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.