I’m currently reading Systems, Experts, and Computers: the Systems Approach in Management and Engineering, World War II and After. The book is really more of a collection of papers, each written by a different author. This post is about the second chapter, The Adoption of Operations Research in the United States During World War II, written by Erik Rau.

During World War II, the British and the Americans were actively investing in developing radar technology in support of the war effort, with applications such as radar-based air defense. It turned out that developing the technology itself wasn’t enough: the new tech had to be deployed and operated effectively in the field to actually serve its purpose. Operating these systems required coordination between machines, human operators, and the associated institutions.

The British sent out scientists and engineers into the field to study and improve how these new systems were used. To describe this type of work, the physicist Albert Rowe coined the term operational research (OR), to contrast it with developmental research.

After the surprise attack on Pearl Harbor, the U.S. Secretary of War tapped the British radar pioneer Robert Wattson-Watt to lead a study on the state of American air defense systems. In the report, Wattson-Watt describe U.S. air defense as “insufficient organization applied to technically inadequate equipment used in exceptionally difficult conditions“. The report suggested the adoption of the successful British technology and techniques, which included OR.

At this time, the organization responsible for developmental research into weapons systems was the Office of Scientific Research and Development (OSRD), headed by Vannevar Bush. While a research function like OR seemed like it should belong under OSRD, there was a problem: Bush didn’t want it there. He wanted to protect the scientific development work of his organization from political interference by the military, and so he sought to explicitly maintain a boundary between the scientists and engineers that were developing the technology, and the military that was operating it.

[The National Defense Research Committee] is concerned with the development of equipment for military use, whereas these [OR] groups are concerned with the analysis of its performance, and the two points of view do not, I believe, often mix to advantage.

Vannevar Bush, quoted on p69

In the end, the demand for OR was too great, and Bush relented, creating the Office of Field Service within the OSRD.

Two things struck me reading this chapter. The first was that operational research was a kind of proto-DevOps, a recognition of the need to create a cultural shift in how development and operations work related to each other, and the importance of feedback between the two groups. It was fascinating to see the resistance to it from Bush. He wasn’t opposed to OR itself, he was opposed to unwanted government influence, which drove his efforts to keep development and operations separate.

The second thing that struck me was this idea of OR being doing research on operations. I had always thought of OR as being about things like logistics, basically graph theory problems addressed by faculty who work in business schools instead of computer science departments. But, here, OR was sending researchers into the field to study how operations was done in order to help improve development and operations. This reminded me very much of the aims of the learning from incidents (LFI) movement.

Managers rarely speak of objective criteria for achieving success because once certain crucial points in one’s career are passed, success and failure seem to have little to do with one’s accomplishments.

p44

Almost all management books are prescriptive: they’re self-help books for managers. Moral Mazes is a very different kind of management book. Where most management books are written by management gurus, this one is written by a sociologist. This book is the result of a sociological study that the author conducted at three U.S. companies in the 1980s: a large textile firm, a chemical company, and a large public relations agency. He was interested in understanding the ethical decision-making process of American managers. And the picture he paints is a bleak one.

American corporations are organized into what Jackall calls patrimonial bureaucracies. Like the Prussian state, a U.S. company is organized as a hierarchy, with a set of bureaucratic rules that binds all of the employees. However, like a monarchy, people are loyal to individuals rather than offices. Effectively, it is a system of patronage, where leadership doles out privileges. Like in the court of King Louis XIV, factions within the organization jockey to gain favor.

With the exception of the CEO, all of the managers are involved in both establishing the rules of the game, and are bound by the rules. But, because the personalities of leadership play a strong role, and because leadership often changes over time, the norms are always contingent. When the winds change, the standards of behavior can change as well.

Managers are also in a tough spot because they largely don’t have control over the outcomes on which they are supposed to be judged. They are typically held responsible for hitting their numbers, but luck and timing play an enormous role over whether they are able to actually meet their objectives. As a result, managers are in a constant state of anxiety, since they are forever subject to the whims of fate. Failure here is socially defined, and the worst outcome is to be in the wrong place at the wrong time, and to have your boss say “you failed”.

Managers therefore focus on what they can control, which is the image they project. They put in a lot of hours, so they appear to be working hard. They strive to be seen as someone who is a team player, who behaves predictably and makes other managers feel comfortable. To stand out from their peers, they have to have the right style: the ability to relate to other people, to sell ideas, appear in command. To succeed in this environment, a manager needs social capital as well as the ability to adapt quickly as the environment changes.

Managers commonly struggle with decision making. Because the norms of behavior are socially defined, and because these norms change over time, they are forever looking to their peers to identify what the current norms are. Compounding the problem is the tempo of management work: because a manager’s daily schedule is typically filled with meetings and interrupts, with only fragmented views of problems being presented, there is little opportunity to gain a full view of problems and reflect deeply on them.

Making decisions is dangerous, and managers will avoid it when possible, even if this costs the organization in the long run. Jackall tells an anecdote about a large, old battery at a plant. The managers did not want to be on the hook for the decision to replace it, and so problems with it were patched up. Eventually, it failed completely, and the resulting cost to replace it and to deal with costs related to EPA violations and lawsuits was over $100M in 1979 dollars. And yet, this was still rational decision-making on behalf of the managers, because it was a risk for them in the short-term to make the decision to replace the battery.

Ethical decision making is particularly fraught here. Leadership wants success without wanting to be bothered with the messy details of how that success is achieved: a successful middle manager shields leadership from the details. Managers don’t have a professional ethic in the way that, say, doctors or lawyers do. Ethical guidelines are situational, they vary based on changing relationships. Expediency is a virtue, and a good manager is one who is pragmatic about decision making.

All moral issues are transmuted into practical concerns. Arguing based on morality rather than pragmatism is frowned upon, because moral arguments compel managers to act, and they need to be able to take stock of the social environment in order to judge whether a decision would be appropriate. Effective managers use social cues to help make decisions. They conform to what Jackall calls institutional logic: the ever-changing set of rules and incentives that the culture creates and re-creates to keep people’s perspectives and behaviors consistent and predictable.

There comes a time in every engineer’s career when you ask yourself, “do I want to go into management?” I’ve flirted with the idea in the past, but ultimately came down on the “no” side. After reading Moral Mazes, I’m more confident than ever that I made the right decision.

Here’s a scenario I frequently encounter: I’m working on writing up an incident or an OOPS. I’ve already interviewed key experts on the system, and based on those interviews, I understand the implementation details well enough to explain the failure mode in writing.

But, when I go to write down the explanation, I discover that I don’t actually have a good understanding of all of the relevant details. I could go back and ask clarifying questions, but I worry that I’ll have to do this multiple times, and I want to avoid taking up too much of other people’s time.

I’m now faced with a choice when describing the failure mode. I can either:

(a) Be intentionally vague about the parts that I don’t understand well.

(b) Make my best guess about the implementation details for the parts I’m not sure about.

Whenever I go with option (b), I always get some of the details incorrect. This becomes painfully clear to me when I show a draft to the key experts, and they tell me straight-out, “Lorin, this section is wrong.”

I call choosing option (b) taking the hit because, well, I hate the feeling of being wrong about something. However, I always try to go with this approach because this maximizes both my own learning and (hopefully) the learning of the readers. I take the hit. When you know that your work will be reviewed by an expert, it’s better to be clear and wrong than vague.

Imagine you’re walking around a university campus. It’s a couple of weeks after the spring semester has ended, and so there aren’t many people about. You enter a building and walk into one of the rooms. It appears to be some kind of undergraduate lab, most likely either physics or engineering.

In the lab, you come across a table. On the table, someone has balanced a rectangular block on its smallest end.

You nudge the top of the block. As expected, it falls over with a muted plonk. You look around to see if you might have gotten in trouble, but nobody’s around.

You come across another table. This table has some sort of track on it. The table also has a block on it that’s almost identical to the one on the other table, except that the block has a pin in it that connects it to some sort of box that is mounted on the track.

Not being able to resist, you nudge the top of this block, and it starts to fall. Then, the little box on the track whirs to life, moving along the track in the same direction that you nudged the block in. Because of the motion of the box, the block stays upright.

The ancient philosopher Aristotle believed that there were four distinct types of causes that explained why things happened. One of these is what Aristotle called the efficient cause: “Y behaved the way it did because X acted on Y“. For example, the red billiard ball moved because it was struck by the white ball. This is the most common way we think about causality today, and it’s sometimes referred to as linear causality.

Efficient cause does a good job of explaining the behavior of the first rectangular block in the anecdote: it fell over because we nudged it with our finger. But it doesn’t do a good job of explaining the observed behavior of the second rectangular block: we nudged it, and it started to fall, but it righted itself, and ended up balanced again.

Another type of cause Aristotle talked about was what he called the final cause. This is a teleological view of cause, which explains the behavior of objects in terms of their purpose or goal.

Final cause sounds like an archaic, unscientific view of the world. And, yet, reasoning with final cause enables us to explain the behavior of the second block more effectively than efficient cause does. That’s because the second block is being controlled by a negative-feedback system that was designed to keep the block balanced. The system will act to compensate for disturbances that could lead to the block falling over (like somebody walking over and nudging it). Because the output, a sensor that reads the angle of the block, is fed back into the input of the control system, the relationship between external disturbance and system behavior isn’t linear. This is sometimes referred to as circular causality, because of the circular nature of feedback loops.

The systems that we deal with contain goals and feedback loops, just like the inverted pendulum control system that keeps the block balanced. If you try to use linear causality to understand a closed-loop system, you will be baffled by the resulting behavior. Only when you understand the goals that the system is trying to achieve, and the feedback loops that it uses to adjust its behavior to reach those goals, will the resulting behavior make sense.