Making Sense With Facilitated Systems

Thursday, December 27, 2007

Monday, May 21, 2007

A leisurely snapshot of the USA

How do we in the USA relax? How has that changed over the past few decades? Normally I try to write from a more global perspective, but today's link specifically refers to the USA. Perhaps those of you outside the USA will find it helpful (or amusing) to learn and ponder a bit more about us. Perhaps some of you will comment here, leaving similar information about the culture and nation in which you live.

David Touve and Steven Tepper of the Curb Center for Art, Enterprise and Public Policy at Vanderbilt University have put together "Leisure in America: Searching for the forest amongst the trees." It may seem out of date in some cases (it talks about MySpace and IM but doesn't mention Twitter; then again, it was published in April 2007 :-), and it may lack a bit of statistical rigor (I don't know if differences it cites are statistically significant), but it seems interesting if sometimes paradoxical (which may be an apt description of us as a culture).

If that's who we are, what does it mean for you and your enterprise (in all senses of the word), no matter the field?

Thanks to Andrew Taylor and The Artful Manager for the link and for more information he gives about the related conference.

Labels: business, classical music business, data, making sense

Thursday, May 03, 2007

But is it art?

Apparently The Diagram thought so. They found my TAFTO article (new URL), liked the graphics, and asked to publish one of its diagrams in their issue 7.2.

Perhaps that's another advantage of working slightly outside the mainstream approach—it gets noticed. Drawing a standard type of diagram with different tools made it stand out a bit and may help it communicate more effectively.

What do you think?

By the way, try their Graphviz interactive diagram.

Labels: art, classical music business, communications, system dynamics

Monday, April 09, 2007

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.

Labels: classical music business, making sense, simulation

Monday, April 02, 2007

TAFTO starts now

If you're reading this because you are interested in classical music and the business of classical music, check out Drew McManus's (new URL) Take a Friend to Orchestra (TAFTO) program (new URL). Drew has a varied lineup of people to write about inviting your friends to join you at a live concert. Check out Adaptistration for daily updates, starting today.

I'm honored to be one of those writing a TAFTO column. If you're reading this primarily because you're interested in making sense of tough problems, you might be interested in my contribution that will use a simulation model to explore whether TAFTO is a good idea or not.

If you're curious about past TAFTO essays, check out the 2006 and 2005 contributions (new, common URL).

Labels: classical music business

Wednesday, March 28, 2007

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.

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.

Labels: classical music business, making sense, simulation

Tuesday, March 27, 2007

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.

Labels: classical music business, making sense, simulation

Monday, March 26, 2007

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.

Labels: classical music business, making sense, simulation

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.

Labels: classical music business, making sense, simulation

Friday, March 23, 2007

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.

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.
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.
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.
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.
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.
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:

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.
If you cut the inflow of newcomers to zero, you will make the average age change to a new value.
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!

Labels: classical music business, making sense, simulation

Thursday, March 22, 2007

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.

Labels: classical music business, making sense, simulation

Wednesday, March 21, 2007

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.

Labels: classical music business, making sense, simulation

Tuesday, March 20, 2007

Making musical sense by email, part 4

In response to Greg Sandow's request to see the actual graphics instead of text-mode graphics as shown in my first email to him, I sent him a PDF with the same text and with higher resolution graphics. I also made a few comments in response to his email (lightly edited here). Greg's comments are included with his permission.



> Thanks so very much for sending me your work. It's
> remarkable stuff, much more solid and promising than most
> of what I see. And your tentative conclusion is quite
> challenging (at least for the classical music business) -
> that the model that best predicts the observed decline in
> attendance (am I getting this right?) is the one that
> predicts no new people join the audience at all.

Greg,

Thanks for your response filled with ideas and insights.
Yes, you got that right; the simulation run that best
matches your data is one in which no more young people enter
a concert hall.

> I'd make one request. I'd much prefer to see the original
> graphics. I did look at your e-mail in Courier, but it
> would be a lot clearer if you could attach the charts as
> graphics, or perhaps send a spreadsheet with the charts
> generated inside it.

I'll regenerate the graphs in a higher resolution format.
I've got a few things on my schedule today, but I'll try to
get to it before the end of the day -- no promises until you
see them, though.

> Also, I mustn't forget to congratulate you on your careful
> work with that Knight Foundation tidbit. I've seen that
> too often used to support the idea that all we need is
> music education in our schools, and classical music
> attendance will go up again. As if, in other words,
> there's a causal connection between playing an instrument
> and classical music attendance. As you rightly point out,
> the Knight study makes no such assertion, but people jump
> to the conclusion anyway. And the statement, which I
> believe they make, that playing an instrument is a
> predictor of classical music attendance does muddy the
> waters, since it rather casually puts playing an
> instrument earlier than attendance in some sort of implied
> statistical food chain. That helps people to jump to the
> causal interpretation.

Thanks!  I agree with your assessment that they didn't
really claim that connection, but they didn't make it clear
that they didn't claim it, either.

> Anyhow, I think you debunked this all really nicely. My
> own predisposition is to believe that there's no causal
> connection, but that instrumental playing and classical
> music attendance are both characteristics of some yet to
> be defined demographic slice, the demographic in question
> being the one most likely to attend classical concerts. In
> other words, roughly speaking, playing an instrument and
> going to classical concerts have a common cause, rather
> than one causing the other.

That makes sense; I don't currently know what that factor
is.

> Going back to the analysis you sent me, I do have some
> suggestions for refinements.  > 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.

You understood the model correctly.  As you note later about
your numbers, this model is a surmise, too -- a
simplification of reality to see what behavior the dynamics
of a basic "aging chain" would create.

Your causal explanation makes sense, though; I'll try to get
to add that to the model.  In particular, I could add a high
rate of dropping out in people's twenties, for example,
returning in their fifties.  I don't know if that fits the
pattern, but it would indicate what dynamics that might
create.  The real world, of course, is more complex than the
model.

> When I made so much of the NEA's statistic about the
> number of younger people dropping off, I didn't mean to
> say that this would lead to any immediate and directly
> proportional decrease in total attendance. I was using the
> data more impressionistically. Accepting the apparent
> truth that people in this era mostly go to classical
> concerts when they're older, I surmised that this younger
> generation, once it was 50 years old, would go in smaller
> numbers than previous generations did. I should stress
> that this is nothing more than a surmise - an
> assumption. It seems reasonable, according to common
> sense, but might turn out not to be true.

One of the things about the "operational thinking" this
modeling encourages is that it leads one to make one's
assumptions very explicit.  Sometimes we learn from that
exercise.

> Though when I combine it with certain data about people
> who are currently 50 and above, the conclusion seems even
> more reasonable. One big change during the past generation
> is that older people aren't as committed to high art as
> they used to be. They now "consume" (if that's the word)
> both high and popular art. This makes them less like the
> committed classical music audience of the past, and thus,
> in my view, less likely to attend classical performances
> in the numbers their predecessors did. Now we have younger
> people who go to classical performances far less often
> than their own predecessors did. That suggests that when
> these younger people are in their 50s, they'll be even
> more culturally omnivorous than the present older
> generation is, and thus less likely to be committed
> classical music attenders.

So we might modify the model to show some twenties dropping
out into a two-decade hiatus, rejoining as fifties, while
others never return.  We could vary the percentage that go
each way and see what happens.

> Second (returning to my suggestions for you), you might
> want to refine your model to reflect the fact that the
> total number of orchestral tickets sold includes many
> tickets sold to the same relatively few people - the
> subscribers. Currently, subscriptions amount to about 60%
> of all tickets sold. This number has declined sharply from
> the 80% or so reported a couple of decades ago, and is
> considered likely to decline still further. Still, this
> needs to be an important part of any model that predicts
> future ticket sales. It's not enough simply to predict the
> number of people likely to buy tickets. We need to know
> how many tickets these people are likely to buy, in order
> to get some idea of what the total ticket sales in the
> future are likely to be. Certainly, in the final analysis,
> orchestras are more concerned with the total number of
> ticket sales than with the number of individual people
> attending.

I'm working on a column for Drew's TAFTO series.  In that
somewhat more complex model and with Drew's consultation,
there are three categories (stocks) of people: NotYets,
Nows, and NoMores.  The NotYets are those who don't know if
they'd like classical music concerts because they've never
gone to one.  The NoMores are those who have attended but
have left, never to return for whatever reason.

To your point, the Nows are those who currently make up
audiences.  That's not the number of tickets, though;
depending upon various factors I'm not modeling, Nows may or
may not attend any one particular concert.  In the old days,
Nows would have bought season tickets; today, they may just
buy for concerts they like or that fall on convenient days.

  To be precise, Nows are not even the number of people.  I
  made the assumption that, for many of us, the decision to
  buy a ticket isn't independent of others.  If I want to go
  and my spouse, partner, or friend doesn't, I may not go --
  or we may both go; it's more of a joint decision.  Thus
  I'm modeling "decision-making entities", which lets me
  pick the nice, round number of some presumed 100 million
  total such entities in the USA out of some 300 million
  total people.

In the TAFTO model, I have three factors I'm varying: media
advertising (ADS), word-of-mouth advertising (WOM), and
audience retention (RET).  In other words, that model, while
only a bit more complex, does allow one to specify various
average retention rates for Nows, so it could cover what
you're describing at least in an aggregate sense.

According to my testing so far, WOM has the biggest impact
on the number of Nows, with RET in second place and ADS an
almost imperceptible third.

Now this model isn't well calibrated, so it may be wildly
off base.  It also doesn't say ADS aren't useful; I'm
beginning to conjecture that ADS have their primary role as
getting Nows to buy tickets by informing them of the who,
when, where, and what of a concert.

What it does suggest is that orchestras might fruitfully
spend some significant amount of their marketing on
understanding why people leave.

I've attached a really rough draft of some results (not even
the draft of the TAFTO column) with graphs so you can see
the sort of stuff I've gotten so far.  I'm suspicious of
some of the results, as I note in the text, and mindful that
I promised this article to Adaptistration first, so please
don't share these further without checking with me.  Drew
will have a final version on Adaptistration sometime in
April, I believe.

> I think I'll stop here. But thanks so much again, Bill,
> for doing this careful and important work, and for sending
> it to me. I'll be eager to see more.

Greg, thanks for taking the time to think through this and
come up with suggestions for improvement.  I will try to
make some of those changes and let you see what the results
are.

More later,

Bill

For the benefit of those of you who didn't see the start of this series, the "Drew" I mention above is Drew McManus, author of Adaptistration, a blog on orchestra management. And TAFTO? Well, you'll just have to read about TAFTO yourself.

You can read the PDF version I sent Greg with higher-quality graphs. In the interest of speed, I put that document together rather quickly; as a result, you'll see blank spaces in the text where the word processor forced a graph to a new page because it didn't quite fit on the current page. Had this been an official report, I would likely have typeset it for a more professional appearance.

Which do you prefer: this PDF version or the original text version? Which do you think better facilitates dialog?

Labels: classical music business, making sense, simulation

Monday, March 19, 2007

Making musical sense by email, part 3

At the end of last week, I posted a link to Greg Sandow's thoughts about the future of classical music and an email I sent him describing my reaction to his thoughts.

Here is Greg's initial response, lightly edited and with his permission:


Bill,

Thanks so very much for sending me your work. It's
remarkable stuff, much more solid and promising than most of
what I see. And your tentative conclusion is quite
challenging (at least for the classical music business) -
that the model that best predicts the observed decline in
attendance (am I getting this right?) is the one that
predicts no new people join the audience at all.

I'd make one request. I'd much prefer to see the original
graphics. I did look at your e-mail in Courier, but it would
be a lot clearer if you could attach the charts as graphics,
or perhaps send a spreadsheet with the charts generated
inside it.

Also, I mustn't forget to congratulate you on your careful
work with that Knight Foundation tidbit. I've seen that too
often used to support the idea that all we need is music
education in our schools, and classical music attendance
will go up again. As if, in other words, there's a causal
connection between playing an instrument and classical music
attendance. As you rightly point out, the Knight study makes
no such assertion, but people jump to the conclusion
anyway. And the statement, which I believe they make, that
playing an instrument is a predictor of classical music
attendance does muddy the waters, since it rather casually
puts playing an instrument earlier than attendance in some
sort of implied statistical food chain. That helps people to
jump to the causal interpretation.

Anyhow, I think you debunked this all really nicely. My own
predisposition is to believe that there's no causal
connection, but that instrumental playing and classical
music attendance are both characteristics of some yet to be
defined demographic slice, the demographic in question being
the one most likely to attend classical concerts. In other
words, roughly speaking, playing an instrument and going to
classical concerts have a common cause, rather than one
causing the other.

Going back to the analysis you sent me, I do have some
suggestions for refinements.

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.

When I made so much of the NEA's statistic about the number
of younger people dropping off, I didn't mean to say that
this would lead to any immediate and directly proportional
decrease in total attendance. I was using the data more
impressionistically. Accepting the apparent truth that
people in this era mostly go to classical concerts when
they're older, I surmised that this younger generation, once
it was 50 years old, would go in smaller numbers than
previous generations did. I should stress that this is
nothing more than a surmise - an assumption. It seems
reasonable, according to common sense, but might turn out
not to be true.

Though when I combine it with certain data about people who
are currently 50 and above, the conclusion seems even more
reasonable. One big change during the past generation is
that older people aren't as committed to high art as they
used to be. They now "consume" (if that's the word) both
high and popular art. This makes them less like the
committed classical music audience of the past, and thus, in
my view, less likely to attend classical performances in the
numbers their predecessors did. Now we have younger people
who go to classical performances far less often than their
own predecessors did. That suggests that when these younger
people are in their 50s, they'll be even more culturally
omnivorous than the present older generation is, and thus
less likely to be committed classical music attenders.

Second (returning to my suggestions for you), you might want
to refine your model to reflect the fact that the total
number of orchestral tickets sold includes many tickets sold
to the same relatively few people - the
subscribers. Currently, subscriptions amount to about 60% of
all tickets sold. This number has declined sharply from the
80% or so reported a couple of decades ago, and is
considered likely to decline still further. Still, this
needs to be an important part of any model that predicts
future ticket sales. It's not enough simply to predict the
number of people likely to buy tickets. We need to know how
many tickets these people are likely to buy, in order to get
some idea of what the total ticket sales in the future are
likely to be. Certainly, in the final analysis, orchestras
are more concerned with the total number of ticket sales
than with the number of individual people attending.

I think I'll stop here. But thanks so much again, Bill, for
doing this careful and important work, and for sending it to
me. I'll be eager to see more.

Best,

Greg

I'm curious: how did that compare with the reaction you thought I would have received? Did your thoughts about what changes you'd like to see me make align with his?

And what did you think of Greg's request to see "original graphics"? (I'll come back to that again later.)

Stay tuned for my response to Greg and to see those requested graphics!

Labels: classical music business, making sense, simulation

Friday, March 16, 2007

Making musical sense by email, part 2

As I promised, here's the first installment in a dialog between Greg Sandow and me on the future of classical music. You might want to start by reading Greg's essay "The short version"; then read my first email to Greg (below). Drew McManus was included in this dialog, although the emails you'll see all involve only Greg and me.


Greg,

I've been following your thoughts for a while, and I've been
discussing a few ideas they've provoked with Drew McManus,
now that he and I have collaborated on a column
(http://pegasuscom.com/aar/model7.html) and associated
computer model.

I'd like to share those ideas with you and see what you
think; perhaps they'll be of use, or perhaps you'll be able
to educate me and help me refine my thinking.  I've copied
Drew on this, in case he has some comments he'd like to
make.  As you'll note, this email is a bit lengthy; I hope
it's useful to you.

By the way, I'll be showing some text-mode graphics below in
an attempt to make this an easier email to read.  Hopefully
you can view this email using a non-proportional font such
as Courier so that those graphs make sense.  If that's a
problem, let me know, and I'll create this in another
format.

One thing that caught my attention was your claim of aging
audiences.  In
http://www.artsjournal.com/sandow/2006/11/important_data.html,
you note that, among other facts, the average audience age
went from 45 in 1992 to 49 in 2002.  You think about the
potential causes and implications of such a development.

One way to think of such a problem is to see if we can
"operationalize" it: can we generate an operational
description of the events that we conjecture are playing out
in the real world, is that operational description similar
structurally to the real problem, and is that operational
description capable of generating similar behavior?  (See
the link near the end of
http://facilitatedsystems.com/weblog/2007/01/systems-language-for-business.html
for more on operational thinking.)

In simpler and more specific words, can I create a computer
model that captures your hypotheses, and does that model
behave as your data shows?  Success doesn't mean I've proven
anything, but failure might indicate a need for modified
hypotheses.  Enough success, combined with a bit of
triangulation, can strengthen our belief in those
hypotheses.

I like to start with really simple models, adding complexity
only when it becomes necessary.  Often we can learn the most
from those simple models.

Here's a simple model of an "aging chain" that might
represent classical music audiences.

   ..+- .                                                   . .--...m.-
  -+*.+..                +------------------+               ---% +-m++m-
.-.+m*++                 |                  |              . +###+#*#m.-.
-mm#*#%#.       +--+     |  Aging Chain of  |      +--+    --.#+mm%###++
 +.#%#*+. =====>| A|====>+                  +=====>| B|=====>.#*%-##+#m.-
.+%m*+++ .      +--+     |  Concert-Goers   |      +--+    ..m+%###m##..
..%-+% .                 |                  |               . --++m+%+--.
  ..+.                   +--------+---------+                  ..+-..%+ .
                                   \                           .  +-.-
                                    \
                                     \
                                      V

                                   Average
                                     Age

The "aging chain" in the middle is a series of "stocks," one
for each decade of age (arbitrarily twenties through
sixties; I don't think the dynamics change much if we add or
subtract a decade at one end or the other).  People move
through those stocks, taking ten years to move from one to
the other.  I've aggregated those stocks into one mega-stock
to simplify the graphics; if I had drawn the entire picture,
you'd have seen five rectangles, connected in a chain by
flows (pipes), instead of that one bigger rectangle.

The "clouds" at the left and right simply mean we don't care
where those people come from or where they go, at least for
the sake of understanding concert audiences; that's outside
the purview of this model.  There are two flows, shown here
as valves called "A" and "B" on pipes that flow from one
stock (or cloud) to another.  Flow A represents the number
of new concert-goers per year, and flow B represents the
number of people leaving the concert-going world each year.
In this simple model, 50-year-olds don't all of a sudden
decide to become concert-goers, and 30-year-olds don't all
of a sudden give up on classical music.  Those are
constraints we can lift later, if we want to.

Now it's easy to talk about what changes the number of
concert-goers: if A is greater than B, you get more
concert-goers, while if B is greater than A, you get fewer.
If A = B, the aggregate audience size stays static.

I've shown "Average Age" as a statistic we can calculate to
describe concert-goers, the same as we can describe their
total number.

I created a computer simulation of that model.  I set the
initial population of concert-goers to 1 million, spread
evenly in age across the five decades (stocks) I modeled.
If we have 20,000 new concert-goers each year, we'll exactly
replace the number of concert-goers who depart each year,
and the number of concert-goers will remain constant.

Because you hypothesized that young people had stopped
coming to concerts, I tested the model by running it over a
200-year period with 20,000 new concert-goers in the first
10 years and then half that thereafter.  Would that
replicate the data you were quoting?

Here's the graph of the number of new concert-goers per
year:

20000 AAAA-------------+----------------+----------------+---------------++
      +                +                New Concert-Goers per year   A    +
      |                                                                   |
      |                                                                   |
      |                                                                   |
15000 ++                                                                 ++
      |                                                                   |
      |                                                                   |
      |                                                                   |
      |                                                                   |
10000 ++ AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
      |                                                                   |
      |                                                                   |
      |                                                                   |
      |                                                                   |
 5000 ++                                                                 ++
      |                                                                   |
      |                                                                   |
      |                                                                   |
      +                +                +                +                +
    0 ++---------------+----------------+----------------+---------------++
      0                50              100              150              200

                                       Year

It stays constant at 20,000 per year until year 10,
whereupon it drops to 10,000 per year throughout the rest of
the 200 year time horizon of this simulation.

Here's a graph of the total concert-going population from
that model:

1e+006 AAAAA------------+----------------+---------------+---------------++
       +   AAA          +                +               +   Total   A    +
       |     AA                                                           |
       |       AA                                                         |
800000 ++       AAA                                                      ++
       |          AAA                                                     |
       |            AAA                                                   |
       |              AAA                                                 |
600000 ++                AAAA                                            ++
       |                    AAAAAAA                                       |
       |                           AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
       |                                                                  |
400000 ++                                                                ++
       |                                                                  |
       |                                                                  |
       |                                                                  |
200000 ++                                                                ++
       |                                                                  |
       |                                                                  |
       +                +                +               +                +
     0 ++---------------+----------------+---------------+---------------++
       0                50              100             150              200

As you can see, the number of concert-goers stays at 1
million for 10 years and then begins a steady decline,
stabilizing at 500,000 at about year 80.

Here's the average age:

  48 ++---------------+----------------+-----------------+---------------++
     +           AA   +                +               Average Age   A    +
     |          AA AAA                                                    |
47.5 ++        A     AA                                                  ++
     |        AA      AA                                                  |
     |        A        AA                                                 |
     |       A          AA                                                |
  47 ++      A           A                                               ++
     |      A             A                                               |
     |      A              A                                              |
46.5 ++     A              AA                                            ++
     |     A                AA                                            |
     |     A                 AA                                           |
  46 ++    A                  AA                                         ++
     |    A                    AA                                         |
     |    A                      AA                                       |
     |   A                        AA                                      |
45.5 ++  A                         AAA                                   ++
     |                               AAAA                                 |
     +   A            +                + AAAAAA          +                +
  45 AAAA-------------+----------------+------AAAAAAAAAAAAAAAAAAAAAAAAAAAAA
     0                50              100               150              200

Note that the vertical axis starts at 45, not 0.

At first glance, this seems intriguing.  Audience population
is declining and average age increases, at least for a bit.
Then, under these assumptions, average audience age drops
back to the same 45 years old.  Could that be?  Is it
possible that we're just on the front end of a declining
audience size, and audience age will correct itself
naturally?

There's something else a bit off here, though.  In "Where we
stand (2)," you provide a graph that indicates the mode of
age has gone up about a decade from 1992 to 2002, consistent
with your cohort theory, and you note elsewhere that the
average age rose by about 4 years.  This model only shows a
2.8 year increase in average age, and that's over about 30,
not 10, years.

What if the decline in the number of newcomers was a more
gradual and continuous decline?  Here's an
exponentially-declining number of new concert-goers each
year, starting at 20,000 and declining to half that in 69
years:

20000 AA---------------+----------------+----------------+---------------++
      +AA              +                New Concert-Goers per year   A    +
      |  AA                                                               |
      |    AA                                                             |
      |      AAA                                                          |
15000 ++       AAA                                                       ++
      |          AAA                                                      |
      |             AAA                                                   |
      |               AAAA                                                |
      |                  AAAA                                             |
10000 ++                    AAAA                                         ++
      |                         AAAA                                      |
      |                            AAAAA                                  |
      |                                 AAAAA                             |
      |                                     AAAAAAA                       |
 5000 ++                                          AAAAAAAA               ++
      |                                                  AAAAAAAAA        |
      |                                                           AAAAAAAAA
      |                                                                   |
      +                +                +                +                +
    0 ++---------------+----------------+----------------+---------------++
      0                50              100              150              200

Since the exponential doesn't drop as fast, you might expect
the number of total concert-goers to drop more slowly; since
the number of new concert-goers continues to drop forever,
you might expect the total concert-going population to
continue to decline.  You'd be right:

1e+006 AAAAAA-----------+----------------+---------------+---------------++
       +     AAAA       +                +               +   Total   A    +
       |         AAAA                                                     |
       |            AAA                                                   |
800000 ++              AAA                                               ++
       |                 AAAA                                             |
       |                    AAA                                           |
       |                       AAA                                        |
600000 ++                        AAAA                                    ++
       |                            AAAA                                  |
       |                               AAAAA                              |
       |                                   AAAAA                          |
400000 ++                                      AAAAA                     ++
       |                                           AAAAAA                 |
       |                                                AAAAAA            |
       |                                                     AAAAAAAA     |
200000 ++                                                           AAAAAAA
       |                                                                  |
       |                                                                  |
       +                +                +               +                +
     0 ++---------------+----------------+---------------+---------------++
       0                50              100             150              200

Remember that the drop-off starts in the first year in this
experiment, not the tenth year.

What about the average age of concert-goers?  Since the
decline in new, young concert-goers continues forever, you
might expect the age boost to last forever.  Since the
decline is less drastic, you might expect the age boost to
be less drastic.  Let's see:

47.5 ++---------------+----------------+-----------------+---------------++
     +                +                +               Average Age   A    +
     |                                                                    |
     |                                   AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA
  47 ++                        AAAAAAAAAA                                ++
     |                     AAAAA                                          |
     |                   AAA                                              |
     |                 AA                                                 |
46.5 ++              AA                                                  ++
     |             AAA                                                    |
     |            AA                                                      |
     |           AA                                                       |
  46 ++         A                                                        ++
     |         A                                                          |
     |        A                                                           |
     |       A                                                            |
45.5 ++    AA                                                            ++
     |    AA                                                              |
     |   AA                                                               |
     + AA             +                +                 +                +
  45 AAA--------------+----------------+-----------------+---------------++
     0                50              100               150              200

You'd be right in both cases, although the difference in the
peak average age is miniscule: 47.0971 vs. 47.7867 years.

So neither explanation seems to account for the drastic
aging of the concert-going audience as reported in the data.

What if we take a drastic approach and cut off new
concert-goers totally after 10 years?  Under those
conditions, here is the total concert-going audience:

1e+006 *****------------+----------------+---------------+---------------++
      +   *            +                +               +   Total ****** +
900000 ++   *                                                            ++
      |     *                                                            |
800000 ++    **                                                          ++
      |      **                                                          |
700000 ++      **

Making Sense With Facilitated Systems

Thursday, December 27, 2007

Top postings of 2007

Monday, May 21, 2007

A leisurely snapshot of the USA

Thursday, May 03, 2007

But is it art?

Monday, April 09, 2007

TAFTO 2007

Monday, April 02, 2007

TAFTO starts now

Wednesday, March 28, 2007

Making musical sense by email: the table of contents

Tuesday, March 27, 2007

Making musical sense by email, special feature

Monday, March 26, 2007

Making musical sense by email, appendix B

Making musical sense by email, appendix A

Friday, March 23, 2007

Making musical sense by email, part 7

Thursday, March 22, 2007

Making musical sense by email, part 6

Wednesday, March 21, 2007

Making musical sense by email, part 5

Tuesday, March 20, 2007

Making musical sense by email, part 4

Monday, March 19, 2007

Making musical sense by email, part 3

Friday, March 16, 2007

Making musical sense by email, part 2