In looking at the COVID-19 response globally and here in the United States, I can’t help but remember a concept I learned in calculus class around local versus global minimums and maximums.
While I don’t remember any of the math, (my interpretation of) the concept has reappeared repeatedly in my client work and again in looking at how the U.S. is responding to the COVID-19 pandemic.
In large companies, when every functional leader does what’s best for his or her department (at the exclusion of what’s useful for other departments), what’s best for the company overall usually doesn’t happen.
If the VP of sales maximizes sales without considering what the finance team needs for profitability, collections, or cash management, overall company performance suffers.
In quasi-mathematical terms, the VP of sales tries to achieve a “locally”-optimized outcome. But, by doing so, they fail to enable a “globally”-optimized outcome for the company as a whole.
You also see this phenomenon in team sports. For example, in basketball, if every player on the team personally tries to score as many points as possible, the team’s overall performance suffers.
Sometimes, your teammate has a better (a.k.a. higher-probability) shot. If you’re trying to maximize your team’s points (as opposed to your own), it makes sense to facilitate your teammate scoring points rather than only tallying the points on your personal scorecard.
As it relates to COVID-19, I’m seeing extensive problems with achieving globally-optimal outcomes both within the U.S. and the world as a whole.
Within the U.S., hospital CEOs, governors, and the Federal Government are competing against each other to buy medical equipment (e.g., ventilators) and supplies (e.g., N95 respirator masks) that are running low.
Washington State is trying to outbid California for vitally-needed supplies, and both are outbid by the Federal Government.
Globally, the U.S. is outbidding Canada and Germany for vitally-needed supplies. Canada is threatening to no longer allow Canadian nurses to cross the border to work in hospitals in Detroit.
With each actor doing what’s best for their area, the overall system outcome is suboptimal.
When each man/woman, department, state, or country is out for itself, the overall outcome for the system suffers.
Here’s another example.
As I speak, the hospital systems in New York are being overwhelmed. There aren’t enough hospital beds, critical care beds, or staff members to care for the surge of patients.
Meanwhile, hospitals in nearby Boston and Washington DC haven’t had the caseload of New York. They’ve used this time to expand capacity. They’ve cleared entire floors of their hospital of regular patients to make room for COVID-19 patients and rearranged staffing processes to enable surge staffing.
At the moment, these hospitals are being underutilized for COVID-19 patients relative to their capacity.
In an opinion piece, Drs Michael Rose (Johns Hopkins Hospital) and Sumit Agarwal (Brigham and Women’s Hospital/Harvard Medical School) made the argument that the U.S. Government should be transferring patients from New York, where the hospitals are overcapacity, to those hospitals in neighboring states that are below capacity.
This is, in essence, an argument for global optimization versus local optimization.
Andrew Cuomo, Governor of New York, made a similar global optimization argument with respect to ventilators — the medical devices that help COVID-19 patients continue to breathe while their bodies fight off the virus.
His argument was that New York doesn’t have enough ventilators, but there are ventilators in the federal stockpile and even in other states that aren’t currently being used. He has asked that they be sent to New York, where the patient caseload is expected to hit an apex in the middle of April.
When the early outbreak in New York begins to decline, he would send staff and equipment to other regions that reach their apex on differing timelines.
At the moment, hospital administrators, state governors, and federal government administrators are largely holding on to their ventilators to prepare for when they might need them.
Within the context of local optimization, those are all rational decisions. Yet it seems like a tragedy within a tragedy to have a patient die in New York while a ventilator in a neighboring state isn’t even powered on.
You don’t need a PhD in supply chain management to intuitively recognize that such a scenario is not the most efficient use of resources.
Outside of the medical arena, the two goods that are sold out in many parts of the United States are toilet paper and guns.
Many people in America are stockpiling toilet paper to make sure they don’t run out. Meanwhile, millions of other Americans are buying guns to protect themselves from those who might want to steal their supplies.
These behaviors are local-optimization strategies. Gather my resources. Protect my resources, and shoot my neighbor if I have to.
In contrast, a global-optimization strategy would be to help your neighbor and allow your neighbor to help you.
Maybe you have a neighbor who’s immunocompromised and happens to know how to sew. This neighbor could offer to sew cloth masks for other neighbors. Meanwhile, another neighbor is young, healthy, and can run grocery store errands on behalf of neighbors who have higher medical risk.
Another neighbor is a retired nurse who can be a resource for answering general medical questions related to all the confusing information around COVID-19. The neighbor down the street is tech-savvy and can provide telephone-based technical support for those having difficulty with video conferencing.
Another is a financial planner who perhaps offers to help those on the block navigate the financial support programs being offered by the federal government.
Technically speaking, the “opposite” of local optimization is global optimization.
In simpler terms, the opposite of every man and woman out for themselves is… teamwork and collaboration.
This was true prior to the pandemic and remains true today. However, when fears and emotions run high, it’s easy to forget that we can achieve more together than we can individually.
You may not be able to control the decisions your leaders make, but you do control this decision that you make.
How will you choose?