Clarity vs. Confidence: Starting Conceptual Models Right

Few things are as powerful as a good model of a complex domain. A clear representation of the domain’s key elements and their relationships creates alignment. The model becomes a shared point of reference and shorthand for decision-making.

Good models eschew some complexity. But complex domains aren’t simple. A model that aims to encompass a domain’s full complexity will likely fail at building shared understanding. But a model that over-simplifies won’t be useful.

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Modeling for Automated Organization

Zach Winn reporting in MIT News:

MIT alumnus-founded Netra is using artificial intelligence to improve video analysis at scale. The company’s system can identify activities, objects, emotions, locations, and more to organize and provide context to videos in new ways.

Netra’s solution analyzes video content to identify meaningful constructs in service of more accurate organization. This improves searchability and the pairing of video content with relevant ads. How does this work?

Netra can quickly analyze videos and organize the content based on what’s going on in different clips, including scenes where people are doing similar things, expressing similar emotions, using similar products, and more. Netra’s analysis generates metadata for different scenes, but [Netra CTO Shashi Kant] says Netra’s system provides much more than keyword tagging.

“What we work with are embeddings,” Kant explains, referring to how his system classifies content. “If there’s a scene of someone hitting a home run, there’s a certain signature to that, and we generate an embedding for that. An embedding is a sequence of numbers, or a ‘vector,’ that captures the essence of a piece of content. Tags are just human readable representations of that. So, we’ll train a model that detects all the home runs, but underneath the cover there’s a neural network, and it’s creating an embedding of that video, and that differentiates the scene in other ways from an out or a walk.”

This notion of ‘vectors’ is intriguing — and it sounds like an approach that might be applicable beyond videos. I imagine analyzing the evolution of such vectors over time is essential to deriving relevant contextual information from timeline-based media like video and audio. But I expect such meaningful relationships could also be derived from text.

Systems that do this type of analysis could supplement (or eventually replace) the more granular aspects of IA work. Given the pace of progress in ML modeling, “big” IA (especially high-level conceptual modeling) represents the future of the discipline.

Improving the way videos are organized | MIT News | Massachusetts Institute of Technology

Changes to Sketch’s Conceptual Model

A few days ago, I received an email from Sketch informing me (a customer) of upcoming changes to the app’s licensing model. They come as part of broader changes, which you can read about here.

I was thrilled when I discovered Sketch. For a long time, one of my primary tools was Macromedia Fireworks, a pioneering drawing and prototyping application that blended bitmaps and vectors and featured ‘symbols,’ reusable components that could take variables.

At the time, nothing matched Fireworks for UI work. The component-based paradigm allowed you to design a button and drop it into several screens using different labels. If you tweaked the original button, all instances would change. Amazing.

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Flexibility vs. Ease-of-use

Chris Welch, reporting in The Verge about a new Android tablet feature:

The simply named “Entertainment Space” will be a new section to the left of the home screen on tablets… It’s an all-encompassing hub that brings together video (TV shows, movies, and YouTube), games, and books.

In other words, the feature aggregates the user’s media, making it easier to access. Instead of having to open individual apps to find movies, TV shows, YouTube clips, etc., users can now access a single screen that puts content upfront.

Computers are universal devices — tools for making tools. Depending on what app you’re using, your computer can be a spreadsheet, a music player, a book, a video editor, etc. This flexibility is a big part of what makes computers powerful.

The tradeoff is complexity. Learning to use a single-purpose tool entails forming an accurate mental model of how it works. This can be hard enough. (I’ve been using Excel for decades and still learning new things it can do.)

But when you’re using a platform, you must not only form a model of each tool but also of the means through which you manage tools — where to find them, how to install, launch, and configure them, where to save work-in-progress, etc.

There’s an inherent tension between flexibility and ease of use. System designers oscillate between both extremes. A new device may launch as a single-purpose appliance and evolve towards platformhood.

An example of this is Apple TV. Originally designed as a simple living room media player, today’s models offer a broad range of functions, including the ability to install apps like games and third-party media “stores.”

This flexibility makes the system more powerful but also more complex. In the earlier, simpler version, users could easily choose what content to experience. Now, they must keep track not just of what to experience, but where to do it.

Users of a single-purpose system must only understand a small set of taxonomies. For example, if they’re going to watch movies, they’ll expect to deal with genres, movie studios, directors, etc.

In contrast, a more complex system asks that users understand taxonomies of taxonomies: “this is the type of app where I can expect to see movie genres, whereas this other app over here has levels and health points.”

Features like Entertainment Space aim to square this circle by layering a simplified, content-first experience atop the platform. I expect their effectiveness depends on their discovery algorithms. It’s a tricky design challenge.

Google’s Entertainment Space makes Android tablets look like Google TV – The Verge

Building Bridges to Understanding

Some tasks are easy, like choosing a flavor of ice cream; other tasks are hard, like choosing a medical treatment. Consider, for example, an ice cream shop where the varieties differ only in flavor, not calories or other nutritional content. Selecting which ice cream to eat is merely a matter of choosing the one that tastes best. If the flavors are all familiar, such as vanilla, chocolate, and strawberry, most people will be able to predict with considerable accuracy the relation between their choice and their ultimate consumption experience. Call this relation between choice and welfare a mapping. Even if there are some exotic flavors, the ice cream store can solve the mapping problem by offering a free taste.

Richard H. Thaler, Cass R. Sunstein, Nudge

Thaler and Sunstein are describing part of what I understand as a mental model. New users aren’t blank slates. They approach interactions with a system using preconceptions shaped by prior experiences with analogous systems.

For example, imagine you encounter chocolate as a possible ice cream choice for the first time. (I know, it’s inconceivable. Everyone loves chocolate ice cream. Right? I know I do. Please bear with me.) If you’ve had chocolate candy and any other kind of ice cream before, you may have a rough idea of what to expect. Chocolate has a particular flavor, and ice cream is sweet, cold, and creamy.

Now consider an exotic ice cream flavor such as green tea. You may have had ice cream and green tea before, so you have reference points for both. However, your prior experiences confound your expectations of how green tea ice cream will taste and feel. Ice cream is sweet and cold; green tea is bitter and hot.

So, when choosing between chocolate or green tea ice cream, you’ll have a better model of the former. That is, your expectations of the taste of chocolate ice cream map more closely to your experience of eating it. If you’re feeling adventurous, you may pick green tea anyway. But it’s a gamble. Hence, those (obnoxiously small) free sample spoons in ice cream shops.

The primary function of information architecture is establishing meaningful distinctions. These distinctions appear as choices to users. Users understand those choices in relation to other choices (i.e., as sets of concepts) and in relation to prior interactions with similar choices (i.e., as individual concepts.)

Some of these concepts will be more obvious than others, much like chocolate is a more obvious choice of ice cream flavor than green tea. Users need help when choosing between unfamiliar or ambiguous concepts.

In other words, users need semantic analogs to those free ice cream samples. For example, each choice could include a clear label, plus an icon or a short phrase that clarifies its meaning in this particular context. Ideally, such aids give users a high-level preview of what they can expect to find when they choose that option. (I.e., they “give them a taste of what’s to come.”)

Much of the craft of IA consists of orchestrating the expectations of users as they’re inducted into new systems. This requires building nuanced bridges between users’ (imperfect) mental models and systems’ (complex, unfamiliar) conceptual models. When done successfully, a user‘s confidence in making choices will increase as he or she interacts with the system.

Cover photo: Ruth Hartnupt (CC BY 2.0)


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Overcoming Objections to Modeling

Recently, I asked on Twitter,

What’s the best objection you’ve heard to making conceptual models as part of the design process?

A lively discussion ensued. Some respondents were unclear on what I meant by “conceptual models,” which speaks to the lack of mainstream awareness of this crucial design artifact. (Here’s my latest stab at clarifying.) Others, clear on what conceptual models are, pointed out that the process matters more than ‘deliverables.’ Great point.

But I’m especially interested in the objections. Here are some that represent what I see as the main gist. Chris Avore pointed out that conceptual models are seen as “too hand-wavey or theater-like,” and that they “lead to a few head nods but the world/plan/goal doesn’t change at the end.” To put it bluntly, as Hà Phan did, some people see conceptual models as “bullshit.” (My take: true insofar as they know about modeling at all; I suspect most people don’t.)

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Models Before Screens

Tanner Christensen asked a good question on Twitter:

Peter tagged me on his reply, leading me to respond with a few thoughts. I’m restating (and expanding) them here to keep them from disappearing in Twitter’s fast-moving stream.

Whenever I work on a new navigation system, I start by establishing its ideal user conceptual model. This model must be informed by research. (So, research is the place to start. But that should be self-evident.)

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Citibank’s $500m ‘UI’ Problem

Timothy B. Lee reporting for Ars Technica:

A federal judge has ruled that Citibank isn’t entitled to the return of $500 million it sent to various creditors last August. Kludgey software and a poorly designed user interface contributed to the massive screwup.

Citibank was acting as an agent for Revlon, which owed hundreds of millions of dollars to various creditors. On August 11, Citibank was supposed to send out interest payments totaling $7.8 million to these creditors.

However, Revlon was in the process of refinancing its debt—paying off a few creditors while rolling the rest of its debt into a new loan. And this, combined with the confusing interface of financial software called Flexcube, led the bank to accidentally pay back the principal on the entire loan—most of which wasn’t due until 2023.

My initial reaction on reading this was: wow, $500m is a lot of money — I wonder how bad the UI is? The article provides a screenshot, which it credits to Judge Jesse Furman:

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Three Models

The men who are cursed with the gift of the literal mind are the unfortunate ones who are always busy with their nets and neglect the fishing.

– Rabindranath Tagore, Sadhana

Modeling is the most critical underused design skill. The ability to examine a domain abstractly — to consider its components, how they relate to each other, and how they allow people to achieve their goals — is essential to designing complex systems that balance the needs of users with the organization’s strategic goals and, more broadly, social well-being.

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