The Home Work Divide

For most of my career, home and work were two separate spheres. Being married, I was determined not to let work intrude on home life. I don’t bring my work home with me, be it exams to grade, papers to read, or the emotional baggage that might come from a stressful day. When I’m home, I focus on life outside of work. It’s as if I’ve erected a wall between the two, except that there are small holes in the wall. Stuff does get through. Not much, but it’s not airtight. Most professors do not do this, but it works for me.

Not bringing physical materials home, not checking work e-mail in the evening, not connecting to my office desktop computer via VPN; these all help maintain the barrier. But the mind is not so easily tamed. I might still be thinking of a research problem, an exam question, a conversation with a student, what I need to do tomorrow; these are not so easy to block out. I do try to actively think about other things, and I’m successful for the most part – except for the times when I had lots of administrative responsibilities. Sometimes there are emergencies, and sometimes you have to respond quickly enough so that something doesn’t become an emergency (in the mind of someone else). Maybe that’s why I avoid significant administrative roles when I can.

There are advantages to the separation. Better family life, I think, first and foremost. I feel it keeps my life balanced, since living one’s life consists of many aspects, and I’ve avoided having my identity wrapped up in my career – I think this is psychologically helpful in both the long and short-term. It’s also made me much more efficient when I am at work. I don’t browse the internet for non-work-related things – an easy distraction to fall into. I must finish my class prep the day before my 8am class next morning if I don’t want to do a poor job when I’m with my students. I’m a bit drowsy and not always at my best in the early morning. (Why do I teach at 8am? I hate looking for parking.) I think this has built self-discipline and good habits.

Now Covid-19 has changed the calculus.

Thankfully I’m on sabbatical, and I am a computational chemist, so I’ve not had to make large-scale adjustment like many of my colleagues. I can do my research from home. My laptop screen is smaller, and I have to scp (secure-copy) files back and forth because I’m running the GUI (graphical user interface) locally, but I run production jobs on my high-performance computing cluster via VPN. I can also connect to my work Desktop if I need files related to teaching or service. There’s a tiny hassle going back and forth; I don’t have my large white boards in my office and research lab; and there can be different kinds of noise, interruptions, or distractions at home.

I was not very productive the first week working from home. Finding the right space (I don’t have a home office and never needed one) and just getting used to the strange idea of doing work in my home environment took a little getting used to. Things got better the next week and research was chugging away. Meetings are less efficient via videoconference, but not terrible. Again, I’m on sabbatical so I have few meetings. There’s lots more e-mail, chunks of which I can ignore, again because of sabbatical. I am making it a point to stay connected to my academic advisees and have written them a couple of e-mails since campus “closed down” and all of us dispersed. It’s registration time and I’m enjoying interacting with my students a bit more this past week, and there’ll be more next week!

I’ve had the luxury of thinking about how to structure my classes should similar shutdowns take place, while not teaching classes this semester. While I will be making my classes a little more robust to this sort of interruption, I very much hope that we will be back to in-person classes in the Fall. That’s why I chose to be at a liberal arts college, and it’s an important factor of why students chose to come to my institution. I’m also looking forward to dividing my life again between home and work. Maybe it’s because that’s what I’ve been used to for so many years, and change is difficult. My home-work divide might be rather old-school, but I think it has value. Most of my students when they go out into the workforce will not have that luxury. I’m glad I can work from home if needed. Many other jobs do not have that luxury either.

One good thing from all of this: it’s reminded me to be thankful for the choices I have!

Pandemic: The Board Game

You’re stuck at home because of a worldwide pandemic. What to do? Play Pandemic, the board game! I’m an old hand at Pandemic with 70 games under my belt since 2009. I guess I have plenty of experience figuring out how to save the world! Not, that the team always succeeds – it’s a cooperative game – but I’m pretty sure I have more wins than losses under my belt.

Here’s a blurry picture from a game earlier this month. It’s early in the game: Turn #2, with an eerie beginning (from randomly drawn cards during setup): Huge number of cases in East Asia (red cubes) centering in China, and some in Europe (blue cubes) including Milan. Humph. Looks like Covid-19. Well, maybe a mash-up with SARS given the outbreak in Toronto where one of the players has reduced the disease down to one blue cube. Well, there’s also the outbreak in Kinshasa (yellow cubes), not quite in the historical location for Ebola.

In Pandemic, players zip around the world trying to reduce disease outbreaks while racing to find a cure for each of four regional diseases (color-coded). Cards turned over in the top right infection deck cause (disease) cubes to be added to global cities. Player cards (lower right) serve several purposes. They can be used to fly between cities, build research facilities (you start the game with the CDC in Atlanta represented by an ecru house), and to find cures. A player needs to collect five cards of the same color, then get to a research station to find a cure. This is not easy because there’s a hand limit of seven cards; players can pass cards to one another but only under very limited circumstances.

Spread throughout the player card deck are several Epidemic cards. This is the focus of the excitement and game tension that makes Pandemic (in my opinion) a pleasure to play! An Epidemic does the following: First the Infection Marker is moved up one step; this marker determines how many infection cards are drawn each turn. Next the bottom card of the deck is drawn and three cubes added to that city, representing a new nasty proliferation of the disease. Finally, all the cards in the discard are shuffled and placed back on top of the infection draw deck, which means the cities that recently added cubes are going to have more! Why would this make you shudder? Once a city has three cubes, if its card is drawn again, an outbreak happens – a cube is added to each adjacent city! And if an adjacent city already has three cubes, the outbreak cascades!

Clearly this is a race against time. Players win if they can discover all four cures. But they must do it before the player draw deck runs out or eight outbreaks have taken place, in which case the players lose! Disease wins and we are overrun.

A nice feature of Pandemic is that each player has a specific skill. The medic (white pawn) can remove all disease cubes in a single action; normally only one cube is removed per action. The researcher (not in the pic) allows discovering a cure with four cards of the same color rather than five. The construction expert (green pawn) can build a research station in any city he/she is in for one action. The two other roles involve helping move players around the board and passing cards to each other more easily. Pandemic is for 2-4 players with five of these “roles” to choose from.

Regulating the game difficulty is a matter of shuffling the appropriate number of Epidemic cards into the player deck. The game recommends four Epidemics in your introductory game. I’ve found this works well when teaching the game to new players! It’s usually winnable for newbies but nail-biting to the end! With a few games under your belt, you’d want more of a challenge – the standard five Epidemics. And if you want an extra-difficult game, you can include six Epidemics. (The game does not come with a seventh card.)

Interestingly, in my first 70 games, I had not attempted six Epidemics. After getting used to the standard game, I started including the expansion, Pandemic: On the Brink. Besides providing eight more role cards for variation, the expansion ups the ante in three ways. First, you can replace some or all of your standard Epidemic cards with Virulent Strain Epidemic cards that add more difficulty (each card has its own twist). Second, you can have a fifth disease (purple cubes) that show up as a mutation. Third, you can add an antagonist player, the bioterrorist. I’ve played the first two aplenty, but have yet to introduce the bioterrorist. I very much enjoy the expansion; I feel it breathes new life into the standard board game.

Given Covid-19, I needed a challenge: six standard epidemic cards. Lost the first two games. (The picture is game #2.) Won the third! Not sure if it was a fluke. Have yet to get the game to the table again… except my new distraction is revisiting Bios Megafauna.

The Size of Atoms

How large is an atom? It depends on the element, but generally atom sizes are in the Angstrom (10^-10 meter) range. Too small to see with the naked eye, or even a high-powered light microscope. You can see one with an electron microscope, provided you believe the images you’re seeing on the computer screen.

You could calculate (using quantum mechanics) the most probable distance of an electron from its nucleus in a hydrogen atom. We do this in P-Chem and the answer comes out to be 0.53 Angstroms or 0.053 nanometers. But the electron could be much further from the nucleus. How much further? Don’t know. The probability of finding the electron approaches zero asymptotically as a function of distance, so we know there’s little chance of finding it far away. You could pre-determine a cutoff, by arbitrarily defining an electron’s orbital as enclosing a 90% chance of finding it. (That’s what we tell G-Chem students.) While that’s easy to calculate for the hydrogen atom, it’s not so easy for other elements because the Schrodinger equation can no longer be solved exactly. (Despite beautiful pictures you might see!)

Yet in G-Chem, we happily discuss periodic trends, and the first one we tackle is atomic size! We discuss the factors that affect size. There’s inevitably a beautiful table in the textbook along with actual charts and actual numbers. Clearly some scientists must have measured these to two decimal places of precision! Some are simply misleading – a simple Google search yielded the following colorful graphic among many. There’s one very clear glaring error you should notice if you’ve taken G-Chem!

But if you read the fine print (and hopefully your chemistry textbook discusses this), measuring the atomic radius is tricky. That’s because, with the exception of the noble gases, it’s not easy to encounter atoms hanging out by their lonesome selves, for you to “catch” and measure. Most atoms form chemical bonds with other atoms, and how close they get depends on multiple factors. The whole business turns out to be rather complicated!

The latest iteration comes from a 2016 article by Rahm, Hoffmann, and Ashcroft (Chem. Eur. J. 2016, 22, 14625-14632). They provide high-level quantum calculations along with a rational cutoff for the electron density. Here’s the abstract.

After going through the history of previous charts and tables, they make the case for their version, and provide their own chart and table. I’ve re-created a version below. 

The most interesting thing that jumps out is that the largest atoms are (excepting Li) in Group II. In G-Chem students learn that atomic size decreases across a row so they would expect the Group I elements to be the largest. Why the difference in this study? Part of it has to do with the way cutoff density is defined, and part of it has to do with Pauli exchange and repulsion. The Li vs Be exception is likely related to limited shielding from the core 1s electrons.

There are other nice features of the paper. Their cutoff choice fits very well with experimental data for Van der Waals radii of noble gases. We see the expected contraction due to the transition metal block, and thus the small corresponding rise across the row when we get back into the main group p-block. There’s a particularly nice correlation to electron configurations to explain palladium’s smaller-than-expected radius (see figure below) coming from the relative differences in s versus d contraction.

There are several other interesting tidbits including trends and discussion of cations and anions. Overall, the paper is an interesting read and something I could potentially incorporate into a quantum or inorganic chemistry course. It’s also a reminder how much we don’t know, even about seemingly fundamental things, and that the periodic table is full of surprises. Kinky ones!

Far From a Yawn

If there was a book I would give a young student interested in medicine, it would be Bill Bryson’s The Body: A Guide for Occupants. Written in a lighthearted conversational manner, it is chock full of interesting factoids illustrated by engaging vignettes. Bryson is a master story-teller.

Reading The Body amidst the Covid-19 pandemic has further heightened my senses towards all things human-health-related. When reading the chapter on the immune system, I found myself looking up more details on the internet. The chapter on bipedalism and exercise reminded me to get up, stand, and walk, more regularly; my sedentary seated in front of a computer lifestyle is a problem. I thought about what I eat as I read the chapter on digestion. And of course, I found anything on chemistry interesting; the chapter on endocrinology is appropriately titled “The Chemistry Department”.

There are many historical anecdotes sprinkled throughout the text. Stories of discovery are interesting in their own right, but what really struck me in these accounts was how difficult it could be for others to accept these discoveries. There’s the oft-told story of Semmelweis, who “discovered” the importance of doctors washing their hands properly before surgery, but was disbelieved by many of his contemporaries who couldn’t imagine that they would be the cause of patient infection. Sometimes science was moved forward even when the initial ideas were wrongheaded; for example Atwater’s caloric measurements leading to his proposal that we should all eat lots of meat since it was a superior fuel source!

Bryson debunks some seemingly “well-known facts”. You’ve probably heard that bacteria cells in our body outnumber our own cells ten-to-one. Apparently, this was from a 1972 paper that was guesswork, and that in 2016, scientists making careful measurements found the ratio to vary between two-to-one and one-to-one. Not only had I believed this for years, I also believed that drinking coffee and other diuretics make me lose water overall. Turns out that while “not the most wholesome of options for liquid refreshment, they do make a net contribution to your personal water balance.”

While Bryson illuminates the reader with history, discovery, and many factoids, he also points out, in most chapters, how much we don’t know. We know a lot about the human body, but it’s amazing how much we don’t know. For example, sleep is still mysterious although we know a lot about it. Rising cases in asthma or Crohn’s disease, while attributed (sometimes speculatively) to a variety of factors, remain a bit of a puzzle. Or consider the uvula. We think it helps with food direction down the throat, maybe aids in producing saliva, or helps with creating certain speech sounds including snoring sounds; but when folks have it removed (due to snoring), it’s unclear what else is impaired.

I’ll end by quoting Bryson as he describes one of those things we don’t know, to illustrate his engaging and whimsical writing.

Finally, we should say a word about that mysterious but universal harbinger of weariness, the yawn. No one understands why we yawn. Babies yawn in the womb. (They hiccup, too.) People in comas yawn. It is a ubiquitous part of life and yet what exactly it does for us is unknown. One suggestion is that it somehow connected with shedding excess carbon dioxide, though no one has ever explained in what way. Another is that it brings a rush of cooler air into the head, thus slightly banishing drowsiness, though I have yet to meet anyone who felt refreshed and energized after yawning. More to the point, no scientific study has ever shown a relationship between yawning and energy levels. Yawning doesn’t even correlate reliably with how tired you are. Indeed, when we yawn the most is often in the first couple of minutes after rising from a good night’s sleep, when we are at our most rested.

I highly recommend The Body. It’s far from a yawn!

Higher Education 2030, Augmented

What might higher education look like in 2030? Since I’ve lately been thinking about forecasting, and in light of Covid, I decided to read Academia Next by Bryan Alexander. The author maintains an interesting blog on future trends in higher education; I’ve been following it for five or so years.

The first part of Academia Next discusses present trends relating to higher education: demographics, technology, economic, cultural. In the second half, seven scenarios are envisioned for 2030.

·      Peak Higher Education

·      Health Care Nation

·      Open Education Triumphant

·      Renaissance

·      Augmented Campus

·      Siri, Tutor Me

·      Retro Campus

While written before the present Covid pandemic, one scenario explores what might happen if health care became the largest sector of the U.S. economy. (The book is primarily about the U.S., since that’s where Alexander is based and he’s been following national trends closely.) Another scenario (Siri, Tutor Me) envisions technological changes related to automation, cloud-based robo-tutor among others.

But the scenario that had most of my attention was Augmented Campus, not because it was the most likely, but because I found it intriguing to ponder. For a glimpse of what immersive and widespread augmented reality might feel like, I highly recommend Rainbows End by Vernor Vinge. (It was Alexander’s blog that alerted me to this excellent novel!) In any case, here’s Alexander’s setup to the chapter:

In 2030, American higher education is divided by digital strategy. One stratum is entirely online, with classes offered digitally by either older, bricks-and-mortar institutions or newer and wholly online enterprises. Another type of institution consists of colleges attended largely by commuter students who attend classes but don’t live on campus. The third portion of academia is residential and predicated upon face-to-face learning: for many it is the iconic representation of post-secondary learning. This is the familiar liberal arts college or state university, primarily teaching traditional-age undergraduates. Its engagement with the digital world has expanded, taking advantage of emerging technologies. We can call it the Augmented Campus.

I do think that higher education will continue to divide into at least two categories: the haves and the have-nots. I’m expecting the latter to be all-online as a cost-cutting measure. This is not because online education is always cheaper – my opinion is that online education can be excellent, but it might actually be more expensive to mount a very high-quality program. However, if you’re willing to sacrifice on quality, then there are a variety of ways to slash costs from physical buildings to human personnel. And with diminishing state support, I’m not sure community colleges (Alexander’s second stratum) will be able to survive while maintaining quality without raising fees given the population they are serving. Face-to-face learning may disappear for the have-nots.

For the haves, the Augmented Campus is idealist and idyllic. Limitations you face today can be augmented. Reality is augmented – you might call it super-reality. The description Alexander provides is sci-fi-ish cool with a blend of what’s hot in education lingo: hybrid learning, active learning, creativity, learning through play, leveraging technology… you can read more in his book. The physical space of the classroom and its furniture are no longer strictures. You could even have class outdoors when the weather is nice! As a chemist, I’ve been thinking about labs – especially since Covid has resulted in less-than-desirable alternatives. The augmented lab space could do so much more!

But all this augmentation could be distracting; a sensory overload. I’m not sure I could manage it, but perhaps this is because I had my formative years in an analog old-school environment. For kids used to multiple information streams, with video, textchat, and who-knows-what-else all going on at the same time, augmented reality education could be fully immersive if well-designed. This will further perpetuate the divide between the haves and the have-nots. A have-not student would be truly a fish-out-of-water at the Augmented Campus.

I would personally prefer the Retro Campus. Back to old school, low-tech. I think there’s little chance of that happening, short of an eVirus pandemic that significantly curbs the reach of technology. That might be a worse disaster given how much of our lives (in the first world) are tied to technology. I wonder what’s next.

Exam Pre-Mortem

Continuing my “disaster” reading the past week, I finished Meltdown over the weekend. Like common popular books published in the past decade, it’s a light and quick read, styled with the usual vignettes adored by journalist-types, with a smattering of self-help. I prefer the heavier, more theory-laden, analysis – hence my choice of reading Normal Accidents before Meltdown. The latter draws quite heavily from the former, making my read quicker, however it has thought-provoking moments. One of these is the Pre-Mortem.

We’re more familiar with the post-mortem – dissecting what went wrong after the bad event. The pre-mortem attempts the analysis before, rather than after, things go poorly. The example in Meltdown was asking students at a business school what can be improved, but the experimental design cleverly had two different prompts. One was the typical “what do you think can be improved?” (more elegantly phrased than my paraphrase) and generated the range of standard student views one might see on such evaluations. The other prompt asked the students to imagine they were alumni two years after graduation, learning that the business school was doing poorly and suffering in reputation. The question was to imagine: “What went wrong?” Essentially, a pre-mortem.

As you might have guessed, responses to the pre-mortem question showed larger variation in scope and increased depth of analysis. Some ideas were more far-fetched, but interesting to consider nevertheless, especially if you were doing some risk-analysis planning. A number of years back, when I was faced with a very difficult and significant life-career decision, I did a pre-mortem – except I didn’t recognize it as such at the time. It was not the only part of my analysis (some forecasting was required), but it was an important piece. For a couple of years after, I regularly debated with myself as to the wisdom of my choice, but I can now look back and see that it was the right thing to do at that juncture. As a planner-administrator, I still regularly do forecasts and analysis, however Meltdown reminded me that I should employ the pre-mortem more often.

As a first step, I wonder if I can use the pre-mortem to help my students. Here’s the setup I’m thinking for a pre-exam writing assignment. “Imagine you just got back your exam and you earned a D. You were both surprised and disappointed. Looking back through your exam and your preparation, what went wrong? Discuss whatever comes to mind.”

Would this help the students better prepare for exams? I hope so. I already have “how to study for this class” tips on my syllabi (including ones from previous students), and I’ve built in some formative assessment approaches to prepare students. I’ve now taught a full year of G-Chem where the students used take-home exams with post-exam self-annotation. In P-Chem, I’ve encouraged group study and exam question forecasting, and tried to help the students annotate their problem sets with some “what went wrong” self-reflection. However, these are all post-mortems.

I’d like to think that the post-mortem on the first exam (if a student does poorly) helps in preparing for the second exam. (My classes typically have 3 or 4 exams before the Final Exam, so the first exam shows up quite early in the semester.) But upon reflection, I’m not so sure. There are students who do well on the first exam but then bomb the second (for a variety of reasons, overconfidence being one). There are students who come by my office asking how they can do better on the next exam; I typically turn the question around and ask the student to reflect. And there are students who don’t darken the door of my office, some proportion of whom probably do zero post-mortem on a previous exam. I also hear students in the hallways talking about getting back exams (from other classes), and the reflection is often shallow to non-existent.

So I hope the pre-mortem will help. Maybe I should do it more than once. Maybe it will also help avoid a meltdown. One can only hope.

Complexity, Coupling, Catastrophe

Watching different countries and governments respond to the Covid pandemic has motivated me to (morbidly) read more about disasters. To see how humankind dealt with the unknown unknowns of epidemics or pandemics, I recommend The Pandemic Century: One Hundred Years of Panic, Hysteria, and Hubris. Published in 2019, the author Mark Honigsbaum takes you on a case-study-style tour from the Spanish Flu to Zika (with “parrot fever” in between). The chapter on SARS is particularly interesting, given the present crisis – the same hysteria and hubris echo strongly. Are we learning the lessons of history? Or are we simply being human?

But that’s not the book I want to discuss in today’s post. While breezing through Pandemic Century, I’m also slowly working my way through Normal Accidents by Charles Perrow. The title is misleading, given the impetus for this 1984 book was the analysis of the Three Mile Island nuclear power plant “disaster”, it’s anything but normal. Perrow explains that “normal” here means “inherent to the system”, and that this is an ever-present danger in interactively complex and tightly coupled systems. Things will go wrong. It’s not a question of if but when.

Perrow is a sociologist who studies organizations. I’ve dabbled in this literature as part of my interest in complex systems. Sure enough, garbage can theory makes its appearance, but my focus today is on Complexity and Coupling, two key factors Perrow introduces in his analyses. The title of today’s post comes from the title to Chapter 3 in Normal Accidents.

Complex interactions have numerous subsystem linkages, some seen some unforeseen, multiple paths, feedback loops, and “connections… multiply as other parts or units or subsystems are reached”. They are distinguished from linear interactions (serial, one thing after another), but they are not labeled non-linear, because there are non-linear systems that are comprehensible. Complex systems, according to Perrow, have hidden interactions reducing their comprehensibility. He argues that this is particularly true of processes involving “transformation”, i.e., they “can be described, but not really understood”. He groups nuclear power production, chemical plants, and recombinant DNA technology in this category. Interestingly, these are all underlying processes where you can’t quite “see” what is going on.

Systems can be coupled tightly or loosely. Tightly coupled systems can respond quickly to changes in lockstep; they tend to be classified as more efficient. Loosely coupled systems, on the other hand, “allows certain parts of the system to express themselves according to their own logic or interest” but are more robust to recovery if something breaks. Perrow goes into detail with many examples of what it means to be tightly or loosely coupled not just from an engineering perspective but also from human organizations.   

Besides nuclear power, Perrow analyzes several systems of interest to explore the landscape of complexity and coupling. These include petrochemical plants, aircraft design and air-traffic control, marine accidents, dam and mine accidents, outer space exploration, and recombinant DNA technology. He then places these, and other systems, along a two-axis chart as shown below.

Universities are in the bottom right quadrant. They are interactively complex, but very loosely coupled. Or at least that’s how Perrow views them in 1984. (You can also see trade schools and junior colleges on the chart.) That seems fair. Universities have, in Clark Kerr’s words, become “multiversities”. There are many stakeholders, seemingly divergent goals, and seemingly slow-to-change behemoths. Traditional universities are not known for their nimbleness. In 2020, there is even more multi to the multiversity. The entrance of educational upstarts into an increasingly competitive space has brought about higher education’s own hubris and hysteria. As the “crisis” heightens, university administrations increasingly insist on more centralization to avert seemingly looming disasters. Covid-19 will accelerate the trend towards the All-Administrative University. Never let a good crisis go to waste.

Perrow’s primary polemic is highlighting the serious dangers of nuclear power plants and weaponry, from the technical but also the organizational point of view. These sit in the top right corner of his chart. It has to do with how these systems could and perhaps should organize themselves to deal with the inevitable crises, black swan events notwithstanding. Should these organizations centralize or decentralize? That is the question! Let’s take each of the quadrants in turn.

TOP LEFT: In interactively linear and tightly coupled systems such as dams, power grids, and rail transport, centralization is recommended. You want maximum efficiency out of these systems, and tight-coupling helps with that. Also since the system is not complex, it is possible to respond quickly and concertedly through being centralized. Basically, you want centralization for tight-coupling, and centralization is “compatible” with non-complex systems.

BOTTOM RIGHT: Let’s look at the opposite quadrant, applicable to mining, university, R&D. When you have an interactively complex system, Perrow argues (with the many examples in his book) that decentralization is desirable. When problems crop up, it’s often advantageous to have frontline people (preferably with experience and expertise) be highly involved and empowered to come up with solutions. Loose coupling allows for this, and information can move back and forth between operators and management without severe time crunch constraints.

TOP RIGHT: The problem in this quadrant is that centralization is needed to cope with tight coupling. But interactively complex systems are better solved through decentralized approaches. Thus, neither approach is optimal and Perrow predicts that the organizations of these systems will ping-pong back and forth with a mixture of approaches that will constantly evolve depending on what disaster hits. Organizational structure thus varies reactively.

BOTTOM LEFT: Since the interactions are not complex, and they are loosely coupled, it doesn’t matter which you choose – both centralization and decentralization are compatible. Perrow notes however that “elites” (the bosses) tend to favor centralization over decentralization in most cases. That’s no surprise. One theme that Perrow emphasizes as a sociologist is power relations within the system.

In my recent post on collegiality, my bias towards decentralized “organic” subunits within the university system is apparent. As a mid-level administrator, one of my tasks was to fend off higher-administration from its tendency to recommend centralized solutions in a one-size-fits-all approach. Financial fears have made these tendencies more acute. Administrators want more tight coupling. At the same time administration is increasing in scope and manpower with increasing interactive complexity of the multiversity. This combination will move universities towards the upper right quadrant. More fail-safe systems must be put in place, further increasing interactive complexity. It’s a positive feedback loop.

Even if we were not in Covid crisis, and the university isn’t like a nuclear power plant with catastrophic potential, the move towards increasing centralization and top-down approaches is very worrying. And it’s all in the name of supposedly running leaner, better, more efficient, whatever. Humans are involved. I close by quoting Perrow.

Organizational theorists have long since given up hope of finding perfect or even exceedingly well-run organizations, even where there is no catastrophic potential. It is an enduring limitation – if it is a limitation – of our human condition. It means that humans do not exist to give their all to organizations run by someone else, and that organizations inevitably will be run, to some degree, contrary to their interests. This is why it is not a problem of capitalism; socialist countries, and even the ideal communist system, cannot escape the dilemmas of cooperative, organizational effort on any substantial scale and with any substantial complexity and uncertainty. At some point the cost of extracting obedience exceeds the benefits of organized activity.

P.S. As one studying the origin-of-life, I've also been reflecting on the interplay of complexity and coupling as I learn biochemistry! More about that in a future post.