Carrots, sticks, and making things worse

Every organization has to contend with limits: scarcity of resources, people, attention, or funding, friction from scaling, inertia from previous code bases, or a quickly shifting ecosystem. And of course there are more, like time, quality, effort, or how much can fit in anyone's mind. There are so many ways for things to go wrong; your ongoing success comes in no small part from the people within your system constantly navigating that space, making sacrifice decisions and trading off some things to buy runway elsewhere. From time to time, these come to a head in what we call a goal conflict, where two important attributes clash with each other.

These are not avoidable, and in fact are just assumed to be so in many cases, such as "cheap, fast, and good; pick two". But somehow, when it comes to more specific details of our work, that clarity hides itself or gets obscured by the veil of normative judgments. It is easy after an incident to think of what people could have done differently, of signals they should have listened to, or of consequences they would have foreseen had they just been a little bit more careful.

From this point of view, the idea of reinforcing desired behaviors through incentives, both positive (bonuses, public praise, promotions) and negative (demerits, re-certification, disciplinary reviews) can feel attractive. (Do note here that I am specifically talking of incentives around specific decision-making or performance, rather than broader ones such as wages, perks, overtime or hazard pay, or employment benefits, even though effects may sometimes overlap.)

But this perspective itself is a trap. Hindsight bias--where we overestimate how predictable outcomes were after the fact--and its close relative outcome bias--where knowing the results after the fact tints how we judge the decision made--both serve as good reminders that we should ideally look at decisions as they were being made, with the information known and pressures present then..

This is generally made easier by assuming people were trying to do a good job and get good results; a judgment that seems to make no sense asks of us that we figure out how it seemed reasonable at the time.

Events were likely challenging, resources were limited (including cognitive bandwidth), and context was probably uncertain. If you were looking for goal conflicts and difficult trade-offs, this is certainly a promising area in which they can be found.

Taking people's desire for good outcomes for granted forces you to shift your perspective. It demands you move away from thinking that somehow more pressure toward succeeding would help. It makes you ask what aid could be given to navigate the situation better, how the context could be changed for the trade-offs to be negotiated differently next time around. It lets us move away from wondering how we can prevent mistakes and move toward how we could better support our participants.

Hell, the idea of rewarding desired behavior feels enticing even in cases where your review process does not fall into the traps mentioned here, where you take a more just approach.

But the core idea here is that you can't really expect different outcomes if the pressures and goals that gave them rise don't change either.

During incidents, priorities in play already are things like "I've got to fix this to keep this business alive", stabilizing the system to prevent large cascades, or trying to prevent harm to users or customers. They come with stress, adrenalin, and sometimes a sense of panic or shock. These are likely to rank higher in the minds of people than “what’s my bonus gonna be?” or “am I losing a gift card or some plaque if I fail?”

Adding incentives, whether positive or negative, does not clarify the situation. It does not address goal conflicts. It adds more variables to the equation, complexifies the situation, and likely makes it more challenging.

Chances are that people will make the same decisions they would have made (and have been making continuously) in the past, obtaining the desired outcomes. Instead, they’ll change what they report later in subtle ways, by either tweaking or hiding information to protect themselves, or by gradually losing trust in the process you've put in place. These effects can be amplified when teams are given hard-to-meet abstract targets such as lowering incident counts, which can actively interfere with incident response by creating new decision points in people's mental flows. If responders have to discuss and classify the nature of an incident to fit an accounting system unrelated to solving it right now, their response is likely made slower, more challenging.

This is not to say all attempts at structure and classification would hinder proper response, though. Clarifying the critical elements to salvage first, creating cues and language for patterns that will be encountered, and agreeing on strategies that support effective coordination across participants can all be really productive. It needs to be done with a deeper understanding of how your incident response actually works, and that sometimes means unpleasant feedback about how people perceive your priorities.

I've been in reviews where people stated things like "we know that we get yelled at more for delivering features late than broken code so we just shipped broken code since we were out of time", or who admitted ignoring execs who made a habit of coming down from above to scold employees into fixing things they were pressured into doing anyway. These can be hurtful for an organization to consider, but they are nevertheless a real part of how people deal with exceptional situations.

By trying to properly understand the challenges, by clarifying the goal-conflicts that arise in systems and result in sometimes frustrating trade-offs, and by making learning from these experiences an objective of its own, we can hopefully make things a bit better. Grounding our interventions within a richer, more naturalistic understanding of incident response and all its challenges is a small—albeit a critical one—part of it all.