialog Greg Sandow and I had on the future of classical music. Neither model is a paradigm of modeling artistry; they're simple, quick models to support our dialog and exploration. If we begin to use these more deeply, we'd certainly test and document them more seriously.

Here's the model I used to create the initial graphs I shared with Greg and Drew:

States = {
 twenties, thirties, forties, fifties, sixties
   };

Inputs = {
 newpa,  # Number of young people becoming concert-goers pa
 };

Outputs = {
 input,
 avgage,
 total
 };

decade = 10.0;
bins = 5.0;   # no. of decades modeled
population = 1.0e6;  # total initial concertgoers

Initialize{
 twenties = population / bins;
 thirties = population / bins;
 forties = population / bins;
 fifties = population / bins;
 sixties = population / bins;
}

Dynamics{
 to30spa = twenties / decade;
 to40spa = thirties / decade;
 to50spa = forties / decade;
 to60spa = fifties / decade;
 endingpa = sixties / decade;

 dt(twenties) = newpa - to30spa;
 dt(thirties) = to30spa - to40spa;
 dt(forties) = to40spa - to50spa;
 dt(fifties) = to50spa - to60spa;
 dt(sixties) = to60spa - endingpa;
}

CalcOutputs{
 input = newpa;
 total = twenties + thirties + forties + fifties + sixties;
 avgage = (25.0 * twenties + 35 * thirties + 45 * forties 
  + 55 * fifties + 65 * sixties) / total;
}

I've stripped out most of the comments to make it shorter; I suspect most of you who are familiar with system dynamics modeling (and many who aren't) can probably understand what's going on.

This model is written for the MCSim simulator created by Frédéric Bois and Don Maszle.

Here is the simulation file that runs the various experiments:

Integrate(Lsodes, 1e-6, 1e-6, 1);
OutputFile("simplerun01.out");
StartTime(0.0);

Simulation{
  newpa = NDoses(2,20000.0,10000.0,0.0,10.0);
  PrintStep(input,0.0,200.0,1.0);
  PrintStep(total,0.0,200.0,1.0);
  PrintStep(avgage,0.0,200.0,1.0);
}

Simulation{
  newpa = PerExp(20000.0, 400.0, 0.0, 0.010);
  PrintStep(input,0.0,200.0,1.0);
  PrintStep(total,0.0,200.0,1.0);
  PrintStep(avgage,0.0,200.0,1.0);
}

Simulation{
  newpa = NDoses(2,20000.0,0.0,0.0,10.0);
  PrintStep(input,0.0,200.0,1.0);
  PrintStep(total,0.0,200.0,1.0);
  PrintStep(avgage,0.0,200.0,1.0);
}

END.

For more on the syntax used in these files, see the documentation for MCSim.

Making musical sense by email, part 7

The conversation goes on, or at least I hope it does, but I thought this was likely enough to give you a flavor of what we have done. What do you draw out of that story?

I think there are a few key points to note about the process. Before I start, I should note, in case you missed it, that we've been applying a methodology called system dynamics to the problem Greg Sandow posed. I've only mentioned "system dynamics" twice so far in this series and never in my emails to Greg.

1. System dynamics can help us think more clearly. Part of that comes from the act of making our mental models more explicit and more precise, even when we're still viewing the world from 10,000 meters up. Part comes from the lessons we learn by considering the effect of feedback on the behavior of the system that's creating our problem. In this case, we used simulation to explore the effect of feedback.
   
   In other words, system dynamics can make us (seem) smarter.
   
   
2. Because system dynamics leads us to make our mental models very clear and explicit (even at the 10,000 meter level), we also clarify what it is we don't understand. That can help identify and resolve differences in understanding more quickly.
   
   Because system dynamics has the potential to make us look more unambiguously dumb, it can seem risky; fortunately, the benefit of thinking more clearly usually offsets the risk of clarifying our ignorance in front of others, and the two (clearer thinking and revealing our ignorance) together can help us learn and move forward more quickly.
   
   
3. System dynamics models and system dynamics "interventions" don't need to be big, overarching affairs that take the lead in organizational work; they can be inserted casually and naturally into the conversation as partners in an effort. As you saw, I never once told Greg that I wanted to do or was doing a system dynamics model nor why system dynamics was special. I think I was able to introduce enough information so he could follow what I was saying without inserting too much jargon. I think he was able to assess the utility of the model by what we did discuss.
   
   I think this is healthy. While big programs and up-front acceptance may be important in some cases, I think we respect our clients and our managers if we don't ask them to buy into a process before they see the results. Focus on the problem, not the tool.
   
   There's a benefit for those of us using system dynamics, too. System dynamics isn't the best tool for every problem. If we've sold a client or manager on system dynamics as the way to proceed and it then becomes evident there's a better way for this particular problem, we risk seeming to have failed. We can either change courses and make that risk real, or we can push ahead, staying with a less-than-optimum approach. By inserting system dynamics dynmaics more naturally, we can change courses as circumstances warrant, without having to eat too many of our words along the way.
   
   Of course, that requires that we have skills in (or connections to people with skills in) a variety of approaches.
   
   
4. It's possible to use system dynamics even by email. Text-mode email is quick (relatively), easy, and encourages interaction. Text-mode graphs make it easy to incorporate graphical data into the conversation, and text-mode drawings make it easy to show simple model diagrams. Text-mode models make it easy to convey the full detail of the model we're using to the degree and in the manner it's useful. I like that.
   
   Of course, some graphs and some graphics are too complex to show in text; for that we use other means. And some people, such as Greg, will prefer higher-quality graphs, while others will be quite happy with the text-mode graphs.
   
   
5. You don't have to simulate everything. Sure, our insights about nonlinear feedback systems aren't always very good, but there are times when we learn enough from a simple simulation so that we can carry useful lessons over into the more complex situation we really face. There are times we can draw on past experiences with simulations to understand the problem we're currently facing. There are times when we can glean enough insight out of the model we've created to do a useful, informed static analysis.
   
   In this case, I simulated a simple "aging chain" that simplified Greg's problem rather than building a more complex model that replicated the exact dynamics seen in US orchestra attendance. That model seemed to give insights useful enough to guide our discussion profitably.
   
   
6. You don't have to use system dynamics for every problem. System dynamics works superbly in addressing problems involving feedback, but it's not the only systemic approach we have. Pick the methodology that best suits your problem. Better yet, consider viewing your problem with multiple methodologies to see if that triangulation leads you to consistent solutions; if you get differing recommendations from multiple methodologies, perhaps you have more work to do.
   

There are a few lessons to be drawn from the musical content of this series, too:

1. In general, you won't change the average age of concert-hall audiences over the long haul by reducing the number of newcomers. While you may certainly affect that average age over the short term, the system will recover to its old equilibrium eventually.
   
2. If you cut the inflow of newcomers to zero, you will make the average age change to a new value.
   
3. Based on some results I shared with Greg but didn't show here, the average age recovers more slowly with more drastic drop-offs in newcomers. For sufficiently drastic drop-offs, it may take years or decades to distinguish between a drop to zero and just a drop to a drastically lower number.
   

Because Greg is working on a book on this subject, I encourage you to check out his blog to explore more lessons about the musical content of this series and to enter into a dialog with him. Check out his interests, too; I think you'll find he enjoys a broad spectrum of musical styles.

If you're more focused on orchestra management, see Drew McManus' Adaptistration.

You can also see another model I'm working on that addresses classical music in my upcoming TAFTO contribution; I'll announce that here when it's published.

I'd be remiss if I didn't thank Greg for the dialog we've had and for his willingness to share it here. Thank you, Greg. All of his words are published in this series with his permission.

Stay tuned for the final postings in this series next week, including a full listing of both models, a table of contents with links to each article in the series, and the possibility of a surprise guest essayist!

Making musical sense by email, part 6

The conversation goes on, or at least I hope it does. I hope these snippets have been sufficient to give you a flavor of how Greg Sandow and I have been talking.

Tomorrow, I'd like to give you my impressions of the meaning of all this. Before I do, I'd like to ask you to contribute. How do you interpret what you've read in this series? What value do you see in the approach? What did you see that you didn't expect to see? What didn't you see that you expected?

There's one particular question I asked earlier: do you think my text-mode graphs or the higher-quality graphs would have been better for facilitating dialog if this dialog had been between you and me, assuming the graphs weren't too complex?

I know you're largely a quiet group of readers, but I would like to learn from your impressions—perhaps we could all learn. Add a comment below, or, if you'd prefer, send me an email or give me a call.

I encourage you to take part in this conversation in other ways, too. The future of music is a project that Greg is working on for a future book. If you've got ideas to contribute to that effort, head over there or to his regular blog. If you're more interested in orchestra management, also check out Drew McManus' Adaptistration (you still need to think about the future of music).

If you'd like to talk more about approaches such as this for making sense of the business and organizational problems you face, keep following Making Sense With Facilitated Systems.

Then come back tomorrow to see my thoughts.

Making musical sense by email, part 5

So far, we've seen Greg Sandow's thoughts on the future of classical music and a dialog between Greg and me involving the use of system dynamics simulations to explore his ideas further. Hopefully this is showing you a way to use systems thinking that you may not have seen before; perhaps you'll see ways to apply it more directly in your work, too. We'll talk more about that later.

Based on my initial model, Greg had revealed a few more details of his thinking. In particular, he wasn't necessarily suggesting that young people were abandoning classical music forever; they might just be staying away for a decade or two.

Thanks to Greg's questions, I augmented the model to show what might happen if classical music concert-goers take a hiatus in their thirties and forties. I describe that in the lightly edited email below, including a quotation (with permission) from another email from Greg not yet quoted here:

"Greg Sandow"  writes:

> First, I believe your model posits that people start
> attending concerts, and then continue essentially
> throughout the rest of their lives. I don't know if that's
> true. That is, people might go occasionally when they're
> young, then not go (or not go very often) for many years,
> and then resume going, much more often, when they're
> older. This would be consistent with some of the data, for
> instance the preponderance of the audience in older age
> groups, and also survey results that show people most
> likely to attend regularly when they no longer have
> children at home. This may not have been the pattern in
> past generations, but it appears to be the pattern now.

Greg,

I've modified the model and run some quick and dirty tests
to let you see the first impressions.  If this happens to be
at all interesting, I'll need to do some model testing I
don't have time for right now.

I did think of one other factor that could lead to an aging
of audiences; see the end of the attachment.

> An impressive study done in Indianapolis a few years ago
> showed that people 40 and under learned about arts events
> they attended mainly by word of mouth. No other source of
> information came even close. Orchestras have little
> understanding of this, and have done very little work, as
> far as I know, to understand what makes people decide to
> go to concerts, or - really important - to go once, and
> not return.

Quite interesting.  That's consistent with the assumptions
Drew and I have been making, and it's consistent with the
purpose of TAFTO.

Perhaps the TAFTO model will be of use, when it's published.

Thanks,

Bill

The attachment I mention contains the results I shared with Greg (with one obvious typo fixed). That model is not very well developed, nor have we carried that part of the conversation much further yet, but I thought you might like to see that it was relatively easy to pursue alternative ideas.

TAFTO refers to Drew McManus's Take a Friend to Orchestra initiative. I'll be writing a simulation-supported column for his annual April TAFTO push. You'll have to wait until then to see more about that model.

Making musical sense by email, part 4