live as a pig at the end of 2006

Maybe florida is too warm. I caught the cold right back to Bethlehem. Now everyday, I sleep, eat, sleep. Today, I finally try to do something different and played monopoly. As usually, I defeat computer players with my perfect preformance in stock market. If it were real money, oh...

Hope tomorrow 2007, I can recover and go back to normal status.

Two OR applications in Florida Trip

The first one is queueing theory.
When I was in Magic Kingdom, I found an interesting application of queueing theor - Fast Pass. The procedure is the following. 1) When you see a waiting line is quite long at one attraction say Space Mountain, you can insert your ticket in one machine to get a fast pass which indicates you return during the specific time usually one or two hours laters. 2) Then when you return after playing other stuffs, you can go through the fast pass line.
There are several key points. 1) Once you get your fast pass for one attraction, you cannot get fast pass for another attraction until your current fast pass expire. For example, if I get fast pass for Space Mountain from 1pm to 2pm. I can only get fast pass for Splash Mountain after 2pm. 2) The number of fast pass in one attraction must be limited. Since the available time for the fast pass is different at different attraction, the hotter the attraction is, the longer time difference between available time of fast pass and current will be. In the last attraction-Jungle Cruise we go at 5pm, its fast pass is unavailable. We need to wait 40mins to play.
The decision to make for the visitor. 1) What is the sequence of attraction I should follow. 2) Which attraction should I get the fast pass.
The decision to make for the Theme Park. 1) How much fast passes to available at each attraction and each time periods.
Game theory might not be suitable in this case. Probably, something like priority queue plus abandon and other customer behavior can be used to formulate the problem.

My experience of fast pass in Unversal Studio at CA is different. We got the fast pass after we experienced a half hour technique difficulty at Jurassic Park. Then we can get into fast pass line for every attraction. But actually, you need to spend 30$ to get one fast pass. So the decision for the theme park is how much fast pass to offer and how much to charge.

The second one is network flow. It is very wise decision to buy a GPS just before this trip. We don't need to print every map. And we can avoid the traffic jam in the high way by going through the local. It was very crucial when we tried to catch up our flight back. And the calculation of route is very fast, it is much faster than O(A) I learned last semester. But actually it can only calculate the route from two points. If I want to go several points a day, I need to manually choose the sequence of points I need to goal. If GPS can provide the function of solve small TSP problem such as 5 points, it will be wonderful. Since real map satisfy triangle inequality, there is heuristic to get solution within 1.5 time of optimal solution. So the calculation time won't increase a lot.

Final week is over

This should be my second last final week. Now I am enjoying the final quarter of game between Rocket and Laker, two of my favorite NBA teams. As usual, I don't have mood to study once there is two or three weeks to final week. This year this mood is especially strong cause Rocket comes strong this season. After I finish the trip to Florida, I should use my time sheet again to do my time management which I haven't done after INFORMS.

Textbook: Convex Analysis and Optimization

Today I want to search how to prove a function is unimodal. I get the following book, Convex Analysis and Optimization by Dimitri P. Bertsekas. What I like Dimitri P. Bertsekas most is that he always post his class slide accompanying his book. Another example is his book Dynamic Programming and Optimal Control.

How to Bring Our Schools Out of the 20th Century

Time shows the views how American people would like education to be in the 21th century. Let's compare to Chinese education I received

• Knowing more about the world. (China: Almost all high school will teach English and we learn history of world. USA: where fewer than half of high school students are enrolled in a foreign-language class and where the social-studies curriculum tends to fixate on U.S. history.)
• Thinking outside the box. (This is weakness of Chinese education. But it seems with no child left behind(NCLB), American goes to Asian type education more and more.)
• Becoming smarter about new sources of information. (This one is hard to tell. Only thing I can say is that more knowledge I have, the smarter I can deal with new information)
• Developing good people skills. (This is also a weakness of Chinese people. There is a saying that 'three monk don't have water'. And I found the counterpart in English:Everybody's business is nobody's business. So it is also a problem in USA. I think it is due to how people get measured. Maybe in USA, there is fairer measure of team work than China. For example, almost every teacher ask students to put down name of people who help their homework)

One surprising thing is to see the following question for a second grade student: How many ways can you combine nickels, dimes and pennies to get 20¢? This question can be solved by recursion. It would be interesting to see how teachers tell students the way to solve the problem.

Three Major Assumptions in Marketing and my customerized assumptions for MS/OR

It is interesting to know the major assumptions in marketing.

• The customers are complete. It means that they can compare any two items. 'complete' here is similar to the definition in the math. There is no hole
• The customers are transitive. If the customer prefer A to B and B to C, then they prefer A to C.
• The more choices, the better.

Is any major assumption in MS/OR field? I never see anything like those three major assumptions in Marketing. But we can have similar one.

• The decision makers are complete. It means that they can compare any two solutions.
• The decision makers are transitive. If the decision makers prefer A to B and B to C, then they prefer A to C.
• The larger decision variable sets, the better result the decision makers can get.

Don't need to get up at 8:30 every weekday now

This week is the final week. During the whole semester, I have classes beginning around 9:00 everyday. Now everything is over and I can get up whenever I am awake.

Bless T-Mac!

Although YM got 23pt in the fourth quater and hit the clutch shot, it cannot make me happy. It is because T-Mac got hurt his back in today's game with Wizards. Rocket has very good beginning of this season. All we need is to wait for T-Mac get recovered with 80% degree compared with his status vs Dallas during the playoff at 04-05.

I really like T-Mac this season. He changes his style from scoring to controling the game. His basket IQ is a key for the success of Rocket. With T-Mac and YM, I can spend almost 3 hours every game day. But now it seems Rocket will go to lottery again with this injury, even though YM has very strong performance this season. Let's hope it is just minor injury and nothing is related to old one.

My current efficiency drops so fast!

Maybe towards the end of the semester, I become stupid. Or I look too much NBA and related news. My instinct is gone very quickly. Even one set network homework takes me almost the whole sunday. And I still haven't finished it. I am looking foward winter break now and need to control online time.

Future is Present

This is one of the beautiful thing you can learn from math course. In financial calculus class, the first half class is devoted to binomial tree model. In that model, you rebalance your portfolio in the near future (Delta t_i) based on the information you know during Delta t. But in continuous model, Delta t becomes delta, future collapses to present. You need to do rebalance all the time. If not, you will be arbitraged by others.

If you don't want others to arbitrage you, you'd better to know future is present and try you best continuously.

tricky tricky airline price

I know a little bit about revenue management, such as littlewood's rule. In my mind, the price of air ticket should stochastically increase. When I try to book the tickets to Orlando from the southwest during the winter break, the price change of air ticket is quite dymatically as I can imagine. The lowest one way price in my case is $59. This price will be randomly available at each day. Moreover, the number of available seats also change at each day. That might be due to change of reservation by the other people. But it may not be entirely true.

Yesterday when other couple reserved one ticket at one time slot(depart at 12:00) and tried to reserved anther one at the same time slot, it failed since there was only one slot remaining before they reserved. So they changed to 10:00 now. Today that slot is available too. It seems that someone change or cancell their reservation yesterday. But it is not just that. I changed mine 12:55 to 10:00 in order we can go togather. But I cannot find available seats for 12:55 today(Yesterday 12:55 is unavailable). One possible explanation is that demand at 12:55 is stochastically higher than demand at 10:00 with respect to the same price. So southwest move the tickets available from me to higher price. But I don't think this is the whole story. What is the control mechnism of air ticket price at southwest is still a puzzle to me.

Btw: southwest allows you to change your reservation without any penalty. That is why "Last August, low-cost king Southwest Airlines carried more passengers than any other U.S. airline, the first time an LCC has claimed the top spot." reported by Time

Stochastic processes@wikipedia

I haven't seen most of them. A long way to go!

Poster presentation next year at INFORMS charpter@Lehigh?

Today Lehigh's INFORMS chapter had its first meeting this semester. Basically we discuss who will be invited as INFORMS chapter seminar speaker next semester. After that, we need to figure out a way to spend increased budget. There is one idea comes out during the discussion. That is we will provide T-shirt having ISE logo. I like this idea a lot. At least we need something to connecting people togather.

Right after meeting, I get an idea about create poster presentation next year. Now in our department, those optimization people have very strong connection. They have their own CORAL and IP seminar. But the connection outside optimization is quite loose. So hope this poster presentation will let people know what the other people do. And every poster presenter should wear the ISE T-shirt.

I also get idea to create subdivision leader in our chapter, say, optimization, stochastic, finance, scm&rm. Each leader should arrange 3 weeks long seminar. This is just coming from IP seminar yesterday.

Maybe we can also create the connection between IE and other department student, so that we can see what cool stuff we can do for other fields.

And just now, I send an email to our president about giving a talk to the first year phd student of the career path of phd. It is very often to mention in the company but seldom done by university. I guess after that we can also create Q&A page for all IE student.

I believe by doing more stuff like that. We can create the best INFORMS charpter ever and sell OR to more people.

If we can get enough people joining us, we can also make the newsletter once a semester. Maybe some short introduction about what happens to every member in our chapter. What had done last semester and what we will do next sememster.

Keeping your direction, don't just follow fashion

This Friday, Kevin Shang gave us a talk about multi-echolen inventory problem. It is very classical problem but there aren't many people focusing on this area. And the barrier to this area is higher than most other areas related to inventory theory. So most people like to chase research 'fashion'. Somebody told me that a few years ago, sessions of SCM are full of people. But in this Informs meetings, RM seems more popular. A lot of people talk about dynamic pricing etc.

But Shang sticks to multi-echolen inventory problem after he passed his qualify exam. And he told me that he will continue to explore this area. It should be appreciate. Although our field just like a branch of applied math, it doesn't mean that everybody should just apply similar math technics to different applications. Once the math is too hard to go on in one area, then everybody shift their research interests. I don't mean that we shouldn't do so. In contrast, we need some people to do so to spread out our field. But what I mean is that we need some people to develop some unique math methods to solve those unsolved problems. Like developing in physics can push math forward. Why won't our IE people do the similar things? (Btw: I am not talking about those optimization people in IE fields)

Actually, I am a guy, who cannot focus on one thing for a long time. I like to find tons of applications, which can be easy in math. But I will find an area which can attract me for at least ten years. Of course, I will do other things on the same time.

What should I do in the next Informs meeting?

• Since I already get to know some friends from this conference, I hope I can meet each one individually and have deep conversation. Also because, I will have more time to do research from the fourth year, I hope to seek more opportunities to work on the different subjects with different people with their own expertise
• Prepare some deeper work to present. That's the only way to impress people, so that they are willing to work with you if possible.
• Get to know job market
• Create the chance to talk with those faculties
• Last but the most important, live at a hotel nearby the convention center. I run into trafic every day and get too less sleep every day during conference. And I haven't recovered yet.

internal force(nei gong) or external technic(zhao shu)?

I went to an Informs conference from Nov. 4 to Nov. 8. Everything goes well. Meeting and making friends is the most important thing for me since our department only has very few people sharing the same interests with me. I need to extend my network. Second thing is to present my work. It is ok since nobody left during my talk and I got two questions and handled well in my opinion.

But there is one fundamental question occuring several times during the conference. On one hand, we need to have good papers before we graduate. It means that I need to work hard on generating papers, which implies that I need to read a lot of literature. As Long told me that we need to accumulate several tricks which are used by different authors again and again in their papers but no one collect and publish those as a reference book. This is absolutely right. Even those great mathematicians has only a few tricks. On the other hand, I like to take as much courses as I can since I believe only when you have complete skill set, you can consider problems from different angles which gives you more insight. Also you can do something crossing the different fields.

In Chinese Wuxia novel, there are two kind of persons. One is working on internal force(nei gong) . It takes a long time to master. The other person is working on external technic(zhao shu). It is fast to learn but easy to hit the bottleneck. In the short run, the people working on zhao shu can easily beat the previous type of people since the stuff they learn is more practical. But in the long run, the previous people can outperform the people only having zhao shu since it has more power.

In this conference, when people start to talk about those 'famous' people's name, I feel a little bit embarrassing since I cannot recognize a lot of those 'big' names. I start to think whether I spend too much time in taking course other than reading related literature. For example, the next semester, I plan to take two courses and audit another two courses again. But I stick to finish those courses since I won't get any time to do so after I graduate. And only when you have enough nei gong, you can pick up those tricks quickly. I am not worried about my ability to come up ideas. Actually, during this Informs, I generate several new ideas. For now, I need to focus on developing those nei gong and try to work on only one or two ideas. After next semester, I would devote most of time into research but not now.

MSOM 2006 submission statistics

Till the latest data, there are 154 submission for the regular issues and 36 submission for the special issue.

For the regular issues, there only 4 papers(2.6%) get accepted. Among these 4, there are 2 papers get accepted without going through review process. 8 papers(5.19%) get minor revision and 35 papers(22.73%) get major revision.

For the special issue, 4 papers(11%) get major revision and 4 papers have got the decision. All other paper got rejected.

You can access data from http://msom.pubs.informs.org/msomdata.xls

The accpetion ratio is very low. MSOM currently publishs 4 issues a year. And each issue contain around 6 papers. So the acception ratio is around 24/(154+36)=12%. That means around 2/3 major revision paper will get rejected finally.

Lehigh ranked 18th undergraduate B-school

It is a little bit surprising to see Lehigh can get 18th rank in that category. ANyway, it is still a good news.

Beamer's columns

\frame
{
\frametitle{Interview}

\begin{columns}
\begin{column}{0.5\textwidth}
\begin{itemize}
\item current symptoms
\item past medical history
\item family history
\item social history
\item review of systems
\end{itemize}
\end{column}

\begin{column}{0.5\textwidth}
\begin{itemize}
\item detect hypertension
\item recognize emergency
\item recognize reversible causes
\item find chronic target organ damage
\item discover co-morbid conditions
\item plan therapy
\end{itemize}
\end{column}
\end{columns}
}

You can have as many columns, of whatever widths, as you like. Note that
within a column, \textwidth will correspond to the column width, not the
width of the beamer slide, so it works pretty much like minipage.
Material within columns is vertically centred, and you can have part of
the slide in normal (single column) format, and other parts in more than
one column.

Insert List into Tabular in Latex

Here is the code. But I still cannot figure out how to do under Beamer. The ultimate goal is to insert the Figure into the left top corner and put the list on the right.

\documentclass[12pt]{article}
\makeatletter % Yes, it's horrible
\def\spacehack{%
\@minipagetrue% % Disables spacing above
\expandafter\everypar\expandafter{% % Add stuff to \everypar
\the\everypar% % Do what was there before
\@minipagefalse% % Clear the mystic flag
\everypar={}% % And reset \everypar
}%
\let\@oldbs=\\% % Remember old \\ command
\def\\{\nointerlineskip\@oldbs}% % Turn off final odd spacing
}
\makeatother

%
\begin{document}
%
\begin{tabular}{p{40mm}p{40mm}p{40mm}}\hline
& Advantages & Disadvantages \\ \hline
Method 1 & \spacehack\begin{enumerate}
\setlength{\itemsep}{-\parsep}
\item item one
\item item two
\item item three
\end{enumerate} & \spacehack\begin{enumerate}
\item item one
\item item two
\end{enumerate} \\[-12pt] \hline
Method 2 & \begin{enumerate}
\item item one
\end{enumerate} & \begin{enumerate}
\item item one
\item item two
\end{enumerate} \\ \hline
Method 3 & \begin{enumerate}
\item item one
\end{enumerate} & \begin{enumerate}
\item item one
\end{enumerate} \\ \hline
\end{tabular}

\end{document}

How to be a good lecturer?

You need

• Have interesting problem (not to be too hard and too specific, especially when your audience background is mixed
• Always talking something that is easy to understand.
• More figures than words and formula
• I will add more here

How to be a good listener?

From those experience, I see there are several keys to be a good listener.

• Try to understand the main point
• Try to answers all the questions other people ask
• Try to think all possible ways to attack their theory
• A good lecturer is neccessary

How I remember those speakers' presentation(2004, Fall)?

Yesterday I read through the advice to the young scientist. It gives some advice about how to do a good talk. The first advice is 'Every lecture should make only one main point. ' And the fouth advice is 'Give them something to take home.'. What can make people remember you. Here is the answer.

It is not easy to follow this advice. It is easier to state what features of a
lecture the audience will always remember, and the answer is not pretty.
I
often meet, in airports, in the street, and occasionally in embarrassing
situations, MIT alumni who have taken one or more courses from me. Most of the
time they admit that they have forgotten the subject of the course and all the
mathematics I thought I had taught them. However, they will gladly recall some
joke, some anecdote, some quirk, some side remark, or some mistake I made.

So I want to recall all the seminar in our department I attend to see what I remember from those talks. Then I may know how to improve my presentation. And how to be a good audience. Here I need to clarify. The talks I don't remember now may be due to that I don't have enough knowledge at that time. I begin from 2004, Fall which is my first semester here.

2004 Fall, I attended all the seminars. The following is the talks that I still have an impression.

08/27/04 David Drake Lehigh Phd student: This is first seminar I attended. The only thing I still can remember is that he thought HongKang is nice place.

9/17/04 Suvrajeet Sen. Program director at NSF. This was the time that every professor was going to take notes. I remember that Dr. Ralphs and Linderoth asked some questions about software patent.

11/5/04 Aurelie Thiele. Lehigh. I know newsboby model can also extend to revenue management. But the terminology is different. Basically, for the simple concave problem, the optimal solution is achieved at marginal cost equal to marginal revenue. In the revenue management, if we have high fare class and low fare class. The number of seats reserved to high fare class is achieved at expected revenue brought by high fair class is equal to expected revenue brought by low fare class.

12/3/04 Krishnan Anand, Upenn, There was an interesting situation that sometimes information may cause increase of inventory. I also remember there is an question related to Wal-mart. Later on, I find a good explanation and email it to Prof. Anand.

An example of converge in probability but not almost sure converge

This is one homework question in stochastic process class. I cannot come up even one example by myself. First I use goole, nothing valuable is found. Then I http://books.google.com/. I get a book named 'Counterexamples in Probability and Statistics'. The example is X_n=H_n, where H_n is Bernoulli variable with probability 1/n to be 1. X_n converge to 1 in probability. But Prob(H_n = 1 infinity often)=1 by Borel Cantelli Lemma.

2 hours to clearn up the inbox

I store all the emails in the box and only delete some when I get the warning about storage. Last time I did clearning is last semester. Today I spent 2 hours to move every email to the proper local folders. It is a good chance to see what I have done since the end of last semester. Although the outcome of research isn't that breakthrough, I am still satisfied with how I deal with those multi-tasks. Now I have fewer things to do. I can put more emphasis on each thing I am doing.

Advice for the Young Scientist

Another related article mentioned is Ten Lessons I Wish I Had Been Taught

My presentation@IP seminar: "rondom thoughts about optimization"

Yesterday I did my first outside class presentation Since my research is not mainly about optimization, I have to make up some stuff to fit the audience's interest. Also I hope that my talk can create some coordination between students in our department. So I just provide several of my ideas which I think that might be attractive to other people.

The result of presentation is OK, at least people don't feel bored during that 50 min. But I am sure that I don't achieve my target fully, since no one shows strong interests. Larry told me that my confidence overcomes my spoken English. Prof. Huang shows a different approach to solve one of my problems about LP and regression after presentation, where there is no need to change matrix A but only constants on RHS. Also I find another way too. We can just change one column instead of replacing the rows. Prof. Linderoth also provides me how do sampling in multi-stage stochastic programming. Prof. Ralphs reminds me how to get dual solution easily. Ash shows interest on my network formulation and I explain to him about bootstrap idea further. Zumbul told me that she has her own problem and want to coordinate with me. Finally my wife told me that I put too much contact with those professors and lose contact to the students.

Finally, I should say I need focus. After that I hope get any coordination with other students.

I have three SCM books now

After struggling for a while, I final decided to buy two more SCM books. They are

• The logic of logistics
• Foundations of stochastics inventory theory

Plus I already have

• Foundations of Inventory Management

Also I copy

• Inventory Control

What else do I need to collect?

The best deal for the Economist ever

Today when I got books from Amazon. I found such good coupon. There is only $100 for 51 issues of Economist plus $25 Amazon coupon. I wish that I could have to read.

Prediction of Nobel Prize in Economics 2006

I guess it would belong to Bhagwati, Dixit and Krugman since their topic is more interesting to me.

measurable or nonmeasurable

Now I am doing Real analysis homework, it is hard to imagine those strange sets. The course till now is all about trying to show that close properties about measurable set and functions. It is hard to come up your own counter example and deal with those 'strange' stuffs like Cantor, Borel, nonmeasure sets.

Luckly by keeping doing the homework even though the score is as normal as my other courses, I am still not lost in the class. It should be considered as the hardest course among all ones I take and audit. Maybe it is time to go office hour next time.

Arby's coupon

Yesterday night, I came to Arby's to have my dinner to save my time. I use the coupon Arby's sends out to me every month. I just raised my hypothesis before I order:
People eat insides are more price intensive so that more people use coupon than those who drive through.
Supposing my hypothesis is true, if Arby's send out the coupon to everyone no matter whether they prefers to eat inside or drive through, then it may lose potential revenue since it increases the chance of using coupon among the driving through persons. After I observe during my dinner, four transaction occurs all including coupon. That increases my confidence on my hypothesis.

A naive way is to replace current strategy with only putting coupon inside the store.

Again, we can build a model to help Arby's to improve their revenue

oonumerics.org

It provide information about different package in the following catogories

• Linear Algebra
• Arrays and Images
• Neural Networks, genetic algorithms, machine learning, data mining
• High-Energy Physics and Quantum Chemistry
• Multiprecision, arbitrary precision data types
• Differential Equations
• Automatic differentiation and interval arithmetic
• Visualization
• Graph Theory/Combinatorics
• Language interoperability/scripting
• Transforms
• Optimization
• Miscellaneous
• Tools

But there are several packages in each catogory. If there is someone providing comparison, it will be great.

JabRef

A quite nice .bib document organizer.
It makes me comfortable to write down my comments to every paper I read.

Power sum

sum(i) = n(n+1)/2
sum(i^2) = n(n+1)(2n+1)/6
sum(i^3) = n^4/4 + n^3/2 + n^2/2
...

A site contains optimization online resource

When I check the statistics for COR@L today
I get the following link, which provide some good online resource for optimization.
http://plato.asu.edu/sub/tutorials.html

The 3rd weekly report

This week, I work for 56 hours. Due to easier homework of Graph and Network, I only spent 28.5 hours in study. Also I make a little progress on one of my research project.

Beside work, I watch the football game between Lehigh and Princeton. I sit too low so that sometimes I cannot clearly catch what was going on in the field. Next time I will sit high enough.

I also watch the film The Illusionist. It is a wonderful movie. Originally I think I would fall in sleep during the movie. But I cannot stop enjoying it and have no chance to have a nap.

(-1)^(1/3)=0.5000 + 0.8660i ?

This time, I meet another problem about math operation in software.
In matlab, when you try
(-1)^(1/3)
What result do we get. It is not -1 but 0.5000 + 0.8660i
You need to use nthroot(-1,3) to get -1.

This time, excel gives me the answer I want.

I am so frustrated now. This time I use more than 4 hours to debug my program which contains similar operations in my matlab code.

Do I know too little about basic math or Matlab provides too much.

My 2nd weekly report

This week, my course work increase 1 hour. It seems that 30 hours per week is average workload 4 courses taught all by professors from math department. They usually have more homework than IE courses. But it is worth doing that.

But research work drops to 23 hours per week. That's because I finish all the coding work for one of my project. I need to increase it to above 25 hours a week.

Also I have played tennis twice this week. I am still seeking the feeling about backhand, which I lost for a month after I changed my habit and cannot go back.

EXCEL vs GOOGLE

When I try to verify the correctness of a sequence. I need to compute the following fomula

=-1*((0.4^4-3*(0.4^3)+0.4^2)*10+(0.4^3-2*(0.4^2))*15+(0.4^2-0.4)*20+0.4*25+30)+100*(0.4^4-2*(0.4^3)+1)+50*(-0.4^4+2*(0.4^3))

Excel gives 66.144. I takes me more an hour to debug. Finally I find it is not my fault

But google gives 63.58400

Actually google gives the correct result.

How can I trust excel anymore?

I still remember two year before Excel can generate negative number from [0,1] random variable. Luckly I get the patch to fix it. This time, I don't how I should do.

chalk and slide

This semesters, all my attended courses are taught from Math department. Those professors like to present their material by chalk on the blackboard. In contrast, most of my IE courses are taught through slides. But I have to say that I love chalk style. You can see how the professor thinks from chalk but not from slides. Also when you take down the notes, you not only use ears to listen, but also use eyes to look and hand to write down. The more 'sensors' you use during the class, the more and the longer you can remember.

One possible reason for the IE professors to use slides more than chalks is because IE professors uses more computer than Math professors. So the technology not always create better way to learn. But slides is healthy way to teach since the more chalk you use, the more chalk ash you absorb. Also use slides may have huge scale of economic. If let me choose which way I use. I would be half and half. Just like last semester nonlinear programming class. Use the slides to show main ideas, use chalks to show the details.

My 1st weekly timesheet

Although, it isn't 5:00pm at Sep. 3, I like to post my 1st weekly timesheet. The total hours on study are 29.5 and on research are 31. I am quite satisfied with my work in the 1st week. I will go on this self monitor mechanism

Andre Agassi & NP-complete

I just finished watching a unbelievable tennis game. No match can be better than this one. All the unbelievalbe games have equivalent elements, like all NP-complete problems are equivalent.

Poincaré conjecture, Perelman, Fields Medal

There are a lot of quarrel about how much credit everyone involving to solve Poincaré conjecture should get. But the most important person, Grigory Perelman, a Russian mathematician, deline which I think is more valuable than Nobel prize.

It shows that the fundemental research still need genius and have genius. Not all the work need to be done through cooperation and fund.

There is good post from MITBBS.com(A chinese forum for people in North American)

It's time to change my own time clock

This new semester, I have the classes beginning around 9:00am every weekday from next week. I cannot believe the summer was over. The time is passing so quickly. Unfortunately, I cannot achieve the goal I set at the beginning of this semester except auditting the class and playing tennis. I really need to push myself hard this semester.

I take two courses: network and graph, financial calculus. Also I am going to audit: real analysis, advanced stochastic process.

But my eagerest goal is to finish two projects on hand so that I can put my time into real research. For my research, I wanna to do more theoritical side than computational side.

There is something I want to get rid of. I am going to shrink my online time. It really wastes my time. To accomplish this target, first not bring my laptop into school unnecessarily, second not open computer after I get up and before I go to bed, third do not go to code unless it is worth doing, fourth don't try to read basketball information everyday especially when new NBA season begins.

At the same time there is something I am going to get used to. Since I am moving to a two bedroom apartment recently, there is a study room for me. I can study home now. Also I get a new mattress, it is so comfort that I hope I can read a paper everyday on the bed before I fall into sleep.

Hopefully, I will increase my ability to manage my time and be more productive. I am during the process of designing a weekly agenda. It needs the balance between work and life. And I am going to fill out my timesheet everyweek as I need to do as an employee. Also I would send my weekly report one day before I meet Larry with detail checklist of what I am doing during the work. I learn it from Wasu. He told me that is really efficient way. I will post the formats of my timesheet and weekly report later. And publish the result of my timesheet and the number of jobs I do every week.

In the ideal situation, this gonna to set my high expectation more likely to achieve.

The connection of EE and SCM

Finally, I found a connection between EE ( my undergraduate major ) and SCM ( my current research interest ).

Yesterday, I call my 4 year roommate at JiaoTong Univ. , who is now at EE@UCSD. I happen to ask whether OR can be applied into his area. Originally, I just want to advocate the usage of OR and provide him a potential tool. It seems no many people use LP, IP, NLP to his area. After I push him think hard, he gave me a very interesting problem in the physical network design. He thinks that using large scale optimization model to solve real time network flow is not his favorite. i.e. he don't like the idea that doing optimization at server level and then telling what the router should do. Instead, he like to solve the problem at the router level and the objective is not far away from the global control.

That's the exact idea of centralized vs decentralized, which is one of the key concept of SCM. But the research on the complex supply network is far from mature. So I cannot give him a plausible method during the phone call.

Also his current internship at Motorola is to implement at heuristics to solve IP at cell phone. Although he uses the different langurage, it seems branch and bound is way to complex for the chip of cell phone.

Anyway, it is always interesting to find the people at different area trying to solve the similar problem.

Personal cost minimization on air ticket purchase

This week, Time privdes a very interesting website to help customer to make the decision about 'To Buy or not to buy' air ticket.
http://www.farecast.com

Etzioni's site Farecast.com
original name was, of course, Hamlet--provides the lowest fare on a route, a
90-day price history and, using a novel predictive algorithm, a tip to "buy now"
or "wait," along with a figure indicating how confident Farecast is in its
advice. (Flyers buy directly from the airlines.)

It is surprising that this kind of website is not developed by the OR people (especially revenue management people ) but a CS professor Oren Etzioni from University of Washington.

OR people need to establish much more sucessful story to advocate importantce of OR. Like a disucssion raised in the Coin-OR workshop, we need more credentials to demonstrate the impact of OR. If OR can become HOT in job market, the pressure of academic people can also release and they can focus on the question they are more interesting but time consuming.

DIMACS workshop on Coin-OR

This week, I attend DIMACS workshop on Coin-OR. I am not pure optimization people and have not implemented any project based on any package of Coin-OR before. But I am really astonished to see those really really busy people devotes their time to the open source software on the optimization, even if it may be necessary for them to develop code for their own research. This fresh experience brings me to a new world. Besides this, this trip gives me a chance to know other people in our department better.

During the workshop, I get to know a person from Archstone-Smith working on the revenue management. He came here and try to find a package to replace Cplex which their software is based on. But it turns out that he is a little disappointed on it. The reasons are warmly discussed in the last presentations. They may be the common concern about open source software, legal and support issue. It would be nice to see a company making money by supporting Coin-OR. The problem is whether there is such demand? Then it leads to the issue about awareness of OR either in public or among other research areas. As Stephen Nash point, there are big and interesing questions in other science fields such as how universe begins. If everyone include me know the biggest problems in OR area, the future of Coin-OR would be bright. Otherwise, it is just a tool of researchers.

I hope there is one day a book introducing how to model OR by using Coin-OR through a well spread tool such as Excel like VBA for Modelers: Developing Decision Support Systems Using Microsoft® Excel. And the reader reviews is over 20. This is similar to Leo Lopes presents at the last part of his presentation to show how to use web service through VBA.

The presentation file can be found in workshop website.

The world cup champion predicted by UBS

UBS reports shows Italy would win the champoin. Maybe gamesters should not just read the sports news but also business new.

Many influencing factors
Along with "football fever", the team around
UBS Wealth Management Chief Economist Klaus Wellershoff was interested to find
out what variables are important in predicting World Cup success. In doing so,
they discovered that many things that appear to be obvious are, in fact, not
crucial to winning the World Cup. An example of this would be the size of a
country's population, which is often incorrectly correlated to the amount of
potential athletic talent. The FIFA rankings, which list the top national soccer
teams, also prove to be of limited use when it comes to making a prediction: the
FIFA list compiles the sporting success of the individual teams but assigns
equal value to all wins, no matter how strong the opponent.

GLPK under windows

I experience a hard time to use GLPK under windows. 2 months ago, I can run the program under vc6. But yesterday, linkage errors persist to appear. Finally, I get the answer that I didn't specify glpk.lib in the project settings. But last time, I also not set it. Anyway, I get a blog for the GLPK. http://glpk.blogspot.com/. The one and the only one post is about how to set GLPK under VC 6.0 and Borland C++ 5.0.

Careers in the Decision, Risk and Management Sciences

The article from science websiste discusses the career development.

The 10,000-member Institute for Operations Research and the Management Sciences
(INFORMS) has members working for homeland security, transportation, health
care, law enforcement, the military, and telecommunications. These people apply
scientific methods to help improve decision-making, management, and operations
for many different kinds of organizations, says Barry List, director of
marketing and public relations at INFORMS. "By using techniques such as
mathematical modeling to analyze complex situations, operations research
provides the power to make more effective decisions and build more productive
systems based on more complete data; consideration of all available options;
careful predictions of outcomes and estimates of risk; and the latest decision
tools and techniques

Usually, people in OR often thinks what people should do. Even under game theory, there is a strict restriction on the people behavior. So it would be fun to study what people would do under specific situation. Based on that, operations research analysists can conduct optimal response to the party they serves. Moreover, the strategy of avoiding the irrational impact from others can be developed.

Diaper and Beer

From WSJ:

Tesco's computers often turn up counterintuitive results. Shoppers who buy diapers for the first time at a Tesco store can expect to receive coupons by mail for baby wipes, toys -- and beer. Tesco's analysis showed that new fathers tend to buy more beer because they are home with the baby and can't go to the pub.

Detailer can be founded in a blog called Data Doghouse.
Giving causation to the correlation given by the statistics is quite interesting:
More interesting and probable OR people can do is the following:
We have N items with correlation matrix, and we want to put them in the shelf togather to gain maximum profit. It is quite similar to the facility layout problem. And we may also hope to change the layout as little as we can, since the customer use to current layout.

Minority Report

Is it true?

In Richmond, Va., police use predictive analysis to determine the
probability that a particular type of crime--armed robbery, auto theft,
murder--will occur in a specific area at a given time. Police lieutenants who
command the city's 12 sectors use desktop computers linked to the system to
decide where to deploy a mobile task force of 30 officers. "Based on the
predictive models, we deploy them almost every three or four hours," Police
Chief Rodney Monroe says.
Officers have arrested 16 fugitives and confiscated
18 guns based on the system's guidance. In the first week of May, Richmond had
no homicides, compared with three in the same week last year. Monroe attributes
that outcome, in part, to moving officers around based on the calculated
probability of shooting incidents. "It's more proactive," Monroe says. "We're
not waiting for a homicide to occur."

In the news, it shows the softwares apply several methods such as Markov decision process, stream mining, and support vector machines to predict the future. Also it is good news to inventory management.

IBM last week introduced an inventory management application for retailers
that uses built-in predictive analytics and replenishment rules to monitor
product inventory, develop safety stock, and recommend orders based on an
analysis of historical demand. The commercial app has been used by IBM
consultants for years.

Only 2.4% compound annual growth rate for SC planning

Although the total market size for supply chain planning is already above 1 billion. But such low compounded annual growth rate indicates wave of SCM would be past soon.

The worldwide market for Supply Chain Planning is expected to grow at a
compounded annual growth rate (CAGR) of 2.4 percent over the next five
years.
The market was $1.05 billion in 2005 and is forecasted to be over
$1.18 billion
in 2010, according to a new ARC Advisory Group study, "Supply
Chain Planning
Worldwide Outlook: Market Forecast and Analysis Through
2010."

Nevertheless, I think the slow increase rate is due to lack of professionals. So companies cannot convert software to the profit.

Super Bowl of Operations Research

Franz Edelman Award this year came to Warner Robins Air Logistics Center. They use "Critical Chain" to reduce repair and overhaul in the depot from thirteen to seven and repaired time drop 33%. When I try to find a little more detail from www.Informs.org, unfortunately there is even no further information. As I think "Critical Chain" is pretty similar to critical path. Maybe it considers undergoing tasks through repair network instead of static analysis of critical path and adjust resource correspondingly. I am not sure whether it will consider stochastical demand and repair time. If it doesn't invovle stochastical element in their model, it still has much space to improve.

If I get a chance to join this competition, I would like to implement my new idea about advertisement in TV.

Currently, the Ad during the program is quite even distributed. If I was watching a program and there is Ad, I would change the channel. I may find more attractive program or even attractive. Even if I cannot find one, I may switch back to the current channel without watching much Ad. Suppose now we use nearly exponential distributed Ad news. I think it may prevent people sticking changing channel.

First they cannot clear idea how long the Ad will be so they will change back quicker even if they change back

Second since it is exponential distributed, when you are back you will still see same expected length of Ad. given it is still Ad time

Third since there is more time each person watching Ad, the company can reduce total Ad length. It brings more satisfication of customer.

Ph.D. Thesis Research: Where do I Start?

I just finish reading this article. It is worth reading. The main questions this article hope to address is indicated from the title. So which topic I should choose?

New and Important. Everyone knows it. But how to find such topic is still the art. Although this article is for Economy major. But it is applicable to other major. Also it is not limited to how to pick a topic, it also mentions other important success factors for a PhD student.

I post some section titles as well as my opinion:
1. How Do I Find “The Right Topic”? (No right topic)
2. How do I know if I have an interesting topic? (Find a real world counterpart, discuss with others...)
3. Where do I start? Strategies for Research:
3.1 If you want to write applied theory, read empirics.
3.2 If you want to write empirics, read theory.
3.3 There is a “Research Frontier”; Your job is to find it.
3.4 Go to weekly departmental seminars in your field. ( I like it and hope to ask a question every time)
3.5 Go to seminars of potential new assistant professors at your school. ( I will try this summer at IP seminar)
3.6 Read the working papers of the intellectual leaders in your narrowly focused research area. 3.7 Read the best journals selectively. ( Still have no time to read or at least scan MS, OR, MSOM regularly)
3.8 Talk, Talk, Talk! Write, Write, Write! ( Need to practice more )
3.9 Question Authority! ( That's what I like to do )
3.10 DonÂ’t Take Courses! ( I don't agree this. But it is true that I will choose to audit rather than taking)
3.11 DonÂ’t teach! ( The author must be a American who doesn't need to worry about teaching as a professor )
3.12 Dealing with advisors. ( Yeah. "the risks of saying nothing far outweigh the costs of occasionally saying something stupid (so long as you also occasionally say something interesting!).")
3.13 Your advisor is too nice!
3.14 Present your work whenever possible.
3.15 Consider writing your first paper jointly.
3.16 Writing matters. ( Still a long way to go )
3.17 Presentation matters. ( Is it easier than writting? )
3.18 Inspiration is where you find it

The semester is over and the summer is coming

I finished all the final exams today. Now it is the time to set ambitious targets for this summer besides routine research work.

Three books to read
Zipkin: Foundations of Inventory Management
Ross: Stochastic Processes
Royden: Real Analysis

One course to audit
Derivatives and Risk Management

Sports
Running for half an hour three times a week
Doing Exercise at Gym for one hour two times a week
Tennis once a week

The benefit 'Dummy'

Today's nonlinear class, I learned a trick by creating dummy variables and constrains to decompose the problem.
The original problem is

min f_1(x) - f_2(x)
st. x \in X_1 \cap X_2

Then we can transform it to

min f_1(y) - f_2(z)
st. y = z
y \in X_1 and z \in X_2

Then we can write the dual of transformed problem and do the decomposition.

Suppose f_1 and f_2 is convex.
We imagine the geometric meaning, for each Lagrange vector for the constrain y = z. We can find the corresponding tangent lines of the f_1 and -f_2. If their x happens to be the same, then we done. Otherwise we update Lagrange vectors. The way to update Lagrange vector is based on sign of difference of two tangent points.

Operational statistics: Do things togather rather than separately

This friday Max Shen came to give talk on the 'operational statistics'. The idea is that when you seperate forecast and decision, you lose the potential saving. For example, if we don't know the demand distribution exactly under newsboy setting, we will forecast the parameters and make the order decision. It gives us lower profit. But if you directly design order quantity function instead of predict demand parameters and transform it to order quantity, we can gain a lot. It's managerial insight behind this paper to do things togather than separately.

But I just wonder what makes it work mathematically.

At seminar, I ask the question about unbaised and baised paramters. After I think it a little, I believe, by transform their order function into sample estimation space. It is likely that we get the baised estimation but with smaller variance. Then there is map between sample estimation space to the revenue space. Now we have two kinds of distribution of estimation. One is unbaised with large variance and the other one is baised with smaller variance. Suppose the map is concave, you can image the latter one will have bigger revenue if that estimation is well designed. So from statistical perspective, we need to balance how bais and how big variance of your estimation from the point of map between estimation and your objective function.

It is still hard for me to express these subtle things real time. Usually I have a very rough thought and need a discussion to clarify what I really think finally. Messy minds.

My computer restart when simulated multistage scenario tree program runs for three days

I didn't anticipate that this simulation takes such long time before. Originally I think it may take just 24 hours. I try 100 times smaller sample size, it takes only 15 mins. After running for one days, I realize that simulation time isn't linear on the sample size in this problem. Because I create a linked list to compare the scenario it already creates, then comparison complexity is more close to O(n^2) not O(n) at first 1 or 2 period.

Next time I need to consider more before running big problem and store the intermediate result.

But it is so strange that my computer will restart itself. Maybe it is due to Microsoft automatical update.

close loop and open loop

Yesterday, I read an article. The system described seems to me as close loop system. But instead it is called open loop system. So I search google and wikipedia today. Now I am total confused.
http://www.atp.ruhr-uni-bochum.de/rt1/syscontrol/node4.html
As I understand, for the open loop the state used to adjust control is variable outside the system. That is, if the system is disturbed, the control won't response.

But in http://en.wikipedia.org/wiki/Open-loop_controller
It defines

An open-loop controller is a type of reactive
controller which computes its input into the system using only the current
state and its model of the system. It does not use feedback to determine if its
input has achieved the desired goal.

By this definition, current state can be affected by disturbance. That is, if the system is disturbed, the control may response later instead of immediately.

Which understanding is right?

My comments on Getting More from Call Centers

1. Set priority Q. Use routing software to send different type of customer with different tast to the corresponding agent. The problem is what type queue it will be V, M, N or others? If it is combined with time variant demand, how to set the parameters.

2. Agent learning. In the article, it said

Ideally, supervisors should spend 70 percent of their time coaching, and the
number of agents they monitor should reflect the team's role, so that, say, a
general-service queue would be supported by a 1:18 coach-to-agent ratio, while a
vital sales or support queue that needed more coaching would enjoy a 1:14 ratio.
Coaches should combine side-by-side training with remote listening (in which the
agent is unaware of being monitored) and should provide immediate feedback in
both cases. The coach is also responsible for sharing best practices with
agents.

The ratio is surprisingly low. Is this ratio indeed reasonable?

3. How to evaluate quality decreasing via outsourcing. What is the relationship between quality and reveneue

4. What performance measures are effective. Since I have do several projects related to the PM, it is one of most interests to me. The performance measures mentioned in the article are total revenue per month, average handling time,... It also mentions that without good design, the effect of call churning will happen. Like balance score card, it is crucial to know how to convert the individual targets to the overall score which is related to the incentive.

I never see any research paper on the part 4 before. Maybe human resource research will be more relavent than our OR field. But it is important issue.

Good Article!

Another article about call center from McKinsey

I don't have time to read this articel Getting More from Call Centers. I think this article may give us insight on revenue side rather than cost side. It may be the trend for research work related to call center.

Patent in OR

Time has an article Patently Absurd. In this article, it said that BUY IT NOW is a patent filed by MercExchange in Mid-90's. A jury awarded this company $35 million in damages from Ebay.

Actually, oppositely to the opinion given by Time. I support patent in OR areas. Why finance people can make much more money than OR people in general. It is because they can earn money for company directly. Actually, I think OR can make at least the same contribution as financial people to the society. We need to use patent to protect ourselves.

For SCMers, maybe the mechanism of contact is the possible direction.
For Optimizers, specific algorithem related to industry may be possible.
For Queueing people, the ways to distinguish priority customer can be considered.

OR history

Today Dr. Saul I. Gass gave a talk about "How Did O.R. Get From There to Here? '. The content of this talk is from the book An Annotated Timeline of Operations Research. It is always interesting to hear the anecdote of famous people in the OR area. Most interesting point is made almost at the end of talk. Dr. Saul I. Gass shows a regression line of OR important activities over time. The most productive time for OR field is just after world war II. Now the trend of increases slows down.

My first response to this phenomena is due to powerful computer. Computer makes people 'dummy'. Also too many talent people may be attracted to IT industry.

After seminar, I think dissolved Soviet Union makes USA or even the whole west lack the motivation to do fundamental research. Without enough demand and abundance funding, researchers need to spend too much time to seek funding. I think research labs are even much important than universities.

Global Optimization Quadratic Programming

When we discuss nonlinear optimization, usually we seek a local solution rather than global solution. This week, in the class, I got to know a way to get glocal solution for quadratic programming (any type matrix is ok). The main idea is to use branch and bound ( similar to IP problem ). In the IP, we relax integer variables. In the QP, for each inequality we can use a concave function bounded above and convex function bounded below. By doing that, we can get a convex set. Then we solve relaxed problem, if it happens to be in the boundry. We are done. Otherwise, branch and solve the problem. As IP, which variables you are going to branch is important decision. Here, how you branch the area is also a key question.

Also any IP problem can be reformed as binary problem. And any binary problem can be reformed as quadratic problem, like x(1-x)=0.

It is interesting that you can connect two distinct optimization areas togather.

You and Your Research

A really good article about how to do research. Sometimes, I image what if I would be a member in Bell Lab now. It was golden time for the researchers. Now let's see how they do research.

------------------------
You and Your Research March 7, 1986
You and Your Research
by Dr. Richard W. Hamming
INTRODUCTION OF DR. RICHARD W. HAMMING
As a speaker in the Bell Communications Research Colloquium Series, Dr. Richard W. Hamming of the Naval Postgraduate School in Monterey, California, was introduced by Alan G. Chynoweth, Vice President, Applied Research, Bell Communications Research.
Alan G. Chynoweth: Greetings colleagues, and also to many of our former colleagues from Bell Labs who, I understand, are here to be with us today on what I regard as a particularly felicitous occasion. It gives me very great pleasure indeed to introduce to you my old friend and colleague from many many years back, Richard Hamming, or Dick Hamming as he has always been know to all of us.
Dick is one of the all time greats in the mathematics and computer science arenas, as I’m sure the audience here does not need reminding. He received his early education at the Universities of Chicago and Nebraska, and got his Ph.D. at Illinois; he then joined the Los Alamos project during the war. Afterwards, in 1946, he joined Bell Labs. And that is, of course, where I met Dick - when I joined Bell Labs in their physics research organization. In those days, we were in the habit of lunching together as a physics group, and for some reason this strange fellow from mathematics was always pleased to join us. We were always happy to have him with us because he brought so many unorthodox ideas and views. Those lunches were stimulating, I can assure you.
While our professional paths have not been very close over the years, nevertheless I’ve always recognized Dick in the halls of Bell Labs and have always had tremendous admiration for what he was doing. I think the record speaks for itself. It is too long to go through all the details, but let me point out, for example, that he has written seven books and of those seven books which tell of various areas of mathematics and computers and coding and information theory, three are already well into their second edition. That is testimony indeed to the prolific output and the stature of Dick Hamming.
I think I last met him - it must have been about ten years ago - at a rather curious little conference in Dublin, Ireland where we were both speakers. As always, he was tremendously entertaining. Just one more example of the provocative thoughts that he comes up with: I remember him saying, “There are wavelengths that people cannot see, there are sounds that people cannot hear, and maybe computers have thoughts that people cannot think.” Well, with Dick Hamming around, we don’t need a computer. I think that we are in for an extremely entertaining talk.
THE TALK
It’s a pleasure to be here. I doubt if I can live up to the Introduction. The title of my talk is, “You and Your Research” It is not about managing research, it is about how you individually do your research. I could give a talk on the other subject - but it’s not, it’s about you. I’m not talking about ordinary run-of-the-mill research; I’m talking about great research. And for the sake of describing great research I’ll occasionally say Nobel-Prize type of work. It doesn’t have to gain the Nobel Prize, but I mean those kinds of things which we perceive are significant things. Relativity, if you want, Shannon’s information theory, any number of outstanding theories - that’s the kind of thing I’m talking about.
Now, how did I come to do this study? At Los Alamos I was brought in to run the computing machines which other people had got going, so those scientists and physicists could get back to business. I saw I was a stooge. I saw that although physically I was the same, they were different. And to put the thing bluntly, I was envious. I wanted to know why they were so different from me. I saw Feynman up close. I saw Fermi and Teller. I saw Oppenheimer. I saw Hans Bethe: he was my boss. I saw quite a few very capable people. I became very interested in the difference between those who do and those who might have done.
When I came to Bell Labs, I came into a very productive department. Bode was the department head at the time; Shannon was there, and there were other people. I continued examining the questions, “Why?” and “What is the difference?” I continued subsequently by reading biographies, autobiographies, asking people questions such as: “How did you come to do this?” I tried to find out what are the differences. And that’s what this talk is about.
Now, why is this talk important? I think it is important because, as far as I know, each of you has one life to live. Even if you believe in reincarnation it doesn’t do you any good from one life to the next! Why shouldn’t you do significant things in this one life, however you define significant? I’m not going to define it - you know what I mean. I will talk mainly about science because that is what I have studied. But so far as I know, and I’ve been told by others, much of what I say applies to many fields. Outstanding work is characterized very much the same way in most fields, but I will confine myself to science.
In order to get at you individually, I must talk in the first person. I have to get you to drop modesty and say to yourself, “Yes, I would like to do first-class work” Our society frowns on people who set out to do really good work. You’re not supposed to; luck is supposed to descend on you and you do great things by chance. Well, that’s a kind of dumb thing to say. I say, why shouldn’t you set out to do something significant. You don’t have to tell other people, but shouldn’t you say to yourself, “Yes, I would like to do something significant.”
In order to get to the second stage, I have to drop modesty and talk in the first person about what I’ve seen, what I’ve done, and what I’ve heard. I’m going to talk about people, some of whom you know, and I trust that when we leave, you won’t quote me as saying some of the things I said.
Let me start not logically, but psychologically. I find that the major objection is that people think great science is done by luck. It’s all a matter of luck. Well, consider Einstein. Note how many different things he did that were good. Was it all luck? Wasn’t it a little too repetitive? Consider Shannon. He didn’t do just information theory. Several years before, he did some other good things and some which are still locked up in the security of cryptography. He did many good things.
You see again and again, that it is more than one thing from a good person. Once in a while a person does only one thing in his whole life, and we’ll talk about that later, but a lot of times there is repetition. I claim that luck will not cover everything. And I will cite Pasteur who said, “Luck favors the prepared mind.” And I think that says it the way I believe it. There is indeed an element of luck, and no, there isn’t. The prepared mind sooner or later finds something important and does it. So yes, it is luck. The particular thing you do is luck, but that you do something is not.
For example, when I came to Bell Labs, I shared an office for a while with Shannon. At the same time he was doing information theory, I was doing coding theory. It is suspicious that the two of us did it at the same place and at the same time - it was in the atmosphere. And you can say, “Yes, it was luck.” On the other hand you can say, “But why of all the people in Bell Labs then were those the two who did it?” Yes, it is partly luck, and partly it is the prepared mind; but ‘partly’ is the other thing I’m going to talk about. So, although I’ll come back several more times to luck, I want to dispose of this matter of luck as being the sole criterion whether you do great work or not. I claim you have some, but not total, control over it. And I will quote, finally, Newton on the matter. Newton said, “If others would think as hard as I did, then they would get similar results.”
One of the characteristics you see, and many people have it including great scientists, is that usually when they were young they had independent thoughts and had the courage to pursue them. For example, Einstein, somewhere around 12 or 14, asked himself the question, “What would a light wave look like if I went with the velocity of light to look at it?” Now he knew that electromagnetic theory says you cannot have a stationary local maximum. But if he moved along with the velocity of light, he would see a local maximum. He could see a contradiction at the age of 12, 14, or somewhere around there, that everything was not right and that the velocity of light had something peculiar. Is it luck that he finally created special relativity? Early on, he had laid down some of the pieces by thinking of the fragments. Now that’s the necessary but not sufficient condition. All of these items I will talk about are both luck and not luck.
How about having lots of “brains” ? It sounds good. Most of you in this room probably have more than enough brains to do first-class work. But great work is something else than mere brains. Brains are measured in various ways. In mathematics, theoretical physics, astrophysics, typically brains correlates to a great extent with the ability to manipulate symbols. And so the typical IQ test is apt to score them fairly high. On the other hand, in other fields it is something different. For example, Bill Pfann, the fellow who did zone melting, came into my office one day. He had this idea dimly in his mind about what he wanted and he had some equations. It was pretty clear to me that this man didn’t know much mathematics and he wasn’t really articulate. His problem seemed interesting so I took it home and did a little work. I finally showed him how to run computers so he could compute his own answers. I gave him the power to compute. He went ahead, with negligible recognition from his own department, but ultimately he has collected all the prizes in the field. Once he got well started, his shyness, his awkwardness, his inarticulateness, fell away and he became much more productive in many other ways. Certainly he became much more articulate.
And I can cite another person in the same way. I trust he isn’t in the audience, i.e. a fellow named Clogston. I met him when I was working on a problem with John Pierce’s group and I didn’t think he had much. I asked my friends who had been with him at school, “Was he like that in graduate school?” “Yes,” they replied. Well I would have fired the fellow, but J. R. Pierce was smart and kept him on. Clogston finally did the Clogston cable. After that there was a steady stream of good ideas. One success brought him confidence and courage.
One of the characteristics of successful scientists is having courage. Once you get your courage up and believe that you can do important problems, then you can. If you think you can’t, almost surely you are not going to. Courage is one of the things that Shannon had supremely. You have only to think of his major theorem. He wants to create a method of coding, but he doesn’t know what to do so he makes a random code. Then he is stuck. And then he asks the impossible question, “What would the average random code do?” He then proves that the average code is arbitrarily good, and that therefore there must be at least one good code. Who but a man of infinite courage could have dared to think those thoughts? That is the characteristic of great scientists; they have courage. They will go forward under incredible circumstances; they think and continue to think.
Age is another factor which the physicists particularly worry about. They always are saying that you have got to do it when you are young or you will never do it. Einstein did things very early, and all the quantum mechanic fellows were disgustingly young when they did their best work. Most mathematicians, theoretical physicists, and astrophysicists do what we consider their best work when they are young. It is not that they don’t do good work in their old age but what we value most is often what they did early. On the other hand, in music, politics and literature, often what we consider their best work was done late. I don’t know how whatever field you are in fits this scale, but age has some effect.
But let me say why age seems to have the effect it does. In the first place if you do some good work you will find yourself on all kinds of committees and unable to do any more work. You may find yourself as I saw Brattain when he got a Nobel Prize. The day the prize was announced we all assembled in Arnold Auditorium; all three winners got up and made speeches. The third one, Brattain, practically with tears in his eyes, said, “I know about this Nobel-Prize effect and I am not going to let it affect me; I am going to remain good old Walter Brattain.” Well I said to myself, “That is nice.” But in a few weeks I saw it was affecting him. Now he could only work on great problems.
When you are famous it is hard to work on small problems. This is what did Shannon in. After information theory, what do you do for an encore? The great scientists often make this error. They fail to continue to plant the little acorns from which the mighty oak trees grow. They try to get the big thing right off. And that isn’t the way things go. So that is another reason why you find that when you get early recognition it seems to sterilize you. In fact I will give you my favorite quotation of many years. The Institute for Advanced Study in Princeton, in my opinion, has ruined more good scientists than any institution has created, judged by what they did before they came and judged by what they did after. Not that they weren’t good afterwards, but they were superb before they got there and were only good afterwards.
This brings up the subject, out of order perhaps, of working conditions. What most people think are the best working conditions, are not. Very clearly they are not because people are often most productive when working conditions are bad. One of the better times of the Cambridge Physical Laboratories was when they had practically shacks - they did some of the best physics ever.
I give you a story from my own private life. Early on it became evident to me that Bell Laboratories was not going to give me the conventional acre of programming people to program computing machines in absolute binary. It was clear they weren’t going to. But that was the way everybody did it. I could go to the West Coast and get a job with the airplane companies without any trouble, but the exciting people were at Bell Labs and the fellows out there in the airplane companies were not. I thought for a long while about, “Did I want to go or not?” and I wondered how I could get the best of two possible worlds. I finally said to myself, “Hamming, you think the machines can do practically everything. Why can’t you make them write programs?” What appeared at first to me as a defect forced me into automatic programming very early. What appears to be a fault, often, by a change of viewpoint, turns out to be one of the greatest assets you can have. But you are not likely to think that when you first look the thing and say, “Gee, I’m never going to get enough programmers, so how can I ever do any great programming?”
And there are many other stories of the same kind; Grace Hopper has similar ones. I think that if you look carefully you will see that often the great scientists, by turning the problem around a bit, changed a defect to an asset. For example, many scientists when they found they couldn’t do a problem finally began to study why not. They then turned it around the other way and said, “But of course, this is what it is” and got an important result. So ideal working conditions are very strange. The ones you want aren’t always the best ones for you.
Now for the matter of drive. You observe that most great scientists have tremendous drive. I worked for ten years with John Tukey at Bell Labs. He had tremendous drive. One day about three or four years after I joined, I discovered that John Tukey was slightly younger than I was. John was a genius and I clearly was not. Well I went storming into Bode’s office and said, “How can anybody my age know as much as John Tukey does?” He leaned back in his chair, put his hands behind his head, grinned slightly, and said, “You would be surprised Hamming, how much you would know if you worked as hard as he did that many years.” I simply slunk out of the office!
What Bode was saying was this: “Knowledge and productivity are like compound interest.” Given two people of approximately the same ability and one person who works ten percent more than the other, the latter will more than twice outproduce the former. The more you know, the more you learn; the more you learn, the more you can do; the more you can do, the more the opportunity - it is very much like compound interest. I don’t want to give you a rate, but it is a very high rate. Given two people with exactly the same ability, the one person who manages day in and day out to get in one more hour of thinking will be tremendously more productive over a lifetime. I took Bode’s remark to heart; I spent a good deal more of my time for some years trying to work a bit harder and I found, in fact, I could get more work done. I don’t like to say it in front of my wife, but I did sort of neglect her sometimes; I needed to study. You have to neglect things if you intend to get what you want done. There’s no question about this.
On this matter of drive Edison says, “Genius is 99% perspiration and 1% inspiration.” He may have been exaggerating, but the idea is that solid work, steadily applied, gets you surprisingly far. The steady application of effort with a little bit more work, intelligently applied is what does it. That’s the trouble; drive, misapplied, doesn’t get you anywhere. I’ve often wondered why so many of my good friends at Bell Labs who worked as hard or harder than I did, didn’t have so much to show for it. The misapplication of effort is a very serious matter. Just hard work is not enough - it must be applied sensibly.
There’s another trait on the side which I want to talk about; that trait is ambiguity. It took me a while to discover its importance. Most people like to believe something is or is not true. Great scientists tolerate ambiguity very well. They believe the theory enough to go ahead; they doubt it enough to notice the errors and faults so they can step forward and create the new replacement theory. If you believe too much you’ll never notice the flaws; if you doubt too much you won’t get started. It requires a lovely balance. But most great scientists are well aware of why their theories are true and they are also well aware of some slight misfits which don’t quite fit and they don’t forget it. Darwin writes in his autobiography that he found it necessary to write down every piece of evidence which appeared to contradict his beliefs because otherwise they would disappear from his mind. When you find apparent flaws you’ve got to be sensitive and keep track of those things, and keep an eye out for how they can be explained or how the theory can be changed to fit them. Those are often the great contributions. Great contributions are rarely done by adding another decimal place. It comes down to an emotional commitment. Most great scientists are completely committed to their problem. Those who don’t become committed seldom produce outstanding, first-class work.
Now again, emotional commitment is not enough. It is a necessary condition apparently. And I think I can tell you the reason why. Everybody who has studied creativity is driven finally to saying, “creativity comes out of your subconscious.” Somehow, suddenly, there it is. It just appears. Well, we know very little about the subconscious; but one thing you are pretty well aware of is that your dreams also come out of your subconscious. And you’re aware your dreams are, to a fair extent, a reworking of the experiences of the day. If you are deeply immersed and committed to a topic, day after day after day, your subconscious has nothing to do but work on your problem. And so you wake up one morning, or on some afternoon, and there’s the answer. For those who don’t get committed to their current problem, the subconscious goofs off on other things and doesn’t produce the big result. So the way to manage yourself is that when you have a real important problem you don’t let anything else get the center of your attention - you keep your thoughts on the problem. Keep your subconscious starved so it has to work on your problem, so you can sleep peacefully and get the answer in the morning, free.
Now Alan Chynoweth mentioned that I used to eat at the physics table. I had been eating with the mathematicians and I found out that I already knew a fair amount of mathematics; in fact, I wasn’t learning much. The physics table was, as he said, an exciting place, but I think he exaggerated on how much I contributed. It was very interesting to listen to Shockley, Brattain, Bardeen, J. B. Johnson, Ken McKay and other people, and I was learning a lot. But unfortunately a Nobel Prize came, and a promotion came, and what was left was the dregs. Nobody wanted what was left. Well, there was no use eating with them!
Over on the other side of the dining hall was a chemistry table. I had worked with one of the fellows, Dave McCall; furthermore he was courting our secretary at the time. I went over and said, “Do you mind if I join you?” They can’t say no, so I started eating with them for a while. And I started asking, “What are the important problems of your field?” And after a week or so, “What important problems are you working on?” And after some more time I came in one day and said, “If what you are doing is not important, and if you don’t think it is going to lead to something important, why are you at Bell Labs working on it?” I wasn’t welcomed after that; I had to find somebody else to eat with! That was in the spring.
In the fall, Dave McCall stopped me in the hall and said, “Hamming, that remark of yours got underneath my skin. I thought about it all summer, i.e. what were the important problems in my field. I haven’t changed my research,” he says, “but I think it was well worthwhile.” And I said, “Thank you Dave,” and went on. I noticed a couple of months later he was made the head of the department. I noticed the other day he was a Member of the National Academy of Engineering. I noticed he has succeeded. I have never heard the names of any of the other fellows at that table mentioned in science and scientific circles. They were unable to ask themselves, “What are the important problems in my field?”
If you do not work on an important problem, it’s unlikely you’ll do important work. It’s perfectly obvious. Great scientists have thought through, in a careful way, a number of important problems in their field, and they keep an eye on wondering how to attack them. Let me warn you, ‘important problem’ must be phrased carefully. The three outstanding problems in physics, in a certain sense, were never worked on while I was at Bell Labs. By important I mean guaranteed a Nobel Prize and any sum of money you want to mention. We didn’t work on (1) time travel, (2) teleportation, and (3) antigravity. They are not important problems because we do not have an attack. It’s not the consequence that makes a problem important, it is that you have a reasonable attack. That is what makes a problem important. When I say that most scientists don’t work on important problems, I mean it in that sense. The average scientist, so far as I can make out, spends almost all his time working on problems which he believes will not be important and he also doesn’t believe that they will lead to important problems.
I spoke earlier about planting acorns so that oaks will grow. You can’t always know exactly where to be, but you can keep active in places where something might happen. And even if you believe that great science is a matter of luck, you can stand on a mountain top where lightning strikes; you don’t have to hide in the valley where you’re safe. But the average scientist does routine safe work almost all the time and so he (or she) doesn’t produce much. It’s that simple. If you want to do great work, you clearly must work on important problems, and you should have an idea.
Along those lines at some urging from John Tukey and others, I finally adopted what I called “Great Thoughts Time.” When I went to lunch Friday noon, I would only discuss great thoughts after that. By great thoughts I mean ones like: “What will be the role of computers in all of AT&T?'’, “How will computers change science?” For example, I came up with the observation at that time that nine out of ten experiments were done in the lab and one in ten on the computer. I made a remark to the vice presidents one time, that it would be reversed, i.e. nine out of ten experiments would be done on the computer and one in ten in the lab. They knew I was a crazy mathematician and had no sense of reality. I knew they were wrong and they’ve been proved wrong while I have been proved right. They built laboratories when they didn’t need them. I saw that computers were transforming science because I spent a lot of time asking “What will be the impact of computers on science and how can I change it?” I asked myself, “How is it going to change Bell Labs?” I remarked one time, in the same address, that more than one-half of the people at Bell Labs will be interacting closely with computing machines before I leave. Well, you all have terminals now. I thought hard about where was my field going, where were the opportunities, and what were the important things to do. Let me go there so there is a chance I can do important things.
Most great scientists know many important problems. They have something between 10 and 20 important problems for which they are looking for an attack. And when they see a new idea come up, one hears them say “Well that bears on this problem.” They drop all the other things and get after it. Now I can tell you a horror story that was told to me but I can’t vouch for the truth of it. I was sitting in an airport talking to a friend of mine from Los Alamos about how it was lucky that the fission experiment occurred over in Europe when it did because that got us working on the atomic bomb here in the US. He said “No; at Berkeley we had gathered a bunch of data; we didn’t get around to reducing it because we were building some more equipment, but if we had reduced that data we would have found fission.” They had it in their hands and they didn’t pursue it. They came in second!
The great scientists, when an opportunity opens up, get after it and they pursue it. They drop all other things. They get rid of other things and they get after an idea because they had already thought the thing through. Their minds are prepared; they see the opportunity and they go after it. Now of course lots of times it doesn’t work out, but you don’t have to hit many of them to do some great science. It’s kind of easy. One of the chief tricks is to live a long time!
Another trait, it took me a while to notice. I noticed the following facts about people who work with the door open or the door closed. I notice that if you have the door to your office closed, you get more work done today and tomorrow, and you are more productive than most. But 10 years later somehow you don’t know quite know what problems are worth working on; all the hard work you do is sort of tangential in importance. He who works with the door open gets all kinds of interruptions, but he also occasionally gets clues as to what the world is and what might be important. Now I cannot prove the cause and effect sequence because you might say, “The closed door is symbolic of a closed mind.” I don’t know. But I can say there is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing - not much, but enough that they miss fame.
I want to talk on another topic. It is based on the song which I think many of you know, “It ain’t what you do, it’s the way that you do it.” I’ll start with an example of my own. I was conned into doing on a digital computer, in the absolute binary days, a problem which the best analog computers couldn’t do. And I was getting an answer. When I thought carefully and said to myself, “You know, Hamming, you’re going to have to file a report on this military job; after you spend a lot of money you’re going to have to account for it and every analog installation is going to want the report to see if they can’t find flaws in it.” I was doing the required integration by a rather crummy method, to say the least, but I was getting the answer. And I realized that in truth the problem was not just to get the answer; it was to demonstrate for the first time, and beyond question, that I could beat the analog computer on its own ground with a digital machine. I reworked the method of solution, created a theory which was nice and elegant, and changed the way we computed the answer; the results were no different. The published report had an elegant method which was later known for years as “Hamming’s Method of Integrating Differential Equations.” It is somewhat obsolete now, but for a while it was a very good method. By changing the problem slightly, I did important work rather than trivial work.
In the same way, when using the machine up in the attic in the early days, I was solving one problem after another after another; a fair number were successful and there were a few failures. I went home one Friday after finishing a problem, and curiously enough I wasn’t happy; I was depressed. I could see life being a long sequence of one problem after another after another. After quite a while of thinking I decided, “No, I should be in the mass production of a variable product. I should be concerned with all of next year’s problems, not just the one in front of my face.” By changing the question I still got the same kind of results or better, but I changed things and did important work. I attacked the major problem - How do I conquer machines and do all of next year’s problems when I don’t know what they are going to be? How do I prepare for it? How do I do this one so I’ll be on top of it? How do I obey Newton’s rule? He said, “If I have seen further than others, it is because I’ve stood on the shoulders of giants.” These days we stand on each other’s feet!
You should do your job in such a fashion that others can build on top of it, so they will indeed say, “Yes, I’ve stood on so and so’s shoulders and I saw further.” The essence of science is cumulative. By changing a problem slightly you can often do great work rather than merely good work. Instead of attacking isolated problems, I made the resolution that I would never again solve an isolated problem except as characteristic of a class.
Now if you are much of a mathematician you know that the effort to generalize often means that the solution is simple. Often by stopping and saying, “This is the problem he wants but this is characteristic of so and so. Yes, I can attack the whole class with a far superior method than the particular one because I was earlier embedded in needless detail.” The business of abstraction frequently makes things simple. Furthermore, I filed away the methods and prepared for the future problems.
To end this part, I’ll remind you, “It is a poor workman who blames his tools - the good man gets on with the job, given what he’s got, and gets the best answer he can.” And I suggest that by altering the problem, by looking at the thing differently, you can make a great deal of difference in your final productivity because you can either do it in such a fashion that people can indeed build on what you’ve done, or you can do it in such a fashion that the next person has to essentially duplicate again what you’ve done. It isn’t just a matter of the job, it’s the way you write the report, the way you write the paper, the whole attitude. It’s just as easy to do a broad, general job as one very special case. And it’s much more satisfying and rewarding!
I have now come down to a topic which is very distasteful; it is not sufficient to do a job, you have to sell it. ‘Selling’ to a scientist is an awkward thing to do. It’s very ugly; you shouldn’t have to do it. The world is supposed to be waiting, and when you do something great, they should rush out and welcome it. But the fact is everyone is busy with their own work. You must present it so well that they will set aside what they are doing, look at what you’ve done, read it, and come back and say, “Yes, that was good.” I suggest that when you open a journal, as you turn the pages, you ask why you read some articles and not others. You had better write your report so when it is published in the Physical Review, or wherever else you want it, as the readers are turning the pages they won’t just turn your pages but they will stop and read yours. If they don’t stop and read it, you won’t get credit.
There are three things you have to do in selling. You have to learn to write clearly and well so that people will read it, you must learn to give reasonably formal talks, and you also must learn to give informal talks. We had a lot of so-called ‘back room scientists.’ In a conference, they would keep quiet. Three weeks later after a decision was made they filed a report saying why you should do so and so. Well, it was too late. They would not stand up right in the middle of a hot conference, in the middle of activity, and say, “We should do this for these reasons.” You need to master that form of communication as well as prepared speeches.
When I first started, I got practically physically ill while giving a speech, and I was very, very nervous. I realized I either had to learn to give speeches smoothly or I would essentially partially cripple my whole career. The first time IBM asked me to give a speech in New York one evening, I decided I was going to give a really good speech, a speech that was wanted, not a technical one but a broad one, and at the end if they liked it, I’d quietly say, “Any time you want one I’ll come in and give you one.” As a result, I got a great deal of practice giving speeches to a limited audience and I got over being afraid. Furthermore, I could also then study what methods were effective and what were ineffective.
While going to meetings I had already been studying why some papers are remembered and most are not. The technical person wants to give a highly limited technical talk. Most of the time the audience wants a broad general talk and wants much more survey and background than the speaker is willing to give. As a result, many talks are ineffective. The speaker names a topic and suddenly plunges into the details he’s solved. Few people in the audience may follow. You should paint a general picture to say why it’s important, and then slowly give a sketch of what was done. Then a larger number of people will say, “Yes, Joe has done that,” or “Mary has done that; I really see where it is; yes, Mary really gave a good talk; I understand what Mary has done.” The tendency is to give a highly restricted, safe talk; this is usually ineffective. Furthermore, many talks are filled with far too much information. So I say this idea of selling is obvious.
Let me summarize. You’ve got to work on important problems. I deny that it is all luck, but I admit there is a fair element of luck. I subscribe to Pasteur’s “Luck favors the prepared mind.” I favor heavily what I did. Friday afternoons for years - great thoughts only - means that I committed 10% of my time trying to understand the bigger problems in the field, i.e. what was and what was not important. I found in the early days I had believed ‘this’ and yet had spent all week marching in ‘that’ direction. It was kind of foolish. If I really believe the action is over there, why do I march in this direction? I either had to change my goal or change what I did. So I changed something I did and I marched in the direction I thought was important. It’s that easy.
Now you might tell me you haven’t got control over what you have to work on. Well, when you first begin, you may not. But once you’re moderately successful, there are more people asking for results than you can deliver and you have some power of choice, but not completely. I’ll tell you a story about that, and it bears on the subject of educating your boss. I had a boss named Schelkunoff; he was, and still is, a very good friend of mine. Some military person came to me and demanded some answers by Friday. Well, I had already dedicated my computing resources to reducing data on the fly for a group of scientists; I was knee deep in short, small, important problems. This military person wanted me to solve his problem by the end of the day on Friday. I said, “No, I’ll give it to you Monday. I can work on it over the weekend. I’m not going to do it now.” He goes down to my boss, Schelkunoff, and Schelkunoff says, “You must run this for him; he’s got to have it by Friday.” I tell him, “Why do I?'’; he says, “You have to.” I said, “Fine, Sergei, but you’re sitting in your office Friday afternoon catching the late bus home to watch as this fellow walks out that door.” I gave the military person the answers late Friday afternoon. I then went to Schelkunoff’s office and sat down; as the man goes out I say, “You see Schelkunoff, this fellow has nothing under his arm; but I gave him the answers.” On Monday morning Schelkunoff called him up and said, “Did you come in to work over the weekend?” I could hear, as it were, a pause as the fellow ran through his mind of what was going to happen; but he knew he would have had to sign in, and he’d better not say he had when he hadn’t, so he said he hadn’t. Ever after that Schelkunoff said, “You set your deadlines; you can change them.”
One lesson was sufficient to educate my boss as to why I didn’t want to do big jobs that displaced exploratory research and why I was justified in not doing crash jobs which absorb all the research computing facilities. I wanted instead to use the facilities to compute a large number of small problems. Again, in the early days, I was limited in computing capacity and it was clear, in my area, that a “mathematician had no use for machines.” But I needed more machine capacity. Every time I had to tell some scientist in some other area, “No I can’t; I haven’t the machine capacity,” he complained. I said “Go tell your Vice President that Hamming needs more computing capacity.” After a while I could see what was happening up there at the top; many people said to my Vice President, “Your man needs more computing capacity.” I got it!
I also did a second thing. When I loaned what little programming power we had to help in the early days of computing, I said, “We are not getting the recognition for our programmers that they deserve. When you publish a paper you will thank that programmer or you aren’t getting any more help from me. That programmer is going to be thanked by name; she’s worked hard.” I waited a couple of years. I then went through a year of BSTJ articles and counted what fraction thanked some programmer. I took it into the boss and said, “That’s the central role computing is playing in Bell Labs; if the BSTJ is important, that’s how important computing is.” He had to give in. You can educate your bosses. It’s a hard job. In this talk I’m only viewing from the bottom up; I’m not viewing from the top down. But I am telling you how you can get what you want in spite of top management. You have to sell your ideas there also.
Well I now come down to the topic, “Is the effort to be a great scientist worth it?” To answer this, you must ask people. When you get beyond their modesty, most people will say, “Yes, doing really first-class work, and knowing it, is as good as wine, women and song put together,” or if it’s a woman she says, “It is as good as wine, men and song put together.” And if you look at the bosses, they tend to come back or ask for reports, trying to participate in those moments of discovery. They’re always in the way. So evidently those who have done it, want to do it again. But it is a limited survey. I have never dared to go out and ask those who didn’t do great work how they felt about the matter. It’s a biased sample, but I still think it is worth the struggle. I think it is very definitely worth the struggle to try and do first-class work because the truth is, the value is in the struggle more than it is in the result. The struggle to make something of yourself seems to be worthwhile in itself. The success and fame are sort of dividends, in my opinion.
I’ve told you how to do it. It is so easy, so why do so many people, with all their talents, fail? For example, my opinion, to this day, is that there are in the mathematics department at Bell Labs quite a few people far more able and far better endowed than I, but they didn’t produce as much. Some of them did produce more than I did; Shannon produced more than I did, and some others produced a lot, but I was highly productive against a lot of other fellows who were better equipped. Why is it so? What happened to them? Why do so many of the people who have great promise, fail?
Well, one of the reasons is drive and commitment. The people who do great work with less ability but who are committed to it, get more done that those who have great skill and dabble in it, who work during the day and go home and do other things and come back and work the next day. They don’t have the deep commitment that is apparently necessary for really first-class work. They turn out lots of good work, but we were talking, remember, about first-class work. There is a difference. Good people, very talented people, almost always turn out good work. We’re talking about the outstanding work, the type of work that gets the Nobel Prize and gets recognition.
The second thing is, I think, the problem of personality defects. Now I’ll cite a fellow whom I met out in Irvine. He had been the head of a computing center and he was temporarily on assignment as a special assistant to the president of the university. It was obvious he had a job with a great future. He took me into his office one time and showed me his method of getting letters done and how he took care of his correspondence. He pointed out how inefficient the secretary was. He kept all his letters stacked around there; he knew where everything was. And he would, on his word processor, get the letter out. He was bragging how marvelous it was and how he could get so much more work done without the secretary’s interference. Well, behind his back, I talked to the secretary. The secretary said, “Of course I can’t help him; I don’t get his mail. He won’t give me the stuff to log in; I don’t know where he puts it on the floor. Of course I can’t help him.” So I went to him and said, “Look, if you adopt the present method and do what you can do single-handedly, you can go just that far and no farther than you can do single-handedly. If you will learn to work with the system, you can go as far as the system will support you.” And, he never went any further. He had his personality defect of wanting total control and was not willing to recognize that you need the support of the system.
You find this happening again and again; good scientists will fight the system rather than learn to work with the system and take advantage of all the system has to offer. It has a lot, if you learn how to use it. It takes patience, but you can learn how to use the system pretty well, and you can learn how to get around it. After all, if you want a decision ‘No’, you just go to your boss and get a ‘No’ easy. If you want to do something, don’t ask, do it. Present him with an accomplished fact. Don’t give him a chance to tell you ‘No’. But if you want a ‘No’, it’s easy to get a‘No’.
Another personality defect is ego assertion and I’ll speak in this case of my own experience. I came from Los Alamos and in the early days I was using a machine in New York at 590 Madison Avenue where we merely rented time. I was still dressing in western clothes, big slash pockets, a bolo and all those things. I vaguely noticed that I was not getting as good service as other people. So I set out to measure. You came in and you waited for your turn; I felt I was not getting a fair deal. I said to myself, “Why? No Vice President at IBM said, ‘Give Hamming a bad time’. It is the secretaries at the bottom who are doing this. When a slot appears, they’ll rush to find someone to slip in, but they go out and find somebody else. Now, why? I haven’t mistreated them.” Answer, I wasn’t dressing the way they felt somebody in that situation should. It came down to just that - I wasn’t dressing properly. I had to make the decision - was I going to assert my ego and dress the way I wanted to and have it steadily drain my effort from my professional life, or was I going to appear to conform better? I decided I would make an effort to appear to conform properly. The moment I did, I got much better service. And now, as an old colorful character, I get better service than other people.
You should dress according to the expectations of the audience spoken to. If I am going to give an address at the MIT computer center, I dress with a bolo and an old corduroy jacket or something else. I know enough not to let my clothes, my appearance, my manners get in the way of what I care about. An enormous number of scientists feel they must assert their ego and do their thing their way. They have got to be able to do this, that, or the other thing, and they pay a steady price.
John Tukey almost always dressed very casually. He would go into an important office and it would take a long time before the other fellow realized that this is a first-class man and he had better listen. For a long time John has had to overcome this kind of hostility. It’s wasted effort! I didn’t say you should conform; I said “The appearance of conforming gets you a long way.” If you chose to assert your ego in any number of ways, “I am going to do it my way,” you pay a small steady price throughout the whole of your professional career. And this, over a whole lifetime, adds up to an enormous amount of needless trouble.
By taking the trouble to tell jokes to the secretaries and being a little friendly, I got superb secretarial help. For instance, one time for some idiot reason all the reproducing services at Murray Hill were tied up. Don’t ask me how, but they were. I wanted something done. My secretary called up somebody at Holmdel, hopped the company car, made the hour-long trip down and got it reproduced, and then came back. It was a payoff for the times I had made an effort to cheer her up, tell her jokes and be friendly; it was that little extra work that later paid off for me. By realizing you have to use the system and studying how to get the system to do your work, you learn how to adapt the system to your desires. Or you can fight it steadily, as a small undeclared war, for the whole of your life.
And I think John Tukey paid a terrible price needlessly. He was a genius anyhow, but I think it would have been far better, and far simpler, had he been willing to conform a little bit instead of ego asserting. He is going to dress the way he wants all of the time. It applies not only to dress but to a thousand other things; people will continue to fight the system. Not that you shouldn’t occasionally!
When they moved the library from the middle of Murray Hill to the far end, a friend of mine put in a request for a bicycle. Well, the organization was not dumb. They waited awhile and sent back a map of the grounds saying, “Will you please indicate on this map what paths you are going to take so we can get an insurance policy covering you.” A few more weeks went by. They then asked, “Where are you going to store the bicycle and how will it be locked so we can do so and so.” He finally realized that of course he was going to be red-taped to death so he gave in. He rose to be the President of Bell Laboratories.
Barney Oliver was a good man. He wrote a letter one time to the IEEE. At that time the official shelf space at Bell Labs was so much and the height of the IEEE Proceedings at that time was larger; and since you couldn’t change the size of the official shelf space he wrote this letter to the IEEE Publication person saying, “Since so many IEEE members were at Bell Labs and since the official space was so high the journal size should be changed.” He sent it for his boss’s signature. Back came a carbon with his signature, but he still doesn’t know whether the original was sent or not. I am not saying you shouldn’t make gestures of reform. I am saying that my study of able people is that they don’t get themselves committed to that kind of warfare. They play it a little bit and drop it and get on with their work.
Many a second-rate fellow gets caught up in some little twitting of the system, and carries it through to warfare. He expends his energy in a foolish project. Now you are going to tell me that somebody has to change the system. I agree; somebody’s has to. Which do you want to be? The person who changes the system or the person who does first-class science? Which person is it that you want to be? Be clear, when you fight the system and struggle with it, what you are doing, how far to go out of amusement, and how much to waste your effort fighting the system. My advice is to let somebody else do it and you get on with becoming a first-class scientist. Very few of you have the ability to both reform the system and become a first-class scientist.
On the other hand, we can’t always give in. There are times when a certain amount of rebellion is sensible. I have observed almost all scientists enjoy a certain amount of twitting the system for the sheer love of it. What it comes down to basically is that you cannot be original in one area without having originality in others. Originality is being different. You can’t be an original scientist without having some other original characteristics. But many a scientist has let his quirks in other places make him pay a far higher price than is necessary for the ego satisfaction he or she gets. I’m not against all ego assertion; I’m against some.
Another fault is anger. Often a scientist becomes angry, and this is no way to handle things. Amusement, yes, anger, no. Anger is misdirected. You should follow and cooperate rather than struggle against the system all the time.
Another thing you should look for is the positive side of things instead of the negative. I have already given you several examples, and there are many, many more; how, given the situation, by changing the way I looked at it, I converted what was apparently a defect to an asset. I’ll give you another example. I am an egotistical person; there is no doubt about it. I knew that most people who took a sabbatical to write a book, didn’t finish it on time. So before I left, I told all my friends that when I come back, that book was going to be done! Yes, I would have it done - I’d have been ashamed to come back without it! I used my ego to make myself behave the way I wanted to. I bragged about something so I’d have to perform. I found out many times, like a cornered rat in a real trap, I was surprisingly capable. I have found that it paid to say, “Oh yes, I’ll get the answer for you Tuesday,” not having any idea how to do it. By Sunday night I was really hard thinking on how I was going to deliver by Tuesday. I often put my pride on the line and sometimes I failed, but as I said, like a cornered rat I’m surprised how often I did a good job. I think you need to learn to use yourself. I think you need to know how to convert a situation from one view to another which would increase the chance of success.
Now self-delusion in humans is very, very common. There are enumerable ways of you changing a thing and kidding yourself and making it look some other way. When you ask, “Why didn’t you do such and such,” the person has a thousand alibis. If you look at the history of science, usually these days there are 10 people right there ready, and we pay off for the person who is there first. The other nine fellows say, “Well, I had the idea but I didn’t do it and so on and so on.” There are so many alibis. Why weren’t you first? Why didn’t you do it right? Don’t try an alibi. Don’t try and kid yourself. You can tell other people all the alibis you want. I don’t mind. But to yourself try to be honest.
If you really want to be a first-class scientist you need to know yourself, your weaknesses, your strengths, and your bad faults, like my egotism. How can you convert a fault to an asset? How can you convert a situation where you haven’t got enough manpower to move into a direction when that’s exactly what you need to do? I say again that I have seen, as I studied the history, the successful scientist changed the viewpoint and what was a defect became an asset.
In summary, I claim that some of the reasons why so many people who have greatness within their grasp don’t succeed are: they don’t work on important problems, they don’t become emotionally involved, they don’t try and change what is difficult to some other situation which is easily done but is still important, and they keep giving themselves alibis why they don’t. They keep saying that it is a matter of luck. I’ve told you how easy it is; furthermore I’ve told you how to reform. Therefore, go forth and become great scientists!
(End of the formal part of the talk.)
DISCUSSION - QUESTIONS AND ANSWERS
A. G. Chynoweth: Well that was 50 minutes of concentrated wisdom and observations accumulated over a fantastic career; I lost track of all the observations that were striking home. Some of them are very very timely. One was the plea for more computer capacity; I was hearing nothing but that this morning from several people, over and over again. So that was right on the mark today even though here we are 20 - 30 years after when you were making similar remarks, Dick. I can think of all sorts of lessons that all of us can draw from your talk. And for one, as I walk around the halls in the future I hope I won’t see as many closed doors in Bellcore. That was one observation I thought was very intriguing.
Thank you very, very much indeed Dick; that was a wonderful recollection. I’ll now open it up for questions. I’m sure there are many people who would like to take up on some of the points that Dick was making.
Hamming: First let me respond to Alan Chynoweth about computing. I had computing in research and for 10 years I kept telling my management, “Get that !&@#% machine out of research. We are being forced to run problems all the time. We can’t do research because were too busy operating and running the computing machines.” Finally the message got through. They were going to move computing out of research to someplace else. I was persona non grata to say the least and I was surprised that people didn’t kick my shins because everybody was having their toy taken away from them. I went in to Ed David’s office and said, “Look Ed, you’ve got to give your researchers a machine. If you give them a great big machine, we’ll be back in the same trouble we were before, so busy keeping it going we can’t think. Give them the smallest machine you can because they are very able people. They will learn how to do things on a small machine instead of mass computing.” As far as I’m concerned, that’s how UNIX arose. We gave them a moderately small machine and they decided to make it do great things. They had to come up with a system to do it on. It is called UNIX!
A. G. Chynoweth: I just have to pick up on that one. In our present environment, Dick, while we wrestle with some of the red tape attributed to, or required by, the regulators, there is one quote that one exasperated AVP came up with and I’ve used it over and over again. He growled that, “UNIX was never a deliverable!”
Question: What about personal stress? Does that seem to make a difference?
Hamming: Yes, it does. If you don’t get emotionally involved, it doesn’t. I had incipient ulcers most of the years that I was at Bell Labs. I have since gone off to the Naval Postgraduate School and laid back somewhat, and now my health is much better. But if you want to be a great scientist you’re going to have to put up with stress. You can lead a nice life; you can be a nice guy or you can be a great scientist. But nice guys end last, is what Leo Durocher said. If you want to lead a nice happy life with a lot of recreation and everything else, you’ll lead a nice life.
Question: The remarks about having courage, no one could argue with; but those of us who have gray hairs or who are well established don’t have to worry too much. But what I sense among the young people these days is a real concern over the risk taking in a highly competitive environment. Do you have any words of wisdom on this?
Hamming: I’ll quote Ed David more. Ed David was concerned about the general loss of nerve in our society. It does seem to me that we’ve gone through various periods. Coming out of the war, coming out of Los Alamos where we built the bomb, coming out of building the radars and so on, there came into the mathematics department, and the research area, a group of people with a lot of guts. They’ve just seen things done; they’ve just won a war which was fantastic. We had reasons for having courage and therefore we did a great deal. I can’t arrange that situation to do it again. I cannot blame the present generation for not having it, but I agree with what you say; I just cannot attach blame to it. It doesn’t seem to me they have the desire for greatness; they lack the courage to do it. But we had, because we were in a favorable circumstance to have it; we just came through a tremendously successful war. In the war we were looking very, very bad for a long while; it was a very desperate struggle as you well know. And our success, I think, gave us courage and self confidence; that’s why you see, beginning in the late forties through the fifties, a tremendous productivity at the labs which was stimulated from the earlier times. Because many of us were earlier forced to learn other things - we were forced to learn the things we didn’t want to learn, we were forced to have an open door - and then we could exploit those things we learned. It is true, and I can’t do anything about it; I cannot blame the present generation either. It’s just a fact.
Question: Is there something management could or should do?
Hamming: Management can do very little. If you want to talk about managing research, that’s a totally different talk. I’d take another hour doing that. This talk is about how the individual gets very successful research done in spite of anything the management does or in spite of any other opposition. And how do you do it? Just as I observe people doing it. It’s just that simple and that hard!
Question: Is brainstorming a daily process?
Hamming: Once that was a very popular thing, but it seems not to have paid off. For myself I find it desirable to talk to other people; but a session of brainstorming is seldom worthwhile. I do go in to strictly talk to somebody and say, “Look, I think there has to be something here. Here’s what I think I see …” and then begin talking back and forth. But you want to pick capable people. To use another analogy, you know the idea called the ‘critical mass.’ If you have enough stuff you have critical mass. There is also the idea I used to call ‘sound absorbers’. When you get too many sound absorbers, you give out an idea and they merely say, “Yes, yes, yes.” What you want to do is get that critical mass in action; “Yes, that reminds me of so and so,” or, “Have you thought about that or this?” When you talk to other people, you want to get rid of those sound absorbers who are nice people but merely say, “Oh yes,” and to find those who will stimulate you right back.
For example, you couldn’t talk to John Pierce without being stimulated very quickly. There were a group of other people I used to talk with. For example there was Ed Gilbert; I used to go down to his office regularly and ask him questions and listen and come back stimulated. I picked my people carefully with whom I did or whom I didn’t brainstorm because the sound absorbers are a curse. They are just nice guys; they fill the whole space and they contribute nothing except they absorb ideas and the new ideas just die away instead of echoing on. Yes, I find it necessary to talk to people. I think people with closed doors fail to do this so they fail to get their ideas sharpened, such as “Did you ever notice something over here?” I never knew anything about it - I can go over and look. Somebody points the way. On my visit here, I have already found several books that I must read when I get home. I talk to people and ask questions when I think they can answer me and give me clues that I do not know about. I go out and look!
Question: What kind of tradeoffs did you make in allocating your time for reading and writing and actually doing research?
Hamming: I believed, in my early days, that you should spend at least as much time in the polish and presentation as you did in the original research. Now at least 50% of the time must go for the presentation. It’s a big, big number.
Question: How much effort should go into library work?
Hamming: It depends upon the field. I will say this about it. There was a fellow at Bell Labs, a very, very, smart guy. He was always in the library; he read everything. If you wanted references, you went to him and he gave you all kinds of references. But in the middle of forming these theories, I formed a proposition: there would be no effect named after him in the long run. He is now retired from Bell Labs and is an Adjunct Professor. He was very valuable; I’m not questioning that. He wrote some very good Physical Review articles; but there’s no effect named after him because he read too much. If you read all the time what other people have done you will think the way they thought. If you want to think new thoughts that are different, then do what a lot of creative people do - get the problem reasonably clear and then refuse to look at any answers until you’ve thought the problem through carefully how you would do it, how you could slightly change the problem to be the correct one. So yes, you need to keep up. You need to keep up more to find out what the problems are than to read to find the solutions. The reading is necessary to know what is going on and what is possible. But reading to get the solutions does not seem to be the way to do great research. So I’ll give you two answers. You read; but it is not the amount, it is the way you read that counts.
Question: How do you get your name attached to things?
Hamming: By doing great work. I’ll tell you the hamming window one. I had given Tukey a hard time, quite a few times, and I got a phone call from him from Princeton to me at Murray Hill. I knew that he was writing up power spectra and he asked me if I would mind if he called a certain window a “Hamming window.” And I said to him, “Come on, John; you know perfectly well I did only a small part of the work but you also did a lot.” He said, “Yes, Hamming, but you contributed a lot of small things; you’re entitled to some credit.” So he called it the hamming window. Now, let me go on. I had twitted John frequently about true greatness. I said true greatness is when your name is like ampere, watt, and fourier - when it’s spelled with a lower case letter. That’s how the hamming window came about.
Question: Dick, would you care to comment on the relative effectiveness between giving talks, writing papers, and writing books?
Hamming: In the short-haul, papers are very important if you want to stimulate someone tomorrow. If you want to get recognition long-haul, it seems to me writing books is more contribution because most of us need orientation. In this day of practically infinite knowledge, we need orientation to find our way. Let me tell you what infinite knowledge is. Since from the time of Newton to now, we have come close to doubling knowledge every 17 years, more or less. And we cope with that, essentially, by specialization. In the next 340 years at that rate, there will be 20 doublings, i.e. a million, and there will be a million fields of specialty for every one field now. It isn’t going to happen. The present growth of knowledge will choke itself off until we get different tools. I believe that books which try to digest, coordinate, get rid of the duplication, get rid of the less fruitful methods and present the underlying ideas clearly of what we know now, will be the things the future generations will value. Public talks are necessary; private talks are necessary; written papers are necessary. But I am inclined to believe that, in the long-haul, books which leave out what’s not essential are more important than books which tell you everything because you don’t want to know everything. I don’t want to know that much about penguins is the usual reply. You just want to know the essence.
Question: You mentioned the problem of the Nobel Prize and the subsequent notoriety of what was done to some of the careers. Isn’t that kind of a much more broad problem of fame? What can one do?
Hamming: Some things you could do are the following. Somewhere around every seven years make a significant, if not complete, shift in your field. Thus, I shifted from numerical analysis, to hardware, to software, and so on, periodically, because you tend to use up your ideas. When you go to a new field, you have to start over as a baby. You are no longer the big mukity muk and you can start back there and you can start planting those acorns which will become the giant oaks. Shannon, I believe, ruined himself. In fact when he left Bell Labs, I said, “That’s the end of Shannon’s scientific career.” I received a lot of flak from my friends who said that Shannon was just as smart as ever. I said, “Yes, he’ll be just as smart, but that’s the end of his scientific career,” and I truly believe it was.
You have to change. You get tired after a while; you use up your originality in one field. You need to get something nearby. I’m not saying that you shift from music to theoretical physics to English literature; I mean within your field you should shift areas so that you don’t go stale. You couldn’t get away with forcing a change every seven years, but if you could, I would require a condition for doing research, being that you will change your field of research every seven years with a reasonable definition of what it means, or at the end of 10 years, management has the right to compel you to change. I would insist on a change because I’m serious. What happens to the old fellows is that they get a technique going; they keep on using it. They were marching in that direction which was right then, but the world changes. There’s the new direction; but the old fellows are still marching in their former direction.
You need to get into a new field to get new viewpoints, and before you use up all the old ones. You can do something about this, but it takes effort and energy. It takes courage to say, “Yes, I will give up my great reputation.” For example, when error correcting codes were well launched, having these theories, I said, “Hamming, you are going to quit reading papers in the field; you are going to ignore it completely; you are going to try and do something else other than coast on that.” I deliberately refused to go on in that field. I wouldn’t even read papers to try to force myself to have a chance to do something else. I managed myself, which is what I’m preaching in this whole talk. Knowing many of my own faults, I manage myself. I have a lot of faults, so I’ve got a lot of problems, i.e. a lot of possibilities of management.
Question: Would you compare research and management?
Hamming: If you want to be a great researcher, you won’t make it being president of the company. If you want to be president of the company, that’s another thing. I’m not against being president of the company. I just don’t want to be. I think Ian Ross does a good job as President of Bell Labs. I’m not against it; but you have to be clear on what you want. Furthermore, when you’re young, you may have picked wanting to be a great scientist, but as you live longer, you may change your mind. For instance, I went to my boss, Bode, one day and said, “Why did you ever become department head? Why didn’t you just be a good scientist?” He said, “Hamming, I had a vision of what mathematics should be in Bell Laboratories. And I saw if that vision was going to be realized, I had to make it happen; I had to be department head.” When your vision of what you want to do is what you can do single-handedly, then you should pursue it. The day your vision, what you think needs to be done, is bigger than what you can do single-handedly, then you have to move toward management. And the bigger the vision is, the farther in management you have to go. If you have a vision of what the whole laboratory should be, or the whole Bell System, you have to get there to make it happen. You can’t make it happen from the bottom very easily. It depends upon what goals and what desires you have. And as they change in life, you have to be prepared to change. I chose to avoid management because I preferred to do what I could do single-handedly. But that’s the choice that I made, and it is biased. Each person is entitled to their choice. Keep an open mind. But when you do choose a path, for heaven’s sake be aware of what you have done and the choice you have made. Don’t try to do both sides.
Question: How important is one’s own expectation or how important is it to be in a group or surrounded by people who expect great work from you?
Hamming: At Bell Labs everyone expected good work from me - it was a big help. Everybody expects you to do a good job, so you do, if you’ve got pride. I think it’s very valuable to have first-class people around. I sought out the best people. The moment that physics table lost the best people, I left. The moment I saw that the same was true of the chemistry table, I left. I tried to go with people who had great ability so I could learn from them and who would expect great results out of me. By deliberately managing myself, I think I did much better than laissez faire.
Question: You, at the outset of your talk, minimized or played down luck; but you seemed also to gloss over the circumstances that got you to Los Alamos, that got you to Chicago, that got you to Bell Laboratories.
Hamming: There was some luck. On the other hand I don’t know the alternate branches. Until you can say that the other branches would not have been equally or more successful, I can’t say. Is it luck the particular thing you do? For example, when I met Feynman at Los Alamos, I knew he was going to get a Nobel Prize. I didn’t know what for. But I knew darn well he was going to do great work. No matter what directions came up in the future, this man would do great work. And sure enough, he did do great work. It isn’t that you only do a little great work at this circumstance and that was luck, there are many opportunities sooner or later. There are a whole pail full of opportunities, of which, if you’re in this situation, you seize one and you’re great over there instead of over here. There is an element of luck, yes and no. Luck favors a prepared mind; luck favors a prepared person. It is not guaranteed; I don’t guarantee success as being absolutely certain. I’d say luck changes the odds, but there is some definite control on the part of the individual.
Go forth, then, and do great work!
(End of the General Research Colloquium Talk.)
Transcription of the Bell Communications Research Colloquium Seminar7 March 1986
J. F. KaiserBell Communications Research445 South StreetMorristown, NJ 07962-1910jfk@bellcore.com