Engineered

Review of "Turning Point: Policymaking in the Era of Artificial Intelligence"

I recently read this book, by Darrell West and John Allen, respectively a Vice President and the President of the Brookings Institution. It was a little dry and numbers-heavy but there were some interesting chapters. I particularly liked their comments on the trends re: car insurance (basically they argue that with the advent of driverless cars, car insurance should go down [p.72], although I think that insurance companies will make up for it in the home insurance premiums), and also I was fascinated by the discussion about the zoning rules and congestion p.96. It is a really great point that vehicles like Amazon Prime delivery trucks put more wear and tear on the roads and they also bring with them a whole range of issues from double-parking to storage issues to parcel theft to apartment buildings that have no loading dock. I enjoyed thinking about the environmental impact of truck delivery as well as the joint warehouse and distribution optimization problems to meet customer needs. Also I had never heard about the freight master plan in Seattle but it is indeed a thing! You can read more about it here. Rating: three stars out of five, which on Goodreads scale means I liked it (enough).

Analytics for Decision Support

I often get questions on how I teach business analytics, so I figured it'd be as good a blog topic as any, especially since I had a great conversation this morning with a graduate student who took that class her first semester and now has included me on her dissertation committee. The analytics course I teach (or taught until last year, since I've been teaching something else this year) is an overview to analytics and has no pre-requisite. It also has an undergraduate section (senior elective) and a graduate section. While the undergraduate version of the course is taken by students in our major, who are therefore well prepared by that time in their studies, the graduate version of the course is open to any graduate student in the school of engineering, which creates a much wider distribution of backgrounds and preparedness. Oh, and there is a distance (graduate) section too, and before the pandemic it was asynchronous, so ideally you have to make the course engaging for asynchronous viewing. 

I start by providing an introduction to the analytics cycle, also known as CRISP-DM (Cross-Industry Standard Process for Data Mining) and go over select articles from Harvard Business Review, especially Competing on Analytics by Thomas Davenport, which got the analytics field started back in 2006 (fifteen years ago!) and the spin-off articles he has co-authored, such as Data Scientist: The Sexiest Job of the 21st Century, from 2012. If I teach the course again, I will probably update the readings with one of his most recent articles such as 4 Ways to Democratize Data Science in Your Organization, from 2021. Also I think it is good for students or recent grads to read publications that the people they aspire at becoming read, and Harvard Business Review is definitely high on that list, although it is a little expensive if they can't get a relative to buy them a subscription as graduation gift (although perhaps HBR gives students and young alumni special rates). 

After the high-level introduction to data science, I usually dive into R and cover linear regression, logistic regressions, classification and regression trees (including random forests for the graduate students) before I take a little break from all the math and cover visualization in Tableau and R's ggplot and then return to clustering (hierarchical and k-means) before finishing with linear, integer and (my favorite topic) nonlinear optimization. I do tell the students that in practice visualization or descriptive analytics is done first, but in the past I've liked putting it halfway through the semester because it gives the students a nice little break at a time where they often have midterms. Maybe if I teach the course again I will just cover the topics in the normal sequence (descriptive analytics first, then predictive, then prescriptive) because it is closer to the real data science process. (Interestingly enough, I started teaching Tableau because a student of mine at Lehigh had just gone back from a summer internship at a consulting company where they had learned Tableau and told me I should teach it to my next cohort of students. Always keep in touch with alumni of your courses! They may have great information to share with you.)

My main book is The Analytics Edge, by my PhD advisor Dimitris Bertsimas along with his co-authors Allison O'Hair and William Pulleyblank. What I like even more than the book is the "Analytics Edge" course website on edX,org, which was instrumental in helping me understand how to pace a course that basically everyone can enroll in. While the book itself consists mostly of case studies without code, the appendix has a fantastic introduction to R for beginners and the book itself also has several good assignments for each chapter, solved in the instructor's manual. Sadly, I have taught the course for so many semesters that I am reluctant at using those assignments again, due to previous students' answers floating around the Internet.

So what I do instead is to use CSV datasets from kaggle.com. In an ideal world, we would have the data in databases and we would extract it to CSV in SQL queries so the students would get to see the entire process, but the semester has only so many weeks, and it's unrealistic to try to do too much. So what I do is I go over the datasets that have received many up-votes and look for something that would illustrate well whatever topic I covered. (I change the datasets every semester, because of websites like coursehero.com where students like to dump their assignments for the next crop of students to look at.) Kaggle.com is such a wonderful thing to happen to data science, just like GitHub.com . I also use a dataset from kaggle.com for the final project.

Somewhere along the way, I ask students to write an essay on ethics in data science, using a list of readings I provide to show them the dangers of data science being misused, and then also asking them to add a few more papers on an angle that is particularly interesting to them. This is where the double majors try, when appropriate, to tie the different parts of their curriculum together. I vividly remember a double major in management science and philosophy who was just so happy to use his entire training to write a particularly meaningful essay. 

Now the Analytics Edge book provides a great introduction to R but it is only an introduction, and to meet the needs of all the students in the class including the more advanced ones, a few semesters ago I started also using the R for Marketing Research and Analytics textbook by Chris Chapman and Elea McDonnell Feit. Elea was once the chair of an Analytics conference while I was on the conference committee, which is how I got to know her and learn about her book, and if you don't have her book, you should get it. The book website is tremendously useful with lots of codes and slides for all the chapters. What I like about the book and the related code is that it introduces students to more advanced features and packages in R, so students who are further away on the learning curve when the semester starts still get to extract something out of the course. Many of the advanced students have also told me that the course, in the way I teach it, provides a nice summary of multiple dimensions of management science, so it is a nice way to wrap up one's graduate degree too.

Somewhere toward the end of the course, I also give a test (I don't have a final exam, only a final project, but I do have one exam in the course). What always amazes me is that you can distinguish most easily the students who are at the top from those who are not from the optimization (or prescriptive analytics) questions. Most students can learn the basics on predictive analytics, so they know, say, you put about 70% of the data in the training set and the rest in the testing set, or you use linear regression to predict continuous variables and logistic regression to predict 0-1 variables. But the weaker students have tremendous difficulty in modeling optimization problems correctly, and I think creating good optimization models from data will be the next frontier that will distinguish the top data scientists from the others, especially given the fact that few Master's programs in data science have time to cover optimization in any depth, due to the amount of material they have to teach the students in only a few semesters.

At the end of the course, I try to give one lecture on big data. This way, if the students have to interact with employees who specialize in big data, they will have some idea of how it is done. (The main course objective is to give the students the background to contribute meaningfully to such conversations, rather than turning them into data scientists overnight, which no single course can do.)

A final piece of advice I give my students, and if you have read thus far (let me know in the comments or @ me or DM on Twitter) you deserve to have it too, is that if you are interested in this field it is good practice to check out job offerings periodically for the required and recommended competences. This is in fact a piece of advice I give in the context of career management. Once a student has a job, it is useful to remain competitive on the job market by keeping track of what the companies are asking for. This may help students identify gaps or software they had never heard of that they would benefit to learn asap so that their skills are in top shape. So when they see in the list of required skills something they are not good at or have never heard of, they need to develop a strategy on how they can become better at it, so that they can be given a chance if the opportunity arises. I'll try to write a blog post on a couple of job ads as an example, when I am done grading all my end-of-semester papers!

How not to lead a team

For those of you who follow SMU football, you know by now that SMU's football head coach Sonny Dykes, in only his fourth year as head coach after a rocky career elsewhere, has decided to jump ship to go to SMU's local arch-rival, TCU. This, in itself, is his own career decision. But he has mishandled the process in an epic manner in letting it affect his coaching and players' performance in away games ever since TCU legendary coach Gary Patterson got fired at the end of October, and yesterday the players learned he was leaving from social media, after he had addressed them earlier this week and refused to say anything about it. It appears that he spent this past week getting some of his top staffers to follow him to TCU.

I can't pretend I understand much about football, but I know something about students, and it rankles me how it looks like a white coach took advantage of his black players and recruits to get a higher salary at a university whose football program is not particularly better than SMU's, apparently fueled by an ego trip (the rumor is that SMU was offering him $4m/year, although the team has collapsed every November). This November there were rumors about his leaving for TCU breaking on the eve of every away game, and sure enough the players uncharacteristically underperformed the day after.

Yesterday the players learned from social media that their head coach, who had made such a show of stating the team was a Great Family, was joining TCU, after he spent the whole week refusing to give a clear answer. Earlier this week five top recruits decommitted from SMU, and it is unlikely to be a coincidence. Apparently he has spent the past week preparing for his move to TCU, while still on rival SMU's payroll, and is taking several of the assistant coaches with him. This is a man who was fired by UC Berkeley and was, as I understand it, without a coaching job when SMU came knocking after Chad Morris left for Arkansas. 

Of course everyone has to do what is best for their career, but the students aren't being treated right in this case, especially the seniors, who have the last game of their collegiate career and most likely their entire career this afternoon and have become embroiled in a great saga fueled by ego, ambition and money. The whole thing is such a mess that even the mayor of Dallas tweeted that it was. How do you help 20-year-olds, many of which come from disadvantaged communities, deal with the realization that the authority figures who are supposed to help them get better and achieve their full potential have thrown them under the proverbial bus, after all this talk of Great Family and SMU being Dallas's university and recruiting from DFW? For quite a few of them (at this university and elsewhere), football is their chance to shine and they have been trying to find an environment that would let them shine since they realized they had a talent in it. And now that's what the coach does? It is a betrayal of the students to have let contract negotiations tarnish the end of those students' football season. No matter how many games the coach later wins (if any), this will remain the biggest takeaway of his career, that he was capable of something like this, treating students from underrepresented communities as disposable objects. Again, I don't care much about football, but I care about not exploiting people who depend on me.

(Let me emphasize again that people change jobs all the time. That's not what bothers me. What bothers me is how he has treated students, especially students of color, as pawns in his great chess game to get the best deal for himself. The seniors only have one Senior Day and the gall of the head coach and the coaches going with him who are going to show their face at this game demonstrates a level of hubris that cannot be sustained even for the biggest egomaniacs.)

It is also worth pointing out that if the coach can betray SMU students he can betray TCU students too. And it is also worth pointing out that SMU has a far better academic reputation in Texas and beyond than TCU does. If the head coach wants to take advantage of the commits by making them de-commit from the best university in North Texas, with the alumni network SMU has locally (a university that was once described to me, by someone unaffiliated to SMU, as having the same reputation in North Texas as Stanford has nationally), well, the only reasonable thing that can be said is that hopefully karma is for real, because taking advantage of younger people who have a lot fewer advantages in life to further one's own career is a big low. It's not like he had an extraordinary win record at SMU either.

It is also worth pointing out that SMU is launching a capital campaign with a new football operations center planned, and so the next head coach will have amazing resources at his disposal to lift SMU to new heights. It has always seemed to me that, in its quest for athletic championships, the university was after regaining the respect that vanished after it was struck with a "death penalty" in the 1980s. We are at a great time to cement the respect the team earned back the past few years. We have great programs in place to help student-athletes thrive in their transition after eligibility. We have the best donors and the best alumni. The next head coach is going to be lucky to be here. As for Sonny Dykes and the assistant coaches who left with him for our rival, we can send them postcards detailing the progress of the new football operations center, funded by donors who truly care about SMU football as opposed to wanting to exploit it for their personal benefit as the head coach did. There are thousands of head coaches in the US. When all is said and done, the only thing that history will remember of Sonny Dykes is that he betrayed his SMU players.

Aurelie Thiele is SMU's Faculty Senate Past President.

It is time to revive this blog! Let's start with a book I read last week, Tim Cook: The Genius Who Took Apple to the Next Level by Leander Kahney. This is to prepare for the engineering management course I will be teaching next semester, which is a required course of all engineering undergraduates. This semester I had assigned Failure is not an Option by Gene Kranz (former NASA flight director) and I loved it, being an engineering MIT nerd, but some of the students didn't like it as much because they had a hard time seeing how it would be applicable to them (and also they didn't understand why they had to take this course since they don't expect to be managers for a while, and of course since my specialty is optimization and I've never been a manager in industry I was not the best person to make the case they should care). So for the coming iteration of engineering management I am reading a couple of other books to find something more suitable. I wanted something available in paperback for cost reasons, not too long because it is only one element of one course (so I was wary of the Steve Jobs bio by Walter Isaacson, for instance) and hopefully something with a bit of diversity because some of the students who are not straight while males were not enthusiastic about the lack of diversity in the Kranz book (what can I say, in the 1960s you just didn't have many engineering managers who were not straight white males and even fewer who achieved enough to be the topic of a book).

I loved the Tim Cook book for many reasons, which I will try to dump in this post. First, I think Tim Cook is a much better leader than Steve Jobs. Jobs was abrasive and, in my opinion, an egomaniac a little too confident in his genius. Maybe that worked twenty years ago, but those days people won't follow too many leaders with such characteristics. Also, Jobs didn't care about sustainability, and Cook has created a lot of programs to achieve sustainability and climate change. Again, twenty years ago maybe CEOs could get away with not caring about sustainability, but with all the talk about climate change, it is not something visionary leaders can do any longer. Cook has also pioneered new products such as the Apple Watch. I also, of course, like that Cook was educated as an industrial engineer and started his career in operations management. There is also an interesting chapter in the book about the controversy surrounding the FBI's request to Apple to develop a backdoor to the iPhone, which raises valuable questions about data privacy - a hot topic these days. Further, Cook is openly gay following his coming out in an op-ed he wrote for Bloomberg Businessweek in 2014, which helps show that CEO positions aren't just for white males, and he has made a strong push toward diversity, for instance on Apple's board. (Of course, challenges remain: while the percentage of underrepresented minorities working at Apple has increased, they tend to be mostly employed in the Apple stores rather than in higher-paying positions as managers.)

Another thing I like is that Harvard Business Publishing Education has many case studies about the events described in the book. This past semester I used the case studies that the previous instructor had been using for years, but the students pointed out that they were too dated (and they were). So I am thinking about also assigning case studies for instance on sustainability, the Apple Watch, etc. Then I would probably also assign some non-Apple cases on related topics so that the entire semester is not about Apple.

Here are a few of the relevant cases on the Harvard Business Publishing Education website:

1. Apple Inc: Managing a Global Supply Chain
2. Apple Inc: Global Supply Chain Management
3. Apple and its Suppliers: Corporate Social Responsibility
4. Apple Pay
5. Apple Watch: Managing Innovation Resistance
6. Financial Policy at Apple (more likely to end up in the engineering finance course)
7. Building a Backdoor to the iPhone: an Ethical Dilemma
8. Apple in China
9. Apple's Future: Apple Watch, Apple TV and/or Apple Car?

Interesting cases would also be:

1. China Moves Up the Value Chain: FoxConn's Dilemma
2. Toyota: The Accelerator Crisis (from 2010)
3. Cisco: Collaborating on New Product Introduction (from 2009 but relevant to new product introduction in engineering management)
4. Lean Process Improvement at Cleveland Clinic (also a little older)
5. Wyeth Pharmaceuticals: Spurring Scientific Creativity with Metrics
6. GlaxoSmithKline: Reorganizing Drug Discovery (from 2005)
7. IDEO: Human-Centered Service Design
8. Vertex Pharmaceuticals: R&D Portfolio Management
9. Ant Financial
10. Implementing LEAN operations at Caesar's Casinos
11. Disruption in Detroit: Ford, Silicon Valley and Beyond
12. Zara: An Integrated Store and Online Model

I only plan to use 12 cases since there are only 13 weeks of the semester after the first introductory week and who knows if we will have a mega-snowstorm again after Texas's continued failure to winterize the electricity grid, so it is best to have one week's worth of margin. This semester I was covering the cases on Thursdays and on Tuesdays we would talk about the assigned book (Failure is not an option this semester) and about another engineering management textbook I liked (I may also change that textbook but that will be the topic of another post.)

I probably should give some thought as to how to keep the course engaging although the sections of the discussion-heavy course are getting stuffed with as many students as there is a classroom of that size, but since leadership seems opposed to opening another section to maintain the quality of the student experience that is a hallmark of education at a private university, I'll take it as a signal that I shouldn't worry about the inability to do small-group discussions in the classroom the course was dumped in next semester or the surge in enrollment over the past year. It is a pity because what the students seemed to like most about the course was to talk with each other about their opinions of the cases and their experiences as summer interns, but there is nothing I can do about it, except taking the students out to hold discussions on the campus lawn, weather permitting. At least I will have tried to make it interesting for me to teach, and hopefully some students will find the material interesting to learn. 

Review of "Tribal Leadership"

 I picked up Tribal Leadership: Leveraging Natural Groups to Build a Thriving Organization by David Logan, John King and Halee Fischer-Wright at Half Price Books the other day, and it was a very interesting read. The authors argue there are five stages for employees:

• Stage 1 is aggressive hostility (“life sucks”, needless to say most of those people don’t last long, or are unemployed more than they’re employed),
• Stage 2 is apathetic passivity (“my life sucks” where people don’t put in their best effort for the company that employs them for various reasons),
• Stage 3 is “lone warrior ethos (“I’m great… but you’re not”)
• Stage 4 is the stage to aim for (“we’re great”, where a company doesn't name a rival as an adversary but, say, a disease it wants to eradicate, like cancer or poverty)
• Stage 5 is basically worldwide enlightenment or what the authors call innocent wonderment (“life is great”).

I liked a lot the advice the authors give to help managers guide employees toward a higher stage. To move a Stage 2 person toward Stage 3, introduce that person to a Stage 3 employee, who will typically enjoy mentoring people to make them more like himself or herself (but won’t want them to become more successful than him/her). In other words, create dyads (groups of two people). To move a Stage 3 person toward a Stage 4, introduce the Stage 3 employee to two people, i.e., create triads. Note that you shouldn’t do that too early, i.e., don’t try that with a Stage 2 person.

I just loved the concept of triads, and started thinking about triads in academia. (The authors say their concepts apply to higher ed, but the two examples they give are USC’s Steven Sample and Stanford’s David Kelley.) Because a tribe is typically 50 to 150 people, a university is often a tribe of tribes, where each school is a tribe and the Deans are the Tribal Leaders.

Traditional higher ed is organized in terms of dyads: for instance, the student comes to see the professor in office hours, or for a research meeting, or even in class the exchange of information is from the teacher to the student even if many students are in class at the same time. It seems that a lot of universities are at Stage 2 or 3. But triads offer the opportunity to do something quite innovative in academia.

An obvious example is team-teaching, but it is more complicated than having Professor 1 teach the first half of a course and Professor 2 the second half (this would be a succession of dyads.) What has to happen is that the two professors interact with each other so that their thinking and teaching is affected by their interaction.

This would be also true of a student double-majoring who is assigned an advisor in each major: the traditional model would be of two dyads, but a triad model would allow the student to get the most benefit from the double major because the two advisors would also interact with each other. Another triad would be of a student with an advisor in the major and an advisor for the core curriculum (the curriculum every student has to take no matter which major he chooses.)

Because each school is basically its own tribe, there would be great value in having triadic relations across schools: for instance, the Dean of the Graduate School introducing two professors from different schools to each other. Some grant programs require at least two principal investigators, each from a different department, but if it is only those two it is a dyadic relationship. Cluster hires are most meaningful when they can introduce people from different departments and schools to each other. Other triadic relationships would include an undergraduate student, a graduate student and a faculty member on a research project. An interesting comment the authors make is that Stage 4 leaders they have met typically only have meetings with 2 or more other employees: they don’t “waste their time” with one-on-one coffee chats anymore, for instance. Instead, they prefer playing “matchmakers”. The authors also argue that Tribal Leaders should first elucidate what the tribe stands for (values) and what it lives for (noble cause).

I do think the authors could have explored some topics in more depth. For instance, what do you do when management thinks the company is at a Stage 4 when most employees are actually at Stage 2? There would have been a good opportunity to explore resonant vs dissonant/delusional leadership. Also, a key concept in the book is tribal pride, but it is not explored in any detail at all. How many managers think of their work as giving their employees a reason to feel proud about their company? It seems to me that a lot of Stage 2 employees are at Stage 2 precisely because they think their job and their company is nothing to be proud of. A manager who doesn’t try to see the situation from his employees’ eyes, thinking “what are they proud of when they come to work?” is probably not excelling at his job, because he’s not thinking about how to get top loyalty from his people.

The book also has a helpful “cheat sheet” in Appendix A that goes over the key features of each stage and the leverage points of managers to upgrade tribal culture.

Overall, a very worthy read!  

Look what I found! aka some shameless self-promotion

So I was looking through some old boxes and look what I found! My Lehigh teaching evaluations! (At least for ISE 316: Optimization Models and Applications, a senior elective following the "introduction to operations research" course. I also have those for ISE 447: Financial Optimization somewhere but finding both on the same day would have been too much of a miracle.) Here you go. 

And here are some screenshots for those of you who don't like clicking on links. 

 

 

 

 

 

 

 

When I moved to Texas I got rid of a lot of stuff Pennsylvania-related stuff but I did keep the paper evaluations that had nice comments on them, as a memory of the impact I got to have during those years and the fact that students recognized the work I put into the course. (I don't have that kind of impact now, so I am definitely glad I brought this stack of papers to Texas with me.)

 

 

 

Although I wish I had won a teaching award back in the days to reflect that, I realize the odds I was up against, since I only taught seniors and above when others were teaching students earlier on, so the entire group of undergraduate majors would know them and only a third of them would know me when voting time came around.

 

 

 

Also, here are my summary evaluations for ISE 316. 

 

 

 

This may be the summer I finally make a textbook out of my ISE 316 notes! I have been thinking about it for years, but I might actually do something about it this time.

Statement of support and solidarity for the AAPI community

Whereas diversity and inclusion are core values of Southern Methodist University, benefiting every member of our community by providing a wide range of perspectives based on our varied identities and life experiences,

Whereas incidents of verbal and physical violence targeting members of the Asian-American and Pacific Islander (AAPI) communities have surged nationally since the beginning of the COVID-19 pandemic, including the tragic shootings in Atlanta on March 16, 2021 that took the lives of eight innocent people, six of whom Asian women, and have been preceded by years of rising anti-Chinese sentiment in the United States,

Whereas a hate crime is defined by Oxford’s English Dictionary as a “crime, typically one involving violence, that is motivated by prejudice on the basis of race, religion, sexual orientation, or other grounds”,

Let it be resolved that the SMU Faculty Senate stands with the Asian-American Pacific Islander community in vehemently condemning these hate crimes and any hate crimes,

Let it be further resolved that the SMU Faculty Senate resolutely opposes bigotry, discrimination and xenophobia in all their forms, and expresses its unflinching support, staunch commitment and deep appreciation to AAPI students, faculty, staff, alumni and friends in these trying times,

And let it be further resolved that the SMU Faculty Senate calls on all SMU faculty to stand in solidarity with the AAPI community, offer support and compassion to anyone impacted directly or indirectly by these hate crimes, and report any hate crime to the appropriate authorities.

This statement was unanimously approved by the SMU Faculty Senate Executive Committee earlier this afternoon.

Demographics and the Demand for Higher Education

Today's post is about Demographics and the Demand for Higher Education by Nathan D. Grawe, which is 150 pages about a model the author created to estimate the probability of attending college by race, region and ethnicity. The book is very dry and it reminded me of a HBR cover a few years ago, which exhorted readers not to let numbers undermine their strategy. I think by this point, everyone who works in higher education knows the demographic trends are very worrisome, and I appreciated that the author quantified it and attempted to provide a fine-grained description of how the national trends will play out across the country and various groups. But I think overall all the discussion of the numbers (an increase of 3% here! a decrease of 10% there!) masks the big picture.

Here are a couple of thoughts that crossed my mind while I was reading. First, there is all this talk about how undergraduate student enrollment is projected to drop by the end of the decade, and the author does have one line buried in the book on how the end of the 2020s is definitely not going to be a good time to look for a tenure-track position, but there is no talk about what it is going to mean for the number of graduate students admitted in PhD programs, especially programs that tend to support their students through teaching assistantships and that predominantly graduate students to go into the professorate (in other words, the humanities). The size of a doctoral program matters to its ranking, for instance in the Carnegie classification, and so I don't expect graduate programs to decrease their size willingly, but if an administration provides fewer teaching assistantships to reflect decreases in enrollment, that's probably something students should know before they accept. So programs that guarantee funding for a number of years will be at an advantage because of the increased precarity in the others.

But it seems that there will be, at many institutions, an increase in contingent faculty ("not tenured track") and I am not sure if the students who are now about to pursue doctoral studies understand it will apply to them. The doctoral programs with a track record of placing their students in academia will be at an advantage, but I think most of the faculty at top institutions come from a narrow set of schools. So the upcoming demographic changes at the undergraduate level will affect graduate education in some profound ways, at least in some disciplines. Also, if we think about this a little bit longer, universities that face the most financial pressure may well discontinue certain programs (that is one way to get rid of tenured professors too) and I wonder how the rise of Zoom will play out in that respect. For instance you could imagine that local (ailing) universities will ally to each focus on their strengths and allow their students to enroll in the partner universities' courses via Zoom to take courses that are no longer offered at their institution. 

The other comment I wanted to make on this is that the rise of the importance of the high-tuition/high-aid model and in particular the importance of the full-pay student is something I find very worrisome. In academia some of us like to say the student is the product of higher education, and the consumer is society. In teaching we try to do what is best for society by giving the product the right features (right courses etc). But if students start viewing themselves as the consumer of higher education, it changes the dynamics and the time horizon, which gets shifted to the short term. If a student is full-pay, he probably has other avenues to get outstanding work/educational experiences, such as internships through his parents' companies or social networks, since said parents are clearly very successful. Then he may not attend college with the same goals as a student receiving need-based or merit-based aid. He may also get the impression his tuition buys him a passing grade by default.

The interesting part about full-pay students is that universities want to attract them without giving them money (which would go against the definition of having full-pay students) and so they need to attract them based on amenities and experiences, which are expensive to build and maintain, as well as social/alumni networks. I can't end this discussion about full-pay students without being reminded of a comment a colleague of mine made at Lehigh, and I paraphrase: we tend to think that American full-pay students come from families with a lot of money, but certain full-pay foreign students have even more money. Back at Lehigh I used to have quite a few very good Turkish undergraduate students who had been sent to the States because they hadn't done great at the entrance exam to the Turkish universities and their parents were aware of the Lehigh ISE name (often because a parent had gotten a degree from Lehigh). And of course there was the appeal of getting their children away from the country.  

Finally, the author comments on the different outlook for two-year and four-year colleges. I thought when I read this that this called for a different strategic model for two-year colleges. For instance, just like regional and national universities hope to attract full-pay students, there could be a greater emphasis for two-year colleges to attract retirees eager for continuing education that would stimulate their mind, like art history or foreign languages. Overall the book completely ignores not-for-credit education (continuing education especially online). It may represent only a small fraction of revenues today, but it could help alleviate the financial pressures down the road. If two-year colleges focus on retirees and provide an enjoyable experience for them toward the end of their life, they may increase the number of them who will include two-year colleges in their will.  

One last thought (and then I will really be done, I promise) was about the fact that elite universities are likely to respond to rising demand by increasing selectivity. The author writes (p.120) "[elite schools] may opt to pass on some highly attractive Asian American students for the sake of a more representative student body" and (p.121) "Asian Americans passed up by elite schools would almost surely be snatched up by national schools facing decreasing total enrollments." (It may also mean that national schools would see more students transfer out as the top students, aware that they deserved to have been admitted to a better school, may apply to transfer more aggressively after a year or two.) So attracting top Asian Americans may become a strategy for national schools eager to move up in the rankings. 

On rising through the rankings

The rankings in the title are of course The Rankings, aka the love-to-hate but always-so-important U.S. News rankings. I finally came across data about universities' U.S. news rankings over the years; specifically, a website called publicuniversityhonors.com has the U.S. News rankings for 57 leading universities from 1983 to 2007. It shows how the rankings for universities that were in the top 50 at least at some point during that period fared over the years. (Some rankings are not provided for the years where U.S. news only grouped universities by tiers.)  

The reason I am interested in rankings over time is that I am trying to better understand which university have been able to make a noticeable, long-lasting rise through the rankings while others have just bobbled up and down along with the tweaks U.S. News makes to its rankings every year. It is great to have as aspirational peer M.I.T. or Harvard or Penn or Notre Dame or Vanderbilt, but if those universities have been at the top for a very long time, it may be useful to also have as aspirational peers universities that have been able to rise through the rankings - in other words, aspirational peers not only in terms of the ranking they currently occupy or the quality of the education they deliver or the strategic positioning they have in higher ed, but in terms of trajectory path. 

Here is what I found for the period for 1983 to 2007:

• U.C. Berkeley dropped from #5 in 1983 to #24 in 1989 back to #13 in 1990 and stayed in the #20s between 1995 and 2007.
• University of Chicago has gone from #6 to #14 back to #9 in the time period considered.
• University of Michigan Ann Arbor was initially ranked #7 and then stayed in the #20s between 1997 and 2007.
• University of Illinois at Urbana-Champaign was at #8 and then after years of unavailable data reemerged at #45, and stayed in the #40s.
• (there are a lot of public universities that dropped in the rankings during that time frame, probably due to funding issues, so I won't go over all of them)
• Washington University in St Louis seems to have been able to slightly move up in the rankings during that time period.
• Interestingly enough, back in those days Lehigh was apparently in the high #30s, perhaps due to its strong engineering reputation, while it has been solidly in the high #40s for a few years now.
• University of Southern California, like WashU, seems to have been able to slightly move up in the rankings during that time period.
• UNC Chapel Hill slipped in the rankings during that time period. 
• U.Virginia also slipped slightly.
• Penn rose. 

Of course that data only tells part of the story, for instance UCSB used not to be in the top 50 but then managed to stay there very steadily during the time period considered, which is an accomplishment too, and now they are ranked #30. It would definitely be best if the data was available until 2021 rather than 2007.

Anyway, I have to wonder whether some private universities benefited from a widespread decrease in funding (?) of the public universities, pushing high-quality professors and students toward the private universities that were able to take advantage of the moment. Our previous provost often referred to USC and I remember him gifting faculty (maybe at the Emerging Leaders seminar?) The Contrarian's Guide to Leadership. USC continues to be ranked #24 in US News (acceptance rate: 11%, graduation rate: 92%, average cost after aid: $30k), while WashU is #16 (acceptance rate: 14%, graduation rate: 94%, average cost after aid: $27k) and Penn is #8 (acceptance rate: 8%, graduation rate: 95%, average cost after aid: $25k). (In case my readers are wondering about Lehigh's stats: acceptance rate: 32%, graduation rate: 89%, average cost after aid: $31k, and SMU's: acceptance rate: 47%, graduation rate: 79%, average cost after aid: $37k. All numbers courtesy of Google.com.) USC, WashU and Penn are universities I'd love to know more about, even if they are not necessarily aspirational peers, but to better understand how they pulled off their rise through the rankings and managed not to slip back down. 

Just like something disrupted negatively public universities in the US News rankings from 1983 to 2007, the upcoming demographic change in the high school student population promises to disrupt the rankings to the advantage of universities that can embrace this shift. The advantage, I think, will be for universities that have exceptional programs in place to welcome first-generation students and/or students of underrepresented minorities.