When an incident happens, one of the causes is invariably identified as human error: somebody along the way made a mistake, did something they shouldn’t have done. For example: that engineer shouldn’t have done that clearly risky deployment and then walked away without babysitting it. Labeling an action as human error is an unfortunately effective way at ending an investigation (root cause: human error).
Some folks try to make progress on the current status quo by arguing that, since human error is inevitable (people make mistakes!), it should be the beginning of the investigation, rather than the end. I respect this approach, but I’m going to take a more extreme view here: we can gain insight into how incidents happen, even those that involve operator actions as contributing factors, without reference to human error at all.
Since we human beings are physical beings, you can think of us as machines. Specifically, we are machines that make decisions and take action based on those decisions. Now, imagine that every decision we make involves our brain trying to maximize a function: when provided with a set of options, it picks the one that has the largest value. Let’s call this function g, for goodness.
(The neuroscientist Karl Friston has actually proposes something similar as a theory: organisms make decisions to minimize model surprise, a construct that Friston calls free energy).
In this (admittedly simplistic) model of human behavior, all decision making is based on an evaluation of g. Each person’s g will vary based on their personal history and based on their current context: what they currently see and hear, as well as other factors such as time pressure and conflicting goals. “History” here is very broad, as g will vary based not only on what you’ve learned in the past, but also on physiological factors like how much sleep you had last night and what you ate for breakfast.
Under this paradigm, if one of the contributing factors in an incident was the user pushing “A” instead of “B”, we ask “how did the operator’s g function score a higher value for pushing A over B”? There’s no concept of “error” in this model. Instead, we can explore the individual’s context and history to get a better understanding of how their g function valued A over B. We accomplish this by talking to them.
I think the model above is much more fruitful than the one where we identify errors or mistakes. In this model, we have to confront the context and the history that a person was exposed to, because those are the factors that determine how decisions get made.
The idea of human error is a hard thing to shake. But I think we’d be better off if we abandoned it entirely.
Some additional reading on the idea of human error:
- “Those found responsible have been sacked”: some observations on the usefulness of error by Richard Cook and Christopher Nemeth
- Behind Human Error By David Woods, Sidney Dekker, Richard Cook, Leila Johannesen, Nadine Sarter