Grizzlies, Pandas, and Optimal Ecological Structures

Last year, a group of students in one of my classes did a project on designing a grizzly bear habitat, inspired by the work at Cornell’s wonderful Institute for Computational Sustainability. In that project, the goal was to pick out a collection of geographic areas that formed a contiguous zone that the grizzly’s could move freely through. As the ICS description says:

Land development often results in a reduction and fragmentation of natural habitat, which makes wildlife populations more vulnerable to local extinction. One method for alleviating the negative impact of land fragmentation is the creation of conservation corridors, which are continuous areas of protected land that link zones of biological significance.

My colleague, Willem van Hoeve, had worked on variants of this problem and had some nice data for the students to work with. The models were interesting in their own right, with the “contiguity” constraints causing the most challenge to the students. The results of the project were corridors that were much cheaper (by a factor of 10) than the estimates of the cost necessary to support the wildlife. The students did a great job (as Tepper MBA students generally do!) using AIMMS to model and find solutions (are there other MBA students who come out knowing AIMMS? Not many I would bet!). But I was left with a big worry. The goal here was to find corridors linking “safe” regions for the grizzlies. But what keeps the grizzlies in the corridors? If you check out the diagram (not from the student project but from a research paper by van Hoeve and his coauthors), you will see the safe areas in green, connected by thin brown lines representing the corridors.    It does seem that any self-respecting grizzly would say:  “Hmmm…. I could walk 300 miles along this trail they have made for me, or go cross country and save a few miles.”  The fact that the cross country trip goes straight through, say, Bozeman Montana, would be unfortunate for the grizzly (and perhaps the Bozemanians).  But perhaps the corridors could be made appealing enough for the grizzlies to keep them off the interstates.

I thought of this problem as I was planning a trip to China (which I am taking at the end of November).  After seeing a picture of a ridiculously cute panda cub (not this one, but similarly cute), my seven-year-old declared that we had to see the pandas.  And, while there are pandas in the Beijing zoo, it was necessary to see the pandas in the picture, who turned out to be from Chengdu.  So my son was set on going to Chengdu.  OK, fine with me:  Shanghai is pretty expensive so taking a few days elsewhere works for me.

As I explored the panda site, I found some of the research projects they are exploring.  And one was perfect for me!

Construction and Optimization of the Chengdu Research Base of Giant Panda Breeding Ecological System

I was made for this project!  First, I have the experience of watching my students work on grizzly ecosystems (hey, I am a professor:  seeing a student do something is practically as good as doing it myself).  Second, and more importantly, I have extensive experience in bamboo, which is, of course, the main food of the panda.  My wife and I planted a “non-creeping” bamboo plant three years ago, and I have spent two years trying to exterminate it from our backyard without resorting to napalm.  I was deep into negotiations to import a panda into Pittsburgh to eat the cursed plant before I finally seemed to gain the upper hand on the bamboo.  But I fully expect the plant to reappear every time we go away for a weekend.

Between my operations research knowledge and my battle-scars in the Great Bamboo Battle, I can’t think of anyone better to design Panda Ecological Systems.  So, watch out “Chengdu Research Base on Giant Panda Breeding”:  I am headed your way and I am ready to optimize your environment.  And if I sneak away with a panda, rest assured that I can feed it well in Pittsburgh.

This is part of the INFORMS September Blog Challenge on operations research and the environment.

The Importance of Accurate Data

I have been spending the last couple of weeks assigning faculty to courses and helping staff think about scheduling issues. I wish I could say that I have been using operations research techniques to do this sort of work. After all, most of my work has been in some form of timetabling optimization. But that has not been the case: for the most part I have simply done the work manually. Partially this is because I inherited a schedule that was 90% done, so I was really in a “rework” phase. But the main reason is that I am new at this job, so I don’t really understand the constraints (though I think I have a pretty good idea of the objective and variables). Gene Woolsey of the Colorado School of Mines had the philosophy that his students had to go out and do a job before they could do any modeling or optimization. So students worked production lines or helped drivers deliver packages first. Only after spending a few weeks on the job, could they think about how operations research could improve things. If I was Gene’s student, I would definitely pick an application in sports or entertainment rather than, say, high-rise steelwork.  For now, I am emulating that approach by first handling the courses manually then thinking about optimization.

Doing the course assignment and scheduling has been eyeopening, and a little worrisome. Just as I worry at the beginning of the season for every sports league I schedule (“Why are there three teams in Cleveland this weekend?”), I worried over the beginning of the fall term as the first of my assignments rolled out. Would all the faculty show up? Would exactly one faculty member show up for each course? Oh, except for our three co-taught courses. And …. etc. etc.

It turns out there is one issue I hadn’t thought of, though fortunately it didn’t affect me. From the University of Pennsylvania (AP coverage based on the Under the Button blog entry):

PHILADELPHIA (AP) — University of Pennsylvania students who were puzzled by a no-show professor later found out why he missed the first day of class: He died months ago.

The students were waiting for Henry Teune (TOO’-nee) to teach a political science class at the Ivy League school in Philadelphia on Sept. 13.

University officials say that about an hour after the class’s start time, an administrator notified students by email that Teune had died. The email apologized for not having canceled the class.

I hadn’t thought to check on the life status of the faculty.  I guess I will add “Read obituaries” to my to-do list.

Operations Research: The Sort of Decisions That Will Get You Fired

I just saw an ad for “Moneyball”, a new movie based on the book by Michael Lewis. A baseball manager (Billy Beane of the Oakland Athletics) used analytics (“Sabremetrics” in the baseball world) to choose players who were undervalued by the rest of the baseball world.  Beane had a constrained optimization problem:  he had to get as many wins as possible with a highly binding budget constraint.  His solution to that problem was to concentrate on statistics that seemed to be undervalued in the market, notably “on base percentage” (if you don’t know baseball, this gets a bit opaque, but getting on base is not as “sexy” as hitting home runs:  home run hitters are expensive; players that just get on base were cheap at the time).

There is a great line in the ad.  A colleague (the “stats guy”) of Beane says:

This is the type of decision that will get you fired!

Brad Pitt, playing Beane,  looks worried, but perseveres.  See the ad at about 25 seconds the official ad at about 18 seconds.

[Unofficial ad deleted.]

I love that line, since it really does sum up what operations research (and make no mistake: “Moneyball” is an operations research film) is all about. When you do operations research, you create models of reality. You do not create models of decisions. The decisions come from the models. And sometimes, the decisions don’t look at all like what you expected. And that is when it gets interesting.

Sometimes these unexpected decisions are due to modeling failures: you have forgotten a constraint, or a key modeling assumption turns out to not only be incorrect (assumptions almost always have some level of incorrectness) but critically incorrect. Optimization is really good at putting solutions right where the models are the weakest. And so you modify the model, not in order to change the decision, but in order to better represent reality. And you get new decisions. And you iterate between modeling and decisions until you reach a model that you believe represents reality. At that point, the decisions are of two types. They might tell you to do what you are doing, but do it better. And that is comforting and probably improves the decision making in the organization.

Or they tell you to do something completely different. And that is when you get to “Decisions that might get you fired.” That is when you need to decide whether you believe in your model and believe in the decisions it has generated. It would certainly be easy to change the model, not to reflect reality, but to force the decisions you believe are right. But if you really believe the model, then you need to avoid that easy path. You really need to decide whether you believe in the model and the resulting decisions.

I worked with a company a few years ago on their supply chain design. The results of the model came back over and over again saying two things: there were too many distribution centers, a result everyone believed, and it was far better for each distribution center to specialize in particular products, rather than have every center handle every product. The latter decision went deeply against the grain of the organization, and objection after objection was raised against the model. It would have been easy to put in a constraint “Every distribution center has to handle every product”. But there was no justification for this constraint except the ingrained belief of the client. In fact, we could show that adding the constraint was costing the organization a significant amount of money. Eventually, at least some of the organization bought into the decisions and began devising specialized distribution centers, but it was gut-wrenching, and perhaps career threatening. After all the discussion and fighting against the decisions, I am convinced those were the right choices: the organization had to change, not just improve.

“Operations Research: The Sort of Decisions That Will Get You Fired” doesn’t have the ring of “The Science of Better”. But the insights OR can get you may lead to radically different solutions than the incremental changes the SoB campaign implied. And those are the changes that can fundamentally change firms and organizations. And careers.

Teaching and Research

For the last few years, I have been dabbling in academic administration, first as Associate Dean for Research and now as Senior Associate Dean, Education here at the Tepper School of Business.  While there are frustrations in this position (“There are how many courses not covered?  And are all the adjuncts on vacation in Aruba now?”), some aspects are wonderful.  Working with new faculty is a great pleasure,  a pleasure that alone almost offsets the hassles.  I love the excitement and the energy and the feeling that anything is possible.

This was easy on the research side of the organization:  my job was to create a great research environment (subject to resource constraints, of course!), and that was very rewarding to do.  On the education side, my job is a bit different.  While some faculty love teaching, for others it seems to take time away from what they really want to do: research.  How can they do any research if they have to do any teaching?

Teaching is hard, and takes time and energy.  Does it take time away from research?   While I can talk to new faculty about how teaching and research intersect, and how one builds on the other, I can see a fair amount of eye-rolling.  Of course, I would say that:  that’s my job!  And when I explain that the entire “sports scheduling” part of my career happened due to an offhand conversation with an MBA student, the response is a mixture of “That’s what I have to look forward to?  Sports Scheduling?” and “Sure, teaching might be OK for practical types, but what about us theory types?”

Thanks to a colleague (thanks Stan!), I think I now have the perfect riposte.  This is from Richard Feynman‘s “Surely you’re joking, Mr. Feynman!”:

I don’t believe I can really do without teaching. The reason is, I have to have something so that when I don’t have any ideas and I’m not getting anywhere I can say to myself, “At least I’m living; at least I’m doing something; I am making some contribution” — it’s just psychological.

When I was at Princeton in the 1940s I could see what happened to those great minds at the Institute for Advanced Study, who had been specially selected for their tremendous brains and were now given this opportunity to sit in this lovely house by the woods there, with no classes to teach, with no obligations whatsoever. These poor bastards could now sit and think clearly all by themselves, OK? So they don’t get any ideas for a while: They have every opportunity to do something, and they are not getting any ideas. I believe that in a situation like this a kind of guilt or depression worms inside of you, and you begin to worry about not getting any ideas. And nothing happens. Still no ideas come.

Nothing happens because there’s not enough real activity and challenge: You’re not in contact with the experimental guys. You don’t have to think how to answer questions from the students. Nothing!

In any thinking process there are moments when everything is going good and you’ve got wonderful ideas. Teaching is an interruption, and so it’s the greatest pain in the neck in the world. And then there are the longer period of time when not much is coming to you. You’re not getting any ideas, and if you’re doing nothing at all, it drives you nuts! You can’t even say “I’m teaching my class.”

If you’re teaching a class, you can think about the elementary things that you know very well. These things are kind of fun and delightful. It doesn’t do any harm to think them over again. Is there a better way to present them? The elementary things are easy to think about; if you can’t think of a new thought, no harm done; what you thought about it before is good enough for the class. If you do think of something new, you’re rather pleased that you have a new way of looking at it.

The questions of the students are often the source of new research. They often ask profound questions that I’ve thought about at times and then given up on, so to speak, for a while. It wouldn’t do me any harm to think about them again and see if I can go any further now. The students may not be able to see the thing I want to answer, or the subtleties I want to think about, but they remind me of a problem by asking questions in the neighborhood of that problem. It’s not so easy to remind yourself of these things.

So I find that teaching and the students keep life going, and I would never accept any position in which somebody has invented a happy situation for me where I don’t have to teach. Never.

If not teaching ruined the minds of those at the Institute for Advanced Study, imagine the effect on us mere mortals!  So teach, already!  And if you want to teach a bit extra, I happen to have a few courses that need to be covered….

Passwords and Reviewing

I was asked to review a proposal today.  Right now, I am feeling a little overwhelmed:  I have a new administrative position (“Senior Associate Dean, Education”) which involves, among 1000 other things, using a 25 year old computer program (ahh, ms-dos days!), I have some sports schedules that have to get out, I have a pile of referee reports, I am getting behind on some editorial duties, and I still have aspirations of publishing something myself once in a while.   But I try to be helpful in the review process:  I recognize how important these are for people’s careers.  This was the proposal too far, however:  the title did not seem particularly relevant, and contained words that I am naturally suspicious of.  But it couldn’t hurt to check it out and see if I might have some unique insight that might be useful.

I go to the funding agency’s website, and find that I have to create an account to view the proposal.  No problem:  account creation is one of my skills.  But I was stymied by the password requirement:

The password must follow these rules:

  • Must be at least 10 characters long
  • Must contain at least two capital letters
  • Must contain at least two lowercase letters
  • Must contain at least two numbers
  • Must contain at least two special characters: ~!@#$%^&*()_-+={[}]|:;>,<.?

Ummmm….. let’s see.  I certainly can type in some nonsense that I can’t possibly remember, hoping that the reset simply goes to my email account (which has a pretty good password, but not one that meets those requirements).  Or I can … “Thanks, but my schedule precludes my taking on more at this time.”  Really… my reviewing of a funding proposal requires this amount of nonsense in a password?

xkcd, as it often does, got it right (I believe the 2^44 comes from choosing 4 of the 2000 or so most common words):

A New ISI Operations Research Journal

I have mixed feelings about things like journal impact studies.  Once a ranking is announced, forces come in to play to game the ranking.  For journals, I have seen things like “helpful suggestions” from the editor on references that should be added before the paper can be accepted (“Perfectly up to you, of course:  let me see the result before I make my final decision”).    Different fields have different rates, making it difficult to evaluate journals in unfamiliar fields.  Overall, I don’t know what to make out of these numbers.

I think I am particularly annoyed about these rankings since my most cited paper (according to Google) doesn’t even exist, according to “Web of Knowledge“, the current face of what I knew as the Science Citation Index.  According to “Web of Knowledge”, my most cited papers are “Voting schemes for which it can be difficult to tell who won the election”, and “Scheduling a major college basketball conference”.  If you go to Google Scholar or, better yet, use Publish or Perish to provide an interface into Scholar, my most cited works are the volume I did with David Johnson on the DIMACS Challenge on Cliques, Coloring, and Satisfiability and “A column generation approach for graph coloring” (with Anuj Mehrotra).  “Voting Schemes…” and “Major College Basketball…” come in third and fifth.  Now I understand that the volume is difficult to work with.  Editors of refereed volumes don’t often do much research in putting together the volume, though I would argue that this volume is different.  But where is “Column generation approach…” in Web of Knowledge?  How can my most referred-to (and certainly one of my better) papers not exist there?

It turns out that in 1996, when “Column generation was published”, INFORMS Journal of Computing, where it was published, had not been accepted by ISI, so, according to it and its successors, INFORMS Journal of Computing, Volume 8, does not exist (indexing seems to have started in volume 11).  Normally, this wouldn’t matter much, but we do keep track of “most cited” papers by the faculty here, and it hurts that this paper is not included.  And including it would increase my Web of Knowledge h-index by one (not that I obsessively check that value more than a dozen times in a year and wonder when someone is going to cite the papers that just need one or two more cites in order to ….., sorry, where was I?).

This is a long way of saying that while I am not sure of the relevance of journal rankings and ISI acceptance, I certainly understand its importance.  So it is great when an operations journal I am involved in, International Transactions in Operational Research, gets accepted into ISI.  ITOR has done a great job in the last few years in transitioning into being a good journal in our field.   The editor, Celso Ribeiro, has worked very hard on the journal during his editorship (I chaired the committee that chose Celso, so I can take some pride in his accomplishments).  ITOR is a journal from the International Federation of Operational Research Societies (IFORS), so this is good news for them too.  Some schools only count journals with ISI designation.  ITOR gives a new outlet for faculty in those schools.

Congratulations ITOR and Celso!

Social Networks and Operations Research

Until recently, I pretty well had a handle on my use of social networks.   Rather than try to use a single social networking system in multiple ways, I have used different systems in different ways and for different networks.

  • I have a blog, of course, and I use that to pontificate on various aspects of operations research.  While the communication is primarily one-way, I see this as a network since 1) I have enough regular commentators that I feel there is some two-way conversation, and 2) there is a network of OR bloggers (the “blogORsphere”) and our various posts often riff off each other, particularly now that INFORMS provides a monthly topic for us to use in common (this post is part of the July challenge on OR and social networks).    You can get a feed of all the OR blogs either in the sidebar at my page or through a Google Reader site. If you have an operations research blog and are not included, please let me know!Feedburner and my log files suggest that each post is read about 3000 times in the first week of posting (after that, each post gets a regular trickle of readers through search).
  • I have a twitter account (@miketrick) where 90% of my tweets have some operations research content (denoted by an “#orms” hash tag).  About 10% of the time, I am griping about some failure in customer service or something similar on non-operations research aspects.   When I post on my blog, a tweet automatically goes out through my twitter account. I follow 183 other twitter users and am followed by just over 700 others, most presumably for the #orms content.
  • I have a facebook account (michael.trick).  Again, a post on my blog generates a facebook entry, but I primarily use facebook for my real-life friends and family, and rarely post on operations research (except the blog entries).

And that seemed enough!  But recently, there have been more social networks that I have had to integrate in to my life, and the existing ones have changed.

  • LinkedIn remains a mystery to me.  I certainly have done a fair amount of linking, with 365 direct connections.  Many of these are former students who want to stay connected to me, and I am happy to be connected.  It has even been useful when getting a request like “Do you know anyone at X who can help me with Y”.  And somehow I am getting emails on conversations that are going on at LinkedIn that actually look pretty good.  But when I go to the site, I can never find where those conversations are coming from, and I am just generally overwhelmed with minutia about who has changed their picture and commented on what.  My blog and twitter feed gets mirrored at LinkedIn, but otherwise this is just not something I have been active in.
  • OR-Exchange is  a Question and Answer site that focuses on operations research and analytics.  In many ways, this was a response to the death (or near death: there are some diehards holding on) of the Usenet group sci.op-research.  That group died under the weight of “solution key sellers”, ersatz conference announcements, mean-spirited responses from curmudgeonly long-timers, and general lack of interest.  So I registered the site or-exchange.com at a Q&A site, and started things off.  Since then, the system has taken a life of its own.  INFORMS now hosts it, and there are a dozen or so very active participants along with a larger number of regulars.  I am not sure the system has really reached critical mass, but I am very hopeful for it.
  • Facebook is moving in a direction that might make it more relevant for my professional life.  Bjarni Kristjansson has put together a group “I like operations research” that is getting some traction.  I put together a page that provides the feed to all of the operations research blogs that I can find (this is the same group of entries that is in the sidebar of my blog).
  • Reddit.com is a very popular way to point out links, and “cavedave” has done a great job in putting together a “sysor” subreddit.  With a couple thousand readers, a post there gets a noticeable bump in readership.
  • Google Plus simply baffles me at the moment.  I have an account, but I don’t know how to treat it.  It seems silly to just recreate a twitter feed in plus, but there doesn’t seem to be a hole in my personal social networking activities  that requires plus.  I had already done the “circles” thing by my different uses of facebook, twitter, and my blog, so it is not a great addition.  But I hate to think I am missing out on something big.  On the other hand, I did spend a couple of days on Google Wave, so I am a little hesitant to simply leap on this bandwagon.

As I look through all of this, I can’t help but reflect on how fragmented this all is.  Wouldn’t it be great to have a real community site where all of us in operations research can get together to share thoughts, papers, links, and more?  Bjarni is working hard at pushing INFORMS in the direction of providing such a community site.  But the sad thing is that we had such a site more than ten years ago, when social networking was in its infancy.  ILOG, through people like Irv Lustig, created a site e-optimization.com.  It lasted a couple of years, but could not survive the pressures of the dot-com crash.  INFORMS keeps a snapshot of the site (with limited functionality), and it is still impressive long after it shuttered its doors.

And, as I look closer to all of the activity, I am amazed that there is not more.  Why are there not hundreds of operations research blogs, instead of the couple of dozen that I list?  Why doesn’t every doctoral student in operations research have a twitter account?  Is there a social networking world I am missing?  If not, where is everybody?

Of course, if you are reading this, then you are in my social network, and I am very grateful that you are.

 

New paper at the OR Forum: Little’s Law

There is a new paper at the OR Forum area of Operations Research.  It has been 50 years since the publication of “Little’s Law” (roughly, the length of a queue is the arrival rate rate into the queue times the average wait, so if 5 people per hour arrive into a queue, and the average wait is 20 minutes (1/3 hour), then the average number in the queue is 5/3).  This formula is amazing because it holds under very broad conditions.  There are lots of applications where you know two of arrival wait, queue length, and waiting time, and Little’s Law lets you determine the third.  I spent a half hour today waiting in line at the Alhambra doing Little’s Law in my head (“300 people to be admitted in one half hour slot, one server, 95 degrees, no clouds, looks like 2 people fainting per minute, makes my wait time…”)

John Little has written an article reflecting on 50 years of Little’s Law and Ed Kaplan, Tim Lowe, Sridhar Tayur and Ron Wolff have provided commentaries on the article.  Check it all out at the OR Forum.

Explaining Operations Research to, and being, a Muggle

In J.K. Rowling’s Harry Potter books, “Muggles” are people who have no magical ability, and, indeed, no knowledge of the magical world.  The term “Muggle” is not exactly a compliment, and veers towards a pejorative.  From the first book:

Hagrid: “I’d like ter see a great Muggle like you stop him,”
Harry: “A what?”
Hagrid: “A Muggle. It’s what we call non-magic folk like them. An’ it’s your bad luck you grew up in a family o’ the biggest Muggles I ever laid eyes on.”

It is a little hard to see anything positive about Muggle in this exchange!  Muggles are often willfully blind to the magic that goes on around them (though sometimes an Obliviator or two can help muggles forget something a little too spectacular), and are generally far less interesting than the magical folk.

But Ms Rowling is pretty even handed in its treatment of the magical world/non-magical world divide.  Just like non-magical folk have no understanding of Quidditch, dementors, Patronus charms and the rest, the magical world is equally confused about the non-magical world:

Arthur Weasley: What exactly is a rubber duckie for?

The definition of a Muggle depends on where you stand!

Now what does this have to do with operations research (other than being the theme of this month’s INFORMS Blog Challenge, of which this article forms my entry)?  A wonderful aspect of working in operations research, particularly on the practical side of the field, is that you both work with Muggles and get to be a Muggle.

Working with Muggles is pretty obvious.  We in operations research have these magical powers to do incredible feats.  Have a problem with millions of decisions to make?  Easy!  Well, easy as long as we can assume linear objective and constraints, and that the data is known and fixed, and …. (magic even in Harry Potter’s world has limitations).  But for the Muggles to believe our results, we do have to spend time explaining what we do and the assumptions we work with.  So we trot out some simple examples, like the traveling salesman problem (“Consider a traveling salesman who has to visit the cities on this napkin”:  don’t laugh!  That is the first real conversation I had with the woman who eventually decide to marry me!).  And we explain why the problem is hard (“Consider how many tours there are”).  And sometimes we convince the whole world of the difficulty so well that they don’t listen to the next part:  “Despite the difficulty, we can really solve these models”.  There are whole swathes of the world, including, it seems, much of computer science, that believes that 30 city traveling salesman instances are a true challenge, requiring an immediate application of approximation algorithms or heuristic methods.   But then we solve interesting problems, and the Muggles begin to believe.  And that is very satisfying.

But it gets even better when we in operations research get to be the Muggles.  And this happens a lot on the practical side of operations research because we get to work with a lot of very smart and very interesting people outside of our field.  A few years ago, I worked with the United States Postal Service to redesign its processing and distribution network.  I know a lot about optimization and models and algorithms.  About all I knew about postal delivery is that a very friendly guy comes by practically every day around 11, and he prefers if I park my car back about two feet so he can cut through the driveway easier.  Turns out there is a heck of a lot more to the Postal Service than Postman Pete and his walk through our neighborhood.  So I got to be the Muggle and to learn about the issues in getting mail from one side of the country to the other in a reasonably timely fashion.  There is “First Class Mail” and “Third Class Mail”, but no “Second Class Mail”.  Why?  Well, that’s quite a story, let me tell you!  And after a few months, I felt that I had passed my first class in the Magic of Mail, but was nowhere near losing my Muggle designation.  But I knew enough to create a few models, and I could explain them to the Wizards of the Mail, and they could correct my Muggle understanding of mail processing (“No, no, no:  a flat could never be handled in that way:  that is only for Third Class, silly!”).  And eventually we arrived at models that did a pretty good job of representing the mail system.  I was a bit less of a Muggle about mail, and they were a bit less Mugggley about operations research.

Over the years, I have started as a Muggle about cell-phone production, sports scheduling, voting systems, and a number of other areas.  And I got to read about these areas, and talk to smart people about issues, and, eventually, become, if not a Wizard, then at least a competent student of these areas.

Some fields are, by their nature, inward looking.  The best operations research is not, and that is a true pleasure of the field.