While there are all sorts of actions people can take to signal that they are trustworthy, sometimes simply making a promise can get the job done. When two parties will be dealing with each other for an indeterminate amount of time it is advantageous to both if they are viewed as trustworthy. Lying would mean being punished in the future by the other party. In this way, talk isn’t simply cheap–it’s a credible signal.
Now, it’s true that the Obama administration achieves its policy goals once the House passes the Senate bill, and doesn’t need a follow-up reconciliation bill except insofar as it’s necessary to guarantee House passage. But the reconciliation bill is going to consist of a lot of popular provisions that Democrats will be eager to vote for — canceling the Cornhusker Kickback, boosting middle-class tax credits, delaying the excise tax and instead raising taxes on the rich.
Moreover, the House is only going to pass the Senate bill first if it gets ironclad assurance on the reconciliation bill from the administration and the Senate. Why would Obama and the Senate nakedly double cross the House? It would mean never being able to pass a piece of legislation again. The reputations of the double-crossers would be destroyed, both inside Washington and, to a lesser extent, nationally. No remotely rational politician, no matter how evil, would do something like that.
What makes (or, would make) this a credible signal of trustworthiness by Senate Democrats? Continue reading
Nathan at Flowing Data puts words to an idea I’ve had for a while, but could never figure out how to communicate. He writes, “[T]he most important things I’ve learned [in statistics courses] are less formal, but have proven extremely useful when working/playing with data.” Some of the lessons learned include:
[T]rends and patterns are important, but so are outliers, missing data points, and inconsistencies.
[I]t’s important not to get too caught up with individual data points or a tiny section in a really big dataset.
[D]on’t let your preconceived ideas influence the results.
The more you know about how the data was collected, where it came from, when it happened, and what was going on at the time, the more informative your results and the more confident you can be about your findings.
[A]lways ask why. When you see a blip in a graph, you should wonder why it’s there. If you find some correlation, you should think about whether or not it makes any sense. If it does make sense, then cool, but if not, dig deeper.
The point is that regardless of whether you are formally trained in and choose to leverage sophisticated statistical methods, there is a great deal to be gained by thinking like a statistician. I would actually go farther here and say that the statistician part is somewhat besides the point. Thinking like a methodologist is the key.
I would agree with Nathan that the most translatable skills that I learned in graduate school are methodological in nature. More specifically, there isn’t a particular technique that is most useful, but rather a mode of thinking that allows me to approach problems in a rigorous fashion.
Just yesterday, my colleagues and I were discussing the merits of health supplements. Specifically, we were talking about the extent to which there is scientific evidence to back up their effectiveness claims. Who knew that I would pop open my Google Reader to find that one of the latest creations from Information is Beautiful focuses on–wait for it–the scientific evidence for supplements!
The visualization is not only beautiful, but also clever, informative, and user friendly. It takes the most popular supplements and plots them from top to bottom in terms of how strong the scientific evidence is for their relative effectiveness in treating certain conditions. Additionally, viewers can sort the data by indication to see which supplements are backed (or not) by scientific evidence.
The universe of health supplements is indeed a noisy one, but data visualizations such as this one can help make sense of it all. Good data visualizations can help separate signals from noise by clearly presenting a large amount of data in a simple, relational way. In this case, you have the most popular supplements ranked by the degree to which scientific evidence backs their claims. It is pretty easy to see which claims are valid, and which appear to be nothing but empty promises (and a big waste of money).
Alan Turing, one of the most brilliant minds to have graced the earth, devised a test to determine whether or not a machine possessed intelligence. This test became known as the Turing Test.
A pair of researchers have developed a similar test to determine whether humans can tell the difference between actual financial market returns and randomly generated data:
[Jasmina Hasanhodzic and Andew Lo] have devised a simple experiment.
They have created a computer game in which a player is shown two time-series of data. One is real data from a financial market such as the US Dollar Index, or the spot price of Gold. The other is the same data randomly rearranged. The player has to guess which is the real series and is immediately told whether the guess is right or wrong.
They found that very quickly subjects were able to determine what was randomly generated data and what was actual data. Actual data was much smoother and therefore easy to spot against the bumpy, randomized data.
It is an interesting experiment, but it doesn’t break any ground in terms of human cognition. We’ve long known that humans seem hardwired to quickly visualize patterns, likely for evolutionary reasons since it is advantageous to quickly recognize predators. We’ve all experienced the sensation of effortlessly seeing faces or objects in clouds, for example. This experiment just confirms the notion.
It’s that time again. This past month really flew by (and not just because there were less days in it). This time around I have removed a few posts that seem to be garnering lots of views due to very common searches (e.g. my post on the plasma knife). Too many short click-throughs to really count them as ‘viewed’ in my book.
So here they are, the top 10 viewed posts for February. As always, thanks to everyone that has visited!
What we see in reality are millions of corpses of businesses and ideas that have made their impact (or not) and then petered out into oblivion without leaving much more than a memory. Some of them get bought and swallowed by a bigger company, others have their ideas copied and commodotized and many just don’t have the business or financial chops to make it all work for more than a few years.
So what if instead of worrying about all that you just decided at the beginning you were going to end it all six years in?
I love questions like this, and Noah is great at asking them. He suggests that such an arrangement may solve the problems that arise when management sacrifices the long-term interests of the company for the short-term, making decisions that optimize their current job security but may create problems for the firm down the road:
Company management doesn’t know how long the company will last, so they optimize for the now (they also don’t know how long their jobs will last, but I’ll get to that in a minute). It may be overly hopeful, but as long as one choose a reasonable time-frame (5-10 years) I wonder if you couldn’t lift the decision-making out of the immediate.
It is an interesting idea, but I think that what Noah is mostly interested in here is a shift in how employment is structured (i.e. knowing up front when one’s job will terminate), rather than how businesses as a whole are set up. (If the business will shutter its doors in 10 years what precisely are the long-term interests of the firm?) Additionally, he focuses more on the employment issue towards the end of the post. In either case, I think that on the whole the uncertainty that exists in terms of business and employment termination is superior to expiration dates. Here’s why: If businesses were set up at the outset with a planned time frame at the end of which the business would wind down it would likely play havoc with their ability to compete in the marketplace. Additionally, the beneficial economic conditions that obtain through competition would be warped. Why? Because businesses would not have to operate in the shadow of the future. Continue reading
Temple Grandin recently spoke on the topics of neurodiversity and autism at TED 2010. Her talk echoed many of the points discussed by Tyler Cowen in his Create Your Own Economy(which I reviewed briefly here). Grandin’s talk focuses on the different types of thinkers that exist, particularly along the autism spectrum. She lists three:
Photo Realistic Visual: Poor at Algebra (not math, per se)
Pattern Thinkers: Excel at music and math
Verbal Mind: Poor at drawing, excel at remembering facts & details
Grandin herself is a Photo Realistic Visual thinker. As she says, “I think in pictures, I don’t think in language.” She goes on to discuss why this is relevant, in particular from a problem-solving/design perspective. Additionally, she makes some good points about how educational institutions need to think about how to accommodate these different types of thinkers, as they will undoubtedly learn differently.
Jen Prout of The Full Belmonty recently posted about the various organizational and management lessons to be learned by watching CBS’s new reality show, “Undercover Boss”. Each week, the show follows a CEO as they go undercover, posing as a new hire or trainee, working at various locations. The basic plot is that the CEO’s learn quite a bit about what policies are working and which aren’t as they immerse themselves in the front lines of their business. Jen rightly points out that anyone watching the show should immediately notice the significant communication problems that exist at the firms whose CEO’s decide to take part in the show. In a well run organization, CEO’s would have an idea of certain problems with their business before ever having to go undercover in this way:
It appears that these undercover bosses are employing a one-way, top-down management approach and not actively engaging in a two-way dialogue with their employees to make more informed and effective operating decisions. Employees are an organization’s best source of knowledge and can be their greatest asset for building a successful company if the employees are properly engaged.
True enough. Any organization that does not employ adequate feedback mechanisms are just asking for trouble. However, watching the show made me think of something else–research methodology. Continue reading