Labeling a root cause is predicting the future, poorly

Why do we retrospect on our incidents? Why spend the time doing those write-ups and holding review meetings? We don’t do this work as some sort of intellectual exercise for amusement. Rather, we believe that if we spend the time to understand how the incident happened, we can use that insight to improve the system in general, and availability in particular. We improve availability by preventing incidents as well as reducing the impact of incidents that we are unable to prevent. This post-incident work should help us do both.

The typical approach to post-incident work is to do a root cause analysis (RCA). The idea of an RCA is to go beyond the surface-level symptoms to identify and address the underlying problems revealed by the incident. After all, it’s only by getting at the root at the problem that we will be able to permanently address it. When doing an RCA, when we attach the label root cause to something, we’re making a specific claim. That claim is: we should focus our attention on the issues that we’ve labeled “root cause”, because spending our time addressing these root causes will yield the largest improvements to future availability. Sure, it may be that there were a number of different factors involved in the incident, but we should focus on the root cause (or, sometimes, a small number of root causes), because those are the ones that really matter. Sure, the fact that Joe happened to be on PTO that day, and he’s normally the one that spots these sorts of these problems early, that’s interesting, but it isn’t the real root cause.

Remember that an RCA, like all post-incident work, is supposed to be about improving future outcomes. As a consequence, a claim about root cause is really a prediction about future incidents. It says that of all of the contributing factors to an incident, we are able to predict which factor is most likely to lead to an incident in the future. That’s quite a claim to make!

Here’s the thing, though. As our history of incidents teaches us over and over again, we aren’t able to predict how future incidents will happen. Sure, we can always tell a compelling story of why an incident happened, through the benefit of hindsight. But that somehow never translates into predictive power: we’re never able to tell a story about the next incident the way we can about the last one. After all, if we were as good at prediction as we are at hindsight, we wouldn’t have had that incident in the first place!

A good incident retrospective can reveal a surprisingly large number of different factors that contributed to the incident, providing signals for many different kinds of risks. So here’s my claim: there’s no way to know which of those factors is going to bite you next. You simply don’t possess a priori knowledge about which factors you should pay more attention to at the time of the incident retrospective, no matter what the vibes tell you. Zeroing in on a small number of factors will blind you to the role that the other factors might play in future incidents. Today’s “X wasn’t the root cause of incident A” could easily be tomorrow’s “X was the root cause of incident B”. Since you can’t predict which factors will play the most significant roles in future incidents, it’s best to cast as wide a net as possible. The more you identify, the more context you’ll have about the possible risks. Heck, maybe something that only played a minor role in this incident will be the trigger in the next one! There’s no way to know.

Even if you’re convinced that you can identify the real root cause of the last incident, it doesn’t actually matter. The last incident already happened, there’s no way to prevent it. What’s important is not the last incident, but the next one: we’re looking at the past only as a guide to help us improve in the future. And while I think incidents are inherently unpredictable, here’s a prediction I’m comfortable making: your next incident is going to be a surprise, just like your last one was, and the one before that. Don’t fool yourself into thinking otherwise.

On work processes and outcomes

Here’s a stylized model of work processes and outcomes. I’m going to call it “Model I”.

If you do work the right way, that is, follow the proper processes, then good things will happen. And, when we don’t, bad things happen. I work in the software world, so by “bad outcome” a mean an incident, and by “doing the right thing”, the work processes typically refer to software validation activities, such as reviewing pull requests, writing unit tests, manually testing in a staging environment. But it also includes work like adding checks in the code for unexpected inputs, ensuring you have an alert defined to catch problems, having someone else watching over your shoulder when you’re making a risky operational change, not deploying your production changes on a Friday, and so on. Do this stuff, and bad things won’t happen. Don’t do this stuff, and bad things will.

If you push someone who believes in this model, you can get them to concede that sometimes nothing bad happens even though someone didn’t do everything can quite right, the amended model looks like this:

Inevitably, an incident happens. At that point, we focus the post-incident efforts on identifying what went wrong with the work. What was the thing that was done wrong? Sometimes, this is individuals who weren’t following the process (deployed on a Friday afternoon!). Other times, the outcome of the incident investigation is a change in our work processes, because the incident has revealed a gap between “doing the right thing” and “our standard work processes”, so we adjust our work processes to close the gap. For example, maybe we now add an additional level of review and approval for certain types of changes.

Here’s an alternative stylized model of work processes and outcomes. I’m going to call it “Model II”.

Like our first model, this second model contains two categories of work processes. But the categories here are different. They are:

What people are officially supposed to
What people actually do

The first categorization is an idealized view of how the organization thinks that people should do their work. But people don’t actually do their work their way. The second category captures what the real work actually is.

This second model of work and outcomes has been embraced by a number of safety researchers. I deliberately called my models as Model I and Model II as a reference to Safety-I and Safety-II. Safety-II is a concept developed by the resilience engineering researcher Dr. Erik Hollnagel. The human factor experts Dr. Todd Conklin and Bob Edwards describe this alternate model using a black-line/blue-line diagram. Dr. Steven Shorrock refers to the first category as work-as-prescribed, and the second category as work-as-done. In our stylized model, all outcomes come from this second category of work, because it’s the only one that captures the actual work that leads to any of the outcomes. (In Shorrock’s more accurate model, the two categories of work overlap, but bear with me here).

This model makes some very different assumptions about the nature of how incidents happen! In particular, it leads to very different sorts of questions.

The first model is more popular because it’s more intuitive: when bad things happen, it’s because we did things the wrong way, and that’s when we look back in hindsight to identify what those wrong ways were. The second model requires us to think more about the more common case when incidents don’t happen. After all, we measure our availability in 9s, which means the overwhelming majority of the time, bad outcomes aren’t happening. Hence, Hollnagel encourages us to spend more time examining the common case of things going right.

Because our second model assumes that what people actually do usually leads to good outcomes, it will lead to different sorts of questions after an incident, such as:

What does normal work look like?
How is it that this normal work typically leads to successful outcomes?
What was different in this case (the incident) compared to typical cases?

Note that this second model doesn’t imply that we should always just keep doing things the same way we always do. But it does imply that we should be humble in enforcing changes to the way work is done, because the way that work is done today actually leads to good outcomes most of the time. If you don’t understand how things normally work well, you won’t see how your intervention might make things worse. Just because your last incident was triggered by a Friday deploy doesn’t mean that banning Friday deploys will lead to better outcomes. You might actually end up making things worse.

When a bad analysis is worse than none at all

One of the most famous physics experiments in modern history is the double-split experiment, originally performed by the English physicist Thomas Young back in 1801. You probably learned about this experiment in a high school physics class. There was a long debate in physics about whether light was a particle or a wave, and Young’s experiment provided support for the wave theory. (Today, we recognize that light has a dual nature, with both particle-like and wave-like behaviors).

To run the experiment, you need an opaque board that has two slits cut out of it, as well as a screen. You shine a light at the board and look to see what the pattern of light looks like on the screen behind it.

Here’s a diagram from Wikipedia, which shows the experiment being run with electrons rather than light , but is otherwise the same idea.

Original: NekoJaNekoJa Vector: Johannes Kalliauer, CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0, via Wikimedia Commons

If light was a particle, then you would expect each light particle to pass through either one slit, or the other. The intensities that you’d observe on the screen would look like the sum of the intensities if you ran the experiment by covering up one slit, and then ran it again by covering up the other slit. It should basically look like the sum of two Gaussian distributions with different means.

However, that isn’t what you actually see on the screen. Instead, you get this pattern where there are some areas of the screen with no intensity at all: where the light never strikes the screen. On the other hand, if you run the experiment by covering up either slit, you will get light at these null locations. This shows that there’s an interference effect, the fact that there are two slits leads the light to behave differently from being the sum of the effects of each slit.

Note that we see the same behavior with electrons (hence the diagram above). Both electrons and light (photons) exhibit this sort of wavelike behavior. This behavior is observed even even if you shine only one electron (or photon) at a time through the slits.

Now, imagine a physicist in the 1970s hires a technician to run this experiment with electrons. The physicist asks the tech to fire one electron at a time from an electron gun at the double-slit board, and record the intensities of the electrons striking a phosphor screen, like on a cathode ray tube (kids, ask your parents about TVs in the old days). Imagine that the physicist doesn’t tell the technicians anything about the theory being tested, the technician is just asked to record the measurements.

Let’s imagine this thought process from the technician:

It’s a lot of work to record the measurements from the phosphor screen, and all of this intensity data is pretty noisy anyways. Instead, why don’t I just identify the one location on the screen that was the brightest, use that location to estimate which slit the electron was most likely to have passed through, and then just record that slit? This will drastically reduce the effort required for each experiment. Plus, the resulting data will be a lot simpler to aggregate than the distribution of messy intensities from each experiment.

The data that the technician records then ends up looking like this:

Experiment	Slit
1	left
2	left
3	right
4	left
5	right
6	left

Now, the experimental data above will give you no insight into the wave nature of electrons, no matter many experiments are run. This sort of experiment is clearly not better than nothing, it’s worse than nothing, because it obscures the nature of the phenomenon that you’re trying to study!

Now, here’s my claim: when people say “the root cause analysis process may not be perfect, but it’s better than nothing”, this is what I worry about. They are making implicit assumptions about a model of incident failure (there’s a root cause), and the information that they are capturing about the incidents is determined by this model.

A root cause analysis approach will never provide insight into how incidents arise through complex interactions, because it intentionally discards the data that could provide that insight. It’s like the technician who does not record all of the intensity measurements, and instead just uses those measurements to pick a slit, and only records the slit.

The alternative is to collect a much richer set of data from each incident. That more detailed data collection is going to be a lot more effort, and a lot messier. It’s going to involve recording details about people’s subjective observations and fuzzy memories, and it will depend on what types of questions are asked of the responders. It will also depend on what sorts of data you even have available to capture. And there will be many subjective decisions about what data to record and what to leave out.

But if your goal is to actually get insights from your incidents about how they’re happening, then that effortful, messy data collection will reveal insights that you won’t ever get from a root cause analysis. Whereas, if you continue to rely on root cause analysis, you are going to be misled about how your system actually fails and how it really works. This is what I mean by good models protect us from bad models, and how root cause analysis can actually be worse than nothing.

Don’t be like the technician, discarding the messy data because it’s cleaner to record which slit the electron went through. Because then you’ll miss that the electron is somehow going through both.

You can’t prevent your last outage, no matter how hard you try

I don’t know anything about your organization, dear reader, but I’m willing to bet that the amount of time and attention your organization spends on post-incident work is a function of the severity of the incidents. That is, your org will spend more post-incident effort on a SEV0 incident compared to a SEV1, which in turn will get more effort than a SEV2 incident, and so on.

This is a rational strategy if post-incident effort could retroactively prevent an incident. SEV0s are worse than SEV1s by definition, so if we could prevent that SEV0 from happening by spending effort after it happens, then we should do so. But no amount of post-incident effort will change the past and stop the incident from happening. So that can’t be what’s actually happening.

Instead, this behavior means that people are making an assumption about the relationship between past and future incidents, one that nobody ever says out loud but everyone implicitly subscribes to. The assumption is that post-incident effort for higher severity incidents is likely to have a greater impact on future availability than post-incident effort for lower severity incidents. In other words, an engineering-hour of SEV1 post-incident work is more likely to improve future availability than an engineering-hour of SEV2 post-incident work. Improvement in future availability refers to either prevention of future incidents, or reduction of the impact of future incidents (e.g., reduction in blast radius, quicker detection, quicker mitigation).

Now, the idea that post-incident work from higher-severity incidents has greater impact than post-incident work from lower-severity incidents is a reasonable theory, as far as theories go. But I don’t believe the empirical data actually supports this theory. I’ve written before about examples of high severity incidents that were not preceded by related high-severity incidents. My claim is that if you look at your highest severity incidents, you’ll find that they generally don’t resemble your previous high-severity incidents. Now, I’m in the no root cause camp, so I believe that each incident is due to a collection of factors that happened to interact.

But don’t take my word for it, take a look at your own incident data. When you have your next high-severity incident, take a look at N high-severity incidents that preceded it (say, N=3), and think about how useful the post-incident incident work of those previous incidents actually was in helping you to deal with the one that just happened. That earlier post-incident work clearly didn’t prevent this incident. Which of the action items, if any, helped with mitigating this incident? Why or why not? Did those other incidents teach you anything about this incident, or was this one just completely different from those? On the other hand, were there sources of information other than high-severity incidents that could have provided insights?

I think we’re all aligned that the goal of post-incident work should be in reducing the risks associated with future incidents. But the idea that the highest ROI for risk reduction work is in the highest severity incidents is not a fact, it’s a hypothesis that simply isn’t supported by data. There are many potential channels for gathering signals of risk, and some of them come from lower severity incidents, and some of them come from data sources other than incidents. Our attention budget is finite, so we need to be judicious about where we spend our time investigating signals. We need to figure out which threads to pull on that will reveal the most insights. But the proposition that the severity of an incident is a proxy for the signal quality of future risk is like the proposition that heavier objects fall faster than lighter one. It’s intuitively obvious; it just so happens to also be false.

Good models protect us from bad models

One of the criticisms leveled at resilience engineering is that the insights that the field generates aren’t actionable: “OK, let’s say you’re right, that complex systems are never perfectly understood, they’re always changing, they generate unexpected interactions, and that these properties explain why incidents happen. That doesn’t tell me what I should do about it!”

And it’s true; I can talk generally about the value of improving expertise so that we’re better able to handle incidents. But I can’t take the model of incidents that I’ve built based on my knowledge of resilience engineering and turn that into a specific software project that you can build and deploy that will eliminate a class of incidents.

But even if these insights aren’t actionable, that they don’t tell us about a single thing we can do or build to help improve reliability, my claim here is that these insights still have value. That’s because we as humans need models to make sense of the world, and if we don’t use good-but-not-actionable models, we can end up with actionable-but-not-good models. Or, as the statistics professor Andrew Gelman put it in his post The social sciences are useless. So why do we study them? Here’s a good reason back in 2021:

The baseball analyst Bill James once said that the alternative to good statistics is not no statistics, it’s bad statistics. Similarly, the alternative to good social science is not no social science, it’s bad social science.

The reason we do social science is because bad social science is being promulgated 24/7, all year long, all over the world. And bad social science can do damage.

Because we humans need models to make sense of the world, incidents models are inevitable. A good-but-not-actionable incident model will feel unsatisfying to people who are looking to leverage these models to take clear action. And it’s all too easy to build not-good-but-actionable models of how incidents happen. Just pick something that you can measure and that you theoretically have control over. The most common example of such a model is the one I’ll call “incidents happen because people don’t follow the processes that they are supposed to.” It’s easy to call out process violations in incident writeups, and it’s easy to define interventions to more strictly enforce processes, such as through automation.

In other words, good-but-not-actionable models protect us from the actionable-but-not-good models. They serve as a kind of vaccine, inoculating us from the neat, plausible, and wrong solutions that H.L. Mencken warned us about.

Tradeoff costs in communication

If you work in software, and I say the word server to you, which do you think I mean?

Software that responds to requests (e.g., http server)
A physical piece of hardware (e.g., a box that sits in a rack in a data center)
A virtual machine (e.g., an EC2 instance)

The answer, of course, is it depends on the context. The term server could mean any of those things. The term is ambiguous; it’s overloaded to mean different things in different contexts.

Another example of an overloaded term is service. From the end user’s perspective, the service is the system they interact with:

From the end user’s perspective, there is a single service

But if we zoom in on that box labeled service, it might be implemented by a collection of software components, where each component is also referred to as a service. This is sometimes referred to as a service-oriented architecture or a microservice architecture

A single “service” may be implemented by multiple “services”. What does “service” mean here?

Amusingly, when I worked at Netflix, people referred to microservices as “services”, but people also referred to all of Netflix as “the service”. For example, instead of saying, “What are you currently watching on Netflix?”, a person would say, “What are you currently watching on the service?”

Yet another example is the term “client”. This could refer to the device that the end-user is using (e.g., web browser, mobile app):

Or it could refer to the caller service in a microservice architecture:

It could also refer to the code in the caller service that is responsible for making the request, typically packaged as a client library.

The fact that the meaning of these terms is ambiguous and context-dependent makes it harder to understand what someone is talking about when the term is used. While the person speaking knows exactly what sense of server, service or client they mean, the person hearing it does not.

The ambiguous meaning of these terms creates all sorts of problems, especially when communicating across different teams, where the meaning of a term used by the client team of a service may be different from the meaning of the term used by the owner of a service. I’m willing to bet that you, dear reader, have experienced this problem at some point in the past when reading an internal tech doc or trying to parse the meaning of a particular slack message.

As someone who is interested in incidents, I’m acutely aware of the problematic nature of ambiguous language during incidents, where communication and coordination play an enormous role in effective incident handling. But it’s not just an issue for incident handling. For example, Eric Evans advocates the use of ubiquitous language in software design. He pushes for consistent use of terms across different stakeholders to reduce misunderstandings.

In principle, we could all just decide to use more precise terminology. This would make it easier for listeners to understand the intent of speakers, and would reduce the likelihood of problems that stem from misunderstandings. At some level, this is the role that technical jargon plays. But client, server and service are technical jargon, and they’re still ambiguous. So, why don’t we just use even more precise language?

The problem is that expressing ourselves in unambiguous isn’t free: it costs the speaker additional effort to be more precise. As a trivial example, microservice is more precise than service, but it takes twice as long as to say, and it takes an additional five letters to write. Those extra syllables and letters are a cost to the speaker. And, all other things being equal, people prefer expending less effort than more effort. This is why we don’t like being on the receiving end of ambiguous language, because we have to put more effort into resolving the ambiguity through context clues.

The cost of precision to the speaker is clear in the world of computer programming. Traditional programming languages require an extremely high degree of precision on behalf of the coder. This sets a very high bar for being able to write programs. On the other hand, modern generative AI tools are able to take natural language inputs as specifications which are orders of magnitude less precise, and turn them into programs. These tools are able to process ambiguous inputs in ways that regular programming languages simply cannot. The cost in effort is much lower for the vibe programmer. (I will leave evaluation of the outcomes of vibe programming as an exercise for the reader).

Ultimately, the degree in precision in communication is a tradeoff: an increase in precision means less effort for the listener and less risk of misunderstanding, at a cost of more effort for the speaker. Because of this tradeoff, we shouldn’t expect the equilibrium point to be at maximal precision. Instead, it’s somewhere in the middle. Ideally, it would be where we minimize the total effort. Now, I’m not a cognitive scientist, but this is a theory that has been advanced by cognitive scientists. For example, see the paper The communicative function of ambiguity in language by Steven T. Piantadosi, Harry Tily, and Edward Gibson. I touched on the topic of ambiguity more generally in a previous post the high cost of low ambiguity.

We often ask “why are people doing X instead of the obviously superior Y“. This is an example of how we are likely missing the additional costs of choosing to do Y over X. Just because we don’t notice those costs doesn’t mean they aren’t there. It means we aren’t looking closely enough.

Model error

One of the topics I wrote about in my last post was about using formal methods to build a model of how our software behaves. In this post, I want to explore how the software we write itself contains models: models of how the world behaves.

The most obvious area is in our database schemas. These schemas enable us to digitally encode information about some aspect of the world that our software cares about. Heck, we even used to refer to this encoding of information into schemas as data models. Relational modeling is extremely flexible: in principle, we can represent just about any aspect of the world into it, if we put enough effort in. The challenge is that the world is messy, and this messiness significantly increases the effort required to build more complete models. Because we often don’t even recognize the degree of messiness the real world contains, we build over-simplified models that are too neat. This is how we end up with issues like the ones captured in Patrick McKenzie’s essay Falsehoods Programmers Believe About Names. There’s a whole book-length meditation on the messiness of real data and how it poses challenges for database modeling: Data and Reality by William Kent, which is highly recommended by Hillel Wayne, in his post Why You Should Read “Data and Reality”.

The problem of missing the messiness of the real world is not at all unique to software engineers. For example, see Christopher Alexander’s A City Is Not a Tree for a critique of urban planners’ overly simplified view of human interactions in urban environments. For a more expansive lament, check out James C. Scott’s excellent book Seeing Like a State. But, since I’m a software engineer and not an urban planner or a civil servant, I’m going to stick to the software side of things here.

Models in the back, models in the front

In particularly, my own software background is in the back-end/platform/infrastructure space. In this space, the software we write frequently implement control systems. It’s no coincidence that both cybernetics and kubernetes derived their names from the same ancient Greek word: κυβερνήτης. Every control system must contain within it a model of the system that it controls. Or, as Roger C. Conant and W. Ross Ashby put it, every good regulator of a system must be a model of that system.

Things get even more complex on the front-end side of the software world. This world must bridge the software world with the human world. In the context of Richard Cook’s framing in Above the Line, Below the Line, the front-end is the line that bridges the two world. As a consequence, the front-end’s responsibility is to expose a model of the software’s internal state to the user. This means that the front-end also has an implicit model of the users themselves. In the paper Cognitive Systems Engineering: New wine in new bottles, Erik Hollnagel and David Woods referred to this model as the image of the operator.

The dangers of the wrongness of models

There’s an oft-repeated quote by the statistician George E.P. Box: “All models are wrong but some are useful”. It’s a true statement, but one that focuses only on the upside of wrong models, the fact that some of them are useful. There’s also a downside to the fact that all models are wrong: the wrongness of these models can have drastic consequences.

And, while It’s a true statement, but what it fails to hint at how bad the consequences can be when a model is wrong. One of my favorite examples involves the 2008 financial crisis, as detailed by the journalist Felix Salmon’s 2009 Wired Magazine article Recipe for Disaster: The Formula that Killed Wall Street. The article described how Wall Street quants used a mathematical model known as the Gaussian copula function to estimate risk. It was a useful model that ultimately led to disaster.

Here’s A ripped-from-the-headlines example of image of the operator model error, how the U.S. national security advisor Mike Waltz accidentally saved the phone number of Jeffrey Goldberg, editor of the Atlantic magazine, to the contact information of White House spokesman Brian Hughes. The source is the recent Guardian story How the Atlantic’s Jeffrey Goldberg got added to the White House Signal group chat:

According to three people briefed on the internal investigation, Goldberg had emailed the campaign about a story that criticized Trump for his attitude towards wounded service members. To push back against the story, the campaign enlisted the help of Waltz, their national security surrogate.

Goldberg’s email was forwarded to then Trump spokesperson Brian Hughes, who then copied and pasted the content of the email – including the signature block with Goldberg’s phone number – into a text message that he sent to Waltz, so that he could be briefed on the forthcoming story.

Waltz did not ultimately call Goldberg, the people said, but in an extraordinary twist, inadvertently ended up saving Goldberg’s number in his iPhone – under the contact card for Hughes, now the spokesperson for the national security council.

…

According to the White House, the number was erroneously saved during a “contact suggestion update” by Waltz’s iPhone, which one person described as the function where an iPhone algorithm adds a previously unknown number to an existing contact that it detects may be related.

The software assumed that, when you receive a text from someone with a phone number and email address, that the phone number and email address belong to the sender. This is a model of the user that turned out to be very, very wrong.

Nobody expects model error

Software incidents involve model errors in one way or another, whether it’s an incorrect model of the system being controlled, an incorrect image of the operator, or a combination of the two.

And, yet, despite us all intoning “all models are wrong, some models are useful”, we don’t internalize that our systems our built on top of imperfect models. This is one of the ironies of AI: we are now all aware of the risks associated with model error with LLMs. We’ve even come up with a separate term for it: hallucinations. But traditional software is just as vulnerable to model error as LLMs are, because our software is always built on top of models that are guaranteed to be incomplete.

You’re probably familiar with the term black swan, popularized by the acerbic public intellectual Nassim Nicholas Taleb. While his first book, Fooled by Randomness, was a success, it was the publication of The Black Swan that made Taleb a household name, and introduced the public to the concept of black swans. While the term black swan was novel, the idea it referred to was not. Back in the 1980s, the researcher Zvi Lanir used a different term: fundamental surprise. Here’s an excerpt of a Richard Cook lecture on the 1999 Tokaimura nuclear accident where he talks about this sort of surprise (skip to the 45 minute mark).

And this Tokaimura accident was an impossible accident.

There’s an old joke about the creator of the first English American dictionary, Noah Webster … coming home to his house and finding his wife in bed with another man. And she says to him, as he walks in the door, she says, “You’ve surprised me”. And he says, “Madam, you have astonished me”.

The difference was that she of course knew what was going on, and so she could be surprised by him. But he was astonished. He had never considered this as a possibility.

And the Tokaimura was an astonishment or what some, what Zev Lanir and others have called a fundamental surprise which means a surprise that is fundamental in the sense that until you actually see it, you cannot believe that it is possible. It’s one of those “I can’t believe this has happened”. Not, “Oh, I always knew this was a possibility and I’ve never seen it before” like your first case of malignant hyperthermia, if you’re a an anesthesiologist or something like that. It’s where you see something that you just didn’t believe was possible. Some people would call it the Black Swan.

Black swans, astonishment, fundamental surprise, these are all synonyms for model error.

And these sorts of surprises are going to keep happening to us, because our models are always wrong. The question is: in the wake of the next incident, will we learn to recognize that fundamental surprises will keep happening to us in the future? Or will we simply patch up the exposed problems in our existing models and move on?

Models, models every where, so let’s have a think

If you’re a regular reader of this blog, you’ll have noticed that I tend to write about two topics in particular:

Resilience engineering
Formal methods

I haven’t found many people who share both of these interests.

At one level, this isn’t surprising. Formal methods people tend to have an analytic outlook, and resilience engineering people tend to have a synthetic outlook. You can see the clear distinction between these two perspectives in the transcript of Leslie Lamport’s talk entitled The Future of Computing: Logic or Biology. Lamport is clearly on the side of the logic, so much so that he ridicules the very idea of taking a biological perspective on software systems. By contrast, resilience engineering types actively look to biology for inspiration on understanding resilience in complex adaptive systems. A great example of this is the late Richard Cook’s talk on The Resilience of Bone.

And yet, the two fields both have something in common: they both recognize the value of creating explicit models of aspects of systems that are not typically modeled.

You use formal methods to build a model of some aspect of your software system, in order to help you reason about its behavior. A formal model of a software system is a partial one, typically only a very small part of the system. That’s because it takes effort to build and validate these models: the larger the model, the more effort it takes. We typically focus our models on a part of the system that humans aren’t particularly good at reasoning about unaided, such as concurrent or distributed algorithms.

The act of creating and explicit model and observing its behavior with a model checker gives you a new perspective on the system being modeled, because the explicit modeling forces you to think about aspects that you likely wouldn’t have considered. You won’t say “I never imagined X could happen” when building this type of formal model, because it forces you to explicitly think about what would happen in situations that you can gloss over when writing a program in a traditional programming language. While the scope of a formal model is small, you have to exhaustively specify the thing within the scope you’ve defined: there’s no place to hide.

Resilience engineering is also concerned with explicit models, in two different ways. In one way, resilience engineering stresses the inherent limits of models for reasoning about complex systems (c.f., itsonlyamodel.com). Every model is incomplete in potentially dangerous ways, and every incident can be seen through the lens of model error: some model that we had about the behavior of the system turned out to be incorrect in a dangerous way.

But beyond the limits of models, what I find fascinating about resilience engineering is the emphasis on explicitly modeling aspects of the system that are frequently ignored by traditional analytic perspectives. Two kinds of models that come up frequently in resilience engineering are mental models and models of work.

A resilience engineering perspective on an incident will look to make explicit aspects of the practitioners’ mental models, both in the events that led up to that incident, and in the response to the incident. When we ask “How did the decision make sense at the time?“, we’re trying to build a deeper understanding of someone else’s state of mind. We’re explicitly trying to build a descriptive model of how people made decisions, based on what information they had access to, their beliefs about the world, and the constraints that they were under. This is a meta sort of model, a model of a mental model, because we’re trying to reason about how somebody else reasoned about events that occurred in the past.

A resilience engineering perspective on incidents will also try to build an explicit model of how work happens in an organization. You’ll often heard the short-hand phrase work-as-imagined vs work-as-done to get at this modeling, where it’s the work-as-done that is the model that we’re after. The resilience engineering perspective asserts that the documented processes of how work is supposed to happen is not an accurate model of how work actually happens, and that the deviation between the two is generally successful, which is why it persists. From resilience engineering types, you’ll hear questions in incident reviews that try to elicit some more details about how the work really happens.

Like in formal methods, resilience engineering models only get at a small part of the overall system. There’s no way we can build complete models of people’s mental models, or generate complete descriptions of how they do their work. But that’s ok. Because, like the models in formal methods, the goal is not completeness, but insight. Whether we’re building a formal model of a software system, or participating in a post-incident review meeting, we’re trying to get the maximum amount of insight for the modeling effort that we put in.

Resilience: some key ingredients

Brian Marick posted on Mastodon the other day about resilience in the context of governmental efficiency. Reading that inspired me to write about some more general observations about resilience.

Now, people use the term resilience in different ways. I’m using resilience here in the following sense: how well a system is able to cope when it is pushed beyond its limits. Or, to borrow a term from safety researcher David Woods, when the system is pushed outside of its competence envelope. The technical term for this sense of the word resilience is graceful extensibility, which also comes from Woods. This term is a marriage of two other terms: graceful degradation, and software extensibility.

The term graceful degradation refers to the behavior of a system which, when it experiences partial failures, can still provide some functionality, even though it’s at a reduced fidelity. For example, for a web app, this might mean that some particular features are unavailable, or that some percentage of users are not able to access the site. Contrast this with a system that just returns 500 errors for everyone whenever something goes wrong.

We talk about extensible software systems as ones that have been designed to make it easy to add new features in the future that were not originally anticipated. A simple example of software extensibility is the ability for old code to call new code, with dynamic binding being one way to accomplish this.

Now, putting those two concepts together, if a system encounters some sort of shock that it can’t handle, and the system has the ability to extend itself so that it can now handle the shock, and it can make these changes to itself quickly enough that it minimizes the harms resulting from the shock, then we say the system exhibits graceful extensibility. And if it can keep extending itself each time it encounters a novel shock, then we say that the system exhibits sustained adaptability.

The rest of this post is about the preconditions for resilience. I’m going to talk about resilience in the context of dealing with incidents. Note that all of the topics described below come from the resilience engineering literature, although I may not always use the same terminology.

Resources

As Brian Marick observed in his toot:

As we discovered with Covid, efficiency is inversely correlated with resilience.

Here’s a question you can ask anyone who works in the compute infrastructure space: “How hot do you run your servers?” Or, even more meaningfully, “How much headroom do your servers have?”

Running your servers “hotter” means running at a higher CPU utilization. This means that you pack more load on fewer servers, which is more efficient. The problem is that the load is variable, which means that the hotter you run the servers, the more likely your server will get overloaded if there is a spike in utilization. An overloaded server can lead to an incident, and incidents are expensive! Running your servers at maximum utilization is running with zero headroom. We deliberately run our servers with some headroom to be able to handle variation in load.

We also see the idea of spare resources in what we call failover scenarios, where there’s a failure in one resource so we switch to using a different resource, such as failing over a database from primary to secondary, or even failing out of a geographical region.

The idea of spare resources is more general than hardware. It applies to people as well. The equivalent of headroom for humans is what Tom DeMarco refers to as slack. The more loaded humans are, the less well positioned they are to handle spikes in their workload. Stuff falls through the cracks when you’ve got too much load, and some of that stuff contributes to incidents. We can also even keep people in reserve for dealing with shocks, such as when an organization staffs a dedicated incident management team.

A common term that the safety people use for spare resources is capacity. I really like the way Todd Conklin put it on his Pre-Accident Investigation Podcast: “You don’t manage risk. You manage the capacity to absorb risk.” Another way he put it is “Accidents manage you, so what you really manage is the capacity for the organization to fail safely.”

Flexibility

Here’s a rough and ready definition of an incident: the system has gotten itself into a bad state, and it’s not going to return to a good state unless somebody does something about it.

Now, by this definition, for the system to become healthy again something about how the system works has to change. This means we need to change the way we do things. The easier it is to make changes to the system, the easier it will be to resolve the incident.

We can think of two different senses of changing the work of the system: the human side and the the software side.

Humans in a system are constrained by a set of rules that exist to reduce risk. We don’t let people YOLO code from their laptops into production, because of a number of risks that would expose us to. But incidents create scenarios where the risks associated with breaking these rules are lower than the risks associated with prolonging the incident. As a consequence, people in the system need the flexibility to be able to break the standard rules of work during an incident. One way to do this is to grant incident responders autonomy, let them make judgments about when they are able to break the rules that govern normal work, in scenarios where breaking the rule is less risky than following it.

Things look different on the software side, where all of the rules are mechanically enforced. For flexibility in software, we need to build into the software functionality in advance that will let us change the way the system behaves. My friend Aaron Blohowiak uses the term Jefferies tubes from Star Trek to describe features that support making operational changes to a system. These were service crawlways that made it easier for engineers to do work on the ship.

A simple example of this type of operational flexibility is putting in feature flags that can be toggled dynamically in order to change system behavior. At the other extreme is the ability to bring up a REPL on a production system in order to make changes. I’ve seen this multiple times in my career, including watching someone use the rails console command of a Ruby on Rails app to resolve an issue.

The technical term in resilience engineering for systems that possess this type of flexibility is adaptive capacity: the system has built up the ability to be able to dynamically reconfigure itself, to adapt, in order to meet novel challenges. This is where the name Adaptive Capacity Labs comes from.

Expertise

In general, organizations push against flexibility because it brings risk. In the case where I saw someone bring up a Ruby on Rails console, I was simultaneously impressed and terrified: that’s so dangerous!

Because flexibility carries risk, we need to rely on judgment as to whether the risk of leveraging the flexibility outweighs the risk of not using the flexibility to mitigate the incident. Granting people the autonomy to make those judgment calls isn’t enough: the people making the calls need to be able to make good judgment calls. And for that, you need expertise.

The people making these calls are having to make decisions balancing competing risks while under uncertainty and time pressure. In addition, how fluent they are with the tools is a key factor. I would never trust a novice with access to a REPL in production. But an expert? By definition, they know what they’re doing.

Diversity

Incidents in complex systems involve interactions between multiple parts of the system, and there’s no one person in your organization who understands the whole thing. To be able to effectively know what to do during an incident, you need to bring in different people who understand different parts of the system in order to help figure out what happens. You need diversity in your responders, people with different perspective on the problem at hand.

You also want diversity in diagnostic and mitigation strategy. Some people might think about recent changes, others might think about traffic pattern changes, others might dive into the codebase looking for clues, and yet others might look to see if there’s another problem going on right now that seems to be related. In addition, it’s often not obvious what the best course of action is to mitigate an incident. Responders often pursue multiple courses of action in parallel, hoping that at least one of them will bring the system healthy again. A diversity of perspectives can help generate more potential interventions, reducing the time to resolve.

Coordination

Having a group of experts with a diverse set of perspectives by itself isn’t enough to deal with an incident. For a system to be resilient, the people within the system need to be able to coordinate, to work together effectively.

If you’ve ever dealt with a complex incident, you know how challenging coordination can be. Things get even hairier in our distributed world. Whether you’re physically located with all of the responders, you’re on a Zoom call (a bridge, as we still say), you’re messaging over Slack, or some hybrid combination of all three, each type of communication channel has its benefits and drawbacks.

There are prescriptive approaches to improving coordination during incidents, such as the Incident Command System (ICS). However, Laura Maguire’s research has shown that, in practice, incident responders intentionally deviate from ICS to better manage coordination costs. This is yet another example of flexibility and expertise being employed to deal with an incident.

The next time you observe an incident, or you reflect on an incident where you were one of the responders, think back on to what extent these ingredients were present or absent. Were you able to leverage spare resources, or did you suffer from not being to? Were there operational changes that people wanted to be able to make during the incident, and were they actually able to make them? Were the responders experienced with the sub-systems they were dealing with, and how did that shape their responses? Did different people come up with different hypotheses and strategies? What is it clear to you what the different responders were doing during the incident? These issues are easy to miss if you’re not looking for them. But, once you internalize them, you’ll never be able to unsee them.

You’re missing your near misses

FAA data shows 30 near-misses at Reagan Airport – NPR, Jan 30, 2025

The amount of attention an incident gets is proportional to the severity of the incident: the greater the impact to the organization, the more attention that post-incident activities will get. It’s a natural response, because the greater the impact, the more unsettling it is to people: they worry very specifically about that incident recurring, and want to prevent that from happening again.

Here’s the problem: most of your incidents aren’t going to repeat incidents. Nobody wants an incident to recur, and so there’s a natural built-in mechanism for engineering teams to put in the effort to do preventative work. The real challenge is preventing and quickly mitigating novel future incidents, which is the overwhelming majority of your incidents.

And that brings us to near misses, those operational surprises that have no actual impact, but could have been a major incident if conditions were slightly different. Think of them as precursors to incidents. Or, if you are more poetically inclined, omens.

Because most of our incidents are novel, and because near misses are a source of insight about novel future incidents, if we are serious about wanting to improve reliability, we should be treating our near misses as first-class entities, the way we do with incidents. Yet, I’d wager that there are no tech companies out there today that would put the same level of effort into a near miss as they would to a real incident. I’d love to hear about a tech company that holds near miss reviews, but I haven’t heard any yet.

There are real challenges to treating near misses as first-class. We can generally afford to spend a lot of post-incident effort on each high-severity incident, because there generally aren’t that many of them. I’m quite confident that your org encounters many more near misses than it does high-severity incidents, and nobody has the cycles to put in the same level of effort for every near-miss as they do for every high severity incident. This means that we need to use judgment. We can’t use severity of impact to guide us here, because these near misses are, by definition, zero severity. We need to identify which near misses are worth examining further, and which ones to let go. It’s going to be a judgment call about how much we think we could potentially learn from looking further.

The other challenge is just surfacing these near misses. Because they are zero impact, it’s likely that only a handful of people in the organization are aware when a near miss happens. Treating near misses as first class events requires a cultural shift in an organization, where the people who are aware of them highlight the near miss as a potential source of insight for improving reliability. People have to see the value in sharing when these happens, it has to be rewarded or it won’t happen.

These near misses are happening in your organization right now. Some of them will eventually blossom into full-blown high-severity incidents. If you’re not looking for them, you won’t see them.