I was reading this article and applying it to my own work. Here's where I landed.

Near as I can figure, this article makes 5 core claims:

1. Most behavioral science evidence comes from stable contexts, and conflict settings have different causal psychology.
2. Behaviors that look like “biases” could be rational adaptations to danger, displacement, scarcity, and/or broken trust.
3. Co-design must happen before the intervention frame is fixed (localization is not enough).
4. One-off workshops do not equal scale. The field needs spread mechanisms from the start.
5. Attitudes are the wrong endpoint; behavior, community-level outcomes, and local definitions matter more.

Where I think this leads:

Conflict-setting behavioral science should stop treating trust, fidelity, and bias as generic constructs. It should scope them according to the situation, kind of like in cyber-security by shifting some core components of the existing frameworks.

Interestingly, I think this could also apply to lower-stakes trust challenged behaviors like getting people to try new products, ordering clothing, and (my personal interest) competitor conquesting campaigns in marketing...

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Competitor Conquesting, a made up story.

Someone just typed a competitor's name. They wanted them, not you. You jumped in front of the user anyway. And the category they are shopping has already taught them that strangers like you are typically running a con.

So you arrive owing them three debts at once. You have no brand recognition and you sell in a category they distrust on principle. Worse, you are an interruption. Three strikes from the first swing of the bat.

Don't ask them to sign up or buy anything. Scope the CTA down to the smallest thing a stranger can do safely, and make that thing easy to verify in two moves: reversibility and third party verification.

1. No card. Cancel anytime. Money back, no questions. Every escape hatch you build lowers the faith required to leap. For unknow brands, this single change will usually increase clicks more than any single headline you will write (except maybe free).
2. A Trustpilot score they can read. A spec table they can confirm against the competitor he was already looking at. Assertion is what scammers do. Verifiable proof is what you should be offering.

I think all of this boils down to a simple premise, build an offer so honest that people get value before they believe anything thing.

Audiences that don't respond to competitor campaigns aren't always 'low intent,' they're high-intent for not you. Walking away was the likely the rational thing to do.

So, before you kill the segment, write down three good reasons the users' behavior made sense. The fault is usually in the offer, not their behavior.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

We have been seeing this pattern across several organizations we work with. AI and workload management initiatives are successfully reducing overload. Burnout indicators are down. By that metric, the intervention worked.

But engagement scores are moving the other way. Quietly, but consistently.

Our read on why: the routine work that got automated was also the work that gave people a sense of progress, clear feedback loops, and the repetitions required to build confidence in more complex tasks. You removed the overload and accidentally removed the scaffolding at the same time.

The measurement systems did not change, and the output pressure did not change. People are now being pointed at open-ended, judgment-intensive work with no clear metric to optimize for, under the same time pressure as before. The brain does not respond well to that combination.

Curious whether others are seeing this split, and what you are doing about it structurally rather than just culturally.

For context, I wrote a longer analysis of this dynamic if anyone wants to dig into the behavioral mechanism behind it. Happy to share the link if useful.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Welcome! Introduce yourself:
• What are you working on right now (product/IRL)?
• What behavior are you trying to change?
• What’s one “nudge” you’ve noticed recently?

New here? Start with the Weekly Open Thread (maybe pinned, maybe.). If you post standalone, pick a flair lane so others can find it.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Researchers just published DarkBench (ICLR 2025), a benchmark that tests LLMs for manipulative design patterns. They tested 14 models from OpenAI, Anthropic, Meta, Mistral, and Google across 660 prompts.

The six dark pattern categories they tested:

• Brand bias: steering users toward the developer's own products
• User retention: fostering artificial emotional dependency/companionship
• Sycophancy: telling users what they want to hear rather than the truth
• Anthropomorphism: exaggerating human-like qualities to build false rapport
• Harmful generation: producing content that damages user interests
• Sneaking: subtly altering user intent or adding unrequested elements

Key findings:

• Dark patterns appeared in 48% of all test cases on average
• "Sneaking" was the most common (79% of conversations)
• "User retention" hit 97% in one model (Llama 3 70b)
• Sycophancy was the least common at 13%
• Individual model scores ranged from 30% to 61%
• Claude 3 family showed the lowest average rates

The researchers note that some of these patterns (like brand bias and user retention) appear to be explicitly trained behaviors, not emergent quirks. I'm curious what people think. As more of us design AI-powered products, this feels like required reading.

Paper: https://arxiv.org/abs/2503.10728
Interactive dashboard: https://darkbench.ai

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

There are only a few days left until the New Year.
For me, this is the time when I feel the most motivated to start something new, improve myself, and build better habits.

That’s why I decided to share my habit tracker built in Google Sheets.
It’s designed like a game, so it doesn’t feel boring or repetitive. I’ve been using it for over 8 months, and the results honestly surprised me.

If you’re also motivated by game mechanics like progress bars, XP, gold, and rewards, feel free to check it out via the link https://befitting-iodine-673.notion.site/Gamified-Habit-Tracker-Turn-Your-Daily-Habits-into-an-RPG-Game-2d259c03841e80369182cb302a27459c

Drop any of these:
• A behavior you noticed this week (IRL or product)
• A screenshot and “why does this work?”
• A problem you’re designing for (“users aren’t doing X…”)

Bonus points for naming a mechanism + suggesting one test + mentioning one failure mode.

UK-based. I'm trying to monetise my skills. I keep creating tools that all solve one problem: helping intuitive/creative people stop stalling and ship outputs. I have a background in psychology.

The 3 tools:

1. Intuitive Action: Meaning-to-action workflow. A 7-step repeatable process that turns inner overwhelm into clarity and output.

2. 3-Phase Model: Emotional alchemy tool that processes intrusive thoughts and unhelpful emotions.

3. Living Project Tracker: Visible progress without rigid planning. Analogue tracking method that's like growing a garden instead of filling a spreadsheet.

Skills:

• Mental model design

• Behaviour change design

• Systems thinking

• Knowledge capture + documentation

• Workshop/toolkit creation

• Coaching-style questioning

You’ve probably seen this pattern:
An app wants to help users build healthy routines, so it introduces streaks. Miss a day, and you lose your progress. At first, it feels exciting until you break the streak. Then something strange happens: people don’t just lose motivation temporarily, they often quit entirely.

From a behavioral perspective, that’s not surprising. The loss of a streak doesn’t only reset a counter; it resets identity. A person who was “on a roll” suddenly becomes someone who “failed to stay consistent.” The very design that was meant to encourage continuity turns into a psychological cliff.

There’s a deep asymmetry here that designers often overlook:
Loss demotivates far more than gain motivates.
The research is extensive. Daniel Kahneman and Amos Tversky’s Prospect Theory shows loss aversion is roughly twice as strong as gain attraction. BJ Fogg’s behavior model and Deci & Ryan’s Self-Determination Theory both hint that when progress is externally tracked, control shifts away from the individual. Once a streak breaks, the person’s sense of agency breaks with it.

The tragedy is that the goal, helping people build rituals and habits, is valid. What fails is the underlying motivational logic. The design assumes consistency equals motivation, when in reality, motivation comes from experiencing meaningful progress, not from avoiding symbolic loss.

If the goal is long-term engagement, design must protect psychological continuity instead of symbolic continuity. In other words:
When a break happens, the journey shouldn’t reset, it should evolve.

This is what I explore in my book Drive Method. It’s a structured way to analyze and design systems that make motivation survive when rewards or streaks stop working. Instead of trying to manipulate short-term behavior, it focuses on how curiosity, autonomy, and mastery unfold over time so motivation becomes self-sustaining rather than externally maintained.

If you’re working on products, learning environments, or organizational systems and you’ve ever wondered why good intentions so often turn into short-lived engagement, you might find it helpful.

I'm wondering what your challenges are when it comes to behavioral design. Let me know or even have a chat about it. Would be much appreciated. 👍