Becoming a more global player 9

For those with the right opportunity, learning to speak another language well can be a great way to get a more global view of the world and of your own culture. I just ran across Benny Lewis' blog today, and it gives away the secret I stumbled onto years ago: don't speak your native language!

For a significant period, I lived and worked in a small town where not even the second language was English. One might argue the first language was Alemannisch, the second German, and the third French. I was stubborn, and I forced myself to speak the local language. I seem to recall almost six months of headaches and tiredness in the evenings after struggling to be productive in a different language. Then one morning I woke up realizing I had just dreamed my first dream in German. From then on, I thought in German. What I learned from then on, I learned in German, and I eventually had to translate some of those concepts to English. I still think in German from time to time. Benny Lewis' experience mirrors mine precisely, except that I think I took a bit longer. As a practicing engineer at the time, a significant part of my day was spent in design and calculation, so I may not have had as much opportunity to converse as he.

So, if you get a chance to live in a different country, I strongly encourage you to try this approach. It will be hard—very hard—at first, but the payoff is great.

You might also like his How to speak a language pretty well, starting from scratch, in just two months. You might also be interested in the previous posts in my series.

The lazy employee revisited

For some reason, In praise of the lazy employee was one of my more popular postings. Now Fast Company is saying much the same thing in Hard Work's Overrated, Maybe Detrimental.

"Just listen to your heart" supports the Fast Company article at least as far as decision making goes.

What did Schellnhuber say this time?

Some of you may recall that Hans Joachim Schellnhuber and his research team produced a very good article on potential tipping points in the environment. I happened to find an interview with him in Der Spiegel entitled 'Industrialized Nations Are Facing CO2 Insolvency.'

What do you think?

Growth from a German perspective

Der Spiegel just published Can Economies Function without Growth?, which directly relates to some of the conversations we've held here in the past.

What do you think?

Sustainability by Cairns

Every once in a while, I run across a classic article I'd like to share. I've run across this one by John Cairns, Jr. several times, and I'm ready to recommend that you read it.

Cairns published Will the real sustainability concept please stand up? (link fixed) in 2004. He provides much insight in the space of four pages.

If those insights made you think (and learn), check out his Web site for more of his papers.

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?

Making more sense with numbers, part 8

I'm not a professional statistician; I am a professional who uses statistics in the course of my work. Increasingly I'm drawn to Bayesian approaches. Various people have asked me what Bayesian statistics is; when I was asked for the elevator speech version recently, I was stumped. I'll try to make up for it here.

Statistical problems have three parts: the setup, the calculation, and the presentation of results. By my understanding, Bayesian and classical (frequentist) statistics differ in all three.

In the setup, Bayesian statistics starts with the development of a probabilistic model and a set of prior probabilities for the parameters of interest. Classical statistics seems to start with the development of a null hypothesis (what if there is no effect from whatever intervention being considered) and an alternative hypothesis. There's a difference in how one considers information one has before the data collection starts. Some have taken Bayesian approaches to task for the sometimes subjective form of those prior probabilities, but others have pointed out that classical approaches also have their subjective moments in assuming that the particular nature of the classical assumptions apply in a particular situation. Some point out that one can pick prior probabilities in a way that doesn't rely on subjective assessments; those tend to be the weakly informative priors you can read about. I'm intrigued by this part of the difference, but it's not the telling difference for me.

In the calculation, the classical statistical approach relies on selecting the appropriate test to decide if one should accept or reject the null hypothesis or to calculate confidence intervals for parameters of interest. As some have pointed out, this is not always an easy task, and the tests are not always easily matched to complex problems. With unique problems, one may have to modify the problem to match the method or invent new methods to match the problem.

The Bayesian approach relies on basic probability models, which makes it easier to develop an approach that meets the specific problem at hand. This is a telling difference for me.

There is a problem. Except for the simpler cases (for example, see the original original Making sense with numbers), it's often hard to carry out the integration involved in making the calculations. Markov chain Monte Carlo (MCMC) approaches make that much more approachable, but they're not things one carries out on the back of an envelope.

Finally, there's the presentation of the data. This, too, is telling for me. While the classical approach gets tied up in explaining precisely what it means to reject the null hypothesis or what a confidence interval means, the Bayesian result means exactly what most of us likely think when we hear a statistical result: it states the probability of a particular event we care about happening.

I'm still looking for a short, easy-to-read but complete elevator speech from a statistician on the topic that's consistent with some of what Andrew Gelman writes (I think he has some excellent writing on the subject, but I'm not sure I've found anything that fits the elevator speech model). In the meantime, Bayesian Statistical Inference for Psychological Research may help some begin to understand, even as it's somewhat old chronologically. Some might enjoy Why we (usually) don't have to worry about multiple comparisons. shows a simple but powerful application of Bayes Theorem, although it's rather more simple than what one would recognize today as Bayesian analysis.

Objections to Bayesian statistics actually does contain an elevator speech about Bayesian inference, even if it is a bit mathematically concise: "'Bayesian inference' represents statistical estimation as the conditional distribution of parameters and unobserved data, given observed data."

It's a bit longer than an elevator speech, but Dr. David Lucy of Lancaster University does have a short introduction to Bayesian methods that may help; it's part of his CFAS415a course materials.

If you've got a great but simple introduction that can explain the difference between Bayesian and classical inference well, please add it to the comments here! Thanks.

Gender and diversity

At a recent Bainbridge Graduate Institute Intensive, I found a short note affixed to my nametag. It was placed there by their Diversity & Social Justice Committee, and it read, "Men who want to support women in our struggle for freedom and justice should understand that it is not terrifically important to us that they learn to cry; it is important to us that they stop the crimes of violence against us." (Andrea Dworkin)

I would write more, except that I think Dworkin said it well.

A systems take on math and science education?

Richard Hake, Emeritus Professor of Physics, Indiana University, recently posted an article describing how US colleges and universities are gradually coming to the view that they can't simply blame US secondary schools for the quality of math and science education incoming college students have, for the teachers and administrators of those secondary schools are themselves almost all products of the US college and university system.

This seems like closed-loop (feedback) thinking in action. Check out his Mobilization for Math/Science Education - Role of Higher Education.

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.

System dynamics applied to music

One of the project teams from last year's system dynamics class in the Information School at the University of Washington will be presenting their work at the International System Dynamics Society Conference this summer in Albuquerque, New Mexico. Look on the tentative schedule for "Exploring the Dynamics of Music Piracy" by Trond Nilsen, Brian Houle, Douglas Kuzenski, and Arpan Sheth, or check out their abstract, paper, and models.

Congratulations Trond, Brian, Doug, and Arpan! For the rest of you, check out their work. Perhaps it will shed light on a subject you've talked about.

Creating sustainability in complex ecosystems

I recently had the privilege of teaching a course in system dynamic for Willamette University's Sustainable Enterprise certificate program. The course lasted two days, with a follow-up two-hour web seminar. We focused on qualitative system dynamics, but we treated it at a somewhat more rigorous level than many such courses, I think.

I'm writing because of one particular lesson I learned—we all learned. Early in the course, we used a simulation game to help people have a common, shared experience of interacting in a challenging system environment.

As with many such games, the expected result is that people fail in making the system work. Typically, the debrief is used to help people understand the ways of thinking that led them into trouble and to prepare them for the material that's to come.

Unexpectedly, this class managed their challenges quite sustainably. While their skill wrecked the planned flow of that part of the session, I was really pleased to see their skill in action. We spent some time talking about what made them successful and how that might carry over to real-world situations. Their insights were useful enough that I wanted to share them (with the students' permission) with a larger audience: you.

I first asked what made them succeed in the game and what provided the most challenges.

Goals were the first. While the game tells them the goal they should have, they rapidly realized that focusing on the stated goals would lead to ruin, and so they decided to set a much longer-term goal.

Communications was the second factor. After the first round, they began to spend most of their time huddled in the center of the room, talking animatedly through their decision-making processes instead of working in isolated teams.

They noted that delays provided a key challenge. As they worked to establish trust in the social system they had set up, they were both trusting other teams' commitments and verifying that they were indeed living up to their commitments. That takes time: commitments made today may not show up for quite a while.

Those delay effects were complicated by the natural delays in the system. Without revealing the game we used, I will say that the dynamics of the game included natural delays between actions and results that complicated decision making.

Some noted this seemed analogous to the situation OPEC finds itself in. They rely on mutual agreement to limit production as a way to manage prices. If anyone in OPEC breaks that agreement, the system can collapse. OPEC's problems are complicated by uncertain demand and uncertain prices, factors that had no analogy in our game.

Math skills created another success factor, which some may find surprising. A subset of the players rather immediatedly began developing quite a useful understanding of their system based on a mathematical model they developed. Once others saw that their results were accurate, everyone became driven by the data. Without some in the group being able to pull that off, they would likely not have succeeded.

Interestingly, trust and math worked together. At one point, the analyst team made a numerical error and then made an especial effort to communicate that they had made that error to others so that the others would be able to differentiate that error from a breaking of the trust relationship. Apologies were key. Information and the lack of information thus played a key role in the group's success. Even then, it took time for the others to regain their trust in the analysts' team.

Playing into this was the lack of external shareholders. Everyone on the teams had a serious take in the workings of the game; no one was in it just for the "money." Similarly, there were no new entrants into the field who might have upset the cartel relationship they had crafted.

I then asked them what they'd advise people in the real world.

Collaboration was the first clear answer. Work together across groups to align goals and actions.

They then said, "knowledge is power." After a bit of reflection and revision, they revised that to "timely, transferrable, actionable knowledge is power."

They felt it was important for everyone to be clear on a vision.

They would encourage people to watch their egos and to be visibly trustworthy.

At one point, in an attempt to test the strength of their commitment (okay, as an attempt to derail their commitment), I as facilitator announced I was the government and was giving them something they really didn't want. (To be accurate, that idea came from Anne Murray Allen, the executive director of the program, who was running the simulation computer.) For a while, I felt as if I were about to experience the French Revolution, as some rather emotionally argued for standing up to government and refusing my help, a bit of resistance I wasn't accepting.

As a result, their last bit of advice was to "Don't trust the wisdom of government, of the private sector, ... of either." In other words, test the data and the reasoning yourselves instead of blindly accepting what others say is good for you.

This was an intense and very exciting two-day workshop. I think those in the class learned a lot; I know I learned as they taught themselves and me (and now perhaps you) how to make sustainability work.

Perhaps I'll see some of you there next year.

M. King Hubbert on growth

I've written about growth from time to time; perhaps you'd be more interested in what M. King Hubbert said in 1974 in Hubbert on the Nature of Growth.

If you'd like to read more on Hubbert's model, see Evaluating Hubbert's Peak and Improvements? by Ben Witten.