Month: October 2008

Interest in ILOG

The “Official Response Document” that ILOG prepared (and posted on their Investor Relations site) responding to IBM’s offer is fascinating reading.  I particularly liked the “Historical Background” (pages 5-10) that begins with IBM’s orginal contacts with ILOG.  From an initial discussion of IBM’s licensing ILOG’s rules software in late 2006, ILOG seemed to quickly come “in play”.  New companies (called Company B, Company C, right up to Company F) began discussions with ILOG on purchasing all or part of it.   Clearly there was a lot of interest in ILOG! By late September, 2007, IBM stopped considering ILOG, only to reopen discussion in April 2008, resulting in a memorandum of understanding by the end of July.

Annex 1 (beginning on page 40 or so of the pdf document) was also interesting since it provides comparable firms (FairIsaac, SPSS and so on) as part of the financial analysis.

Reading through the document and the history, I was struck by how much “rules” was the initial driver of the acquisition, but also by how optimization was seen as a key aspect of the deal.  I don’t know who Companies B, C, D, E, or F were, but I think I am happy that IBM ended up as the partner in this deal.

Of course, all this is just personal opinion.  I don’t own any stock (any more:  I bought at the top and sold at the bottom, which is my general financial strategy) and, as my colleagues in finance remind me, am unqualified to truly evaluate the finances.  But I do recommend reading the document!

Minimum Democracy

A few weeks ago, I pointed out that Barack Obama (or John McCain) could win the upcoming Presidential Election with a tiny fraction of the popular vote.  I wrote:

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.

The current issue of OR/MS Today has a neat article by Winston Yang (University of Wisconsin-Stout) who takes the problem much more seriously.  Rather than allow my extreme variance in turnout, he works with population numbers (which is equivalent to assuming the same turnout rate in every state).  In this case, the minimum popular vote for a winner must be at least 22% or so.  This occurs when a candidate just wins enough states to get 270 electoral votes, and loses (completely) all other states.  Yang then analyzes a number of different ways of allocating electoral votes from the states.  For instance, Maine and Nebraska both use a system where there are electoral districts allocating all but two of the electoral votes, with the two electoral votes then be allocated to the candidate who wins the most popular votes.   Many political thinkers have proposed a number of approaches to allocating the electoral votes.  Yang has a nice graph illustrating the minimum fraction of votes necessary to win for the past elections:

Be sure to check out the full article at OR/MS Today!

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!

New Design

There is now a new design for the page. I wanted to find something that made the “comments” a little more obvious. Further, I think a new design every three years is not too much. I would appreciate your thoughts on this, even if (especially if) you hate it.

One other change is that the “Things I am Reading” page is now “New from the OR Blogs”. The difference is that everything that appears on one of the OR Blogs now appears in the sidebar (not just things I flag). Of course, if it appears that some sites are not providing relevant material (I’m looking at you sci.op-research!), then I can simply drop them from the list.

Thoughts and opinions on the changes are very much welcome.

Added Oct 25. I decided to separate out the newsgroups from the blogs, since otherwise we might get a series of newgroups postings (particularly when they are in a flame ware on something) rather than the blog postings appearing in the left sidebar.

Healthcare, Baseball, and Operations Research

The New York Times had an op-ed today about health care written by Billy Beane, Newt Gingrich, and John Kerry.  Billy is the general manager of the Oakland Athletics baseball team and is the primary subject of the book Moneyball, which looked at how a new look at statistics affects a baseball team’s decisions.  What a strange group of coauthors!  Gingrich and Kerry are politicians from the opposite sides of the political spectrum.  My wife (who pointed out the article to me) thought Gingrich and Kerry were strange coauthors:  add in Beane and you are verging on an alternative universe.

The authors argue that health care has got to take a better look at the data, just like baseball teams look at player data.

Remarkably, a doctor today can get more data on the starting third baseman on his fantasy baseball team than on the effectiveness of life-and-death medical procedures. Studies have shown that most health care is not based on clinical studies of what works best and what does not — be it a test, treatment, drug or technology. Instead, most care is based on informed opinion, personal observation or tradition.

They give a number of examples on what happens when people really look at data:

…a health care system that is driven by robust comparative clinical evidence will save lives and money. One success story is Cochrane Collaboration, a nonprofit group that evaluates medical research. Cochrane performs systematic, evidence-based reviews of medical literature. In 1992, a Cochrane review found that many women at risk of premature delivery were not getting corticosteroids, which improve the lung function of premature babies. Based on this evidence, the use of corticosteroids tripled. The result? A nearly 10 percentage point drop in the deaths of low-birth-weight babies and millions of dollars in savings by avoiding the costs of treating complications.

They conclude with a call for looking at the stats:

America’s health care system behaves like a hidebound, tradition-based ball club that chases after aging sluggers and plays by the old rules: we pay too much and get too little in return. To deliver better health care, we should learn from the successful teams that have adopted baseball’s new evidence-based methods. The best way to start improving quality and lowering costs is to study the stats.

The authors are clearly right.  There seems to be great value to looking at the statistics, and this is a necessary step towards rationalizing the system.  The key is making better decisions.  Some of these decisions seem pretty obvious.  But as the decision making gets more complicated, operations research comes into play.  To go back to the baseball analogy, Beane discovered that players with high “on-base percentage” were undervalued by the market, who were paying big money for sluggers (players who hit home runs) instead.  An obvious better decision is to buy up more of the undervalued players.  A more complicated decision would be to form a team that maximized overall output for a given budget constraint.  More complicated still would be forming a team relative to a budget constraint that was affected by team performance.  These more complicated decisions are not the result of a simple rule (“Buy high OBP players”) but rather the result of much more complicated models.

Managing health care, by its nature, requires complicated decision processes.  And that is where operations research comes in (and why I think OR in health care and medicine are two great areas for our field).

Happy Birthday to the Blog!

This is the blog’s third birthday!  It has been an exciting year, both personally and in the world of operations research.  Personally, at this time last year, I was in New Zealand, waiting for the weather to warm up and wanting to get back in the water.  Since then, we have returned to the US, and I have gotten back into my “normal life” as a Tepper School faculty member.

In the world of OR, this year will go down as a year full of flux in the commercial world.  ILOG is acquired by IBM (not quite yet, but things are underway); Dash is acquired by Fair-Isaac; Gurobi announces its formation.  There were some sad entries, including the passing of Rick Rosenthal, Mike Rothkopf, and Lloyd Clark, among others.   I went to some fantastic conferences, including INFORMS Washington, CPAIOR Paris, and, particularly, IFORS South Africa, a life changing experience.  Operations Research seems to show up more and more in the popular press, and is showing up in books aimed at the general public.

Just a few statistics, providing an update from last year’s birthday post.  I posted 133 times in the year (up from 83, and well past my goal of 2/week).  I’ve had 237 comments in the last year, up a lot from 69, so you too have been much more active!  I get about 3500 visitors per month (up from 2000), and, new this year, Feedburner tells me about 450 people subscribe through google reader and other rss feeds (that don’t typically hit my server).  Spam is running at about 10/day, but my system catches it, so it is no big deal.

A special thanks to the other OR people blogging (check them out in the sidebar).  I like feeling part of a community!

Reading through the past year’s postings, I am reminded strongly what a wonderful job it is to be a professor, and how lucky I am to have ended up in a field as wide and fascinating as operations research.