Jimmy Wales on Constraints for Bad Behavior

If you’ve read Living in Information, you’ll know I think highly of Wikipedia. It’s a very valuable artifact; a convenient agglomeration of the world’s key knowledge. But it’s more than that: Wikipedia is also where that artifact is created — a place, a culture, a system that produces ongoing value for everyone, for free.

Wikipedia co-founder Jimmy Wales is one of the people most responsible for establishing and stewarding that place. In a lengthy interview (audio, transcript) with Tim Ferriss, Wales discusses Wikipedia’s history, the values that make it work, and how it’s different from other social networks.

The entire conversation is worth your attention, but I was particularly taken by the discussion of how to regulate bad behavior. What should users be allowed/forbidden to do? What guardrails should the system have to prevent bad things from happening?

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Tweaking Users’ Mental Models

Allison Johnson, writing in The Verge:

… the original Apple II version [of the video game Karateka] included a delightful little easter egg from the early days of PC gaming — putting in the floppy disk upside down would boot up the game upside down.

According to [Karateka’s creator Jordan] Mechner, the game’s developers hoped that a few people would discover it by accident, and think their game was defective. “When that person called tech support, that tech support rep would once in a blue moon have the sublime joy of saying, ‘Well sir, you put the disk in upside-down,’” Mechner was quoted as saying in a recent profile, “and that person would think for the rest of their life that’s how software works.”

It may seem disingenuous to suggest users would expect that flipping the software media would cause the software itself to flip. But I’ve been surprised at the many ways people misunderstand how computers work.

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Becoming Better Users of Online Information

Shira Ovide reporting for The New York Times:

This week, Amazon acknowledged reality: It has a problem with bogus reviews.

The trouble is that Amazon pointed blame at almost everyone involved in untrustworthy ratings, and not nearly enough at the company itself. Amazon criticized Facebook, but it didn’t recognize that the two companies share an underlying problem that risks eroding people’s confidence in their services: an inability to effectively police their sprawling websites.

Learning from the masses is a promise of the digital age that hasn’t panned out. It can be wonderful to evaluate others’ feedback before we buy a product, book a hotel or see a doctor. But it’s so common and lucrative for companies and services to pay for or otherwise manipulate ratings on all sorts of websites that it’s hard to trust anything we see.

It’s a gross exaggeration to say learning from the masses hasn’t panned out. Overall, online product reviews — uneven as they are — have made us much more informed shoppers. They’re certainly better than the alternatives that existed before the internet. (Mostly: nothing.)

But as with much so much of what we read online, we must develop some skepticism. If a system can be gamed — and if humans have incentives to do so — then the system will be gamed. As a result, we must take what we read as one of several inputs when deciding.

Can platforms improve the accuracy of the information in their systems? Of course they can. As the article notes, Amazon is responding to curtail manipulated reviews and ratings. But we, too, can become better users of such information by honing our online B.S. detection skills.

Amazon’s Open Secret – The New York Times

Abstraction and Implementation

Paul Dourish, in his book Where the Action Is:

Software systems are built from abstractions. The extent to which abstraction is fundamental to software systems can be hard to explain to someone who hasn’t built them; while to someone who has, it can be so ubiquitous as to have become invisible. But the very essence of software system design is the manipulation, combination, and creation of abstractions.

Essentially, computers manipulate information in terms of the presence or absence of energy, which we render as ones and zeros. There’s a wide gap between this binary way of representing reality and how human beings experience reality. We bridge the gap using abstraction.

The essence of abstraction in software is that it hides implementation. The implementation is in some ways the opposite of the abstraction; where the abstraction is the gloss that describes how something can be used and what it will do, the implementation is the part under the covers that describes how it will work.

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Machine Intelligence and the Design of Complex Systems

Adobe’s Patrick Hebron, in an interview for Noema (from September 2020):

If you’re building a tool that gets used in exactly the ways that you wrote out on paper, you shot very low. You did something literal and obvious.

The relationship between top-down direction and bottom-up emergence is a central tension in the design of complex systems. Without some top-down direction, the system won’t fulfill its purposes. However, if it doesn’t allow for bottom-up adjustments, the system won’t adapt to conditions on the ground — i.e., it won’t be actualized as a real thing in the world. What’s needed is a healthy balance between bottom-up and top-down.

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The Key to Understanding Why Things Happen

When a systems thinker encounters a problem, the first thing he or she does is look for data, time graphs, the history of the system. That’s because long term behavior provides clues to the underlying system structure. And structure is the key to understanding not just what is happening, but why.

— Donella H. Meadows, Thinking in Systems

Every year, I introduce systems students to the iceberg model. The model is a helpful way of understanding situations by looking ‘beneath the surface’ of the things we experience, to the structures and mental models they manifest.

In case you’re unfamiliar with the iceberg model, it’s a framework that encourages you to think about situations at four levels:

  1. Events, or the tangible manifestations of the situation; the things we can see, hear, and record — “just the facts.”
  2. Patterns we perceive in events; outcomes that happen not just once but manifest time and again.
  3. Structures that may be causing the patterns we perceive; these could include rules, regulations, incentives, etc.
  4. Mental models that bring these structures into being.

Notice the fourth level is more abstract than the first: we can ascertain events, but we must hypothesize mental models. There’s also a causal relationship between levels: mental models elicit structures that elicit patterns of events.

As a result, events are easier to grok than mental models. But as with pace layers, the deep levels are where the true power lies. A change at the level you can see has less impact than tweaking the mental models that bring it forth. The ability to change minds is an incredibly powerful lever.

The iceberg model is helpful when doing research. Research produces lots of data points: Google Analytics and search logs tell you about usage, landscape analyses tell you about competitors and analogs, user interviews tell you about intent, etc.

But research doesn’t stop with data. Insights only emerge once you spot patterns in data. If lots of people enter the same term into the search box and do not get good results, that tells you something important about your system.

But you can go deeper still. Patterns only tell you what is happening, not why. You should at least have a hypothesis about why things are happening. This calls for understanding the underlying structures and the mental models that enable them.

Collaborating on these levels can be uncomfortable since the work is speculative. Acknowledge the awkwardness upfront. Allow the team to speculate. You’re not making anything normative yet, just understanding why things might be happening.

Knowing causes helps produce better outcomes. You might not know causes precisely, but you can test hypotheses. Ultimately, a better understanding of the system’s structures and underlying mental models will lead to more skillful interventions.

Cover image: NOAA’s National Ocean Survey (CC BY 2.0)

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(Re)drawing System Boundaries

There are no separate systems. The world is a continuum. Where to draw a boundary around a system depends on the purpose of the discussion—the questions we want to ask.

— Donnella Meadows, Thinking in Systems

One of the hardest things about making systems maps is knowing where to stop. You start by identifying the system’s components and how they relate to each other, and soon you find yourself drawn to include the entire world.

Should your map of a new electrical product include details about the power grid? Perhaps not; the grid is assumed infrastructure. But what if the product is an electric car? In that case, access to charging matters, so you may want to reconsider.

Where you draw the boundaries of a system map depends on what questions you’re looking to illuminate. (I keep reminding students about the power of good framing questions.) But drawing a map may also reveal the need for new questions.

Maps help us see unexpected aspects of the situation. We draw them in part to avoid unintended consequences. As such, we must be open to re-drawing their boundaries as the exercise reveals new angles. When we do so, our understanding deepens.

System maps are valuable artifacts per se, but they’re more than that. Map-making is a powerful way of knowing the world and — more importantly — intervening skillfully. It calls for continually asking where the boundaries lie — and reconsidering.

Cover photo by sid (CC BY-NC-ND 2.0)

Not Just a New Feature; a New Compact

David Pierce, writing in Protocol yesterday morning:

Starting on Wednesday, any Slack user will be able to direct message any other Slack user. The new system is called Connect DMs, and works a bit like the messaging apps and buddy lists old: Users send an invite to anyone via their work email address, and if the recipient accepts (everything is opt-in), their new contact is added to their Slack sidebar. The conversations are tied to the users’ organizations, but exist in a separate section of the Slack app itself.

Lorenzo Franceschi-Bicchierai, writing in Vice a few hours later:

On Wednesday, Slack launched a new feature that allows users to message anyone else via direct messages, even if the receiver is outside of the sender’s organization. In other words, the feature allows anyone to connect with you privately on Slack. Critically, even if the feature is turned off on your Slack, you’ll still get an email notification and message from anyone trying to connect with you—including people who don’t work with you and can use this feature to sneak harassment into your inbox.

After experts in content moderation, and several other people, complained about this risk, Slack is already backtracking and limiting the feature, admitting it “made a mistake.”

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How to Keep a Long-term Focus

Point of view is worth eighty IQ points.

— Alan Kay

Sometimes we face situations that demand an immediate response. A few weeks ago, millions of Americans dealt with unexpected weather conditions that disrupted their ability to keep themselves and their families fed and warm. On February 20, the crew of United flight 328 had to deal with an engine that exploded in mid-air. (Fortunately — and through excellent piloting and engineering — the plane landed safely.) Such life-threatening situations call for skillful action now.

Most situations aren’t as urgent as landing a crippled plane or finding shelter in freezing temperatures. And yet, we often feel the stress of urgency in our day-to-day lives. Perhaps we’re on the hook for meeting this quarter’s KPIs, or we’re running late to take our child to her 10 am martial arts class, or we have a big presentation on Tuesday. Whatever the case, we’re under pressure to deliver now.

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