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7 Sep

Fuck Nuance

I stumbled upon quite a provoking sociological paper recently.

It's called Fuck Nuance, by Kieran Healy.

(The paper is free to read. Just Google “Fuck Nuance” and you'll find a PDF on the very first link. But I wrote this piece so that you don't have to do the hard work of parsing it.)

Now, if you have been reading Stoa Daily for a while, you know how much we love detail and nuance in our reasoning. In fact, nuance is something we actively try to promote with these essays — by reasoning out loud in sometimes elegant, and usually messy ways.

But today's piece is my attempt to add some much-needed nuance to the issue of nuance itself.

Fuck Nuance — the paper — goes into how sociology today is fraught with an intellectual craze for nuance, till the point that the demand for nuance obstructs good theory and abstraction that is useful for making progress in the field.

“When faced with a problem that is hard to solve, a line of thinking that requires us to commit to some defeasible claim, or a logical dilemma we must bite the bullet on, the nuance-promoting theorist says,

“But isn’t it more complicated than that?”


“Isn’t it really both/and?”


“Aren’t these phenomena mutually constitutive?”


“Aren’t you leaving out [something]?”


“How does the theory deal with agency, or structure, or culture, or temporality, or power, or [some other abstract noun]?”

This sort of nuance is, I contend, fundamentally antitheoretical. It blocks the process of abstraction on which theory depends, and it inhibits the creative process that makes theorizing a useful activity.”

Essentially, what Kieran suggests is that the relentless pursuit of nuance in theories can sometimes obstruct a good, fruitful theory. This made me pause and ponder about the necessity of simplification and abstraction, especially when we approach complex problems in business.

What intrigued me most was the central premise — the call to forsake the details, to forsake nuance, at least temporarily, to allow a useful theory to develop.

To understand what I mean by a “useful theory”, the idea of instrumental convergence might help.

(Feel free to ignore this bit if you aren't mathematically inclined.)

A useful theory, at its core, is a great abstraction of a fundamental pattern.

Think of “abstraction” as a tool that helps you distill the essential information from a complex system or concept. Now, when you aim to forecast or analyze the behavior of X or things influenced by X, you're primarily concerned with the core details encapsulated in f(X), the abstracted version of X, not the nitty-gritty of X in its entirety.

The abstraction — f(X) — is simply focused on X, insofar as it is useful for fulfilling a certain function and doesn't really care about the nature of X itself. f(X) converges instrumentally and narrows down focus on the outcome and the goal, instead of getting lost in the sprawling complexity of X.

So, when we talk about the goodness of any theory, we are actually making a claim about the general usefulness of the abstraction it presents.

“The most important thing about a theory is whether it is any good. Demands for more nuance actively inhibit the process of abstraction that good theory depends on.”

Don't worry, I understand that the previous section may be hard to follow. Let's look at some examples.

Think about a circle, or what set of objects we call a “circle.”

A “circle” is an abstraction for a 2D closed figure such that every point in the figure is the same distance from some central point. This definition doesn't describe the details of a particular circle at a particular point in the 2D plane. Rather, it describes the nature of all circles.

Mathematically, the following function abstracts the nature of all circles on a 2D plane:

Notice that the function doesn't list out the specifics of any circle in particular, but can be employed to describe any circle on the 2D plane.

Likewise with many other common abstractions like a “chair” or a “table” or a “spoon”. These are words used to describe a category of objects in an instrumental way, while going into the specifics of any particular chair, table, or spoon.


Because for the given context, the particulars of the category being abstracted do not matter.

“Abstraction is a way of thinking where “new ideas or conceptions are formed by considering several objects or ideas and omitting the features that distinguish them.” Abstraction means throwing away detail, getting rid of particulars. We begin with a variety of different things or events—objects, people, countries—and by ignoring how they differ, we produce some abstract concept like “furniture,” “honor killing,” “social-democratic welfare state,” or “white privilege.””

Let's take another example.

A gas consists of millions of individual atoms or molecules moving loosely, such that their natural tendency is to expand and move away from each other over time.

But in thermodynamics, a gas is most usefully described by throwing away information about the individual atoms or molecules themselves, and simply referring to a few properties of the gas like its heat, temperature, density, etc.

We can then usefully predict things about the gas like its pressure, just based on the details we've chosen to keep, without having to worry about individual gas particles. about things like, for e.g., pressure, just based on the reduced information we've kept, without having to think about each individual particle. That reduced information is the abstract layer describing the gas and its properties.

For an even more relatable example, consider an app on your smartphone.

What is an app? An app is an application layer consisting of interface elements that can be easily used by any individual of what's happening at lower levels of abstraction.

When you use a smartphone app, you're essentially interacting with a simplified representation or “facade” of a much more complex system. The app serves as a user-friendly interface that abstracts away the intricate and technical processes happening in the background. You just see and interact with buttons, images, and text, but behind those, there are complex algorithms, databases, and networks operating in sync.

Say, you're using a weather app. You open it, and within a few seconds, you're presented with the current weather information, forecasts, and possibly even satellite imagery. All this data is gathered from various sources, processed, and synthesized in real time to give you the information in a digestible format.

You don't see the vast computational processes, data exchanges, or the myriad of technical details involved in bringing that information to your screen. The app abstracts these complex processes, allowing you to access what you need without getting bogged down with the how and why.

Essentially, abstractions allow you to grasp, address, and interact with new instances more efficiently by “abstracting away” lower-level details.

When I drive a car or operate a fridge, I don't need to think about what makes them work. I just use them on the application layer to achieve my intended goal. To operate a car, I don't need to worry about the details of how an internal combustion engine or an AC motor works. To operate a fridge, I don't need to understand how a compressor works. I only care about whether both of them do the job I intend them to do.

This lack of deep knowledge isn't a handicap. Rather, it is a useful feature of abstracted interfaces. My interaction with these tools only demands an understanding of their user interfaces, not how they were created.

This is only possible by our ability to layer abstractions, where each layer builds upon the others, enhancing our effectiveness at manufacturing tools that can do increasingly complex tasks. In fact, civilization itself is a result of centuries of advancements, significantly propelled by humans creating and implementing novel abstractions that help us do a larger set of activities without ever having to think about them.

And this is precisely what the author also means when they speak of the goodness or usefulness of any theory or abstraction.

But now, the author goes into the reasons why over-indexing on nuance may inhibit the creation of useful theory. This excerpt encapsulates the core essence of the paper:

“Nuance flourishes because of the relative absence of shared standards within the field for the evaluation of theory. These standards can be those of logic, for instance, or model-building, or research methods, or even simply an agreed-upon focus on an empirically delimited area. With one or more of these constraints in place, abstractions become possible and theory can develop.

But in their absence, there is a tendency to fall back on assertions of multidimensionality or worry that one has to “account for” everything at once.

The result is a lot of unproductive blocking. General theory suffers, but so do particular explanations. By calling for a theory to be more comprehensive, or for an explanation to include additional dimensions, or for a concept to become more flexible and multifaceted, we paradoxically end up with less clarity. We lose information by adding detail.”

You see, there's a tradeoff between complexity, abstraction, and generality.

  • Complexity lets us explain a wider range of phenomena
  • Abstraction allows us to reason more easily by filtering unnecessary noise or details
  • Generalization lets us reason over larger domains.

But, there are tradeoffs between the three.

Adding complexity helps us incorporate more things in our categories by adding detail to our models. However, this increased detail makes the models harder to understand and play with. If the model isn't central to our objective but only a peripheral subset, we might be better off using a less complex model.

Abstraction helps us arrive at more straightforward reasoning by distilling information to its core components, shielding us from unnecessary detail. However, over-abstraction might sever our connection to reality, making our models less accurate and less representative of the reality they're trying to map.

Generalization broadens the applicability of our models but at the expense of specific details. It can sometimes lead to oversimplified viewpoints, missing nuances in particular situations. This concept is visible in economics, where broad theories sometimes fail to account for nuanced human behaviors, prompting the development of more concrete models to better align with actual human actions.

In Fuck Nuance, Kieran argues that when it comes to sociology, over-indexing on nuance doesn't let a useful theory develop, because every critic wants to add their own bit of nuance, and this nuance isn't always helpful.

“... someone [is] challenged on the grounds that their theory or research is missing something, or has ignored some dimension, or neglected to adequately address some feature of social reality. Calling for more nuance in this way makes us shy away from the riskier aspects of abstraction and theory-building generally, especially if it is the first and most frequent response we hear. Instead of pushing some abstraction or argument along for a while to see where it goes, we have a tendency to start hedging theory with particulars. People complain that some level or dimension has been left out, and they demand that it be brought back in.

... the heterogeneity of research topics that sociologists pursue means that everyone is tempted to bring the particulars of his or her own empirical case to bear on whatever theoretical idea is being developed. ”

The author also discusses the three common nuance traps we tend to fall into while thinking about a given argument:

1. Nuance of the fine-grain

This happens when someone rejects a theory citing a lack of descriptive detail: particulars that are not necessary to prove the general.

2. Nuance of the conceptual framework

This happens when the theorist attempts to endlessly expand the theoretical system to capture more and more scenarios and detail — only so that the system can make itself immune to rebuttal or disconfirmation by any edge-case in the world.

3. Nuance of the connoisseur

This happens when the critic alleges that the proposed theory lacks richness and texture. It is most unhelpful when said richness or texture is purely idiosyncratic and aims to satisfy one's own individual sense of detail — instead of aiding the objective the theory is aiming for.

“The quality of a theory on principled grounds is ultimately the most important thing about it.”

To help you grok this statement, let me present some hypothetical scenarios in the workplace to understand how a lack of understanding of abstraction and nuance can play out in everyday engagements.

Business Scenario 1: Asking questions at the wrong level of abstraction

We all know that in an early-stage startup, resources are limited, and the focus is usually on rapid growth and scalability. Teams often have to work with a higher level of abstraction to keep up with the pace and not get bogged down in details that might be irrelevant to strategic discussions.

Now, consider a virtual team meeting where a new product launch is being discussed. Arnav, the manager, has a broad vision of where the project should be heading. Nimesh is a team member who is responsible for a part of the project. Currently, Nimesh is focusing too much on the nitty-gritty, hindering the discussion's flow and progression.

Arnav: So, our main goal with this launch is to capture a significant market share in the next quarter. We need to focus on the core features that will make us stand out and attract initial users.

Nimesh: But Arnav, have we decided on the color scheme and the font style for the user interface? I believe these aspects are vital and we should settle them before moving forward.

Arnav: While I appreciate your attention to detail, we are at a stage where we need to conceptualize the broader aspects of the project. The elements you're talking about fall under the realm of aesthetics, which, although important, can be refined as we progress.

Right now, our primary focus should be on creating a good product strategy that outlines our unique selling points, target audience, and marketing channels. As we move forward, we'll delve deeper into the specifics, where your insights on the finer details will be extremely valuable.

Nimesh: I see your point, but don't you think addressing these smaller elements now would give us a head start?

Arnav: It's not about racing to address every minute detail at the outset, Nimesh. At this stage, it's more important to have a clear roadmap that outlines our overarching strategy. If we immerse ourselves in the granular details now, we risk losing sight of our broader goals, and it could significantly stall our project.

Right now, I would urge you to think about questions like

“What are the core features that will make our product unique?”


“What strategies can we employ to reach our target audience effectively?”

These questions will help us to carve out a solid foundation, upon which we can build and refine as we move forward.

Nimesh: Alright, I understand. I'll align my focus more towards the broader objectives for now.

Arnav: Excellent! Let's build a strong base first; the finer details will follow naturally as we progress.

In this scenario, we see that Nimesh is focused on elements that, although important, are at a lower level of abstraction than what is required at the current stage of the project. Arnav guides him to recalibrate the level at which he's thinking about the problem.

The only thing the team collectively needs to agree on in this discussion is the larger direction or approach. The details are important, but they are distracting from the goal this conversation aims to achieve.

I think it demonstrates something that happens quite often at early-stage startups, where an excessive focus on particulars from teammates can hinder the general usefulness of a particular strategy or approach.

Business Scenario 2: Arguing on different levels of abstraction

Let's consider another scenario, this time demonstrating how misunderstandings and arguments can arise, simply because two people are arguing at different levels of abstraction or nuance.

Consider a conversation between Tabish and Nishant, where they're discussing the development of a new interactive learning platform. Tabish is focusing on pedagogical aspects. Nishant is focusing on the technological features of the platform.

Tabish (visibly frustrated): Nishant, I am telling you, we need to concentrate on building a curriculum that caters to different learning styles. We can't just churn out a platform with flashy features but lacks substance.

Nishant (defending): And I'm saying that without advanced features and a solid technical foundation, the platform won't be able to support the diverse curriculum you're advocating for. We need to prioritize the technical development first!

Noticing this heated debate going on in office, Kaustubh, the manager, intervenes.

Kaustubh (calmly): Hold on, both of you. It seems we are having a disconnect here because you are discussing two vital but different aspects of the project. Tabish, you are immersed in the pedagogical abstraction, concentrating on the educational content and how it can cater to various learning styles. Nishant, on the other hand, you are focused on the technicalities.

Both of you are arguing about different things, on different levels. Both perspectives are crucial, and neither can be sidelined.  Now, let's try to frame questions that consider both these aspects, and work collaboratively to address them, instead of focusing on our individual domains exclusively.

Let's discuss how to ensure we're thinking at the right level of abstraction. A good starting point is by asking yourself two questions:

1. What is the primary objective of our current discussion or project phase?

If we're brainstorming core functionalities or unique selling propositions, it's more about the broader strokes. On the other hand, when we're fine-tuning user experience or curriculum details, we need to delve into specifics.

2. Is my input or question going to move the project forward in this current phase?

If the answer is “No”, then it's likely not the right time to address that concern.

Nishant: That makes sense, but how can we ensure we’re doing a good job at it?

Kaustubh: The trick lies in periodic reflection. For instance, after a meeting, take a few moments to reflect on the discussions. Were they aligned with the current project phase? Were there moments when someone got too bogged down in details or, conversely, remained too vague when specifics were needed?

Tabish: Okay, and what if we notice we've been on the wrong level?

Kaustubh: Adjust and communicate. If you find yourself too entrenched in specifics, take a step back and ask,

“How does this fit into our broader goal?”

Conversely, if you're being too generic, challenge yourself to list out specific examples or consequences of your overarching ideas. And importantly, engage with your teammates. A varied perspective is an asset. Use it.

And remember, the level of abstraction will vary depending on the situation. Actively bringing back focus to the objectives when someone is digressing, helps.

Business Scenario 3: Falling prey to nuance-creep and excessively focusing on particulars and edge cases

Let's consider a scenario that will help us better illustrate the conflict between nuance and action and how too much focus on nuance can take away from the conversation instead of adding to it.

Consider a startup townhall, where the team has gathered to discuss how they can improve the product onboarding flow.

Ajinkya is an enthusiastic team member proposing a new, streamlined onboarding process for users. Priya is a team member who is emphasizing a nuanced approach, wanting to cater to every possible user preference during the onboarding.

Ajinkya: I believe we need to simplify our onboarding process. Right now, it takes too long and could potentially discourage new users. I propose we simplify and reduce it to just a few necessary steps, so users can quickly dive into using our app.

Priya: But Ajinkya, I think it's more nuanced than that. What about users who have specific needs and preferences? A reductive process might overlook important details that could enhance the user experience.

Ajinkya: I get that, Priya. But, if we attempt to cater to every individual preference, the onboarding process might become too cumbersome, defeating our initial purpose of making it user-friendly.

Priya: I think you're oversimplifying it. If we ignore these nuances, we might miss out on opportunities to fully satisfy our user base.

The conversation seems to be at a standstill, with both parties strongly advocating for their respective positions. The rest of the team observes silently, sensing the rising tension. Shreya, a discerning team member steps in to mediate and guide the conversation towards a constructive resolution.

Shreya: If I may interject, I think we are experiencing a classic case of nuance versus necessary simplification. Priya, while your concerns are valid, it's also crucial to realize that sometimes catering to every nuance can lead to analysis paralysis, delaying movement on this project.

Priya: But wouldn't it help in creating a more inclusive product?

Shreya: Yes, but let's also not forget the larger goal here — to create a user-friendly product that quickly resonates with a broader user base. Sometimes, simplicity can be a virtue, making the product accessible and easy to navigate for a larger group of people.

And Ajinkya, this doesn't mean we entirely ignore the finer details. We can perhaps initiate a simplified version and, in parallel, work on an advanced setup for users who prefer a more detailed onboarding process. This way, we aren't stalling the progress while still considering how to incorporate edge cases in the onboarding flow.

Ajinkya: Agreed.

This scenario is more common than we'd all like to believe, describing that gnarly trade-off between thoughtfulness and bias for action. Shreya suggests a middle ground that honors both perspectives — the need for swift action and the need for inclusivity.

I personally think Scenario 3 aligns well with the core message in the paper, emphasizing the importance of finding a good trade-off between seeking nuance or dismissing it, based on context and application.

Now that we've touched on how these trade-offs play out in the workplace, let's discuss another key insight from the paper.

In the paper, the author also argues how nuance usually doesn't help if you want to promote your ideas far and wide.

“It is reasonable to want other people to take notice of your work. I argue that nuance is not much use here either. In addition to blocking new ideas and being obnoxious, nuance fails in the long run as a strategy for getting people to read and care about what you have to say.

... We see that the ideas that remain most relevant to the field are not their most nuanced work.”

You might now understand why experienced entrepreneurs on Twitter lean towards more general heuristics, aphorisms, and conjectures that aim to describe a broader aspect of reality, while intentionally sacrificing contextual nuance.

They often get ridiculed by nuance connoisseurs for their lack of nuance and for painting the world with overly broad strokes. But you might now start to see why arriving at simple generalities after wading through a lot of complexity may be a better indicator of reality than nuance that only applies specifically, and not generally.

Nuance can add useful detail, but too much of it can also take away.

To conclude —

All of us, in the course of our daily lives, rely on tools other people have made without having even the slightest clue around how to make those tools ourselves. This applies right from the simplest tools like chairs and mugs, to the most complex ones, like the power distribution system or the personal computer.

But the usefulness of these abstractions allows us to build on top of each other's work. A programmer working with a high-level language like Python doesn't need to understand what's going on at the binary level of operation. This programmer can now focus on creating even higher level of abstracted interfaces using Python, like the software we use every day.

Abstractions are what you get when you choose to throw unhelpful nuance away, in the service of something more generalizable, replicable, and scalable.

Sure, that washing machine has no unique setting for your fancy merino wool shirts, but it doesn't need to, because you're a tiny minority of its target customers who use merino wool. The complexity it would need to add to its system to account for your fancy tastes isn't worth the effort or cost involved. And a good washing machine, by nature, is a robust abstraction that solves for a wide variety of clothes. Adding nuance to it usually doesn't make economic sense.

Hence, to create value at scale, figure out places where abstractions would be useful but haven’t yet been developed, and try to create them. All valuable products and businesses abstract out a set of patterns of human behavior that apply to a large variety of humans.

And the way you do this is by noticing when cool things you come across share properties with other interesting things you've previously seen. The act of finding patterns and connecting the dots kicks off a mental process that leads to useful abstractions that compress and synthesize generalizable takeaways.

But more importantly, understand how much nuance is useful and when. Too much of it generally prevents you from producing something simple that still accurately maps reality. And too little of it leads to producing abstractions that do not map anyone's reality in any meaningful way at all.

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