Creating conditions for innovation

McKinsey Quarterly's (free after you register) interview with Brad Bird, Oscar-winning director of The Incredibles and Ratatouille, has some interesting insights into creating an atmosphere that brings out the creativity in people.

For example:

* Learning about what others do
The Quarterly: Is there anything else you’d highlight that contributes to creativity around here?
Brad Bird: One thing Pixar does—which is a knockoff of old-school, Walt-era 1940s Disney—is to have all kinds of optional classes. They call it “PU,” or Pixar University. If you work in lighting but you want to learn how to animate, there’s a class to show you animation. There are classes in story structure, in Photoshop, even in Krav Maga, the Israeli self-defense system. Pixar basically encourages people to learn outside of their areas, which makes them more complete. Sometimes, people even move from one area to another.
* Improving morale
Brad Bird: In my experience, the thing that has the most significant impact on a movie’s budget—but never shows up in a budget—is morale. If you have low morale, for every $1 you spend, you get about 25 cents of value. If you have high morale, for every $1 you spend, you get about $3 of value. Companies should pay much more attention to morale.
The context is making movies; the content is widely applicable - read the full interview!


Improving hurricane intensity predictions

A bit of hurricane folklore has it that hurricanes have a dry side and a wet side - that is, whether you'll get more wind than rain when a hurricane passes through depends on which side of the center you're on. A new report from the NASA Jet Propulsion Lab points out that not only is this folklore true, it may to improve the ability to predict the intensity of hurricanes.
The researchers found the hurricanes that rapidly intensified tended to exist within a moister large-scale environment than weaker storms. The rapidly intensifying hurricanes had statistically significant higher relative-humidity levels in their environments than storms whose intensity was weakening or unchanged.
 . . .
The team found substantial differences in relative-humidity levels between storm quadrants. One factor may be the shape of the Atlantic basin. Hurricanes in the Atlantic usually travel to the west or northwest -- regions that are drier, climatologically-speaking, than from where the storms originated. This causes the front two quadrants of Atlantic hurricanes to be drier than their rear two quadrants.

A unique result the team found is that in their front-right quadrants, rapidly intensifying hurricanes tended to have sharply higher amounts of upper tropospheric moisture near their centers than they did farther from their centers. 

A previous post linking to some good explanations of why predicting hurricane intensity is so complex is here. NASA is "exploring collaborations" that will allow forecasters to incorporate relative humidity data into hurricane prediction system, so we may be able to see test data in the next few years.


High fructose corn syrup, diet, and studies

There's been a lot of news coverage in the past couple of days about a study, "High fructose corn syrup and diabetes prevalence: A global perspective" by Michael I. Goran and others published this month in the journal Global Public Health. See, for example, here and here. (The NY Times has posted a .pdf of the article.) The paper itself is worth reading for several reasons.
  • It's really interesting. Across the globe, the number of people with diabetes is increasing, from 153 million in 1980 to 347 million in 2008. Most of the increase is coming as "Western-style" diets, ie those with lots of processed foods, carbohydrates, and especially sugar, become more widely consumed. And, as the report says:
    A growing body of evidence supports the hypothesis that in addition to overall sugar intake, fructose is especially detrimental to metabolic health and risk for type 2 diabetes. This is of particular concern given the global changes that are occurring in the use of high fructose corn syrup (HFCS) in food and beverage production . . . (citations omitted)
    The data table shows that, while not all the higher-diabetes countries consume HFCS, all the countries that consume HFCS have a diabetes prevalence that is higher - 20% higher - than countries that do not use HFCS. Note that this is an ecological study, looking at populations, not at individuals, and does not infer causation.
  • It suggests a reason why increased consumption of HFCS can contribute to an increase in rates of diabetes, even though HFCS does not depend on insulin. Instead, HFCS is metabolized by the liver, and does not generate leptin (which makes you feel full). In addition, there is some evidence that fructose helps generate fat, particularly the bad fat around your waste. If you're heavier, you're more likely to develop diabetes. In fact, diabetes-management sites like this one recommend keeping an eye on how much HFCS you consume.
  • Nonetheless, it's important to remember that this study describes correlation, not causation. The New York Times was a little incautious when it quoted Marion Nestle as saying that the study's conclusion was "a stretch." She seems to think so too, and this discussion on her blog is worth reading. Some highlights:
As with all correlational studies, something else could be going on that causes HFCS, sugars of all types, and diabetes to increase.
 And, later on:
Yes, HFCS is sugar(s)—glucose and fructose.  So is table sugar (sucrose).

But the bottom line goes for both: Everyone would be better off eating less sugar(s).


Four Charts and a Video

in which The Atlantic identifies the impact climate change is having already.

Bonus picture:
That's a time-lapse picture of glacial ice melting in the Arctic, illustrating the Guardian's article about a new film, "Chasing Ice." More pictures available at the links.

Image via


Analyzing tweets in real time

The computer giant SGI has teamed up with scientists at the University of Illinois to create the Twitter Global Heartbeat, a "real-time combined population, tone and geographic analysis and heat map visualization of Tweets." That is, the Twitter Global Heartbeat analyzes 10% of Twitter's 500 million daily tweets as they are posted and analyzes the content, tone, and location. The project converts the data to a map, showing hot spots of positive and negative comments. The video above is an analysis of tweets as Hurricane Sandy approached the US coast and moved inland. (I found it thanks to Robert Wright's post in

It's a new service (my choice of videos was Sandy or the US Presidential election). There are also some snapshots and graphs. Here's an example of the former, picturing "Global Sentiment from Live Twitter Feed." (Red is negative, blue is positive.)

The snapshot is November 15 - I can see why the Mideast is such a hotspot of unhappiness, but what was happening in Indonesia then? If you know, let us know in the comments.

You can follow the Twitter Global Hotspot's twitter feed here.


Useful programs for organizing information

Like many other people, I am constantly on the lookout for useful organizing tools. Here are a couple to ponder, and play with, over the Thanksgiving holiday.

Evernote: Evernote's slogan is "Remember everything." It's a free program downloadable to most platforms (computers, tablets, phones) and operating systems. The main product is an application that allows you to create one or many notebooks. You can type in information, copy urls, add photos or drawings. You can share pages or notebooks with co-workers, organize notebooks, and add searchable tags. Content is also searchable.

Best of all, Evernote synchs across platforms, so if you update something in your phone, it will be updated in your computer as well. There's also a handy little plug-in, the web clipper, that lets you clip and copy content or urls. Oh, and you can format your notes as text or lists. I use it as I scan the web, and I've also moved all my recipes into it. It's nice not to be dependent on all those decomposing fragments of paper.

TheBrain: Graphics-minded users find the mind-mapping software TheBrain very helpful.
Here's how the website describes it:
TheBrain moves beyond linear folders and lists, letting you create a network of information organized the way you think about it. You don't have to force any idea or project into a single folder. With TheBrain you can connect things to anything else. TheBrain applies visualization to your information, creating a digital map 
I tend to be more of a word person than an images person, so have found Scapple, which I've just started playing with, to be very helpful as I think things through. It's available in a beta version from Literature and Latte.  Here's a description:
Scapple is a tool for getting early ideas down as quickly as possible and making connections between them. The main advantage of doing this in Scapple instead of on paper is that you don't run out of paper (the Scapple canvas expands to fit as many notes as you want to create), you can move notes around to make room for new ideas and connections, it's easy to delete and edit notes, and it's easy to export your notes into other applications when you know what you want to do with them. 
The beta version is free; the developers ask for comments and feedback. The final version will be very low cost.

So try these programs out, play with them over the holiday, and let me know what interesting uses you come up with for them. I'll be taking next week off from blogging, so will be back November 26. Enjoy the holiday.


Political action on climate change . . . . maybe?

So we've just had a monster storm on the East Coast, and (re-)elected a president who is ready to take action on climate change. As Climate Central puts it, at his first press conference after the election
Obama reaffirmed his view that manmade emissions of greenhouse gases are contributing to global warming, and stated his intent to continue to take action to reduce greenhouse gas emissions. “I am a firm believer that climate change is real, that it is impacted by human behavior and carbon emissions,” he said, noting that, “we have an obligation to future generations to do something about it.”
Here's a short piece from Auden Schendler about how we can go about it. He outlines three approaches - the Charge of the Light Brigade, the Battle of Leye Gulf, and the Battle of Agincourt. Schendler's providing an interesting metaphor. I think his deeper point is well taken - feel free to discuss in the comments.

Image via


If you haven't seen it, read this excellent column by Eduardo Porter, "Charity's Role in America, and its Limits," in today's New York Times. He argues that, while philanthropy in the US is strong, it is not the solution to various social problems. Here's one sample:
In fact, a small portion of philanthropic efforts are aimed at helping those who most need it. A study by Rob Reich, a professor of sociology at Stanford University, concluded that only a small share of charity redistributes income from the wealthy to the poor. A big chunk of the $40 billion donated last year to educational nonprofits went for new buildings and new programs at someone’s alma mater. Donations to schools in affluent school zones tend to help their own children, not those on the other side of the tracks. 

Using measurements - how to get started

Sometimes the hardest part of doing something new is getting started. Here are a couple of ways to find your way into the process of using outcome measures. You don't have to try them in the order I've listed them - sometimes it helps to approach a problem from several angles.

  • Think hard about what you do, and what you want to know about it. What is the end result of your services? If you provide education or tutoring, what sort of improvement do you expect to see? What does that improvement mean in the long run for the students you are serving?
  • Why are you looking at numbers in general, and this process in particular? For example, if an important function is backlogged, you can use numbers to tell that the backlog has been cleared. But then you can take a deeper look, using what you've learned to identify the root cause of the problem. The goal is to prevent it from recurring once you have cleaned it up.
  • What data systems do you have? How can you harness them to provide numbers? Bring in your IT and QI staff. But don't use a number just because you can measure it. 
  • Make sure your measure tells you what you think it does - don't measure something just because it's convenient. One example - it's very easy to measure the number of people who start a program. Maybe comparing that number to the number who complete the program will tell you something you need to know. But if it doesn't don't use it!
  •  Don't rely on a single measurement - you are likely to miss nuance. At the same time, don't try to measure too many things. And, as always with numbers, remember the context.

Here's a good description of an effective use of numbers, from McKinsey Quarterly's Report, "The Global Gender Agenda."
McKinsey’s more general work on transforming the performance of companies shows that those with a clear understanding of their starting point are more than twice as likely to succeed as those that are less well prepared. In a gender diversity context, this understanding means knowing the gender balance at every level of the organization; comprehending the numbers by level, function, business unit, and region; and then monitoring metrics such as pay levels, attrition rates, reasons women drop out, and the ratio between women promoted and women eligible for promotion.
Why go to this expense? Establishing the facts is the first step toward awareness, understanding, and dedication to improvement. Using a diagnostic tool, one company simulated how much hiring, promoting, and retaining of women it would require to increase the number of senior women managers. That approach helped it set an achievable and, just as important, sustainable target that would not compromise a highly meritocratic corporate culture. With an overall target—that 25 percent of managing directors and directors should be women by 2018—and a clear understanding that the bar for promotion could not be lowered, managers now look harder for high-potential women and start working with them earlier to develop that potential.
You can see my earlier post about that report here.

This is another in a series of occasional posts about developing and using outcome measures. You can see a previous post, with links to related posts, here.


The Signal and the Noise, by Nate Silver

I've been a fan of Nate Silver's work since the 2008 election when I, like perhaps many of you, obsessively checked his blog. I've always thought that his writing is clear and that he is transparent - to a point - about his methodology. So I was eager to read his very interesting book, "The Signal and the Noise."

What Silver sets out to do in this book is explore our ability to make predictions based on big data. Silver's main thesis is that we should be using Bayesian statistics to make and judge our predictions about the world. As Silver puts it,
The argument mad by Bayes and Price is not that the world is intrinsically probabilistic or uncertain . . . It is, rather, a statement . . . about how we learn about the universe: that we learn about it through approximation, getting closer and closer to the truth as we gather more evidence. [Italics in original.]
As Silver acknowledges, this approach is not the one we are taught in school (or in classes in the history and philosophy of science. For a review of that approach, read the first third or so of Jim Manzi's book "Uncontrolled." My review of "Uncontrolled" is here.) Instead, Silver argues, we use statistics that focus on our ability to measure events. We ask, given cause X, how likely is effect Y to occur? This approach raises lots of issues, such as separating cause from effect - we get mixed up a lot about the difference between correlation and causality. We mistake the approximation for reality. We forget we have prior beliefs, so allow our conclusions to be biased.

In contrast, Silver explains, the Bayesian approach is to regard events in a probabilistic way. We are limited in our ability to measure the universe, and Pierre-Simon Laplace, the mathematician who developed Bayes' theorem into a mathematical expression, found an equation to express this uncertainty. We state what we know, then make a prediction based on it. After we collect information about whether or not our prediction is correct, we revise the hypothesis. Probability, prediction,  scientific progress - Silver describes them as intimately connected. And then he makes a broader claim:
Science may have stumbled later when a different statistical paradigm, which de-emphasized the role of prediction and tried to recast uncertainty as resulting from the errors of our measurements rather than the imperfections in our judgments, came to dominate in the twentieth century.
Silver describes the use of Bayesian statistics (to greater or lesser rigor) in many contexts, including sports betting, politics, the stock market, earthquakes, the weather, chess, and terrorism. We are better at predictions in some of these contexts than we are in others, and he uses the chapters to illustrate various corollaries to his main theme. In his first chapter, on the 2008 financial meltdown, he identifies characteristics of failed predictions: the predictor focused on stories that describe the world we want, we ignore risks that are hard to measure, and our estimates are often cruder than we think they are. On the other hand, in a chapter about sports data, he makes a compelling case for the premise that a competent forecaster gets better with more information. Throughout, he urges us to remember that data are not abstractions but need to be understood in context.

This is not a how-to book, and it certainly left me with many questions. How do you test social programs using Bayesian analysis? But it is a very good starting point.

Image via


More post-Sandy commentary

This column by Bob Massie of the New Economics Institute describes a series of discussions he had back in 2000 - that's 12 years ago - about the possibility of developing cars powered by hydrogen fuel cells. The idea wasn't that far-fetched; GM unveiled the Hy-Wire concept car in 2002.

Of course, Massie recounts, there were problems with the idea, one of them being how to convert gas stations to supply the fuel. He asked someone from a petroleum company about the problem of fuel cell infrastructure. Here's what he says:
How many gas stations were there in the United States? I asked. About 150,000, he said. How much would it cost to convert a gas station to provide hydrogen? I continued. Anywhere from $500,000 to $1,000,000, he replied. And did every gas station have to be converted to provide adequate supply? No, he said, they anticipated that only about a third of existing stations would need to switch to provide adequate coverage.
I did the math. "So you are saying that the price of converting every gas station in the country at the maximum price would be about $150 billion?" I asked. Yes, he said. "And if the price were only $500,000 and we only did 1/3 of them, we could do the job for $25 billion?" Yes, he agreed. We came to the conclusion that a reasonable cost for the whole job would be about $50 billion.
That looks like a lot of money, but at less than 1/3 of 1% of a single year's GDP, it seemed a bargain price to change America's automotive fuel source so profoundly. The investment had the potential to completely revitalize America's auto industry, alter our fuel sources, and, because hydrogen fuel cell reforming emits less than half the carbon emissions of internal combustion engines, catapult us into a clean energy future. When the discussion came up in the press, however, political and business analysts ridiculed the idea that Congress would ever commit $50 billion to such a switch.
That $50 billion? It's about the cost of the cleanup from Hurricane Sandy, in today's dollars. The cost to convert would have been more than $50 billion, of course, but Massie has a pretty good point, I think. Do you agree?


Election predictions

In case you might have missed it, once again Nate Silver correctly predicted the outcome of the election. You can follow his thoughtful description of his model and regular updates on his blog.

I am reading Silver's book, "The Signal and the Noise," now, and will post a review sometime next week. In the meantime, here is a post describing Silver's outcome (and response) in various media. (That's a screenshot of graphic artist Christoph Niemann's take on it.) Basically,
Not a bad night for the math nerds. However, the truth—which Silver would readily admit—is that he didn't really "predict" anything. The math did ... and the math was based on polls, which are also based on math. He pulled them all together and came back with a number, which was very useful (and comforting to Democrats), but not magic. 
 And here is an post showing how various pundits' predictions succeeded (or, in most cases, did not).


More from McKinsey on Women in the Workplace

Last spring I wrote a post about a new report from McKinsey about its research on the advancement of women in the workplace, particularly large corporations. The McKinsey Quarterly (free once you register) has now followed that with a further report that suggest approaches for increasing the number of women at the higher levels of corporations, government, and academia. As usual with McKinsey, the insights apply to not-for-profits as well.

The conclusion? Well, progress has been made, but structural problems remain:
Firmly entrenched barriers continue to hinder the progress of high-potential women: many of those who start out with high ambitions, for instance, leave for greener pastures, settle for less demanding staff roles, or simply opt out of the workforce. . . And everywhere we look, despite numerous gender diversity initiatives, too few women reach the executive committee, and too few boards have more than a token number of women.
So what is to be done? McKinsey offers four strategies for committed leaders to follow:

1. Treat gender diversity like any other strategic business initiative - ie, set a goal and monitor it regularly. Expect the process to take some time, possibly years. Keep asking about it.

2. Ask for--and talk about--the data - in particular, think about the points where women exit. Why do they leave then? How many women are in the pipeline? Oh, and hold everyone in senior management accountable for those numbers.

3. Establish a culture of sponsorship - everyone, men and women, should sponsor, mentor, support two or three future leaders. And the current CEO/ED should spend time with them on visits.

4. Raise awareness of what a diverse work environment looks like - talk about your efforts; celebrate and publicize success.

And if you're in the US and still reading on November 6, don't forget to vote!


The Northwestern Juvenile Project

Today's New York Times carries a description of the Northwestern Juvenile Project, a longitudinal study of the mental health and outcomes for delinquent youth. The study follows 1800 (now down to 1644) youth with the goal of understanding drug and alcohol abuse, mental disorders, violence, HIV incidence, and paths or barriers to service. The study is directed by Linda A. Teplin of the Department of Psychiatry and Behavioral Sciences.

The design of this study, with its longitudinal approach and emphasis on follow up, is unusual and, from the description in the paper, sounds very well thought through. Because they are expensive, long-term follow up studies are rarely done. The potential for useful information for policy makers and service providers is immense. I haven't yet had time to look at the papers, but the abstracts -- including this one, which concludes that most of the youth in detention had a history of physical abuse -- suggest that there is much to be learned.

You can read more about the study, and find links to the papers that have already come out of it, here.


Sandy's tail lights

This beautiful image displays the impact that Hurricane Sandy had on the skies late in the afternoon of October 30th in Huntsville, Alabama. Each of those solar effects is pretty unusual; to see them together is rarer still.

The picture sent me to the website to get an explanation:
The apparition is almost certainly connected to hurricane Sandy. The core of the storm swept well north of Alabama, but Sandy's outer bands did pass over the area, leaving behind a thin haze of ice crystals in cirrus clouds. Sunlight shining through the crystals produced an unusually rich variety of ice halos.
 Click on the links to find out more about these phenomena.


Understanding Sandy's storm surge

The impact of the storm surge from Sandy on New York City and New Jersey is now becoming clear: it was tremendous. The surge at the Battery reached more than 13 feet above mean low tide, and water flooded streets and subways stations and tunnels. The screenshot is from Climate Central's sea level change forecast map, showing what a 10-foot rise in sea level will look like in lower Manhattan.

Three different but related actions contribute to a storm surge:
  • Wind - which piles water up high
  • Waves - which push the water ahead faster than the water can drain back
  • Pressure - low pressure of a hurricane means that water typically is higher near the eye
You can find a very clear explanation of a storm surge, by Jeffrey Masters of Weather Underground, here.

Why is a storm surge so damaging? All that water is heavy, and carries a lot of force. As Masters explains it,
A cubic yard of sea water weighs 1,728 pounds--almost a ton. A one-foot deep storm surge can sweep your car off the road, and it is difficult to stand in a six-inch surge. Compounding the destructive power of the rushing water is the large amount of floating debris that typically accompanies the surge.
Here's a photograph of some of the local debris deposited by the storm surge:

And yes, while no particular event is due to global warming, it's clear that global warming made Sandy worse: warmer seas meant that the storm was stronger and generated a bigger surge, and higher seas meant there was no place for the water to go but inland. Here's a good explanation.

Update: The Center for Climate and Energy Solutions has produced a useful fact sheet.

And in case you don't believe that Mitt Romney really said that the federal government should get out of the post-disaster aid business, here's a link to the transcript.

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