Presenting results and the election

In operations research, we often have problems presenting our results in a reasonably provocative yet accurate way.  I swear I have spent 10% of my life sitting through powerpoint tables with the presenter saying “Well, you can’t really see the numbers but they really show my approach is better” (and perhaps I spent a further 5% of the time presenting such slides:  I am as bad as many others in this respect).   We have to do better!

My adviser, John Bartholdi, is a big fan of Edward Tufte, to the extent that I can recognize my academic brothers by the Tufte in their bookshelves (I noted it for Kevin Gue at Auburn two weeks ago).  His main message is that thinking about how to present work can be as much effort or even more than the work itself.  But it pays off in improved understanding.

The recent US presidential election is a good example of a display challenge.  There is the simple data:  Barack Obama won the election and John McCain lost.  You can add in the “electoral college” numbers:  Obama with 364 to McCain’s 173 as I write, with one vote still too close to call in Nebraska.  But these numbers don’t give a very deep impression of the election.  Where did Obama do well?  Where did he do poorly?  The standard electoral map (like that shown at pollster.com) gives some impression (Obama blue on the coasts and around the great lakes, generally McCain red in the middle, but with some blue inroads in Colorado and New Mexico):

But this misses a huge amount also.  Some of these areas are highly populated, while some have very, very few people.  In the US, we don’t vote by acreage!

Mark Newman, a physicist and researcher in complex systems, has a great page on different presentations of the election.  The one I like best includes both a scaling of the states to keep their general shape but to scale them to be proportional to population and a mixing of blue and red (making purple) representing how strongly an area voted for Obama and McCain respectively:

I admit it looks a bit weird, resembling some cardiovascular system to my eyes, but Mark’s page walks us through the process.

The standard operations research talk would present this data in six-point fonts in a table that can’t be read by anyone more than 3 feet from the screen.  Perhaps when we get as good at presenting our work as the complex systems people seem to be, our field will get the respect it deserves.

Giving Talks

I am in Auburn Alabama where I just gave a talk to the industrial and systems engineering department on sports scheduling.  I must say that when I left Pittsburgh this morning, I had somewhat mixed feelings.  Of course, I love giving talks, and it is great to go out and see a university I have not seen before.   And I know some people at Auburn and I like them and the research they do (check out Kevin Gue‘s animations of order picking in warehouses:  who knew order picking was so captivating!).  Further, the meetings with people I don’t know offer great opportunities for social capital (I ended up enjoying all of my meetings, particularly the one with Emmett Lodree who is doing really neat work on disaster response and inventory).

But as the alarm went off at 4:30AM so I could make a 7AM flight to Atlanta and then drive an hour and half from Atlanta to Auburn, I was wondering of the value of giving another talk.   I have given versions of my sports scheduling talk a few dozen times (though it is vastly different than what it was even one year ago) and, while it is a fun talk, some of the thrill is gone.

But recently I read a blog entry by Sze San Nah, a doctoral student at the University of Sydney, on her giving her first talk (at the IFORS conference in South Africa).  In her blog entry, she goes through the excitement and terror of giving a talk at a professional conference.   And I thought back on my first talk.  It was at an ORSA/TIMS (or TIMS/ORSA) conference in the mid-1980s.  I was to give a talk on an improved algorithm for polymatroidal flow (a paper I am still extremely proud of:  it was published in Math of OR).  The paper was stuck in a session on manufacturing networks, and the chair of the session introduced it as “Here’s a paper that I can’t even understand the abstract.  I don’t know what it is doing here”.  He proceeded to spend the rest of the session looking out the window.  After that introduction, practically everyone in the room stood up and left, seeing that there was going to be very little manufacturing in my talk.  Fortunately, I think my co-author Craig Tovey had rounded up some people, because about 10 people came into the room, just to hear me talk.  So I stumbled through my talk, and it ended up going reasonably well.  But I was very nervous.

Since then, I have given perhaps one hundred talks at professional meetings and another fifty talks at various universities and research institutions.  And I think the key to giving a good talk is to keep some of the nervousness that Sze San Nah talks about, without letting that nervousness take over.

To come back to Auburn, I had a great day here.  Nervousness was easy, since there were sixty or more people in the room.  But the talk went well, if a little rushed. I am glad I decided not to ignore the alarm clock this morning!

Operations Research and the US Presidential Election

I am in Cork, Ireland, attending the Irish Conference on Artificial Intelligence and Cognitive Science (I gave a talk on sports scheduling and three themes of modern integer programming: complicated variables, large scale local search, and logical Benders constraints). Conversation here (when an American is in the group: presumably without an American conversation is about hurling or something) is on the US Presidential Election. Some of the historical anomalies are a bit confusing. Why is it only now that Barack Obama “accepts” the nomination from the Democratic Party: shouldn’t he have decided on this long, long ago? What if he didn’t accept the nomination?

The most confusing aspect of the election process is our use of the Electoral College to elect the President. Rather than directly electing the President, voters vote for electors, with each state being given a set number of electors. For most states, all of the state’s electors are given over to just one candidate. This makes interpreting the polls quite difficult. One recent poll had Obama (the now-nominee of the Democrats) and McCain (the presumptive Republican) tied at 47% support each. A natural leap was to then assume that the election is a toss-up. But it is really the distribution of support that counts. It is possible to win the election for President of the United States with .00001% of the vote. For instance, suppose only one voter shows up in 49 states, and those voters vote for Obama, and 10,000,000 Republicans vote for McCain in New York, then Obama would lose the national popular vote 10,000,000 to 49 but he would have an overwhelming majority in the electoral college. While the results would never be that extreme, it is certainly possible (and has happened) to win the national popular vote and lose the electoral vote.

Interpreting polls gets more complicated when you try to address the uncertainties in the polls. For instance, the 47% results above are only for those in the survey who had a preference. There are a huge number of “undecided” voters who do not yet have a preference. How should they be handled as we try to figure out who is ahead (I hate this idea of elections as a “horse race”, but if the media is going to see it as a race, they could at least accurately represent the real race)?

Sheldon Jacobson (University of Illinois), Steven Rigdon, and Ed Sewell (both of Southern Illinois University Edwardsville) are addressing this issue by taking the current poll data and determining the probability of winning the election for each candidate. They have a fascinating website that is being constantly updated.

It is worthwhile to read their methodology section.

The mathematical model employs Bayesian estimators that use available state poll results (at present, this is being taken from Rasmussen, Survey USA, and Quinipac, among others) to determine the probability that each candidate will win each of the states. These state-by-state probabilities are then used in a dynamic programming algorithm to determine a probability distribution for the number of Electoral College votes that each candidate will win in the 2008 presidential election.

There is a full paper by the above authors along with Christopher Rigdon.

They point out a few limitations of their approach. Of course, the results are only as good as the poll data: if the poll data is off, then their results are meaningless. Further, they are not (currently) treating Maine and Nebraska correctly: those two states divide their electors by congressional district, while every other state is all-or-nothing.

Currently, they have Barack Obama with an 89% chance of winning, which is pretty high, but down from the 96% chance they had him at on July 31.

Personal Blogs, Personal Views

William Patry, Google’s Senior Copyright Counsel, is ending his blog “The Patry Copyright Blog“. That sentence, in short, gives one of the two reasons he is ending the blog (the other is the horrid state of copyright law, in his view). Patry is a long time copyright lawyer who started the blog while in private practice. Once he joined Google, however, suddenly people projected his blog onto Google. Of course, it is hard to see how to refer to people without saying something about their credentials. Saying, “William Patry, Google’s Senior Copyright Counsel, said this on his blog” seems to give more credence to a view than “William Patry, random guy on the street, said this”, even without making the step to “Google as a company believes this”. But too many people make the last step, making it impossible for Google’s senior copyright counsel to have a personal blog.

I’m primarily an academic, and there seems little harm in referring to me as “Michael Trick, professor at Carnegie Mellon, said …”, since there is a long tradition of celebrating individual views among professors. If everything a university professor said had to be vetted by the university (and who in the university could do such a vetting?), you wouldn’t hear much from professors on any topic. Once in a while, I get identified with organizations. Recently, I was referred to as “Michael Trick, former President of INFORMS” in a blog entry where I have pretty well convinced myself I was not referred to as a “nut with a computer” (though some doubt exists), which is a bit more troublesome. Even in 2002 as President of INFORMS, almost nothing I said was speaking on behalf of INFORMS. Certainly now, if it is not obvious, I don’t speak for INFORMS.

This is also relevant to the recent IBM/ILOG acquisition. We’d all love to hear from the ILOGers and IBMers on what they think it means. I would love to hear it on a personal level on how it will affect their life, their work, our field, and the universe. But it is essentially impossible for anyone at IBM or ILOG to speak personally at this point. Anything they say could be taken as “official” and would draw harsh legal (and company) repercussions. So, until the acquisition is complete, I think we will have to be satisfied with carefully crafted, legally-cleared, official comments.

Operations Management Blog

Gerard Cachon, who I overlapped with in New Zealand, along with Christian Terwiesch have started a blog on operations management issues.  They get to the heart of the issue in naming the blog “Matching Supply with Demand” which is about as good a tag line as I have seen for operations management (it is also the title of their book).    The blog has just five posts so far, but is very much in keeping with the goal of looking at current events from an OM perspective.  Check it out!

Data Visualization

I have always loved Data Visualization (well, always since my adviser John Bartholdi pointed me to Tufte’s classic “Visual Display of Quantitative Information”). I teach data mining here to our MBAs, and have wanted to include the topic, but never knew what to include. Thanks to Stephen Baker of Business Week and his pointer to Many Eyes, I think I am getting an idea. Many Eyes is an IBM site with a goal of making data visualization algorithms and data sets widely available. It is a fantastic place to spend a few hours. As an example of what you can do on the site, here is a tag cloud of my vita (the source is at http://mat.tepper.cmu.edu/trick/vita.pdf):


I think you can find a fair amount about me just by looking at that tag cloud, though I am a bit biased (most ink blots end up looking like me in my eyes). Perhaps even more than by reading through a 12 page vita (by the way, vita (or curriculum vitae) is supposed to meana short account of one’s career and qualifications prepared typically by an applicant for a position”. What ever happened to short? What is the name for the document where you put down every blessed thing you ever did in your academic career?)

The structure of Many Eyes is unusual: you don’t download computer software. Instead, you upload your data (which immediately becomes public, so don’t try this with your financial records) and work with it there. This means that Many Eyes is quickly collecting a huge amount of data (23,256 data sets so far) that it (and you and others) can work with. This “social networking” aspect is unexpected, but I would bet that some interesting results come from it.

Another fascinating site is Wordle, which also creates tag clouds, but does so in a more artistic way. Here is my vita in that form (a couple of versions). I think I will use it during my next salary review!


I think I will need a few more days to recover from my surgery before I can get any useful work done.

Change in attire

Since today was

  1. The first working day after classes ended last week, and
  2. Warm and sunny in Pittsburgh,

I went to work in shorts and sandals. In honor of this, I would like to direct your attention to an article in Inside Higher Ed by Erik M. Jensen entitled “A Call for Professional Attire“. In the article, Jensen notes the standard sartorial choices of professors:

Professors, it’s been said, are the worst-dressed middle-class occupational group in America.

He offers a Uniform Uniform Code:

Faculty members shall, when on college grounds or on college business, dress in a way that would not embarrass their mothers, unless their mothers are under age 50 and are therefore likely to be immune to embarrassment from scruffy dressing, in which case faculty members shall dress in a way that would not embarrass my mother.

A response by the economist Brad Delong brings out how dress needs to change depending on the audience:

With math-oriented students, however, a tie tells them that I spend too little time thinking about isomorphisms.

For the record, when I teach MBAs, I teach the first class in a suit and tie. The second class, I take off the jacket half-way through. The third class, I take off the jacket immediately. The jacket is never to be seen again: I trust my students assume I take it off in my office, though it never leaves my closet. Later in the course, I might lose the tie for a couple of lectures if the course is going well; If the course is going poorly, I put on “power ties” of increasing power until I get the course going well again. When doing video teaching, I used to wear shorts with a shirt and tie, but the new system in place shows all of me, so I am back to wearing big-boy pants for all my classes. I only change the structure of my facial hair in the middle of a course if it is going so poorly that I need to subtly get across the idea of “new beginnings”. And I often wear shorts and am otherwise an embarrassment to my mother outside of teaching days.

Some Miscellany from the INFORMS Practice Conference

Some random things from today’s INFORMS Practice Conference:

  • Sanjay Saigal, popular columnist for OR/MS Today and founder/CEO of Intechne, formerly of ILOG, just to continue a theme, chided me for not pointing to his blog. I actually read his blog, but he normally blogs on non-OR things. It is great (with a great title): check out “Another Argumentative Indian”.
  • I met Sandy Holt, who had given me a blog idea. If you see me, don’t hesitate to say “hi!”: I’d love to meet more of you who have emailed me over the past couple of years.
  • I had a good long chat with Cindy Barnhart. Cindy is the current President of INFORMS, and is an associate dean of engineering at MIT. For a person who seems pretty laid back, she certainly seems to get a lot done!
  • I also had a long chat with Irv Lustig from ILOG. Irv is extremely upbeat about CPLEX at ILOG, just as Alkis Vazacopoulos is very upbeat about Dash and Fair Isaac. As OR becomes more mainstream (in a form known as “Advanced Business Analytics”, with “Business Analytics” being “look at your data!”), it is natural that the software firms would act more like businesses, being bought and sold, and having turnover. Perhaps I overly worry about the various changes.

OK, time for dinner and an early bedtime. My buddy from graduate school, Chris Lofgren, now CEO of Schneider is speaking first thing in the morning.

Me and Kareem

I teach data mining here at the Tepper School, and one example I use of something that is hard to get computers to do is to recognize faces, a task any 2 month old baby can do reasonably well (at least with regards to mothers). But it seems that MyHeritage.com has this licked: given a photo, they do a great job of seeing who your celebrity look-alikes are. And for me, it was uncanny. I can’t tell you the number of times I have walked down the street and have people say “Aren’t you Kareem Abdul-Jabbar?” That’s assuming they are not mistaking me for the Dalai Lama. I am glad I now have this picture so I can clear up any confusion: that is me in the upper left; Kareem is in the lower left. Perhaps the easiest way to distinguish is to note that I still have some hair on the top of my head. Or perhaps that Kareem is the taller.

Check out the face recognition at http://www.myheritage.com/face-recognition

I’m now hard at work to create the algorithm to prove my real look-alike is George Clooney.

Monkeys, Cognitive Dissonance, and Hiding in Plain Sight

If you have taken an undergraduate psychology class, you probably have heard about the experiment where monkeys, having previously shown little or no preference between red, blue, and green M&Ms are offered them in pairs. First blue and red is offered, and the one not chosen (say blue) is offered against green. About 2/3 of the monkeys choose green. Psychologists claim this is a sign of cognitive dissonance: information is ignored or used selectively to confirm biases. If blue is not chosen in the first round, it must be “bad”, so it is less likely to be chosen in the second round. Not so fast! If the monkey does have a preference among the colors, then perhaps the monkey is acting consistently. There are six choices for an ordering of the colors:

  1. R G B

  2. R B G

  3. G B R

  4. G R B

  5. B R G

  6. B G R

Choosing Red over Blue means that cases 3, 5, and 6 do not occur. In 2 of the three remaining cases, Green is preferred to Blue!

This is reminiscent of the infamous Monty Hall problem, as explained in the New York Times, in an article covering the cognitive dissonance issue:

Here’s how Monty’s deal works, in the math problem, anyway. (On the real show it was a bit messier.) He shows you three closed doors, with a car behind one and a goat behind each of the others. If you open the one with the car, you win it. You start by picking a door, but before it’s opened Monty will always open another door to reveal a goat. Then he’ll let you open either remaining door.

Suppose you start by picking Door 1, and Monty opens Door 3 to reveal a goat. Now what should you do? Stick with Door 1 or switch to Door 2?

You should switch doors.This answer goes against our intuition that, with two unopened doors left, the odds are 50-50 that the car is behind one of them. But when you stick with Door 1, you’ll win only if your original choice was correct, which happens only 1 in 3 times on average. If you switch, you’ll win whenever your original choice was wrong, which happens 2 out of 3 times.

Unlike the Monty Hall problem, which is really not much more than a parlor game, the effect of not seeing the monkey issue is more far-reaching. A Yale economist, M. Keith Chen, who noted the issue, believes that this goes to heart of a lot of testing in psychology:

Dr. Chen remains convinced it’s a broad problem. He acknowledges that other forms of cognitive-dissonance effects have been demonstrated in different kinds of experiments, but he says the hundreds of choice-rationalization experiments since 1956 are flawed.

Even when the experimenters use more elaborate methods of measuring preferences — like asking a subject to rate items on a scale before choosing between two similarly-ranked items — Dr. Chen says the results are still suspect because researchers haven’t recognized that the choice during the experiment changes the odds. (For more of Dr. Chen’s explanation, see TierneyLab.)

It is striking that such an obvious point, and one that relies on only the most rudimentary understanding of probability, took decades to see. I wonder if ten years from now, we will be wondering about our current conundrums: “P=NP? Yeah, isn’t it amazing that the field missed the obvious fact that …” Now fill in the “…”.