2 posts on Collaboration

Every decision we make involves weighing tradeoffs, whether that is done consciously or not. From evaluating whether an extra bedroom is worth $800 extra in rent, whether being able to sleep lying down during a long flight is worth the $500 upgrade cost, to whether you should take a pay cut for that dream job.

For complex high-stakes decisions involving many competing tradeoffs, trying to decide with your gut can be paralyzing. The complex tradeoffs that come up when designing products ^[1] fall in that category so frequently that analytical decision-making skills are considered one of the most important skills a product manager can have. I would argue it’s a bit broader: analytical decision-making is one of the most useful skills a human can have.

Structured decision-making is a passion of mine (perhaps as a coping mechanism for my proneness to analysis paralysis). In fact, one of the very first demos of Mavo (the novice programming language I designed at MIT) was a decision-making tool for weighed pros & cons. It was even one of the two apps our usability study participants were asked to build during the first Mavo study. I do not only use the techniques described here for work-related decisions, but for any decision that involves complex tradeoffs (often to the amusement of my friends and spouse).

Before going any further, it is important to note a big caveat. Decision-making itself also involves tradeoffs: adding structure makes decisions easier, but consumes more time. To balance this, I tend to favor an iterative approach, adding more precision and structure only if the previous step failed to provide clarity. Each step progressively builds on the previous one, minimizing superfluous effort.

For very simple, uncontroversial decisions, just discussing or thinking about pros and cons can be sufficient, and the cost-benefit of more structure is not worth it. Explicitly listing pros and cons is probably the most common method, and works well when consensus is within reach and the decision is of moderate complexity. However, since not all pros and cons are equivalent, this delegates the weighing to your gut. For more complex or controversial decisions, there is value in spending the time to also make the weighing more structured.

The tradeoff scorecard

What is a decision matrix?

A decision matrix, also known as a scorecard, is a table with options as rows and criteria as columns, with a column in the end that calculates a score for each option based on the criteria. These are useful both for selection, as well as prioritization, where the score is used for ranking options. In selection use cases, the columns can be specific to the problem at hand, or predefined based on certain principles or factors, or a mix of both. Prioritization tends to use predefined columns to ensure consistency. There is a number of frameworks out there about what these columns should be and how to calculate the score, with RICE (Reach × Impact × Confidence / Effort) likely being the most popular.

The Tradeoff Scorecard is not a prioritization framework, but a decision-making framework for choosing among several options.

Qualitative vs. quantitative tradeoffs

Typically, tradeoffs fall in two categories:

• Qualitative: Each option either includes the tradeoff or it doesn’t. Thing of them as tags that you can add or remove to each option.
• Quantitative: The tradeoff is associated with a value (e.g. price, effort, number of clicks, etc.)

Not all tradeoffs are all equal. Even for qualitative tradeoffs, some are more important than others, and the differences can be quite vast. Some strengths may be huge advantages, while others minor nice-to-haves. Similarly, some weaknesses can be dealbreakers, while others minor issues.

We can model this by assigning a weight to each tradeoff (typically a 1-5 or 1-10 integer). But if quantitative tradeoffs have a weight, doesn’t that make them quantitative? The difference is that the weight applies to the tradeoff itself, and is applied the same to each each option, whereas the value of quantitative tradeoffs quantifies the relationship between tradeoff and option and thus, is different for each. Note that quantitative tradeoffs also have a weight, since they also don’t all matter the same.

In diagrammatic form, it looks a bit like this:

These categories are not set in stone. It is quite common for qualitative tradeoffs to become quantitative down the line as we realize we need more granularity. For example, you may start with “Poor discoverability” as a qualitative tradeoff, then realize that there is enough variance across options that you instead need a quantitative “Discoverability” factor with a 1-5 rating. The opposite is more rare, but it’s not unheard of to realize that a certain factor does not have enough variance to be worth a quantitative tradeoff and instead should be modeled as 1-2 qualitative tradeoffs.

The overall score of each option is the sum of the scores of each individual tradeoff for that option. The score of each tradeoff is often simply its weight multiplied by its value, using 1/-1 as the value of qualitative tradeoffs (pro = 1, con = -1).

While qualitative tradeoffs are either pros or cons, quantitative tradeoffs may not be universally positive or negative. For example, consider price: a low price is a strength, but a high price is a weakness. Similarly, effort is a strength when low, but a weakness when high. Calculating a score for these types of tradeoffs can be a bit more involved:

• For ratings, we can subtract the midpoint and use that as the value. E.g. by subtracting 3 from a 1-5 rating we get value from -2 to 2. Adjust accordingly if you don’t want the switch to happen in the middle.
• For less constrained values, such as prices, we can use the value’s percentile instead of the raw number.

Explicit vs implicit tradeoffs

When listing pros and cons across many choices, have you noticed that there is a lot of repetition? First, several options share the same pros and cons, which is expected, since they are alternative solutions to the same problem. But also because pros and cons come in pairs. Each strength has a complementary weakness (which is the absence of that strength), and vice versa.

For example, if one UI option involves a jarring UI shift (a bad thing), the presence of this is a weakness, but its absence is a strength! In other words, each qualitative tradeoff is present on all options, either as a strength or as a weakness. The decision of whether to pick the strength or the weakness as the primary framing for each tradeoff is often based on storytelling and/or minimizing effort (which one is more common?). A good rule of thumb is to try to avoid negatives (e.g. instead of listing “no jarring UI shift” as a pro, list “jarring UI shift” as as con).

It may seem strange to view it this way, but imagine you were trying to compare and contrast five different ideas, three of which involved a jarring UI shift. You would probably list “no jarring UI shifts” as a pro for the other two, right?

This realization helps cut the amount of work needed in half: we simply assume that for any tradeoff not explicitly listed, its opposite is implicitly listed.

Putting it all together

Your choice of tool can make a big difference to how easy this process is. In theory, we could model all tradeoffs as a classic decision matrix, with a column for each tradeoff. Quantitative tradeoffs would correspond to numeric columns, while qualitative tradeoffs would correspond to boolean columns (e.g. checkboxes).

Indeed, if all we have is a grid-based tool (e.g. spreadsheets), we may be stuck doing exactly that. It does have the advantage that it makes it trivial to convert a qualitative tradeoff to a quantitative one, but it can be very unwieldy to work with.

If our tool of choice supports lists within cells, we can do better. These boolean columns can be combined into one column as a list of all relevant tradeoffs. Then, a separate table can be used to define weights for each tradeoff (and any other metadata, e.g. optional notes).

I currently use Coda for these tradeoff scorecards. While not perfect, it does support lists in cells, and has a few other features that make working with tradeoff scorecards easier:

• Thanks to its relation concept, the list of tradeoffs can be actually linked to their definitions. This means that hovering over each tradeoff displys a popup with its metadata, and that I can add tradeoffs by selecting them from a popup menu.
• Conditional formatting allows me to color-code tradeoffs based on their type (strength/weakness or pro/con) and weight (lighter color for smaller impact).
• Its formula language allows me to show and list the implicit tradeoffs for each option (though there is no way to have them be color-coded too).

There are also limitations however:

• While I can apply conditional formatting to color-code the opposite of each tradeoff, I cannot display implicit tradeoffs as nice color-coded chips, in the same way as explicit tradeoffs, since relations can only display the primary column.
• Weights for quantitative tradeoffs have to be baked in the formula (there are some ways to make them editable, but )

“Oh we can’t possibly do that, it would be way too much work to implement!”

Raise your hand if you’ve ever heard (or worse, said) this during a product design brainstorming session. In my experience, this argument is where a lot of good product design goes to die.

No, I’m not suggesting you should drain engineering resources chasing the perfect UI! Yes, in the end it will all boil down to Impact/Effort ^[1]. But bringing ephemeral constraints such as implementation difficulty up too early is counterproductive.

By ephemeral I’m referring to constraints that are not intrinsic to the problem at hand (these are requirements!), but those that have to do with the current state of technology, the team, the company, or the broader environment.

For example, “This interaction will be very common and needs to be as frictionless and efficient as possible” is a requirement, as it is intrinsic to the use case. Ephemeral constraints are things like:

• Engineering resources
• Performance
• Technical feasibility (to some extent)
• Backwards compatibility

A tool I keep coming back to is what I call a “North Star UI” . A North Star UI is the ideal UI for addressing a set of pain points and use cases in a perfect world with no ephemeral constraints. It answers the question “What would we ship if both we and our users had infinite resources, and all our users were new?”.

I have often mentioned this concept in discussions, and it seemed to be generally understood. However, a quick google search revealed that outside of this blog, there are only a couple mentions of the term across the entire Web, and the only actual definition seems to be a callout in my Eigensolutions essay. That needed to be fixed — if I’ve found this concept so useful, it’s highly likely that others would too!

For the uninitiated, designing a solution that ignores essential constraints seems like a pointless academic exercise. “Why would we spend precious resources on something that’s not feasible? We should be pragmatic, not chase pipe dreams!” I often hear. And yet, perhaps counterintuitively, a solid NSUI can provide so many benefits that over time it actually reduces the amount of resources spent on product design.

What benefits? Let’s dive in.

1. Simplifies problem solving

A common problem-solving strategy in every domain, is to break down a complex problem into smaller, more manageable components and solving them separately. Product design is no different. The concept of a North Star UI breaks down product design tasks into three more manageable components:

1. North Star UI (NSUI): What is the ideal solution?
2. Ephemeral constraints: What prevents us from getting there?
3. Compromises: How close can we reasonably get given the timeframe we have?

These subproblems do not always have the same level of difficulty. Sometimes the NSUI is obvious, and the big product design challenge is navigating the compromises. Other times, the big challenge is figuring out what the NSUI should be and once this is clear, it turns out it’s also perfectly feasible. And most of the time, both are hard, so solving them separately can be a huge help.

2. Facilitates team alignment and helps build consensus

This is a pattern I’ve seen very frequently, across many different teams: disagreements about the NSUI will often masquerade as disagreements about practical constraints, so people will waste time and energy debating the wrong issue.

Here is a story that may sound familiar: Bob will say “X is way too much work, it’s not worth doing”, but what are they actually thinking is “X is a bad idea, so any nontrivial amount of work towards it is a waste”, while Alice thinks that X is an elegant solution that would create an incredible user experience, and is worth a somewhat higher implementation cost. Instead of spending time figuring out whether X is a good idea in itself, they spend their time debating how much work it is and how that could be simplified, but fail to reach consensus because that is not actually the root issue. (Any similarity to real persons, living or dead, is purely coincidental.) 😅

The thing is, when the NSUI is not documented, it still exists, but everyone has their own version. It is important to answer these questions in order, and reach consensus on what the North Star UI is before moving on. We need to be aware of what is an actual design decision and what is a compromise driven by practical constraints.

By articulating these separately, they can also be debated separately, so that when we are at the stage of evaluating compromises, we are all on the same page about what we are trading off and how much it’s worth. How can you do a cost-benefit analysis, without knowing both the cost and the benefit?

NSUIs can even be user tested, using wireframes or prototypes, which can be particularly useful when there are vastly different perspectives within a team about what the NSUI is, or when the problem is so novel that every potential solution is on shaky ground. Even the best product intuition can be wrong, and there is no point in evaluating compromises if it turns out that even the “perfect” solution is not actually a good one.

3. Paves the way for getting there (someday)

Just like the mythical North Star, a NSUI can serve as a guide to steer us in the right direction. Simply articulating what the NSUI is can in itself make it more feasible. No, it’s not magic, just human psychology.

First, once we have a NSUI, we can use it to evaluate proposed solutions: How do they relate to a future where the NSUI is implemented? Are they a milestone along that path, or do they actively prevent us from ever getting there?

Prioritizing solutions that are milestones that get us closer to the NSUI can be a powerful tool in building momentum. Once we’re partway there, it naturally begs the question: how much closer can we get? it is much easier to convince people to move a little further along on the path they are already on, than to move to a completely different path. Even if we can’t get all the way there, maybe we can close enough that the remaining distance won’t matter. And often, the closer you get, the more feasible it becomes. In some cases, simply reframing the NSUI as a sequence of milestones rather than a binary goal can be all that is needed to make it feasible. (perhaps I should call this )

4. Today’s constraints are not tomorrow’s constraints

NSUIs make our design process more resilient and adaptable. I have often seen “unimplementable” solutions become implementable down the line, due to changes in internal or external factors, or simply because someone had a brilliant idea that made the impossible, possible. I have seen this happen so many times that I have learned to interpret “cannot be done” as “really hard — right now”.

When this happens, it’s important to have a solid foundation to fall back on, rather than having to go back to the drawing board because design and constraints were so intertwined we didn’t know where our actual design choices ended and the practical compromises began. With a solid NSUI in place, when constraints are lifted we only need to re-evaluate the compromises.

Change in Engineering Momentum: Sentiment Chips

Here is a little secret that applies to nearly all software engineers: neither feasibility nor effort are fixed for a given task.

Engineers are not automatons that will blindly implement whatever they are told to. Product managers are often content to get engineering to reluctantly agree to implement, but then you’re getting very poor ROI out of your engineering team.

Often all that is needed to make the infeasible, feasible is engineering momentum. Investing the extra time and energy to get engineers excited can really pay off. When good engineers are excited, they become miracle workers. The difference is not small, it is orders of magnitude. Things that were impossible or insurmountable become feasible, and things that would normally take weeks or even months can be prototyped in days.

One way to get engineers excited is to convince them about the value and utility of what they are building. It helps a lot to have them observe usability testing sessions and to be able to back product decisions up with data.

As I discovered last year by accident, there is also another, more …Machiavellian way to build engineering momentum: The NSUI is too hard? Propose a much easier solution that you know engineers would hate, such as one that turns a structured interaction into unstructured data. As much as I wish I could be that strategic 😅, this was not something I had planned in advance, but it was very effective in retrospect: I got an entire backend to work with that I had thought was entirely out of the question!

Change in the Environment: CSS Conic Gradients

background: conic-gradient(in hsl,
	red, orange, yellow, lime,
	cyan, blue, magenta, red);

Sometimes, the environment changes and a previously infeasible or high effort feature becomes feasible or even trivial. An example that comes to mind is CSS conic gradients. Conic gradients are the type of gradient that is created by (conceptually) rotating a ray around a center point.

I originally proposed adding conic gradients to CSS in 2011, and they first shipped in 2018 (in Chrome 69)! Someone observing this timeline without context may just conclude “pffft, standards just take forever to ship”. But there is always a reason, either technical, human, or both. In this case, the reason was technical. Browsers do not implement things like shadows and gradients from scratch, they use graphics libraries such as Skia, Cairo, or Core Graphics, which in turn are also abstractions over the OS-provided graphics APIs.

At the time these libraries did not support any primitive that could be used to render conic gradients (e.g. sweep gradients, mesh gradients, etc.). In the years that followed, one after the other added support for some kind of gradient primitive that could be used to easily render conic gradients, which took the proposal from high to low effort. I also created a polyfill which stimulated developer demand, increasing Impact. These two things together took the Impact/Effort ratio from “not worth it” to “let’s do this, stat” and in 2 years the feature was implemented in every major browser.

Someone has a Lightbulb Moment: Relaxed CSS Nesting Syntax

Sometimes high effort things just take a lot of hard work and there is no way around it. Other times they are one good idea away.

One of my favorite examples, and something I’m proud to have helped drive is the relaxed CSS Nesting syntax, now shipped in every browser. It is such an amazing case study on the importance of having a North Star UI, I even did an entire talk about it at Web Unleashed, with a lot more technical details that I have included here.

In a nutshell, CSS nesting is a syntax that allowed CSS developers to reduce repetition and better organize their code by allowing them to nest rules inside other rules.

table.browser-support {
	border-collapse: collapse;
}
table.browser-support th,
table.browser-support td {
	border: 1px solid silver;
}
@media (width < 600px) {
	table.browser-support,
	table.browser-support tr,
	table.browser-support th,
	table.browser-support td {
		display: block;
	}
}
table.browser-support th {
	border: 0;
}
table.browser-support td {
	background: yellowgreen;
}
table.browser-support td:empty {
	background: red;
}
table.browser-support td > a {
	color: inherit;
}

table.browser-support {
	border-collapse: collapse;
@media (width < 600px) {
&, tr, th, td {
display: block;
}
}
th, td {
border: 1px solid silver;
}
th {
border: 0;
}
td {
background: yellowgreen;
&:empty {
background: red;
}
> a {
color: inherit;
}
}
}

This is one of the few cases where the NSUI was well known in advance, since the syntax was well established in developer tooling (CSS preprocessors). Instead, the big challenge was navigating the practical constraints, since CSS implemented in browsers has different performance characteristics, so a syntax that is easily feasible for a preprocessor may be out of reach for a browser. In this case, the NSUI syntax had been ruled out by browser engineers due to prohibitive parsing performance ^[2], so we had to design a different, more explicit syntax that could be parsed more efficiently.

Initial attempts for a syntax that satisfied these requirements introduced a lot of noise, in the form of an awkward, noisy @nest token that needed to be placed in the beginning of many nested rules.

At this point, it is important to note that CSS Nesting is a feature that once available, it is used all over a stylesheet, not just a couple times here and there. For such widely used features, every character counts. Conciseness and readability of syntax are paramount, especially when conciseness is the sole purpose of this feature in the first place!

Worse yet, these attempts were actively incompatible with the NSUI syntax, as well as other parts of CSS (namely, the @scope rule). This meant that even if the NSUI became feasible later, CSS would need to forever support syntax that would then have no purpose, it would exist just as a wart from the past, just like HTML doctypes.

This proposal sat dormant for a while, since implementors were not exactly in a hurry to ship it. This all changed when State of CSS 2022 showed Nesting as the top missing CSS feature, making Google suddenly very keen to ship it.

A small subset of the CSS Working Group, led by Elika Etemad and yours truly organized a number of breakouts to explore alternatives, an effort that produced not one, not two, but four competing proposals. The one that the group voted to adopt ^[3] was the one I designed with the NSUI in mind, by asking the question: If the NSUI is out of the question right now, how close can we get and still be compatible with it in case it becomes feasible later on?

Once we got consensus on this intermediate syntax, I started exploring whether we could get any closer to the NSUI, even attempting to propose an algorithm that would reduce the number of cases that required the slower parsing to essentially an edge case. A few other WG members joined me, with my co-TAG member Peter Linss being most vocal. This is a big advantage of NSUI-compatible designs: it is much easier to convince people to move a little further along on the path they are already on, than to move to a completely different path. With a bit of luck, you may even find yourself implementing an “infeasible” NSUI without even realizing it, one step at a time.

We initially faced a lot of resistance from browser engineers, until eventually Anders Ruud and his team experimented with variations of my proposed algorithm and actually closed in on a way to implement the NSUI syntax in Chrome. The rest, as they say, is history.