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Microtrends and collaborative filtering?

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I’ve recently been hearing a bit about Mark Penn’s book "Microtrends: The Small Froces Behind Tomorrow’s Big Changes". As this review says, Penn analyzes poll and survey data to identify 75 important microtrends (which appear to correspond to ‘small’ segments of the US population, say at least 3 million) that, he believes, are interesting and important.

How, since Mark Penn is the guy who identified the ‘soccer mom’ demographic for Bill Clinton’s 1996 re-election and is now the chief political advisor to Hillary Clinton, when he talks, people listen.

And when I listend, I’ve found what he has to say interesting. However, since I haven’t read the book, I don’t know exactly how he comes up with his microtrends. It seems like the scientific approach would be to apply clustering algorithms or factor analysis or some such technique, which, as far as I can tell from browsing reviews.

I wonder what such an approach would reveal: if you ran a clustering algorithm, say, on a large survey dataset, would the clusters include Penn’s microtrends? Would one even be able to make sense of the clusters?

How do software patterns evolve?

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I was talking yesterday afternoon with several other lab members about Martin Fowler’s "Patterns of Enterprise Application Architecture." In his book, Fowler admits that most patterns aren’t anything new. "Creating" a software pattern is just naming and describing a software practice already used by some developers. Of course, Fowler presents patterns with amazing clarity and skill, so his contributions are valuable to the development community.

Since our research group is interested in social communities, we were curious about how new software practices evolve into well-known design patterns. Do most patterns start with a bang from a few highly influential, outspoken developers? Is there gradually increased adoption until the pattern reaches a critical mass of people? Another possibility is that as the global software environment changes, many groups of people stumble onto the same pattern at the same time.

What’s your opinion?

 

Apple on Wrong Side of History

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Here’s one of many articles on the battle between Apple and iPhone hackers over the ability to run their applications on the iPhone. Apple seems to be badly misreading the current state of the world.  Hello!  We’re in Web 2.0 here!  The breakthrough is Facebook fielding a full-fledged API for its fans to develop their wild and crazy applications — and to share them with everyone else.  Yet, Apple is playing Microsoft, trying to lock down and protect their ability to deliver proprietary applications that their customers cannot build themselves, extend, or improve. 

Personally, I’m committed to the power of community-maintained resources; I won’t consider buying a device that is locked down like the iPhone and the ITunes Touch.  There’s something about knowing that the machine in your pocket is programmable that makes it more fun to carry — even if you end up not programming it yourself. 

John

 

http://www.engadget.com/2007/10/10/iphone-and-ipod-touch-v1-1-1-full-jailbreak-tested-confirmed/

Future of Computer Science

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Interesting Computer World article on the future of computer science.  A wide range of views.  The ones that impressed me the most were John Canny’s thoughts on what it is that has made computer science uninteresting to so many people.  He argues in several comments that computer science has chosen the path of not caring about social relevance, and that that is now costing us in how interesting we are as a discipline of study, and how much impact we are having on the world.  He points out that much of computer science is just working hard to be a hand-maiden to other sciences.  

I found his arguments compelling.  The suggested fix of moving towards understanding how computers can provide applications that deeply fit what people want to do in their lives is critical.  Right now most computer science programs don’t want to think about users and their problems.  Why is that?  How can it be fixed?

In a New York Times article on the same topic, Jon Kleinberg is quoted about the vast opportunity computer networks are creating for measuring social networks.  Of course, he’s right about the value of the social networks, but I think focusing on the measuring misses the big picture: computers are making possible radical new notions of community that are altering or replacing our traditional geography-bound notions of community.

In a Nature issue devoted to the future of computing, Stephen H. Muggleton, one commentator, discusses the use of computers for advancing biology research.  He
discusses many examples of the use of computer algorithms to improve
the ability of scientists to form and test hypotheses.  He argues that
interfaces to these algorithms will eventually enable human scientists
to perform in a way that will demonstrate "Exceeding human limits".

What do you think the future of computing will bring?

John