Is education always behind the times?

When I was at the university, I majored in electrical engineering and math. Encouraged by Michael Morrison, my physics major roommate at the time, I took an excellent English course taught by Dr. David Minter that was intended for English majors. Normally I would have been leary of my chances at a decent grade, but this was the start of pass-fail options at my school.

We read and wrote a report on a major novel a week, as I recall. One of those was The Education of Henry Adams. Recently, I picked it off the shelf and re-read it. It's amazing how much more sense it made, now that I have a few more life experiences. I was glad to have read it at the time; I was glad to read it again this time.

Within the last few months, I watched L'armée des ombres (Army of Shadows). One of the lessons I drew from both of these is that our educations don't prepare us well for the world in which we find ourselves. The Resistance fighters had to kill a traitor, their very first person to kill. Adams, prepared mentally and culturally for the eighteenth century, had to prepare himself for the beginnings of the twentieth.

Perhaps the lesson is that our educations, as important as they are, always teach us about the problems of the last generation. Our challenge is to apply those and more insights and wisdom to the needs of today. Our challenge is to learn from our education how to learn ourselves, how to complete our education, how to rise to the challenges we face. Taken at face value, my education prepared me for a world that's no longer visible. Perhaps the key thread that ran through my education (and perhaps yours) was the concept that education was not about learning something. Education was about learning how to learn what you needed to learn for the future.

For us, I suspect the needs of the day include figuring out how to live together on a planet that seems increasingly small and learning how to live in an age that is facing climate change, the end of oil, and a transition to equilibrium (or so we can hope).

What is the education of me? What is the education of you? Can we help each other in this cause?

Recognizing one's errors

Justin Kruger and David Dunning of Cornell University published Unskilled and Unaware of It: How Difficulties in Recognizing One's Own Incompetence Lead to Inflated Self-Assessments.

My first reaction is that no one here is subject to the Dunning-Kruger effect; we're brighter than the bank robber described in their first example. My subsequent thought is that I may be letting us off the hook too easily; perhaps we're all subject to the Dunning-Kruger effect in the right domain. None (well, perhaps darn few) of us are highly competent in everything, but we still may be tempted to make pronouncements in knowledge domains where our expertise lags that of our peers. That conclusion is scarier. Knowing oneself is apparently not easy.

Read their article to get some ideas how to test our thinking, and compare that to my earlier postings on scepticism. This sounds related to the idea of confirmation bias, or maybe it's similar to the Lake Wobegon effect.

How do we get around this problem? As best as I can see, life-long learning plays a key role, for it fits with their prediction 4. I suspect careful observation and reflection can help, too, for that might help us recognize our abilities.

Your thoughts?

Thanks to RealClimate for pointing out the article via the Wikipedia article.

Causality

When we evaluate something, we typically are trying to understand and make claims about causal relationships. When we create a system dynamics model, we are mapping and modeling causal relationships. But how do we tell what relationships are causal and which are correlational?

Thanks to a recent pointer on the evaltalk mailing list, here's Sir Austin Bradford Hill's “The Environment and Disease: Association or Causation?” Hill gives nine considerations to ponder.

For a rather shorter read, see xkcd's take on causality. Be sure to see the alt tag.

The (un)Sustainable Commentator on growth

Just to keep the question series on growth going, here's what Wayne Maceyka is saying on The (un)Sustainable Commentator.

Check out Wayne's blog, too, and his extensive list of links in the right-hand column.

Questions on growth: a follow-up

After all of our good questions about growth, we've gone silent, so I was about to change topics. Then I saw The Growth Bubble on Tom Fiddaman's MetaSD and its link to Thomas Friedman's The Inflection Is Near?

It's a Friday night after a long week, so I'll leave it to you to make the editorial comments.

The chicken or the egg?

Perhaps you've been wondering what in the world system dynamics is good for.

System dynamics can help you answer the question, "Which came first: the chicken or the egg?", which you can only do if you look at both the chicken and the egg at the same time.

I was reminded that I wrote that back in 2003 when I saw Tom Fiddaman's SD on Long Waves, Boom & Bust. Click on my name "Where are we in the long wave?" in that post to the the thread in which it occurs.

The metric system and math skills

I once suggested that eliminating the use of the customary system of units in this country would help us learn and compete. Now Richard Slettvet, a local teacher, has made the same point in A logical way to improve math scores.

Is it time to act? Who will you engage?

Feel free to tell others about this post.

Cool tool

If you like making sense of (or with) numbers and use Linux, check out Qalculate!. The screenshots give you an idea of its power and ease of use.

Number 500

I made my first blog posting in May 2004. Then I waited until October of that year to make my second. It started a trend of including environmental issues, not in addition to business issues but because I think environmental issues are business issues. Finally, in March 2005, I began blogging on a more regular basis.

Now, not quite five years after I started, I've reached my 500th posting. I've enjoyed getting to know some of you along the way, and I've enjoyed sharing ideas and getting your comments, insights, and feedback. I invite you to share this link with others and to continue to share your ideas with me. Thanks!

Good graphs

Doing graphs well is important for communicating information (you do use graphs, don't you?). Rafe Donahue has published Fundamental Statistical Concepts in Presenting Data: Principles for Constructing Better Graphics. I think it's well worth our time to read and heed.

Thanks to Andrew Gelman for the tip.

Questions on growth: a pre-evaluation

You've helped me accumulate quite a list of questions about growth, and I thank you. Here they are once again:

1. What if (aggregate) growth went to 0% forevermore? What would it mean to you, to your business, and to your personal life?
   
   
2. Do you think there's reason to believe growth could stay at 0% or below for a very long time? Is that of necessity either good or bad, or does its value depend upon our reaction?
   
   
3. Is it a good idea to try to keep growth positive? Why? Are there any downsides? Are there indeed any limits to growth, either in terms of annual growth rates or the overall size of anything?
   
   
4. As attractive as growth may be in the current worldview, systems ideas would indicate that the longer we try to keep growth going once we've exceeded the carrying capacity of the system (the planet), the worse the eventual and inexorable fall and the lower the eventual sustainable standard of living. If you favor continued growth, how will you overcome those seemingly inviolate systems limitations?
   
   
5. If the systems theories play out in the real world, how do we reasonably make the transition from our current state to a new, equilibrium state in ways that attend to people (social justice, the ability to procure what we need for life, the ability to make a difference or find purpose, etc.) and the natural environment (sustainability, the depletion of nonrenewable resources)?
   
   
6. Can we make such a transition a good thing and not a painful thing?
   
   
7. What do we owe our descendants? For how far into the future do we bear responsibility?
   
   
8. If these systems ideas have merit, what changes in mindset (in worldview) do we need to survive emotionally as well as physically? For but one example, negative growth has long meant failure for people leading businesses, but that could be the way of the future. Can we realistically change our mindsets and our systems so that satisfying needs (instead of generating growth) defines success?
   
   
9. In case these systems ideas don't apply in this situation (e.g., if technology can once again save us even in the face of decreasing energy supplies and rising population), can we design robust actions that work well in either eventuality? How do you answer Tom Fiddaman's questions about the sufficiency of technology?
   
   
10. Can the current economic "engine" be morphed into one based on such a radically different paradigm?
    
    
11. If we change to a different paradigm that's not built on growth, can we figure out how to get money into the hands of all who need it? Or will our reaction to low or zero growth be to trim people out of companies to keep the organizations viable while building unemployment?
    
    
12. Is an economy built on lending inexorably drawn to growth for survival?
    
    
13. How we to encourage understanding that quality of life can still improve while quantity of consumption decreases?
    
    
14. In light of the current economic crisis, how can we best protect the economy's life-supporting functions such food production, health care and ecosystem services amidst the chaos that will undoubtedly trim the less important financial and luxury markets?
    
    
15. What new national and international policies and institutions do we need to design in order to prepare for a transition to a steady state, or true cost, economy that recognizes the need for investments in natural and social capital as well as financial?
    
    
16. What would constitute the analogy of complex relationships those with "imaginary" components?
    
    
17. Will a state of zero net growth become a state of dynamic economic equilibrium, and will this new state actually make markets MORE efficient, and effective at elevating the state of the common man?
    
    
18. Why is our world so hung up on growth to begin with? How did growth get into our DNA? Has it always been there or is it just since the invention of the steam engine?
    
    
19. What do you do if evolution favors individuals or groups who aspire to growth?
    
    
20. What if the US and EU go green and China and Russia don't?
    
    
21. What if growth had to be -X% per year for Y years in order to reach a sustainable steady state (in material throughput)? How might social systems accommodate that peacefully?
    
    
22. What if technology has limited potential?
    
    
23. What would an evolutionary landscape that favored sustainability look like?
    
    
24. Can wealth can go up while the material flow goes down? How?
    
    

I've numbered them this time for easy reference. If you want to see who contributed each question, refer back to the original posting.

Now the next step: how would one go about answering these questions?

Before getting to the process, though, I'm curious how you think we would recognize a good answer. I don't expect that we'll all agree, but, rather than getting into a bunch of statements about our respective positions, I think we might learn more by first thinking about the criteria by which we'll evaluate potential answers.

I suspect our answers to that may be all over the map. As food for thought, I offer up the questions critical systems heuristics offers about motivation, control, expertise, and legitimacy. You can find one article by Bob Williams and Martin Reynolds on Bob's Web site: go to Systems Stuff and scroll down to the article called Critical Systems Thinking. You can see another introduction by Werner Ulrich here. He lists the questions starting on page 11, but you probably need to read the earlier pages to understand what it all means.

Once we have some idea how we'll evaluate potential answers (and how we think we should evaluate them), then we can think about picking approaches, methods, methodologies, or processes we think might be of use for each of these 24 questions.

So what do you think? How will you evaluate the answers to these questions? How do you think you should evaluate them?

Questions on growth

From time to time, I've posted some of my musings about growth. I had a related email discussion recently with a colleague, and one of my emails to him contained questions that I'd like to ask you, too.

We know the world's economies are currently experiencing hard economic times. Yet I wonder if thinking of this in terms of business cycles is the best way today. Jay Forrester has written, "Our greatest challenge now is handling the transition from growth to equilibrium".

If you think of the world in terms of business cycles, getting from bad times to good is a largely waiting game, assuming you've saved enough in the good times to carry you through the bad. Before refrigeration and long-distance transportation of food, that happened on an annual basis, too: you had to save enough food from this year's harvest to survive to next year's. Admittedly, some organizations are good at gaining ground during these periodic "yellow flag" situations, but many of us will just try to survive until the good times return.

If Forrester and others are correct and we're really moving from a long period of growth into an even longer period (possibly forever, at least in practical terms) of relative equilibrium, then dealing with those (equilibrium) times may require a fundamentally different mindset (as Cynthia McEwen and John Schmidt would remind us), not just a changed process.

• What if (aggregate) growth went to 0% forevermore? What would it mean to you, to your business, and to your personal life?


• Do you think there's reason to believe growth could stay at 0% or below for a very long time? Is that of necessity either good or bad, or does its value depend upon our reaction?


• Is it a good idea to try to keep growth positive? Why? Are there any downsides? Are there indeed any limits to growth, either in terms of annual growth rates or the overall size of anything?


• As attractive as growth may be in the current worldview, systems ideas would indicate that the longer we try to keep growth going once we've exceeded the carrying capacity of the system (the planet), the worse the eventual and inexorable fall and the lower the eventual sustainable standard of living. If you favor continued growth, how will you overcome those seemingly inviolate systems limitations?


• If the systems theories play out in the real world, how do we reasonably make the transition from our current state to a new, equilibrium state in ways that attend to people (social justice, the ability to procure what we need for life, the ability to make a difference or find purpose, etc.) and the natural environment (sustainability, the depletion of nonrenewable resources)?


• Can we make such a transition a good thing and not a painful thing?


• What do we owe our descendants? For how far into the future do we bear responsibility?


• If these systems ideas have merit, what changes in mindset (in worldview) do we need to survive emotionally as well as physically? For but one example, negative growth has long meant failure for people leading businesses, but that could be the way of the future. Can we realistically change our mindsets and our systems so that satisfying needs (instead of generating growth) defines success?


• In case these systems ideas don't apply in this situation (e.g., if technology can once again save us even in the face of decreasing energy supplies and rising population), can we design robust actions that work well in either eventuality? How do you answer Tom Fiddaman's questions about the sufficiency of technology?
  


• What other questions do you have?

Let me be clear: aggregate growth of 0% does not imply that there is no dynamism, no growth in the economy. Our need for certain products and services will increase, even as our need for others will decline. I'm only thinking of aggregate growth in posing these questions.

I have lots of questions and only tentative answers. Right now, I really am interested in seeing your questions, as I've seen the power of listing questions before trying to provide answers.

Later, we'll get to considering answers and how we might test those answers for reasonableness and usefulness.

Questions?

Sustainable Energy without the hot air

You may have noticed it's sometimes hard to get good data on issues of current importance. We read and hear adjectives, but we too rarely hear numbers. When we do, they're often presented in ways that are not conducive to clear understanding. I've written about that from time to time, for grounding decisions in good data seems to be a fundamentally important skill.

I've also written about the environment, for I think we do and will face challenges of the sort our ancestors never had to address (There: more descriptive phrases! Relief is on the way.).

Today Andrew Gelman pointed to David MacKay's free book Sustainable Energy -- without the hot air as an example of a book that brings data to the fore of the discussion about sustainable energy. In general, he likes the way the data is portrayed, although he doesn't attempt to vet the book for its content. While I haven't yet double-checked any of the numbers, I have begun to read the book, and I find the data clearly, cogently, and interestingly put (quite a change from William Farr's advice to statisticians). I like that he seems to use a significant number of clear time series ("behavior over time") graphs, and the time horizons are long enough to see useful patterns developing. So far, it appears as if this work will help me put events in perspective; I'll be curious to see what I learn and what reactions I have as I finish the book.

While I'm reading it, I encourage you to get a copy, too, and see what you think. Perhaps we'll all learn something important, both about living on the planet successfully and about presenting data effectively.

Tabbloid!

So you'd like to get the news delivered to your door daily, but you'd really like to pick and choose what news that is. Perhaps, as I've suggested before, you'd like a broader range of stories, perhaps seen from viewpoints in your country and in others. Perhaps you'd like more emphasis on one field than you get in your local newspaper.

Now you can get that delivered to your inbox for free from Tabbloid, an HP development. It's simple, and you get a nice, letter-sized PDF document on the schedule of your choice. This would be great for public transit commuters: print out your personal Tabbloid before leaving for work, and you have custom news of the day you can finish by the time you arrive at your place of work (or at least by the time you arrive home).

There are risks. Just by taking some interesting selections from my current RSS feeds and generating a sample issue, I got a 24-page document that took about 800kB. I know there are other feeds I'd like to include, and I know there are some feeds I selected I'll probably delete soon (I don't read them all daily; I simply skim for useful material when I have time). If I can print for US$0.05 per sheet, that 24-page document will cost me US$1.20 a day—more than I would spend for the local paper even if I bought it at a newstand (duplex printing gets it down to US$0.60, but that's still more than the US$0.50 newstand price of my local paper).

Spending that much daily could drive me to cancel my newspaper subscription, which is part of a dynamic that moves revenue from the news media to the paper (and ink) industry. None of that revenue gets back to the writers of the news; what effect will that have?

Printing my Tabbloid would consume a ream of paper in about a month of workdays. That generates a lot of waste; even with recycling, that doesn't sound like a great idea.

Don't forget that a piece of paper doesn't have hyperlinks, either. Subscribing to a feed whose articles consist heavily of links won't be of much use here.

There are a few features I wish Tabbloid had:

• It would be nice to be able to arrange the order of feeds in the final product, perhaps by dragging and dropping the list of feeds on Tabbloid.
  
• It would be nice to be able to create Tabbloids and then put them on hold for a while or for specified periods. For example, I might want an international news Tabbloid I would only read occasionally, or I might want a different Tabbloid on the weekend than on weekdays. I can probably do the latter with judicious use of additional email accounts, but I'm not sure about the former.
  

Nonetheless, I'll try it for a few days and see what I think. It is a creative idea from HP, and it could be helpful for commuters who lack the time to catch up on their RSS feeds but have time sitting in buses and trains.

Perhaps this is an alternative to advertising-supported news delivery: instead of paying for Tabbloid through ads, perhaps we're paying for it through printing supplies.

Perhaps I can be selective enough to make a much shorter paper (and I won't be printing it for now, at least).

What do you think?

Making more sense with numbers, part 7

Tim O'Reilly tweeted about Florence Nightingale: The passionate statistician. While I'm not a fan of pie charts, Nightingale's work here is impressive, especially given the date. We can probably ignore William Farr's advice about statistical writing, though.

By the way, I got off on my numbering in this series, giving several postings the number 4. You can now find the real Making more sense with numbers, part 5 and Making more sense with numbers, part 6, as well as earlier postings in the series.

Heretics, skeptics, and cynics: your ideal business partners!

Art Kleiner has written The Age of Heretics, celebrating those who are loyal to our organizations but see reality somewhat differently. Günter Grass wrote Aus dem Tagebuch einer Schnecke, celebrating, among other things, skeptics and questioners. Now the TP! Wire Service points to Working best: Cynicism not always workplace hindrance by Bill Repp of the Organization Development Group.

For more on the topic, see The importance of a focus on disconfirmation, Skepticism revisited, and Skepticism, numbers, and making sense.

Secret tool for online meetings

Have you ever sat in an informal (no slides) Web-based meeting and tried to keep up with what's going on? When the meeting was over, have you wondered what was decided?

Here's a small secret: I've found it helpful to use FreeMind, a free mind-mapping tool, to take notes during meetings. I often share Freemind through the Web-based meeting tool so that it works as a virtual flip-chart: everyone can see what I'm recording, people can suggest corrections, and I can hide parts easily when we're addressing other issues. When the meeting is over, I can convert it to PDF or HTML or any of a number of other formats and distribute meeting minutes with no additional work.

That's very related to Bernie DeKoven's technography (be sure to watch the video to understand how this works).

Try it sometime; you might find you like it.

Dealing with risk and uncertainty

Times are uncertain. Risks seem high. We may feel that the price of missteps is high; we know it's hard to decide what steps to take.

In situations such as this, how do you make decisions in and for your organization? How do you plan effective actions? How do you solve the inevitable problems that arise?

The German psychologist Dietrich Dörner, author of The Logic of Failure, has made a career of studying why people make mistakes and what we can do to improve. One of his key pieces of advice is to use computer simulation to get insight about the situations we face so that we can make better decisions in real life.

Perhaps today's uncertainties are your signal that the time is right to apply more systemic approaches in your work and to ground your planning, problem solving, and decision making with simulation that takes into account factors important to your business. Perhaps it's time to test and rehearse your plans before you implement them.

That's what we've been discussing here, and that's how I help others. If you're concerned that your standard approach to business may need augmentation in today's world, perhaps I can help you, too. Drop me an email or give me a call. There's no obligation—only opportunity.

Road Maps to Peace

Yesterday I blogged about what I see as one of the two big problems we face: how we deal with the load we're placing on the planet. I think the other big problem is how we deal with disagreements among those of us who live on this planet. As the load we place on the planet increases, I think the importance of figuring out how to get along will increase, too.

Some of you may know of Rick Steves as a travel guide who also writes newspaper columns, produces TV and radio shows, owns a travel agency, and sells travel gear. He also has a more serious purpose in life, or, perhaps, as I've written about Bernie DeKoven and sustainability, his more serious purpose is inherent in what he does in his travel business.

On September 6, he hosted Lord Alderdice in a discussion called Road Maps to Peace. Lord Alderdice played a key role in helping people work to end the "Troubles" in Northern Ireland, and he has continued his work by helping others to reduce conflict in their regions.

Rather than me taking the time to summarize Lord Alderdice's message, listen to him directly. The recording is about an hour long. I encourage you to listen carefully, with an open mind, to the end, for there are many good ideas. Often we hear people expressing opinions (or express them ourselves) about how peoples should get along; Lord Alderdice is talking about what has worked in helping peoples to get along together. I found ideas that I can apply in interpersonal relationships as well as in thinking about larger political issues we help decide at the ballot box or in dialog with our elected officials as well as in working with or inside organizations.

One message he gives (but not the most important—I'll let you listen to find the essence of his ideas) is that we should all listen to news from other parts of the world so that we gain a broader perspective. That's a message I've given here before. I've long thought we should strive to have personal friends and professional contacts in multiple parts of the world, for I suspect we'd be more likely to work things out and less likely to go to war with countries where we have those connections.

As with my previous message, I really would appreciate your comments on these ideas.

The Marblehead Letter

I've written about my musings on growth a number of times, even as I worried that my ideas might be controversial.

Now I've seen the Marblehead Letter, written by executives at a SoL conference in 2001, and I think those of you reading this blog might find it worthy of your time. Read both the full letter—it's only two pages long, and I think it states its questions better—and the summary, which hints at some of the signatories.

Note that the letter has questions, not answers, and note that the letter comes from people high in the ranks of major organizations.

I discovered this letter by reading Presence: An Exploration of Profound Change in People, Organizations, and Society, courtesy of InBubbleWrap and 800ceoread. For various reasons, that's been a hard book for me to get through, but I'm persevering (and those of you who have a copy can tell the page I've reached by this blog posting). Perhaps I'll blog more about it when I finish it.

What do you think? It's comforting to know that others are considering similar questions to the ones I've been raising. Question 3 is exactly what I want to work on, but reading it brings two thoughts:

• You can't address that one question in a vacuum; you have to consider their other questions and still more (for example) in the process. It is a systems issue on multiple levels.
  
• I wonder if they didn't go far enough in question 3. They want to reconceive growth. I wonder if and how and under what conditions overall sustained growth is possible and good for us. If, in aggregate, it is not (and I have yet to see evidence that the systems mantra of "there are always limits to growth" is false), I want to help find a new and successful way forward. While we have to address the long-term situation, I'm more interested in helping us figure out how to make the transition from growth to sustainability, whether on an organizational, societal, or personal level.
  

Those of you acquainted with some of the literature on growth will realize that a stable system doesn't mean there is no growth. For example, in a business sense, some technologies, products, or services outlive their usefulness, and their companies shrink or perhaps go out of business. Other technologies, products, or services are needed in increasing amounts, and their companies grow. Equilibrium in the aggregate doesn't require equilibrium in the details.

While I'm optimistic we'll figure out a way to deal with this, I still think the issue of growth is an integral part of one of the two major problems we face as a people. From what I read, we may well have exceeded the carrying capacity of the planet. If that be true, then these are important times, for the way we respond can likely have a major effect on the response of the systems in which we live, and the recovery of a system from overshoot can be harsh.

I really would welcome your comments on this.

Two big problems

I've written before about what I see as the two big, high-level problems we face (it's the weekend—I'll let you search for the articles). This week, I plan to publish articles on both. Monday's article on one is mostly written, and I'm beginning to address the second.

Check back next week!

Twittering away my life

Well, I'm not that into it, but I have started twittering. If you're a Twitter user, sign up to follow me, and I'll likely follow you, then, too.

Stocks, flows, and the President's weight

I've noted before the importance of thinking appropriately about stocks and flows. Janice Molloy of Pegasus Communications just wrote "A Weighty Take on Stocks and Flows" for the August 2008 issue of The Systems Thinker, using stocks and flows to communicate the message of a New York Times column by Gail Collins. It's a good tale; if you subscribe to The Systems Thinker, check it out, or check out the original column.

It was fun talking through the implications of these ideas with Janice, creating a few simple models together to clarify Collins' message, and producing the diagrams that Janice used. There is a message hidden in the fun, though: be sure you understand what they really mean when someone says they'll reduce the rate at which something is growing. It may not be all that good a deal.

Looking from the outside in...seven months and a trip later

Last January, Henrik Müller posted an article called "Amerika steht mit dem Rücken zur Wand" ("America stands with its back to the wall"). Now he's made a trip through the USA and written again in "Amerikas schwärzeste Stunde" ("America's Blackest Hour").

If you read German, check out the article, and let me know what you think. If you don't, here's the short version:

• The USA faces as serious a challenge as we've faced in years, perhaps as serious as the Great Depression.
  
• The difference between this challenge and many of the others since the Depression is that individual people are now impacted in real ways.
  
• While we have some number (he doesn't really guess how many) of tough years ahead of us, we have a way in our culture of getting through tough times successfully. He thinks our situation parallels that in Germany from 1993 through 2005, but he thinks we'll be more successful in breaking out.
  

If you do read German, feel free to add key points you think I missed. You can also read his comments in Google's translation.

What do you think? Maybe more importantly, where do you think we should be headed? What should business look like? How about the economy? How about society? How should we get there? Perhaps a return to 2006 isn't what we want or need for 2010. Feel free to add your ideas below.

Prediction, system dynamics, and Future-Fusion

Recently, I made the claim that we're better off focusing on adapting to the present than predicting the future. I've made similar claims in the past, too. I've even given one example in which predictions serve a useful purpose.

That's all a bit simplistic, of course. Even system dynamicists could be said to predict the future in a way: we show behavior over time we feel is more likely to occur (although we may warn people away from point predictions based on a behavior over time graph). In other words, I might suggest that your current policies could produce a boom and bust effect in your business, but I wouldn't want you to draw the conclusion that your business will grow another 172.3% by June 15, 2009 before taking a tumble that afternoon.

Because we all would like to know the future, I've experimented with blending system dynamics and Bayesian analysis to quantify the probability of a particular behavior pattern, for example. Of course, that probability is conditioned on both the historical data and the model being correct, which is a loophole big enough for a good-sized locomotive to run through: models are always incorrect. Still, I think this approach may give more useful insight in certain cases.

Now Kshanti Greene of Stottler Henke Assocates, Inc. has shown me a Bayesian tool they've developed called Future-Fusion, and I've been exploring it a bit. They are using Bayesian networks and the power of groups to get a better handle on what the future holds. Much as Data360 looks at the past, Future-Fusion attempts to look at the future. As of this writing, they've created four test areas which you can explore: the 2008 US presidential election, the Iraq war, corporate strategy, and energy. Try it out: learn how to use the system, see current predictions, and add your own (I think you only have to create a free account if you want to add your own predictions). Perhaps you'll learn something, and perhaps they will, too.

Kshanti has pointed out a recent addition to Future-Fusion that may intrigue some of you: time. They've enhanced their technology to allow limited dynamic execution of a network model, which begins to narrow the gap between Bayesian networks and system dynamics from the Bayesian network side, much as what I've tried has narrowed it from the system dynamics side. To try that out, go to the energy model, select a prediction (e.g., "Reduced SUV sales"), click "view graph," note the numbers, and then click "Next Time Step."

I think this is all still experimental in many ways, but it's a good opportunity to learn a bit about this technology by trying it out on real-life issues. I'll be curious what you discover.

Why are we here?

Why are we here in business, that is?

You can find lots of answers, and "making money" seems to crop up frequently, at least in informal conversations. Some of you know I used to work at Hewlett-Packard. At least at the time, it was governed to a large degree by "Bill and Dave stories": tales of how Bill Hewlett and Dave Packard responded in certain situations.

Today, thanks to Tom von Alten's note on Corporate blogging, I discovered Anna Mancini's blog called From the HP Archives… (she's the HP archivist) and this Dave Packard Quote.

Why are you in business? Why is your company in business? What is your contribution to society?

What is progress?

It's easy to think progress is measured by GDP, trade balances, or the number of things we have; that's what we read and hear about in the news. Yet there's an undercurrent that suggests such views have it all backwards.

The Glaser Progress Foundation has a program area devoted to measuring progress. Go there to see a video or hear an audio of a 1968 speech by Robert Kennedy suggesting that GDP measures all the unimportant things or to research articles they've assembled.

Thanks to Joost Bonsen's Maximizing Progress for the link. Thanks, too, to Cliff Havener and his Meaning : The Secret of Being Alive. I read that years ago, and I'm pretty certain he makes the point that Lord Kelvin was wrong: all the important things—love, peace, faith, art, ...—share the attribute that they can't be measured by numbers. I've looked, though, and can't find the reference; if anyone can provide me the page number, I'd appreciate it.

Making more sense with numbers, part 4

This could be called Monty Hall and cognitive dissonance. John Tierney just published And Behind Door No. 1, a Fatal Flaw, a brief review of the Monty Hall problem and a report on its potential application to psychology, including its potential to invalidate some prior claims about subjects such as cognitive dissonance.

I'll leave the psychological arguments to others; the point is that thinking carefully isn't always as easy as it seems. If you're not convinced, read the start of that article down to "Before I tell you the answer, I have a request," and then write down your answer before proceeding. Then try out the online version to see if you got the right answer, to get a visceral feel for the game, and to see the reasoning.

Once you get the hang of those, try out Monty Hall’s Other Problems.

Do you now think you've got the hang of it? Just to confuse things a bit more, read Behind Monty Hall's Doors: Puzzle, Debate and Answer?, Tierney's 1991 report of playing the game with Monty Hall. By the end, you may have an even deeper appreciation of the challenge of making sense with numbers.

And if you wonder what this might have to do with business, remember that the impetus for Tierney's column was Yale economist M. Keith Chen's application of the Monty Hall problem to psychology. Are similar gotcha's waiting for us in business?

PS: Yes, there is a Making more sense with numbers, part 3.

The importance of a focus on disconfirmation

Here's a lesson from John Sterman's Business Dynamics: Systems Thinking and Modeling for a Complex World section 1.3.7: we gain little to no new insight by observing cases where data supports our hypotheses. We gain much from testing cases where data might disconfirm our hypotheses.

For more on that, see Raymond Nickerson's Confirmation Bias: A Ubiquitous Phenomenon in Many Guises, Bob Dick's Rigour and relevance in action research, the Skeptic's Dictionary entry on confirmation bias, Wikipedia's entry on the same subject, or one of my prior essays on skepticism.

System dynamics, black swans, and the management of business

I'm currently reading Nassim Nicholas Taleb's The Black Swan: The Impact of the Highly Improbable. While I intend to tell you more of what I think when I'm finished, I have an early impression, based on stories such as what he calls "Hume's problem" (or the turkey problem). That's a problem in which everything seems to be getting better and better, only to change direction suddenly and drastically for the worse. In his example, the turkey sees life as a daily succession of friendly humans offering food, only to have it cut short in a manner seemingly quite out of character for life as the turkey has perceived it. (As Taleb points out, it all makes eminent sense to the butcher.)

I think that's part of the reason for system dynamics as yet another tool for thinking and working. As Geoff Coyle points out in his System Dynamics Modelling: A Practical Approach, top management is concerned about things such as the consequences of actions, the likely future, and robustness against uncertainty (p. 15). One of the basic parts of the system dynamics approach is to challenge preconceived notions of the extent of the system causing the current situation: are we looking over a broad enough time span, are we including enough of the actors and actions, and are we paying attention to feedback effects (what Taleb calls recursive effects on p. xxii), where something we do today might come back and affect the situation we face tomorrow?

While there are no guarantees, that unfortunate turkey, had she had good training in system dynamics (or a competent system dynamicist at her side), might have been inspired to look at life over a 5-10 year time span, not just the few months she had experienced. That might have surfaced the fate that led to her demise as part of a regular pattern (albeit one that occurred rarely compared to her lifespan). Had she looked not only at the friendly human feeding her and the other turkeys eating with her, she might have noticed the butcher looking eagerly over the fence from time to time and asked about his role in her life. Had she realized the implications of those observations, she might have decided not to become quite so friendly with her "caretaker," she might have decided not to eat nearly as much (if she were scrawny, might her fate have been different?), and she might even have encouraged the other turkeys to join her in an escape attempt.

Now I don't think that the use of system dynamics conveys infallibility; in fact, that's why I'm reading Taleb's work, to figure out more places my insights may be fallible so that I can make them more robust.

Taleb advocates tinkering as a way to make progress; I see system dynamics as a way to tinker faster and think more effectively in support of your (and my) goal of more effective action.

While my comments may be out of the main focus of Taleb's thesis (system dynamicists tend to focus on the deterministic, not the random, even as they seek to help you be able to respond better in the presence of the random), I don't yet see them in contradiction. I offer them to you in the hopes they are of use to you. Now it's my (and your) task to try to disconfirm them; the longer we can't, the greater the likelihood there's something worth attending to!

If you want to tinker faster with the situation you find yourself in but don't want to risk your business each time you tinker, let's talk.

Thanks to Andrew Gelman for his posts that led me to Taleb's work.

Looking from the outside in...in English

Realizing that the majority of those reading this blog may not read German, I put together a quick summary of Henrik Müller's arguments to which I pointed last Friday.

In his most recent article, he claims that, in a somewhat healthy economy, we have three feedback loops that would stabilize our economy and dampen out our current problems:

• People and the government would spend more to stabilize consumption.
  
  
• Government would borrow more in order to support its temporarily increased spending.
  
  
• The Fed would lower rates to encourage consumption (and, presumably, investment).

He claims all three are at their limits here. He quotes an OECD number that says our savings rate is -1.0%, and housing values are dropping, so we have nothing left to spend.

He says our Federal budget deficit is only 3% of the GDP, and our debt, at 60% of the GDP, is 60% beneath the norm in Europe, so we could increase the debt to try to pull us out. Unfortunately, because we save so little, the only people who can buy that debt are foreigners.

Finally, while the Fed has room to lower the rate, he sees banks as ready to absorb any excess cash rather than loan it out, and he worries about inflationary pressures that may present, thanks in part to an ever-weakening dollar.

In the current political scene, he sees candidates pushing protectionist agendas and hope, while he sees our real hope as lying in global product and capital markets. In fact, the only good news he sees is that the devalued dollar has increased exports and that foreign governments seem ready to invest huge sums in US banks, and he's worried that we don't see that for the good news it is.

What do you think? If you read German (especially if you read it natively), what important points do you think I missed from the two articles?

Looking from the outside in...

One of the things that doing enough system dynamics work teaches one is the benefit of perspective. Sometimes when you're in the middle of something, it's hard to see the forest for the trees. Standing back a ways and, just for a moment, trying to drop any emotions that are tied up in one's current situation can give one better insights.

That's what system dynamics modeling can often do: change a situation in which you're an intimate part to a situation you and your colleagues can look at with a bit of perspective. It also gives you the ability to test ideas on the model before you test them on the real situation.

When we can't get that perspective ourselves, either because of time limitations or because we can't figure out how to do it successfully, reading or hearing what others say about us can sometimes provide us similar perspective. Sure, those outsiders may not understand our situation as well as we do, at least in the details, but they may help us find a better perspective into which to place our more detailed understanding.

If you live and work in the USA, you've no doubt read much about our economic situation recently. I've suggested before that it's healthy to see how others see us. Recently Henrik Müller of the German business magazine manager magazin posted an article entitled Amerika steht mit dem Rücken zur Wand ("America stands with its back to the wall"), a follow-up to an earlier and more data-filled Nach der Orgie ("After the Orgy"). If you read German, or if Google Language Tools suffices, I encourage you to read these articles. He may not be correct in all his assessments, but he may give a better perspective than the headlines in the nightly news about sub-prime mortgages, foreclosures, and other problems. Certainly his earlier article does something I like to see: he gives graphs of at least some key data over a five-decade span, which is more useful for seeing patterns and gaining perspective than merely seeing what has happened this century.

How do your mental models compare with his? What are you doing in your company to adapt? What should you be doing?

A problem in policy or implementation?

There's a discussion about different approaches to solving organizational problems on a mailing list to which I belong. I posted the following follow-up:

Problems in policy implementation may be due to problems in policy design.

(In this context, a policy is a set of rules or guidelines by which we make decisions. )

While it's taken totally out of context here, I think it's very consistent with Deming's ideas, with the lesson that problems in the user manual for a product may really be a problem with the design of a product, with what I've learned as a manager leading change, and with what I've observed as a consultant: if you get the "system" designed well, the implementation may well become significantly more straightforward.

So if you're not getting the results you want out of your organization and if you're tempted to think the problem lies in the people implementing your policies, think again, just to be sure. It's possible that the problem lies in your policies.

That's actually good news, for it means the problem lies in an area you really do control, one where you really can make meaningful, effective changes.

What do you think?

Top postings of 2007

In the last 12 months (to be precise, from last December 28, the day after the Top postings of 2006 entry through December 26, 2007), you have chosen ten top postings on Making Sense With Facilitated Systems as ranked by unique pageviews in Google Analytics.

As I noted last year, there are potential statistical problems with this list. Those who read my blog every day using the main URL don't get counted; both last year's and this year's tallies were made from those who landed on specific URLs as reported by Google Analytics (but excluding visits I may have made). That may be okay; those who linked to specific pages may have cared more about them. Recent entries have a more difficult hurdle, as they haven't been around as long to be viewed. The dates don't quite line up with the calendar year, although I suspect that makes little difference in the results. If you know of a better way, let me know.

10. For some time now, I've been using an open source simulator for my system dynamics work because it seems to help me think more effectively. That doesn't mean I've given up on commercial tools; I still use iThink for creating interactive environments, and I will be teaching IMT 586 at the University of Washington using Vensim PLE (and I may be using it in professional applications, as well). Last April, I combined my interest in the arts with my interest in this new approach to system dynamics in a public article about marketing program for symphony orchestras. You selected TAFTO 2007, the pointer to that article, as number ten on the list.
    



9. I've written several articles about data and numbers. Making more sense with numbers part 3 offered an easy process to plot data you receive in email or reports.
   



8. The words we use can be vitally important in helping us think productively about key business, organizational, and social challenges. In A systems language for business, number eight on the list, I described one team's evolution towards a better language for discussing business issues, thanks to a course they took from me in system dynamics modeling and simulation.
   



7. Good data helps us ground our thinking in reality. Still more on data, a pointer to several online sources of data, captured the number seven spot.
   



6. Growth can create problems (witness any of the bubbles that have occurred over history), but where are good examples of successful companies that intentionally don't grow? Number four on the list is Small Giants: the American Mittelstand?, pointing to a book that answers that question.
   



5. Sometimes old technology still has utility; sometimes it still attracts interest. At number five, Technology comes full circle, a description of my continuing use of a slide rule in my work, certainly fits that description. For those who are interested, it points to a source for new slide rules.
   



4. When I first started work as an engineer, PERT charts were done using mainframe computers or hand-drawn charts. Today, project management has become a profession with a certification process, and automated tools with graphical user interfaces have long since replaced tables of numbers and dates. Your sixth-most-popular entry was Critical chains: a decade later, my revisiting of Eliyahu Goldratt's critical chain theory that linked to Tom von Alten's revisiting of his views on the approach.
   



3. Productivity is obviously important to you. Your third most popular posting of the year was a surprise to me: If you can say it, it's done, an entry about the array programming language J.
   



2. Barry Richmond has a deserved place as an educator and thinker on system dynamics and systems thinking. I posted a link to an article he wrote about systems thinking and followed up with "Scientific thinking" the modern way, a differing view on the application of modern scientific thinking in system dynamics. That was your second favorite posting from 2007.
   



1. The 2007 posting you viewed the most was the series Making musical sense by email, showcasing a conversation between music critic, composer, author, professor, and consultant Greg Sandow and me that used a system dynamics model to explore the aging of audiences for symphony orchestra concerts in the USA. Now I'm curious: was its popularity because of the topic (music), the approach (a somewhat novel approach to using system dynamics), or the fact it was a real conversation between two people? Let me know.
   

All of those postings were made in 2007. It wouldn't be fair to finish this list without noting that some postings from prior years did rank higher than some of these. Here's the all-time top ten list of postings from Making Sense With Facilitated Systems as measured by your viewings in the last twelve months:

15. TAFTO 2007 (2007)



14. Making more sense with numbers part 3 (2007)
    



13. A systems language for business (2007)
    



12. Still more on data (2007)
    



11. Small Giants: the American Mittelstand? (2007)
    



10. Technology comes full circle (2007)
    



9. System Dynamics for Cheapskates (November 2006)
   



8. Critical chains: a decade later (2007)
   



7. If you can say it, it's done (2007)
   



6. "Scientific thinking" the modern way (2007)
   



5. Making musical sense by email (2007)
   



4. System dynamics with MCSim (November 2006)
   



3. In praise of the lazy employee (April 2005)
   



2. System dynamics and program evaluation (June 2005)
   



1. Making sense with numbers (November 2006)
   

That list includes the top ten postings written in 2007 plus the five entries written in prior years that were at least as popular as the top ten 2007 postings.

As 2007 draws to a close, I want to thank you who read Making Sense With Facilitated Systems and to invite you to continue with me in 2008. If you have suggestions or feedback for this blog, contact me.

I would be honored to be of service to you or your organization in 2008. If you're trying to make sense of tough business or organizational challenges, curious how I might be able to help, or just want to talk about some of the issues you face or that I write about, get in touch.

Testing the speed limits

In the Fall 1990 Sloan Management Review, Christoph-Friedrich von Braun published "The Acceleration Trap," calling into question our focus on shorter time to market. He later published the related The Innovation War (Prentice-Hall International Series in Industrial and Systems Engineering) (a book still on my reading list).

Tom Peters blogged about it (briefly). Brice Dattée and Dr. David FitzPatrick published The Acceleration Engine: Pattern of Technological Development, a mathematical exploration of the topic from a slightly different perspective. Barry L. Bayus of the University of North Carolina at Chapel Hill wrote an interesting review of his book. Eugene Garfield wrote another review. Alexander Kandybin and Martin Kihn quoted von Braun's work in The Innovator's Prescription: Raising Your Return on Innovation Investment in strategy+business.

His words haven't been accepted universally, as anyone in a wide range of industries may attest to once they leave work at 9 or 10 in the evening. On the more literate side, Preston Smith wrote From Experience: Reaping Benefit from Speed to Market. It was the subject of a debate (Die Innovationsfalle) at the 2001 CeBIT.

If von Braun is right, there's another risk to growth besides running out of natural physical resources: there's running out of time. It's analogous to an addiction: we need to keep getting more and more of the substance in question (reducing time to market, in this case) to remain satisfied. If we can't maintain our "supply," we crash and go into withdrawal.

In this case, the risk von Braun pointed out was the limit to how short one can make product cycle times and the risks to any financial success that's built on steadily decreasing time to market. Perhaps we can eventually cycle through product generations faster than our customers will accept them (do you want to replace the computer you bought yesterday with a new generation today and then do it again tomorrow?). Perhaps we'll begin to hit physical limits to speed (zero time to market would seem to be a very hard limit to exceed). If we try to break through that limit, whatever it is, and fail, we lose the business benefits we've been sustaining based on constantly improving time to market in the past.

I'm sure many of us are tempted to say, "We don't know if there's a limit or not; we should push forward as hard as we can, and we'll let the real world tell us if there are limits." Unfortunately, if von Braun is correct, hitting those limits won't mean a leveling off; it will mean a crash, and that could have a ripple effect that none of us will enjoy.

Does this mean I'm against reducing time to market or cycle time in general? No. There are many places in our organizations where reducing delays can help, likely including the delay from a customer perceiving a real problem to being able to obtain a product or service to address that problem. Without thought and testing, though, it's hard to make generalizations.

I perceive that time to market reduction is like growth: neither serves as an eternal, prime goal, but each may have its place in at the right time and in the right situation. I do encourage you to do your own reading, think about it, and draw your own conclusions.

How do you know if it's the right time and situation for you? If you'd like to explore ways to discern whether now is such a time and this is such a situation for your organization and how you might create policies that further your goals, let's talk.

A rash generalization

Friend and colleague Jay Forrest and I talk about what I find to be interesting ideas from time to time. Stimulated by an idea he once told me, I've assembled this rash generalization:

• Income (living wages, one hopes) gets made off the production that's both necessary to support the status quo and possible because of the status quo.
  
• Riches can be made off the transitions from one status quo to the other.
  
• Some disasters are made off the mistaking of a transition (from point X to point Y) for a never-ending trend (from point X ever upwards at a constant CAGR) and the effort we expend to try to make it so even after we've passed that situation's limits to growth.
• Other disasters are made when we envy the growth others exhibit and try to force our steady-state situation to match their growth.
• Disasters may take time to materialize. Part of the art of business is recognizing turning points and responding appropriately.

What do you think? Where is your business?

Making more sense with numbers, part 4

In the spirit of helping us all make better sense of data we read, I encourage you to read Mark Liberman's Thou shalt not report odds ratios in his Language Log if you write about data. If you read reports containing data (including the newspaper), read it, too, to help decipher what you read.

It's a somewhat long article, but you'll probably get the message by the end of the first example. (There is a possibly useful pointer to odds ratios and risk ratios on Wikipedia at the end of the article.) If you want another view on the same subject, see Odds ratios should be avoided when events are common, a letter by Douglas Altman, Jonathon Deeks, and David Sackett in BMJ. For an opposing view, see Stephen Senn's response.

If you're not writing for a highly technical audience and making it clear (perhaps through context) what you mean, I agree with the first and second articles.

Thanks to Jeremy Miles for the pointer.

Those curious about the title of this posting can read part 3 and find earlier parts.

You've got to see this graph!

One of the ways we make sense of situations is in how we portray data. I'm a fan of carefully crafted graphics, often trying to follow the lead Edward Tufte sets in his books and workshops.

That's why you have to see this graph on Statistical Modeling, Causal Inference, and Social Science. It ... ah, Phil says it better than I could; go take a look.

When you come back, note that the message is not to copy this design into the next five graphs you do (or at least the ones you show) but to have the courage to show the data in creative ways, breaking a few rules along the way if that helps to convey your information with clarity and integrity.

What he said

See Gill South's report about Systems Thinking, System Dynamics: Managing Change and Complexity by Kambiz E. Maani and Robert Y. Cavana.

The advice?

• Take the time to think.
  
• Patterns, not individual datapoints (problems), are key.
  
• If you want to change an event (fix a problem), you have to change the process (structure) that created the pattern of which the event is a part.
  

As some might say, it's all about context. Others might suggest it's about getting leverage.

If you're facing a challenge in your organization, are you looking at an individual event, or is there a pattern? What's the structure (process) that created that pattern? What can you do to change the structure so that the pattern changes in a way you want? Would you like to talk about it?

Structure influences behavior

That's part of a message I try to convey: structures tend to create patterns, and events are usually part of patterns. If you have to fix (change, eliminate) an event, don't focus on the event. Rather, see if the event is part of a pattern, and focus on the structures that fix that pattern. Then you'll see the events become fixed.

That's the reason I apply system dynamics to organizational problems: it helps us find the pertinent structure.

In It's the invisible structures that get you, Andrew Taylor says it eloquently.

A better way to show data?

We all know that measured data comes with some uncertainty. Perhaps it's measurement error; perhaps it's sampling error. We even expect to see it mentioned explicitly in political polls, but I rarely see it published in business and financial reports. There are likely many reasons for that omission, only one of which is the difficulty of presenting the uncertainty concisely and informatively.

Thomas Louis and Scott Zeger recently published Effective Communication of Standard Errors and Confidence Intervals with a proposed approach to indicating such uncertainties. On the one hand, I like it. It makes a nice, neat display of two, three, or five numbers that can describe a statistic nicely, and it's relatively easy to understand and to incorporate into your reports (the authors give three lines of LaTeX code you can use directly). On the other hand, as Andrew Gelman notes, graphs are still better (thanks to Andrew for the pointer).

What's a person to do? I have some suggestions:

• If you've got data in tables, strongly consider figuring out a graphical approach that conveys your information clearly and effectively instead of using a table, as easy as that might seem. In addition to the ideas in the paper Andrew references, consider boxplots among the potential candidates.
  
  If you don't have time to create useful graphics, consider whether your audience has the time to make sense of your tables. There are usually more of them than there are of you; taking 10 extra minutes to save 20 other people 2 minutes each sounds like a good trade-off (plug in your own numbers).
  
  Of course, if you're doing a balance sheet or income statement, you probably need the numbers at least once, although graphics may still help to convey your message.
  
  
• If you've got isolated bits of data that you're using in flowing text, consider using Louis and Zeger's approach.
  
  It's easy to do in LaTeX and pretty easy in OpenOffice.org's Write. It seems a bit harder in Word (I have Word 2000) because I don't see a way to have subscripts on subscripts, but you can select a smaller font on some numbers.
  
  Unless and until this becomes a standard idiom, you'll probably need an explanatory note somewhere in your report.
  
  
• Consider sparklines as a way to convey graphical data (time series graphs or histograms) in flowing text. Sparklines can be generated with a number of different approaches for a number of different document formats; if you'd rather, you can generate them online.
  
  

Perhaps the real answer is that we now have yet another way to portray data, from which we can pick and choose to fit our current needs.

Scenario planning

Scenario planning is a business process we got largely from Royal Dutch Shell. If you just read The Art of the Long View: Planning for the Future in an Uncertain World, you should know things haven't stood still. Martin Börjesson has a very good reference site worth checking out.

If there's one thing I've learned about scenario planning, it's that it's a literary and creative process, not a mechanistic one. Those scenarios that offer the most value, that help me think the most, are also those that qualify as true stories, not as mechanical selections of various events.

Making more sense with numbers, part 4

Now that you've got an easy way to capture numbers out of emails and documents, how do you get numbers back into emails?

Graphs are great, but perhaps you don't want to use attachments? Check out Gnuplot's dumb terminal mode as a way to create plain text graphics. If you keep it simple, you can convey decent graphical information with plain text (as long as your recipients use a non-proportional font in their email client for plain text emails—a very good idea, anyway). I tested this approach in a public discussion and found some liked it and some didn't.

Perhaps you really do want to include a table of numbers or numbers and words. If you're working in J, it's pretty straightforward to create the table you want and then use J's clipfmt and wdclipwrite verbs to create something you can simply paste into your email or other document.

If you're using J, you can create your (text or other) graphics in Gnuplot, if you prefer, or you can create them in J directly.

Incidentally, this note and its predecessor have addressed specific cases of the more general problem of getting data into and out of J, a problem I think lots of newcomers to J discover early on. It's easy to do powerful calculations in J, but manually transcribing the data from another window into J or from J into another document loses all the benefits. The J Wiki has a page called Interfaces that might help. I've found the Text Files page quite helpful in getting data out of plain text files. Any statisticians reading this might find the interface to R useful.

Making more sense with numbers, part 3

One of the early mantras one hears in statistics is "Plot the data." When I first heard it, it was followed by "by hand"; I suspect that part gets elided these days. Still, the advice is good. It's often easier to make sense of a list of numbers if you can visualize them.

Most of the time, that takes time we don't have. When we get an email or a report with a table of numbers, we know that plotting the numbers means grabbing a piece of graph paper (does your office supply cabinet even stock graph paper anymore?) or opening up your favorite spreadsheet, copying numbers, and drawing a graph. I rarely take the time.

Last week, I got yet another email with a table of numbers showing how something had changed over time. I was curious, so I wrote a short J script (now edited into a one line script) to turn the clipboard into data and another to plot the data.

Voilá! Now I had an easy and quick way to grab and plot data. I tried grabbing data out of an OpenOffice.org Writer document, and it worked, too. Grabbing data out of a Writer table was almost as good; my script lost the shape of the table, but that's easy to fix.

What's more, when you've got it in J, you can also apply various J statistical routines to the data, or you can pass it to R for more advanced statistical processing.

Yet another simple productivity tool, yet another reason to learn J as a tool for thinking and doing, yet another way to make sense with numbers.

I don't really care if you use J or some other tool; just pay appropriate attention to the data you handle. I just happen to think J is a powerful tool for this task (and for many other tasks). If you're learning J, check out the J lab called "An Introductory Course in J" by Henry Rich (thanks to Kip Murray of the University of Houston for pointing that out recently on the J Programming forum. Kip notes that Henry's lab covers a lot of territory very clearly but with a steep learning curve. If you are just seeing J for the first time, check out the J Primer.).

Interested readers might also be interested in tables2graphs.com and Using Graphs Instead of Tables.

So, if you have a table in email that looks like

Year Amount
2000 150
2001 200
2002 250
2003 225
2004 260
2005 254

and you'd like to graph it, one J program is

require 'format misc files plot'
sd=: > @: (". each ) @: |: @: clipunfmt @: wdclipread

Just copy the numbers, and type

plot ;/ sd''

to see your graph. I'll let you figure out how to add options and how to deal with multi-column data tables (it's easy).

Why is this part 3? Because there already has been a first and a second making sense with numbers, of course.

Jane Jacobs

Some time ago, I wrote briefly about Jane Jacobs and her The Death and Life of Great American Cities. I found it in a list of recommendations from Andrew Gelman, which makes me want to go back and review the other books on his list that I haven't read.

I liked three things in particular about this book:

• her lessons about cities
  
• her detailed and interesting descriptions of her observations
  
• her very early use of ideas of complex adaptive systems
  

The first was simple: I had never really thought about the functions sidewalks and side streets play, and I had never thought about how a mix of uses during the day plays into keeping a city safe. I had not understood why some recommendations for urban renewal seemed to work so poorly nor what might be done about it. She made all that clear. I'll trust what she says, for I don't live in a city environment to be able to experience it first-hand.

The second was more important to me. Her book was, in a way, one long series of low-level observations, coupled closely with reasonable and reasoned inferences she would draw from those observations. She never got far away from the observations, so it seemed easy to verify her thinking. Using the ladder of abstraction metaphor, she seemed to stay on the lower rungs, and that made her thinking and her arguments more powerful.

The third surprised me. I didn't really expect an early 1960s book about city planning to dive into complexity theory, but she did it at the end of the book, after building up a remarkable story, and she did it in a way that was quite approachable. If you're curious, you can see an excerpt from "The kind of problem a city is," the last chapter of her book, at Katarxis No. 3.

If I took away lessons from this experience, they would include:

• Observe.
  
• Attend to outliers as well as central tendencies; attend to diversity as well as averages.
  
• Make sure inferences are based on observations, and make the chain to the observations as short and as transparent as reasonable.
  
• Explore new ideas and new theories, for some of them make help make better sense of observations. This admittedly may cause tension with the previous lesson.
  
• Be interesting, which comes in large measure from being interested.
  

I like to give links to other sources you can explore, but there are so many options in this case. P.J. Tayor published Jane Jacobs (1916-2006): An Appreciation in Environment and Planning A. Jacobs gave credit to Warren Weaver in her work on complexity; you can read the part of his work she references in The Rockefeller Annual Report, 1958 (start on page 23 of the PDF). I recommend this highly. If you liked Weaver's article and want to read more about making sense of complex situations in the social sciences, F.A. von Hayek's Nobel Memorial Lecture The Pretence of Knowledge might well belong on your reading list.

But, more than anything, read Jacob's The Death and Life of Great American Cities. It's worth it.

If you can say it, it's done

Even in this day and age, computing is a problem. How many of you us take the time to do some of the calculations mentioned here when faced with business or economic data, and how many of you us just read the analyst's summary and take the analyst's advice?

To some degree, that's because it takes time and effort to double-check such work, and that only gets worse if the subject is complex. It's also because the tools we have aren't always set up to help us do such things on the fly, and we're often on the fly (or in meetings, which can be as challenging).

That's one reason I've encouraged some of you who are interested to learn alternative approaches.

At least one APLer, Randy MacDonnell, has written about APL, "If you can say it, it's done." The same is true, of course, about J, its descendant. I had occasion recently to write a program to calculate whether a certain Monte Carlo simulation was done. I found a quotation by Andrew Gelman describing the Gelman - Rubin statistic:

For any given parameter, R-hat is the estimated posterior variance of the parameter, based on the mixture of all the simulated sequences, divided by the average of the variances within each sequence.

That looked easy enough, so I just wrote it down:

R=: var @: , % mean @: var

In English, that's "the variance of the entire set of data" (var @: ,)
"divided by" (%) "the mean of the variance of each data sequence" (mean @: var).

"If you can say it, it's done."

And you thought this was a blog about business, not programming, right? You were right. While J is a language that can be used by programmers, it's also a language that can be used by you and me to express quantitative ideas more powerfully and concisely than a spreadsheet. If you're ever interested in numerical answers from a spreadsheet, you could be interested in J. Perhaps, for some of you, it's worth downloading and trying out. Much as in learning a foreign (human) language, you won't be able to do much at first, but, eventually, you might be surprised what you can do. In a way, it's as much about thinking than about computing, and yet you can process some pretty large data sets with pretty concise "programs," too.

Thanks to Randy and Andrew for the quotations. For those of you interested in the Gelman-Rubin statistic, Andrew has pointed me to two papers giving more information: his Inference from Iterative Simulation Using Multiple Sequences with Donald Rubin and his General Methods for Monitoring Convergence of Iterative Simulations with Steve Brooks.

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.

Making more sense with numbers

Some time ago, I walked through an exercise to make sense of one sort of numerical claim we often encounter in our daily lives, whether in business reports or the daily news. Now Andrew Gelman has pointed to a presentation by Dick De Veaux called Math is Music – Stats is Literature, pointing out that thinking statistically is hard work.

Andrew recommended it for teachers of statistics; I'd suggest it for anyone interested in making sense of numbers. Even assimilating the lessons of those slides might help us remember to plot the data we've been given and then to look at the graphs, to think about assumptions that are being made, to think critically and be skeptical, and to make better decisions.

Incidentally, you can find links to tools to convert data tables you might see online to graphs in my comments to Andrew's tables2graphs.com.

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.

A somewhat unified view of decision making: the table of contents

I recently completed a four-part series that tries to pull together some of my thinking over the past year about various decision-making models. In the process, it also highlights some of what I offer organizations with whom I work.

To make it easy to find those four articles, I've assembled a list of links and brief descriptions, much as I did with an earlier series on making sense of a tough problem:

1. A somewhat unified view of decision making: introduction
   
2. A somewhat unified view of decision making: part 1 (the basic model)
   
3. A somewhat unified view of decision making: part 2 (simulation)
   
4. A somewhat unified view of decision making: part 3 (learning from experience)
   
5. A somewhat unified view of decision making: part 4 (putting it all together)
   

A somewhat unified view of decision making: part 4

I promised to clean up some loose ends. What are they?

1. How do you do this stuff, really?
   
2. Where does Tom Peters fit in?
   

If you're like many companies, you're set up to make decisions and take action, but you're not necessarily set up to spend much time on explicitly increasing your Actionable Insight (or whatever you more informally call it). You may feel that some of the decisions you face tax the limits of your or anyone else's ability to make sense of them. You may feel you lack the time to get involved in something with the name "action research"; after all, you have a business to run.

That's where an outsider can often help. Such a person can often see things with fresh eyes and may have developed approaches you don't have the time to develop or maintain internally.

For example, I've helped others make sense of complex issues, gain insights, and test potential decisions using system dynamics simulations, and I've got other systems approaches available, in case the first ones we try aren't a great fit.

I've led project retrospectives as a light-weight way for organizations to learn more effectively from their own experiences, and I co-developed a popular set of learning logs to help guide personal as well as group improvement activities.

If we were to work together, you wouldn't lose control over the decisions you make. I'm just there to help you make sense of the situations you face, to help you test the alternatives you might want to try, and to learn more effectively from your experiences.

If you know someone facing complex issues or trying to figure out better ways of making progress, have them give me a call or send me an email. If it's you who is facing those challenges, I invite your call or email, too. Either way, there's no obligation and no charge for an initial conversation.

Now where does Tom Peters fit in? There's one more stock, Willingness to Act. We can have great, high-quality insights, but we don't do anything until we actually step up and do something. Tom is fond of saying we should just get on with it. I encourage you to take a look at some of his books and presentations to get more ideas and inspiration in building up your Willingness to Act. Don't accept everything he says at face value (I certainly don't), but do think seriously about it, and apply what works for you (it may be more than you think!).

A somewhat unified view of decision making: part 3

I last left you with two thoughts about decision making:

• You don't have to pick between the recognition-primed (intuitive) decision model and the more rational models. Both are valuable.
  
• Simulation can be involved in both decision-making models. That particular insight might help us integrate thinking about the two.
  

I'll stop talking about simulation for a bit and talk about Insight Quality.

We all make mistakes. If Insight Quality is measured on a (hypothetical) scale from 0 to 100, it's likely never at 0 and never at 100.

We learn by making mistakes and reacting appropriately to them. If you're a system dynamicist, you recognize that feedback systems work because of an error signal: that difference between what you want and what you've got. So don't worry about making a mistake (especially small ones), but do pay attention to what you do with mistakes.

Insight Quality, like Actionable Insight, declines if left alone. Pick a skill, even riding the proverbial bicycle: if you don't use it for a while, you'll get rusty at doing it, and you'll likely make a few mistakes you wouldn't have made earlier.

Before I go further, I'll admit that the distinctions between Actionable Insight and Insight Quality and how one increases either are a bit artificial. I'll keep them distinct to make my point, but please understand that I understand there's overlap.

How do we improve our organization's or our personal Insight Quality? We need a feedback loop, a means to observe when things aren't going right, so that we can learn from them. In this model, perhaps the easiest spot to observe those situations is when Latent Problems re-emerge (or, almost equivalently, when we see Problems we recognize as old friends).

There is a methodology called action research (or action learning—the distinction between the two is a bit fuzzy, but action research often carries the connotation of being written down and shared). Those describe the actions involved in that entire loop that encompasses both action and learning.

In a way, you can look on simulation and action learning as two different things. One, you might think, comes before the decision to inform you; the other comes after the decision to improve you.

You might also look on them as very similar. With simulation, you're looking at the analog (typically, a computer model) of the real-world situation to gain insights. With action learning, you're also looking at an analog of the situation you're facing, but this analog is the history of your and others' past attempts.

In either case, I submit that we need to blend both in our work. We need rational processes (and simulation is often a valuable approach) to build Actionable Insight, and we need action research or learning to correct lessons we mis-learned or forgot from our rational processes.

Based on these foundations, we need the ability and willingness to make good decisions in a timely fashion, often using a more intuitive (and yet still simulation-based, if we accept the recognition-primed decision model as a description of reality) approach, and we need the wisdom to know when our intuition won't suffice and we need to drop back to a rational model.

So what's left? I've got one more installment planned to tie a few loose ends together and to live up to my promise to fit Tom Peters into the equation.

A somewhat unified view of decision making: part 2

My last posting described the basic model, but it left lots of gaps. For one, Actionable Insight isn't static, as we all know. While some insights maintain their validity for years and some perhaps forever, others either lose validity as new knowledge is developed or lose relevance as their need goes away. I'll show that effect as a flow draining out of Actionable Insight into another cloud, for it doesn't matter what happens to it; we just don't have it anymore. Don't forget to click on the image to see a full-sized, pop-up version.

If Actionable Insight can drain away, how do we refresh it?

We no doubt have as many ways of gaining insight as there are people. Trying to make a comprehensive list might be futile, and it doesn't really contribute to my point. I want to highlight one approach that I think helps integrate the various ideas I've been exploring.

Someone once said, "Don't ever go where you have not been before." You could also say "Don't do anything for the first time"; it sounds similar and seems to make as much (or as little) sense.

With possible apologies to Tom Peters (I'll explain that note in a later column), I think there's a lot of merit to those statements. Actors and musicians practice it faithfully; symphony orchestras and theater companies always have rehearsals before they show their work to an audience. You might bring up improv or jazz as counter-examples, but, as learningimprov.com says, it takes hours of rehearsal to learn how to improvise.

On a more academic note, Dietrich Dörner has made a career of studying decisions and how people make decisions that go awry. He has advocated simulations as a way for us to learn more rapidly than we can in real life and with less risk.

An organizational simulation is, in many ways, nothing more than what musicians and actors do in rehearsals: an opportunity to go through the important parts in private, with the ability to try things different ways, and with little risk. We can learn what works so that things go more smoothly when we do it for real.

For example, I recently did a set of simulations to explore a marketing program for symphony orchestras. In a relatively short time, I was able to create a simulation model of the essence of the problem Drew McManus was trying to address and to apply multiple solutions to see how each might work. The model isn't good enough (nor, likely, should it be) to tell Drew exactly how many additional tickets he'll sell for Friday's concert, but it should give him better insight into what is key for making his program succeed.

Certainly there are other ways we also use to increase our insight; why am I writing about simulation? It's because of the link to Gary Klein's recognition-primed decision model. Gary's model, simply put, is that we tend to simulate situations quickly in our heads. If our plans of action work out, we put them into place in the real world. If they fail, we try a different approach until we find a mental simulation that works. We can do that really quickly, and that's key to our survival.

But there's a catch. As Barry Richmond once said, our mental models aren't always good enough, and our ability to simulate those mental models in our heads doesn't always suffice, thus leaving us with holes in our feet.

I've come to believe that simulations and rehearsals are important ways we prepare to make good, quick decisions later, when we need to. These, plus all the other analytic and rational tools we can bring to bear, are the means by which we enable ourselves to be good at the recognition-primed decision model. Certainly we can do those simulations in other, more manual ways, but, for many situations, the alternatives are tedious and error-prone. Certainly we can and do learn in other ways, and those are important—I'll even talk about some of them later. What simulation almost uniquely provides is the ability to specify a situation and see the logical ramifications of that situation.

All this has been to support two conclusions:

1. The recognition-primed model and more rational decision-making models aren't two choices from which you get to select only one. They can work together, the more rational model providing you the stock of insight you need to make faster, better recognition-primed decisions.
   
2. Simulations (rehearsals for musicians and actors) are great tools to build insight for those recognition-primed decisions, for both are in fact simulations; they just have different constraints. Thus the structure of the learning you get from the one should fit nicely into the structure of the insight you need for the other.
   

There's more to come. In future installments, I'll cover how Insight Quality, action learning, and Tom Peters figure in.

If you know someone who is facing tough organizational or business problems, problems that persist, problems that don't lend themselves to quick, intuitive solutions, introduce us. Perhaps I can help.

A somewhat unified view of decision making: part 1

Yesterday I promised a series trying to integrate my various thoughts on decision making as I've touched on in this blog before. I'll use this diagram of four stocks, denoted by rectangles, to guide the conversation (click on the image to see a pop-up, full-sized version). Let's start with the stock of Problems. As I'm using it here, those are situations that demand our attention or action. In a Kepner-Tregoe sense, they include both problems and decisions. Problems come at us from out of nowhere, it sometimes seems, and that's why I've represented them as coming out of a cloud. Incidentally, "pa" stands for the Latin "per annum" (per year).

We deal with problems by making decisions. If we make good decisions, problems go away. If we make poor decisions, they become Latent Problems, waiting to visit us again another day. Our capacity (and capability) for making decisions is controlled by a stock of Actionable Insights we or our organizations possess. It's also likely controlled by our willingness to act; I can make a heck of a lot of decisions in a hurry if I resolve them all by rolling a die. Whether we make good or bad decisions is controlled, to a large degree, by the quality of our insights (another stock); luck plays a role, too, as do other actors in the situation.

In this model, most decisions are made relatively rapidly, perhaps using the recognition-primed decision model without our even knowing of it. Increasing our insights and the quality of those insights is something we may do as part of OJT. Sometimes, though, we run up against problems we know we must study before we make our decisions, and so we do something explicit (commission a task force, conduct experiments, hire a consultant, etc.) to increase our Actionable Insights.

If any of you offer your observations, either as comments here or as email, I'll try to take them into account as I prepare the next installment, in which I'll begin to connect up some of the missing links.

A somewhat unified view of decision making: introduction

I've written several times about the various methodologies we make decisions: the rational model, as typified by the Kepner-Tregoe approach, Gary Klein's recognition-primed decision model, the Thomas Saaty Analytic Hierarchy Process, and others. Sometimes we get the impression from reading that there's one best approach, but it's often the case that diverse approaches (to most anything) are more robust.

Does that mean we just pick and choose the approach we like when we want?

While there will always be an element of personal choice in deciding which methods to use, I do think we can say more about the varied approaches that helps put them in context. The recognition-primed decision model seems to apply when we have to make a decision in a hurry—not surprising, since firefighters and other emergency response teams were the subjects Gary Klein studied. The rational model seems to apply when our intuitions lead us astray. As Jay Forrester has pointed out, that may happen more often than we'd like to admit (and his approach goes past the rational of Kepner-Tregoe to a more systemic view).

Over the next few days, I intend to walk through my developing views on how one might integrate these ideas, and I hope some of you will chime in with your own insights to help us all learn still more.

While you're waiting for this to start, review some of the ideas that led to this series.

Tom Peters' suggestion on ISO 9000 and Six Sigma

Have you ever known Tom Peters to suggest anything? Well, he didn't start now: see his "Told You So!" rant on ISO 9000, Six Sigma, and the like.

I've said in the past that I don't always agree with what Tom says, but he does almost always make me think. To what thoughts does this lead you?

Debunking myths with data

Normally I don't recommend video blogs, because I know people's time is scarce (or I presume it is; mine is), and it's easier to control one's time by reading than by watching.

This morning, I found Hans Rosling's Debunking Myths about the World. It's a video of his talk at TED showing Trendalyzer's use in helping us think more productively about the world. It's worth its twenty minutes, both to learn a bit about the world and to learn a bit about another way to look at data.

Now look at Gapminder to learn more on your own.

Counterintuitive Behavior of Social Systems

Jay Forrester's (new: link to several of his papers) Counterintuitive Behavior of Social Systems (new URL) is another classic worth reading and re-reading. The article offers food for thought, not final answers. If you start, I encourage you to keep going to the end (I'm saying that, because I know it's 28 pages of text and everyone's time is short). As a bit of encouragement, here are two quotations. First, from p. 24:

Figure 8 shows the world system if several policy changes are adopted together in the year 1970. Population is stabilized. Quality of life rises about 50 per cent. Pollution remains at about the 1970 level. Would such a world be accepted? It implies an end to population and economic growth.

Then, on page 27, he writes, "Our greatest challenge now is handling the transition from growth to equilibrium."

Start at the beginning to get the context for both quotations, and see what you think by the end. These are important issues; don't blindly accept his admittedly somewhat preliminary ideas (see section XI), but don't blindly reject them, either.

Thanks to the HPSIGWiki for the reminder.

A classic: the tragedy of the commons

Every once in a while, it's useful to read or re-read some of the classics. We often hear of "the tragedy of the commons," but how many of us have read the original essay that led to that term? Check out the 1968 essay The Tragedy of the Commons by Garrett Hardin.

Seeing things from another's point of view

There are many good policies that generally help us. One policy is to see things from other people's points of view in addition to our own. It's all too easy to get wrapped up in our own views of the world, believing that what we ourselves see, what we feel, what we think, and how we react must be universally true. It's not.

No matter how much I know that, it still sometimes takes effort to get out of my frame of reference and see things as others might. If I'm working by myself and don't need additional insights, perhaps that's okay. If I need to learn or to work or interact with others (which accounts for most of what I do), it's in my best interest to be able to see things from their view, too, and to be able to reflect on the differences. (Sure, I can try to force things to be my way, but I'll likely build up a stock of resentment in others by such coercion, and that resentment may end up making things harder for me later.)

Another useful policy is to make things more concrete. We often spend much time talking high up on the ladder of abstraction. We're taught that in school, as teachers try to help us see patterns where we once just saw chaos. It can make for efficient conversation when we all know we're talking about the same thing, but it can also make for misunderstanding, confusion, and either unnecessary conflict (when we don't realize we agree) or lost learning (when we don't realize we disagree).

Earlier this week, Brad Trnavsky put both of those together in a field that many of us find challenging: sales. His Creating Feature / Benefit Statements That Work is a good reminder of the importance of seeing things from the other person's point of view, and he gives a concrete example to make sure we understand.

Brad just joined the blogosphere this month. Welcome, Brad!

There's another problem, too

In my last two postings, I've written about what I think is a very important issue we face as individuals, as businesses, and as a society. I think there's another issue that goes hand-in-hand with that issue: how we deal with tough, stressful problems, how we deal with conflict, and how we deal with perceived inequities. In almost any significant situation, we'll have differences of opinion, some small, some very large. (If we didn't, we should perhaps wonder if we're considering all the evidence.) How we deal with those differences can make all the difference as to what sort of outcomes we achieve.

No matter whether we're talking about differences between individuals, differences between groups in our companies or organizations, differences between factions involved in our local or national governments, or differences between nations, we have various approaches. Some approaches are violent; some are not. Some seem to lead to good resolutions; others do not. Some seem coercive, at least to some involved in those deliberations; others do not. Some seem to solve the problem today while creating new problems for the future; others don't.

If we can't figure out how to act effectively in such situations, I fear we'll have increasingly difficult times as stress mounts from climate change, from energy shortages, from perceived inequities (whether between individuals, groups, or nations), or simply from the challenges of doing business in competitive markets.

There are many ways to make communications in such situations more productive. As I've advocated for the use of multiple approaches (triangulation) in making sense of problems, I will say that I don't think any one approach has ability to save all deliberations about differences. Yet I have found the work of Chris Argyis, work he calls action science, to be impressively powerful in helping groups to hold productive discussions, to make breakthroughs in their organizations' abilities to get work done, and, as a nice side benefit, to help people feel good about working in their groups, not because they get their way, but because they get heard. It's based on three premises about productive decision-making in times of conflict and stress:

• Free and open decision making
  
• Testable and tested data
  
• Mutual commitment
  

The first, among other things, means I can't force you to use this approach. I can at best model the behavior I believe in and that I would like you to adopt; I have to give you the right to decide whether you want to follow suit.

The second means that we are willing to test data about all our important assumptions, not just the ones about quantitative data. Perhaps I (think I) know you'll never accept a certain proposal. If I'm following my principles, I'll figure out a effective way to test that assumption on my part (perhaps as simple as asking); otherwise, I'm unilaterally taking one possible solution off the table without us having the ability to talk about it.

While these are all hard in practice, while they require great attention to one's self, and while they sometimes require great courage, the third sometimes seems the hardest. Perhaps you and I disagree about a situation. Perhaps you've made your best case, and I see important issues I perceive as favorable to your position that you didn't bring up. If I'm committed to the first two premises of making free and open decisions with tested and testable data, then I'm committed to bringing up those issues, even if I perceive they weaken my case, for that's the path towards more productive, effective decisions. That may sound easy now; it isn't always so easy when I'm in a discussion involving a strongly-held belief.

I can introduce this approach to a group in perhaps 15 minutes, including some techniques for applying these premises in practice. It's hard work, though; it may take months of active help before a group begins to internalize these ideas in their routine interactions. This work is some of the hardest and yet some of the most rewarding I do. The first time I saw a group I had helped really begin to act this way, I was blown away by the progress they had made. Using these ideas along with others, they had shortened their process times by over 80%. They were highly effective at working together, cutting the time of making decisions from hours and days down to minutes, and the quality of those decisions had improved markedly. Deliberations were sometimes amazingly direct, and yet people felt really good about them. As one said (paraphrasing), "Now I know that people really hear me." If you're curious, you can read about that project in "Emphasis on Business, Technology, and People Cuts Turnaround Time at Hewlett-Packard's Lake Stevens Division." If you'd like to talk about this for your organization, give me a call.

And, when you're thinking about the topics of climate change and oil depletion (or business strategy and customer satisfaction), remember that how we talk about those issues may be as important as what we now think about those issues in achieving good results.

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?

Improving math education: a conjecture

When I lived and worked in Germany, I noticed to my surprise that my engineering colleagues couldn't do fractions. They had no concept of whether 9/16 was bigger or smaller than 35/64, and they couldn't easily solve "simple" problems such as

  3          5
  -  +  ------------
  4     4 +    2
             -----
             4 + 3

Of course, they could figure out that 9/16 is 0.5625, that 35/64 is 0.546875, and that the former is thus bigger than the latter, but they wouldn't immediately convert 9/16 to 36/64 and see it was bigger.

When I expressed my surprise, we discovered that they never studied fractions in school. We decided the only likely reason US students studied fractions in elementary school was to be able to deal with inches, gallons, and the like. From vague recollection, I must have spent the better part of a couple of years in elementary school arithmetic studying fractions plus memorizing how many feet in a mile, pints in a gallon, and teaspoons in a cup (not to mention conversion factors from those units to metric). By comparison, my European colleagues had to learn a set of metric prefixes, the names of basic units of measurement, and the universal conversion factor of 10 (just to show there's a Wikipedia page on almost anything!).

As it's often written that students in other countries are often, on average, well ahead of US students in math skills and that such a gap makes us less competitive in world markets, what if we switched to the metric system (or, more precisely, the International System of Units)? Would that make a year or two long hole in math education that could be filled in with more advanced topics? Would that help us in the USA catch up?

I don't know. I recognize that it's a challenging problem, and there is no silver bullet, not even in my idea. I would be curious to know if anyone has evaluated this approach.

Pie charts: the exception that proves the rule

Pie charts: don't use them. That's been my motto, and it's a (non-) feature of one of the graphics applications I use. Now Masanao at Statistical Modeling, Causal Inference, and Social Science posts the Color of Flags, possibly an interesting use for such a tool. See also Information Extraction from Different Data Representation Forms on a CRT: Charts and Tables by Janice M. Engberg and F. Layne Wallace.

A climate change primer

Why do I write about climate change and the environment on Making Sense With Facilitated Systems? Because I think climate change will affect (and has already begun to affect) all of our businesses and all of our lives.

How do we get up to speed, if we need help? RealClimate posted Start here, which has information for those just getting started at understanding the issue and information for those who want more in-depth specifics.

For a slightly different take on the matter, see Gristmill's How to Talk to a Climate Skeptic (perhaps you're the skeptic you'll be talking with :-). Thanks to Brad DeLong's Grasping Reality with Both Hands for that pointer.

Evolutionary project planning

Rick Davies just posted Evolving storylines: A participatory design process?, a fascinating description of the evolutionary process of iterated variation, selection and retention applied to project planning, to project evaluation, and to storytelling. He raises more questions (for me, at least) than he answers, for he points at a range of variations one can try to see what works best in any one situation.

I think I'll look for opportunities to try it out.

Contribution

Yesterday I wrote about the power of narrative, as inspired by Andrew Taylor's posting. In searching for a link for my article, I discovered a powerful message about contribution in the last half of Presentation Zen's Two Questions: Why does it matter? What's your contribution?, the part I had skipped before. It builds on the three questions meme I wrote about previously, but it uses art to do it in a powerful fashion.

Watch the Benjamin Zander video from start to finish. Yes, it's just over six minutes long. Yes, some of it is probably promoting the speaker. Listen to it anyway; the message is important. If, after listening to it, you're not sure of its application to business, check out the "Fields of Interest" part of Hewlett-Packard's 1966 corporate objectives (scroll to the bottom).

Einstein on growth and Taylor on making sense

I've written about growth and about classical music. Both came together today in Andrew Taylor's The need for narrative.

At first, I thought the Einstein quotation was a great riff on growth, and I only used the first part of the title of this piece. Then I reread Andrew's note and thought again.

Narrative (story-telling) is important in getting your message across. Art obviously plays a big role in effective narrative, from poetry to graphic arts to music (why do you think many commercials showing elegant products feature classical music?). (I read something about that last point recently, but I can't find its source. If anyone knows who blogged about it in the last few weeks, please let me know, and I'll credit the idea.) Art has a way of unleashing emotions and feelings we struggle to express in mere words.

But there's another side, too; we've all read of people who have told great stories unsupported by fact, reason, or, in some cases, ethics. Perhaps we ourselves have been mislead by such stories in the past.

That's why I think it's important to blend stories and reason grounded in data, not necessarily at the same time but certainly in the same deliberation, which may be spread out in time. I suspect we all have our own biases towards one or the other, even as those biases may shift from time to time. That's why I like the PGP approach advocated by Edward Tufte, even as I realize the risk of formulaic approaches to remove all the life from the artful (and perhaps the rational) side.

Use your art to make the rational come alive, and use your reasoning to guide your art in ethical directions and for ethical purposes!

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.

Who are you? What do you do? Why does it matter?

Who are you? What do you do? Why does it matter? Those are the questions posed in Presentation Zen's Two Questions: Why does it matter? What's your contribution?, taken from Marty Neumeir's The Brand Gap: Revised Edition (2nd Edition).

Much like the recent two words exercise, those three questions sound useful. Here's my quick attempt:

Who are you?

See http://facilitatedsystems.com/about.html.

What do you do?

I help people solve problems, test strategies, make sense of confusing situations, work more effectively in groups that are spread around the globe, and have more productive discussions to improve and speed problem solving and decision making. I help organizations work smarter.

Sometimes I do this by using computer simulations (system dynamics) as a testbed for new ideas. Sometimes I use approaches such as action science, which, at its core, is a way to test assumptions about conversations we have at work. Sometimes I simply help you have productive dialog so you can progress towards your goals, no matter whether you're in the same room or distributed around the globe. Sometimes I use other systemic approaches.

Why does it matter?

You lead an organization, you work in an organization, or, for most of us, you do both. People have joined your organization (you joined your organization) to accomplish something they couldn't accomplish alone. Perhaps it's as simple as supporting themselves and their families. Perhaps it's to save the world or to make life easier for certain people (your customers) or to make life possible for certain people. Assuming you're working for an ethical cause (and I suspect that includes everyone here), that's a big deal, and you should feel good about it. Your organization bears responsibility towards the people you serve and the people who have joined you in your cause, and you, no doubt, want to live up to that responsibility.

Perhaps you're doing it as well as can be done, but most organizations, even the best ones, in my observations, have weak spots somewhere. Perhaps company executives have high goals, but they're not sure how to link those to effective actions. Perhaps the company experiences recurring problems, and people aren't sure what causes them or how to fix them (or even that they are fixable). Perhaps strategies are set and plans launched without much testing to see if they fit reality or are likely to be successful. Perhaps executives wish people would step up and take more initiative, while others wish the executives would listen to what's really going on. Perhaps you and your colleagues sit 5,000 km. apart, and you worry that you aren't as effective as you could be if you only sat in the same building.

That's why what I do matters. I help organizations, perhaps yours, fix some of those problems. You do the work and retain control; I shine the flashlight and help you see insights that might help you.

Here's my question to you: where is my brand gap? As Neumeier says, brand is what you say it is, not what I say it is. It's your "gut feeling" about what I do.

You see me and my company, my work, from the outside, while I see it from the inside. What do you see as my brand? Why does (or should) it matter? What are the strengths I didn't mention? Where am I failing to live up to what I see as my brand?

That's not a rhetorical question. As risky as it may seem, I want your answers. Either comment here, send me an email, or give me a call. Listening is one of the key ways companies learn, and I think unstructured listening such as this may be far better than a survey for this question.

Oh, and how would you go through this exercise for your organization?

Tested alternatives

Just two words. That seems related to the "just three words" game that has caught on in some online work as a way to stimulate interaction and ideas.

Gary Harpst, CEO and founder of Six Disciplines, LLC, advocates a two-word approach to strategy as a way to get focus.

In that sense, what does Facilitated Systems do? Gary said it took him 18 years to get to that level on one business he ran, so I may not succeed at a final answer today. What would I say in two words?

Tested alternatives.

I think that's it. I don't make your decisions for you; those are your responsibility and under your control. I provide ways to help you and others come up with and test solutions to your problems and challenges. Sometimes what you need are systemic approaches to addressing issues. At other times, you may need help creating an environment in which productive conversations can take place, whether the problem is reluctance to talk about certain important topics or the challenge of working with people spread around the globe.

Making the move to open source software (don't forget to FLOSS!)

As frequent readers may have detected, I make use of a fair amount of open source software (also called Free/Libre/Open Source Software or FLOSS) in my work. Sure, it's less expensive, but I tend to do it because of other advantages it provides.

You might get the impression that I think switching to open source (or, for that matter, switching to any new software) is easy, and you might be concerned that it's harder than I let on.

You'd be right to be concerned. Solveig Haugland has just published a good article called A very important post for decision-makers considering OpenOffice.org. Read it if you're even considering moving your company to a new system. (If you're only moving yourself, it's a lot easier; you become both the pilot test and the implementation, and you can control the speed and extent of your transition.)

If you'd like to dig more deeply into the economic impacts of FLOSS, you might be interested in Study on the: Economic impact of open source software on innovation and the competitiveness of the Information and Communication Technologies (ICT) sector in the EU, prepared for the European Commission's ICT Task-Force. The final report was published November 20, 2006. I can't get that report to load successfully this morning, but a September 26, 2006 draft of the executive summary does work.

I've not read the entire 287 pages, but I have read the executive summary. I'd like to highlight one point on page 12 of the report:

Avoid lifelong vendor lock-in in educational systems by teaching students skills, not specific applications...

Whether you're a fan of open source software or not, I think that's incredibly important advice. Some software, some products, some processes, and some technologies have incredibly long lives (I'm using an editor that traces its heritage back to the 1970s), but others come and go. It's much better when we, our employees, and our new hires understand general principles and then can apply those principles in specific cases than when we only understand specific cases and have to start all over when we face a new specific case. Think about that as you hire, as you train people, as you learn yourself, and as you consider what gets taught in the educational system where you live.

Knowing what's so

How do we know something is true?

In many cases, we don't. We may have to make good decisions based on partial data, or we'll be left behind.

Sometimes I hear someone wish they could have the certainty one gets from mathematical proofs in their own field.

Papers such as Social Processes and Proofs of Theorems and Programs by De Millo, Lipton, and Perlis have served me as healthy reminders that mathematicians don't have it easy, either. Even for them, the process of knowing something is heavily a social process, even as outsiders may view theirs as an ironclad, logical process.

That's not to say we should be sloppy in our explorations, investigations, and research; we shouldn't. It just says we should have a realistic understanding of the way things are really done.

As an aside, there's an interesting and humorous description of one particularly problematic use of randomized control trials that some of you might enjoy.

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.

Perceptions count

One of the features of many system dynamics models created to make sense of complex situations is the differentiation between the real thing and the perception of the real thing. We people don't often react to reality; we react to what we perceive as reality. That perception typically comes with some distortion and after some delay. To understand the system that created a problem we now face, we need to attend to those perceptions as well as to that reality.

Now there is Wharton's Out of Stock? It Might Be Your Employee Payroll -- Not Your Supply Chain -- That's to Blame which gives an example of how this works in retail. You can download the research paper by Marshall L. Fisher, Jayanth Krishnan, and Serguei Netessine, too.

Thanks to the TP! Wire Service for this lead.

Exploratory data analysis

Most of you (well, I presume most of you; perhaps someday I should do a poll) are busy enough with management and business activities so that you don't have time to become a statistician (or system dynamicist or soft systems expert or facilitator or ...). You rely on others, whether internal or external to your organization, to do the technical work in such areas.

Nonetheless, you see data all the time, and you may have need of simple tools to help make sense of what you're seeing, either before you can get to your statistician or to double-check what you're hearing from a statistician to see if it makes sense.

In the 1970s, statistician John Tukey assembled a body of techniques into a methodology he called "exploratory data analysis" (EDA), and some of its tools may be of use to any of us. While there is software available to perform these techniques, many of them can be done with paper and pencil, on the spot. That's when it likely becomes most useful for those of you managing operations or organizations.

Even that may be too much for the time some of you have. You may need something you can do without even paper and pencil, something you can do to evaluate the results you're hearing or reading.

For example, let's say you're presented the results of doing things two different ways, and the speaker or writer claims that one approach is obviously better than the other (or asks us which is the better approach). Tukey developed a so-called pocket test that you can likely do in your head. It's so easy to describe that the abstract gives almost the entire process.

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.

Skepticism, numbers, and making sense

Don't always trust what you read in print or hear in meetings. Mike Kellermann posted The answer is -3.9% (plus or minus 17.4%) on the Social Science Statistics Blog. Note that he had to dig deeper to understand the real situation. While he was writing about public information, the same guideline applies to internal business communications.

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

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?

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!

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 ++      **                                                        ++
      |        *                                                         |
600000 ++        *                                                       ++
      |          *                                                       |
500000 ++          *                                                     ++
      |            *                                                     |
400000 ++            *                                                   ++
      |              *                                                   |
300000 ++              **                                                ++
      |                **                                                |
200000 ++                ***                                             ++
      |                   ***                                            |
100000 ++                    ****                                        ++
      +                +       *******  +               +                +
    0 ++---------------+-------------*************************************
      0                50              100             150              200

As you can see, the concert-going public drops to nothing
(technically, the model shows fewer than 10,000 people by
year 108); it is already down to 494,703 by year 36 (26
years after young people stopped becoming concert-goers).

What about the average age?

64 ++----------------+----------------+-----------------+----------------++
  +                 +                +               Average Age'*********
62 ++                                           ******************       ++
  |                                   **********                         |
60 ++                           *******                                  ++
  |                        *****                                         |
58 ++                    ****                                            ++
  |                  ***                                                 |
56 ++               **                                                   ++
  |              **                                                      |
54 ++           ***                                                      ++
  |           **                                                         |
52 ++        **                                                          ++
  |        **                                                            |
50 ++      **                                                            ++
  |      **                                                              |
48 ++    **                                                              ++
  |    **                                                                |
46 ++  **                                                                ++
  *****             +                +                 +                 +
44 ++----------------+----------------+-----------------+----------------++
  0                 50              100               150               200

Finally, we're getting drastic changes in average ages.
According to
http://www.artsjournal.com/sandow/2006/11/important_data.html,
the age went from 45 in 1992 to 49 in 2002.  In this model,
it went from 45 in year 10 to 48.75 in year 20.  That's not
a bad match, and the model structure seems reasonable.

What's scary is that's a model of /no/ new concert-goers at
all!  In other words, the data you're showing /could/ be
consistent with a sudden change to essentially no new
audience members forever.  You came close to this same
conclusion in today's "The short version."

Now this model doesn't prove there are no new concert-goers.
There may be other ways to get similar results.  For
example, perhaps it's not true that "once a concert-goer,
always a concert-goer."  Perhaps younger people are starting
to attend concerts and then giving up in droves.  Perhaps
orchestra marketing is drawing in baby boomers who have
never attended concerts.  Perhaps multiple causes are at
work.  Perhaps you have other conjectures.  Any of these
hypotheses could be tested in such a model to see if they
are consistent with the reality you've been observing.

What I think is interesting is that a relatively simple
model can help shed light on the mental models we create to
explain the problems we face.  In this case, the first,
simple approach suggests things may be as you suggest, with
the caution that they /may/ be even more serious than you
indicate.  I'm curious in your thoughts on all this.  I do
apologize for the length of this email; I don't yet know how
to walk someone through a model such as this without taking
a little bit of time.

Drew, did I miss anything fundamental?

I'll be expanding on related ideas using a different model
in a column I'm doing for Drew shortly.  You can see some of
the blog postings I've made about music at
http://preview.tinyurl.com/2consf.  In particular,
http://facilitatedsystems.com/weblog/2006/11/making-sense-with-numbers.html
was a very popular posting about the recent Knight report.
Drew and I have exchanged other thoughts sparked by your
columns, but this note is long enough as it is.

Thank you for your time,

Bill

Think about what your response would have been, had you received such a message. Then come back next week to see Greg's response.

Postscript: When I initially posted this, the fixed-width email section overlapped Blogger's sidebar material. I reformatted the width of the email but left the text-mode graphics in the original size.

Making musical sense by email, part 1

If you've been reading this blog for a while, you probably know I like both system dynamics and classical music. Recently I've been in a conversation with Greg Sandow that involved both subjects, and I thought you might like to read about it:

• Those of you interested in classical music and the classical music business might find the subject matter of direct interest.
  
• Those of you charged with understanding and solving problems (I suppose that's all of you) might find our approach of interest.
  

Greg writes a blog on the future of classical music. He's also a composer, consultant to orchestras, author, and music critic. In addition, he teaches at Julliard and keeps himself busy in other ways.

He's been writing a series of blog essays called "Where we stand," which he's summarized and linked to in "The short version." I found those essays fascinating, for they paint his picture of what the classical music business faces over the next few years, and he offers his reasoning to understand his predictions.

As part of my making sense of his story, I wanted to see if I could "operationalize" his ideas: could I create a model that represented his hypotheses reasonably in both structure and behavior? The process might help me understand them better, it might help me test them, and it might find limits to their application.

Instead of telling you what we did, let me show you. Over the next several days, I'll post lightly edited copies of the emails Greg and I exchanged. That will give you the flavor of what we experienced. At the end, I'll provide my interpretation of what you read. I welcome your contribution to the dialog all along the way. While you're waiting for this to start, I encourage you to explore the links in this posting, for there's a wealth of information to be found.

Why few organizations adopt systems thinking

Russell Ackoff wondered about that in his blog posting today. More than just considering that question, he wrote of ways we can learn to learn more effectively from the decisions we make.

He closes with a request that we end our published articles with a statement how we intend to affect the behavior or thinking of the reader. Here's my list for this short posting:

• I want to get some of you (and me, more of the time) to try his Decision Record idea. That's very related to the learning logs Bob Williams and I developed, with extra emphasis on our decisions not to act. It's very related to a idea from Peter Drucker mentioned on page 101 of Effective Change Management Using Action Research and Action Learning: Concepts, Frameworks, Processes and Applications
• I want you to ask me if I use jargon in ways you don't understand. If you don't understand something, it's likely others didn't, either. It may even be that I didn't understand it, at least not well enough to get my point across. Perhaps we'll all benefit by your asking a question either in a blog comment or by email or phone.

Thanks!

An inverted look at climate change

Peter Schwarz established his credibility some years ago with his scenario work at Royal Dutch Shell. Now a principal and cofounder of the Global Business Network, he along with Nils Gilman and Doug Randall have published a short, 25-page paper called Impacts of Climate Change.

What's different about their story is that they don't talk about climate change and its potential impacts; they look at the human and natural systems upon which we depend and how climate changes might send them towards tipping points. In other words, the path of conjecture is shorter.

I encourage you to read their paper in its entirety. Its claims seem well footnoted, although I have not studied the references yet.

After you read it, I'm curious in your responses. Where do you think our vulnerabilities lie? What can we do to ameliorate the effects of the stresses Schwarz, Gilman, and Randall describe? How should and can we modify the systems in which we live and work to make them more resilient in the face of such changes? Is this the time for a Y2K-like approach to climate change and related issues (no matter your thoughts about Y2K)?

Calling your shots before you make them

In straight pool, you have to call your shots before you make them.

That's a smart approach for working with system dynamics simulation models, too. Most people showing simulation models to others have likely noticed that you can show a person a simulation model result and often get the response, "Sure, that's what I expected. What's the big deal?" If you ask that same person to "call their shot" (draw a graph of the expected behavior of key variables) before you run the model, though, you and they will often discover they won't have a good record of predicting the outcome. That's not because they are dumb; it's because nonlinear feedback systems of the sort in which we usually live and work exhibit behavior most of us find rather unintuitive.

So do I suggest you do this to make people feel foolish? Not at all. I suggest this to help them (and me) learn. When any of us sees a result and says "What's the big deal?", that person likely hasn't learned from the experience. When we call our shots in advance, using our best insights, and then compare our prediction with the results of a simulation, we often learn one of three things:

• Perhaps our current insights are pretty decent after all, and we can be even more confident in our future predictions.
  
• Perhaps our insights aren't so good, and we can use the discrepancy between our insights and the simulation results to hone our intuition.
  
• Perhaps our simulation model is wrong, and we can use the discrepancy to build a better model of the problem we're facing.
  

There's more to this than just working with simulation models. As Bob Williams and I describe more fully in chapter 10 ("Learning Logs: Structured Journals That Work for Busy People") of Effective Change Management Using Action Research and Action Learning: Concepts, Frameworks, Processes and Applications, there are great benefits to be gained from calling our shots and then comparing those shots with what happens in real life. Done carefully, that becomes an action research approach to getting things done while simultaneously learning how to be more effective in the world we live.

Inverted KM

I'm certainly no knowledge-management (KM) expert, but I had occasion to recall Connex, an old Hewlett-Packard KM system today. As I understand KM systems, they generally attempt to capture isolated organizational knowledge and make it more broadly available throughout the organization.

Connex worked the other way. Instead of capturing knowledge, it captured information about experts. If you needed information about subjects X and subject Y, and you had reason to need it from someone who spoke German, you could query Connex and find the name and contact information of any experts who fit that profile.

That seemed easier for the experts, for they didn't have to take the time to write up their expertise without knowing whether it would ever be used. They didn't have to work to make their tacit knowledge explicit, either.

It seemed advantageous for the people needing knowledge, too, for, instead of getting a canned response that might not precisely fit their situation, they'd be able to discuss the matter with the expert and, hopefully, tailor a solution to their particular problem.

What I found really attractive was the social dynamic upon which it was founded. In a KM system, experts might feel as if they were passing along to others what made them unique and valuable. Instead, by listing their profiles with Connex, they enhanced their reputations while benefiting others in the company.

With a bit of searching, I discovered that Connex is not necessarily unique; Motorola has its Compass program, and IBM has its Blue Pages Plus.

Data: fundamental premises

About twenty years ago, I created a slide I called "Fundamental Premises" in reaction to what I saw at the time as an excessively eager approach to data collection in a particular manufacturing environment.

I rediscovered it recently. Here are its five points:

1. One should only take data for a specific purpose; the quantity of data necessary for maintaining historical perspective and a report card is far less than we presently take;
   
2. The value of the flow of information is epsilon less than the value of the flow of products, and the same attention should be paid to making both flows simple, easy to understand, and defect-free;
   
3. Nothing beats talking to people for basic communications, but limited data helps to expand the capability of people to analyze a situation;
   
4. Data collection is almost never free, although the costs are often well hidden;
   
5. Manual data collection may be more valuable than computerized data collection (much as we have learned that manual, Kanban-oriented shop floor control may be preferred to computerized systems); for one thing, it is arguably easier to verify the accuracy of many kinds of data when manually collected and plotted.
   

While the original was an unnumbered list, I've added numbers to make commenting easier.

How might I modify those premises today?

Seemingly contrary to what I wrote in points 3 and 5, I do understand that automated data collection can be valuable, and I do understand that data helps us avoid subjective biases (even as talking with people helps us avoid missing important insights). I've described elsewhere a case in which people on a production line failed to report the most common problem they saw; when the problem was pointed out to them because it was evident in recorded data, they said, "Oh, that's not a problem; it happens all the time." Triangulation is important, as is paying serious attention to the data, not just letting a computer draw a few conclusions and accepting those conclusions without further thought.

I still stand by point 2 and the related point 4. Most of the organizational systems in which we work can be understood as feedback systems, and information feedback is a key determinant of system behavior in such systems. I would suggest that system dynamics can be a tool to help determine what data is important. That data feedback necessary to make the system dynamics model work well may be just the data needed to make the real system work well.

I'd largely stand by point 1, too. It's tempting to squirrel away all the data we can take and then have it just in case we need it. The problem comes in point 4; it costs time and money to ensure we're getting the data we think we're getting. If we don't need particular data, we're tempted to not worry about its accuracy as much. Then, later, if we do decide we need it, it may be hard to determine what it really means or how accurate it really is, and we may make bad and costly decisions by relying on data we only think we have.

What are your fundamental premises regarding data and its use in organizations?

Systems Thinking: The Bigger Picture

I often write about system dynamics, but that's only one aspect of what I do in helping people and organizations make sense of challenges they face, and it's only one of many approaches I might recommend you consider.

Pegasus Communications published my Systems Thinking: The Bigger Picture in their Systems Thinker. Now they've featured an abridged version in the February 2007 Leverage Points, and they're making the full article available for a limited time. Once it's gone, you can still access it for a small fee from the Systems Thinker Web site.