There’s a model for that, and here it is.
Let’s say you are a part of executive leadership at a university of higher education. You and your colleagues have noticed that some departments just aren’t bringing in the cash. You want them gone, but they are departments that have some level of support–be it from donors who want to see the “classics” of liberal arts education stay in the curricula or from those who want to keep departments built around the idea of enhanced diversity.
If you shut them down, you risk looking like a meanie. That’s not good for public relations. You need a way to make the departments disappear without looking like the bad guy. You need to make it seem like the departments themselves are to blame. One way is to make it seem like those departments have not stepped up to serve student needs.
You already know that the departments you want gone have smaller class sizes than your cash cow departments . In a light-bulb moment, you think, ‘If we set a minimum class size, we can have our cake and eat it too!’
A minimum number of student clients taking classes from any department will be seen as a measure of whether the department serves student needs. If some departments fall below a set number, then the public and your donors can’t blame you for cutting them because “they aren’t stepping up.” You can even give them a chance by setting a minimum number. If they don’t hit that number, then you come out golden!
But how do you set that number? What if it’s too low? Those pesky departments may hit the number. If you set it too high, then it could look like you are loading the dice. You need to set a minimum number that is right at the level that the departments you want gone could never hit. Well, at least not all of them could hit, so you could still cut a bunch of them. That’s a good start for an executive leader ).
What you need is a model .
You know that models begin with a purpose . Your purpose here is to determine a number subject to constraints. Let’s call it “the magic number” and give it the symbol M. The number represents how many students, on average, need to take classes from a department to justify the department’s continued existence. The constraints are several. First, the number must appear fair and achievable to the outside world for all departments. Second, the number has to be unachievable by departments that need to go. That sets the model goal – the output you seek is the magic number M.
The next step is to ask what the output depends on. Here is a list:
N: The number of students at your university
Dw: The number of departments you want to keep
Dl: The number of departments you want gone
Cw: The number of classes offered per semester by the keep ’em departments
Cl: The number of classes offered per semester by the loose ’em departments
T: The total number of classes any student takes per semester
Pr: A popularity ratio that expresses the ratio of majors in Dw versus Dl
The popularity ratio, Pr, is something executive leadership can control during admissions. Admit more students who declare majors in your Dw departments. You can also promote your Dw departments during student orientations as the ones that provide the best student outcomes regarding higher-paying jobs and financial mobility. Students will preferentially take more classes in their majors . This allows us to assume that Pr will be a good measure of the ratio of students taking classes from Dw versus Dl.
Now, we are ready to construct the model. The availability of students to take classes in a semester will be N x T.
- The number of students taking classes from your slacker departments, if they could hit your magic number M, would be Cl x Dl x M.
- The number of students taking classes from your productive departments, noting that the numbers will be a factor of Pr above the slacker departments, would be Cw x Dw x Pr x M.
- We can now determine the highest M value your Dl departments could, on average, possibly hit. The model gives us M = (N x T)/(Cl x Dl + Cw x Dw x Pr).
Let’s run through an example. Assume the following values:
N = 5000
Dw = 30
Dl = 20
Cw = 10
Cl = 10
T = 4
Pr = 3
With those values, your magic number is 18. So, to set the scheme in motion, you announce that all departments must, on average, have 20 students in the classes they offer per semester .
That is hardly unreasonable. If a department can’t hit that fair and (seemingly) achievable number, then it’s not serving student clientele, and you have no choice but to cut the department.
But wait, that’s only step one. Let’s move on to step two of the scheme.
Some of your slacker departments might hit M=20, but it will be impossible for all of them to achieve that goal. So, let’s say that enables you to cut half of them (with no blame placed on executive leadership as the departments could not pass a reasonable bar). It would be a waste of facilities to cut some departments and not allow others to grow because, after all, you have cleared out some space, which was wasted space .
So, you grow your Dw departments to offer 12 classes per semester . Using the example numbers, the maximum your Dl departments can hit, even with fewer of them, is 17 students on average in the classes they offer per semester.
It will be clear to the public and your donors that those departments are doing even worse than before . You even gave them added time to show they could step up, and they chose not to. Clearly, they are to blame. You have no choice but to cut them.
Your Dl departments are financial drags on your university, but they may have a clever person or two within them. They may see strategies that could allow them to hit your newly imposed expectations. Maybe they choose to downsize so they have fewer classes, which could make it easier to clear your M requirement. Maybe they have one or two faculty members who can draw large class numbers so that one large class offsets their other classes.
Yet, no worries! Our model has it covered. The big popular class strategy runs into a practical limit. Universities do not have infinite space, so class sizes will be limited by facilities restrictions, which are different for different departments and can be adjusted over time (e.g., if a department is dropping in numbers, then its facilities should clearly be reduced to minimize wasted space).
You only need to limit facilities and space on the departments that did not meet your initial M requirement. That will be fully justified as your performing departments should be rewarded with more space, and space is finite.
The downsize strategy also runs into a practical limit based on the definition of a department. The number of classes a department offers is proportional to the number of faculty within a department . Clearly, a ‘department of one’ is not a department. Is a department of three a department? Arguably so.
Who can make that decision? Whose job is it to make the decision? Well, that’s clearly part of the duties assigned to executive leadership. This adds the following to our model:
Cm: The minimum number of classes a department must offer to be a department
Our main model equation allows you to set that number in a way that will not allow most of your Dl departments to survive by progressive downsizing. Brilliant!
If some departments do hold on via downsizing, you can turn that to your advantage. It makes sense to group them into a single department (they are shrinking out of existence anyway). That new department will satisfy any remaining whiners who believe that a liberal arts education should go beyond vocational training. Just point them to the Department of Art, Philosophy, Ethics, History, Classics, Literature, and Ethonstudies. Voila!
Doing that will also satisfy customers who want to be at an Ivy League-esq university that offers the ‘classics,’ and ranking agencies will be able to check off the ‘well-rounded education’ box. It’s a win-win!
With that done, it’s time to move on to making tenure a thing of the past and developing plans for how AI can replace faculty and staff in your Dw departments. They can’t all be winners, can they? 
Notes and Footnotes
The authors of this article teach scientific modeling at a prestigious university, and we have worked to help start-ups develop business plans using modeling methodologies. This essay offers our modeling insights for free, but know that it will be paywalled soon, so act quickly.
 Your productive departments are the ones that generate commercial profits for your university from marketing their research products. They are the ones who can connect to corporations and/or start-ups to increase the financial footprint of your university. They are the departments that executive leadership promotes, offering students the skills they need to get well-paid jobs (your students deserve the highest-paid jobs – maybe even executive leadership positions). These are the departments ranking organizations, like U.S. News and World Report, score high on student outcomes and income mobility metrics and those touted as offering financially desirable majors. That sort of recognition – that attention – is a cash equivalent in the Attention Economy.
 If you happen to be at a university that still refers to administration as ‘administration’ and to those who work in administration as ‘administrators,’ then you need to correct that. The new way to speak about administration is to refer to it as executive leadership. In keeping with the new speak, all the vice presidents, assistant vice presidents, provosts, vice provosts, assistant vice provosts, deans, dean-lettes, etc., are spoken of as executive leaders. Titles matter (they have historically set a necessary level of hierarchy and respect), so choose your titles wisely. You can also use the thought leader title on your web pages if you like. Those who are not leaders are followers, and that’s how we think of faculty and staff. You and your executive leadership colleagues know this. Still, in public, it is best to refer to non-leaders as ‘your team’ (even though leadership runs the university, you still need workers and followers to operate a university, so don’t alienate them until you cut them loose).
 You could also go to a consulting firm that specializes in ‘efficiency’ and have them do a multi-year study of your university to reach the conclusion you want and help justify it (i.e., “Hey, a prestigious consulting company reached the decision. It wasn’t our call.”). Some companies specialize in those services. They will provide you with multi-page reports with graphs, projections, charts, and excessive details. If you go that route, cool! You will get the desired result. You don’t need to pay those ridiculous fees and spend your valuable time reading reports and listening to presentations. Instead, consider our simple, cheaper, and less time-consuming scheme based on a quantitative model.
 Some consulting companies might also offer you models. To justify high fees, they may attach fancy names to them like “AI-Generated Strategy Scenarios.” Don’t be fooled! These are, at their core, representations of some aspect of reality. Stated another way, they are models. Sure, one can feed model equations and associated assumptions into a computer and add sprinkles of artificial intelligence (AI), big data analytics (BDA), and machine learning (ML) to make it look like what’s offered is beyond the understanding of clients (so they are happy to pay big money – abbreviations like AI, BDA, and ML add to the mystique). Again, don’t be fooled! The core level is still the model equations and the model assumptions themselves. That’s the prize to keep your eye on. Our scheme is focused on that prize free of bells and whistles and ribbons and bows that make the model look like more than it is.
 A rule of thumb is that if, for example, students take four classes per semester, then three of those, on average, will be related to their major. The other class will be part of what is often called “breadth requirements.” That falls under the idea that an education is not the same as vocational training, and students should gain “general knowledge” – also referred to as a “liberal arts education.” For a long time, that antiquated idea has allowed your Dl departments to drag your institution down. Even if those departments don’t have a lot of majors, they will argue that what they offer has value for educating future citizens – instead of just generating future workers. Executive leadership knows that’s naïve thinking in a neoliberal world. Yet, some donors and some of the public buy into that nonsense. That has allowed your Dl departments to slide by without bringing sufficient financial return to your university. Our model will show that imposing proper expectations on these departments can end that.
 Connect this announcement to transformations at your university to enhance fairness and incentivize all departments to be their best selves. Those who see the incentives will increase their student numbers, and those that don’t need to be penalized in the interest of transparency. Make it clear that transformations can’t be delayed. Create a palpable sense of urgency for all departments, faculty members, and staff to deliver on the promise of your bold new transformation plan.
 Herein lies another beauty of our model. Adding any new facilities, space, or support is unnecessary. Our model creates a zero-sum competitive game among your departments. The departments that generate the most revenue gain at the expense of those that don’t. This competition is framed in terms of incentives and transparency . Your department chairs will be eager to show that their departments want to step up and will not see the zero-sum nature of what’s going on until it’s too late. Academics are prone to wanting to be good university citizens and, as such, tend not to openly question leadership decisions (i.e., they want to be team players ). The internal competition our model initiates could even save you money – money that could be used to pave the way for enhanced executive leadership.
 The number of classes a department offers is proportional to the number of faculty within a department (all faculty must teach at least one class a semester to justify a paycheck). Since you have cut some of your Dl departments, you need to make sure students have an adequate number of classes to choose from, so you need to up your faculty numbers (and you have cleared some salary by sacking faculty from the Dl departments that underperformed). It only makes sense that the high-flying departments should grow based on the students served. Why would anyone grow the remaining Dl departments that only barely cleared your reasonable M requirement? The example numbers highlight an added value of our model. For those numbers, you had to let 100 faculty go (it wasn’t your choice; it was theirs). You replaced them with 60 new faculty. Clearly, executive leadership is being fiscally responsible in terms of new hires.
 In modeling terms, our scheme lets you put your Dl departments into a positive feedback loop. The more they try to take action to fight an effect, the worse it gets. The loop nature will make it appear to the outside world that no one has manipulated the result – it’s just the departments’ actions, or lack thereof, that are feeding back on the departments’ demise. Positive feedback loops allow for death spirals that can be initiated with only a small, seemingly insignificant push. Death spiral is an apt term for our model’s designed effect on your Dl departments, all initiated by what appears to be a reasonable policy requirement – a small change – that could never be seen as leading to an inevitable demise. (You’re welcome.)
 Concerning tenure, think about how much more nimble and agile your university could become in its competition with other universities if you removed tenure so that you could fire faculty at will. Consider the benefits of adding job precarity to the competition our scheme will initiate amongst your faculty . Imposed internal competition and precarious job security will get your faculty to work harder to outdo each other so as to hold onto a job for a little longer – a sure formula to enhance your university’s financial competitiveness. Not only would it up your financial competitiveness, but removing tenure would also provide a new leadership tool. A tool that would allow you to suppress any dissent about new transformative measures and bold actions proposed by executive leaders ip. Using AI effectively will allow you to replace the talents of overpaid faculty and staff with a more efficient automated system.
Parting Observations: There are limits to the talents that machines can easily replace. The obvious ones that cannot be replaced are the talents of executive leadership.
Cover photo, Mark Tansey, The Triumph Over Mastery I