r/BehavioralEconomics

I built a website where anyone can add a quarter to a shared tower.
There’s no reward, no social credit, no gamification — just a visible increase in height.

The tower is now 20 quarters tall.
I’m studying whether minimal, non‑monetary feedback loops can drive participation.

https://copperspore.com

I spent the last several months trying to understand why online dating appears to produce so much frustration despite giving people access to vastly more potential partners than any previous matching system.

I eventually came to think the interesting explanation isn't primarily cultural or gender-specific. It's a market-design problem.

The starting point is thickness.

Matching markets generally benefit when more participants enter because the probability of finding a compatible counterparty rises. But beyond some point thickness produces congestion: too many potential transactions, inadequate mechanisms for evaluating them, and difficulty sending credible signals through the resulting noise.

Dating apps appear to combine several features that make this unusually severe:

1. The market is heavily asymmetric.

The large heterosexual platforms have substantially more men than women. That creates scarcity on one side and congestion on the other.

The same marketplace is therefore experienced as two almost opposite products.

2. Signaling is nearly costless.

A swipe or like carries almost no cost.

When expressing interest is cheap, broadly signaling interest can become individually rational. But aggregate cheap signaling destroys information content.

The receiving side then gets more approaches but less information about which approaches represent serious intent.

3. Congestion changes selection behavior.

Experimental research on online dating has found that continued exposure to large sets of potential partners makes participants progressively more rejecting.

In randomized experiments, acceptance probability fell roughly 27% from the first potential partner shown to the last.

The options themselves weren't getting worse.

Exposure to the option set changed the decision-maker.

This is the part I find most interesting: abundance can reduce successful selection rather than improve it.

4. The scarce side adapts too.

When matches become difficult to obtain, the rational response isn't necessarily to continue evaluating every match as a potential long-term partner.

A scarce match can be reclassified into a lower-commitment interaction.

So the congested side becomes more selective while the scarce side becomes less willing to treat the matches that clear as serious candidates.

Neither side needs to be behaving irrationally or maliciously.

Each side is responding rationally to its own incentives.

Yet the aggregate market clears worse.

5. The intermediary has a peculiar objective function.

Historically, intermediaries in courtship—friends, family, community, school, church, neighborhood—had reputational exposure to the outcome.

Modern platforms largely disintermediated those institutions.

But the replacement intermediary has an unusual economic characteristic:

Its revenue is earned while the search continues.

A successful terminal match removes two customers from the market.

That doesn't require anyone inside the company to deliberately prevent successful relationships. It simply means that engagement and successful clearing point in different directions as optimization targets.

6. We therefore measure almost everything except clearing.

Dating companies can measure registrations, active users, likes, matches, conversations, retention, payers and revenue per payer with enormous precision.

What remains remarkably difficult for an outsider to determine is the obvious denominator:

What percentage of people entering the system successfully leave it because they found the durable relationship they wanted?

Hinge is the especially interesting case because the brand promise is literally Designed to Be Deleted.

Yet the public operating metrics overwhelmingly measure people remaining, returning, engaging and paying.

There is some offline feedback—Hinge's "We Met" feature can ask whether a match produced a date and whether someone wants another date—but that is very different from longitudinally measuring relationship formation, duration, permanent successful exits and reactivation after dissolution.

That brought me to a broader hypothesis:

The public "gender war" around online dating may partly be the social symptom of a market-design failure.

Two populations experience radically different sides of the same mechanism.

Both possess accurate information about their own experience.

Neither sees the system producing the other side's experience.

So each concludes that the other population is the problem.

I ended up writing a much longer piece tracing this through matching-market economics, signaling theory, behavioral psychology, the history of courtship, the disappearance of social intermediaries, and eventually the financial statements of Match Group.

The last part became a public-equity short thesis because I realized the sociology generates financial predictions.

If the underlying marketplace is structurally impaired, eventually I would expect to see:

• payer attrition;
• heavier monetization of the participants who remain;
• difficulty expanding the total category;
• growth increasingly sourced from geographic expansion rather than deeper successful adoption;
• and eventually a lower terminal valuation for the companies operating it.

That makes the public company an interesting way of putting an otherwise difficult sociological hypothesis under an empirical clock.

The full essay and sources are here:

https://dljlevfin.substack.com/p/the-undisclosed-denominator

I'm especially interested in criticism of the behavioral mechanism rather than the stock call.

Where does the causal chain break?

Is congestion actually the right framework?

Does cheap signaling necessarily degrade matching efficiency here?

And most importantly: what metric would you use to distinguish a dating marketplace that generates enormous engagement from one that actually clears successfully?

I made an app for learning to spot cognitive biases in real situations.

It covers 188 biases. Each one has a plain definition and an example, no jargon walls. The main mode is a quiz that gives you a short scenario and asks which bias is at play. There are close to 19,000 of these scenarios.

The other mode is Bias Check. You write down a decision you actually made and it walks you through which biases were probably involved. It keeps a history, so after a while you can see which ones keep showing up for you.

The 188 are grouped into 4 categories by the problem they try to solve: too much information, not enough meaning, need to act fast, what to remember. That grouping is what made them stick for me.

Free, with ads and optional in-app purchases.

https://play.google.com/store/apps/details?id=com.random.cognitivebias

Would like to hear if the scenarios feel realistic or too textbook. That is the part I am least sure about.

Pulled the actual health club billing data behind gym contracts after wondering why I always end up picking the same "unlimited monthly" plan, and the layering of independently documented mechanisms turned out to be more extensive than I expected.

Stefano DellaVigna and Ulrike Malmendier tracked 7,752 members across three U.S. health clubs over three years (American Economic Review, 2006). Members who chose a flat monthly contract over $70 attended an average of 4.3 times a month, paying more than $17 per visit, even though a 10-visit pass was sitting right there at $10 a visit. On average they forwent about $600 in savings over the life of the membership. The stranger detail: monthly members were 17 percent more likely to stay enrolled past a year than annual members, despite paying more precisely for the option to cancel anytime. They paid extra for flexibility they consistently declined to use.

John Gourville and Dilip Soman studied a health club that billed twice a year instead of monthly (Journal of Consumer Research, 1998). Attendance spiked hard right after each bill, then decayed steadily until the next one. They called it payment depreciation, the psychological sting of a purchase fades the further you get from paying it, and once the sting is gone, so is the motivation to use what you paid for. Most gyms bill monthly or weekly now, which keeps that sting too small and too frequent to ever spike attendance the way a biannual bill does.

Richard Samuelson and William Zeckhauser's work on status quo bias (Journal of Risk and Uncertainty, 1988) explains what happens next. People default to whatever requires no action, even when a better option sits right next to it and the status quo is actively costing them money. Some cancellation processes lean into this hard, phone-only or mail-only cancellation, narrow in-person windows, while sign-up takes under a minute online.

Hal Arkes and Catherine Blumer's classic sunk cost experiments (1985) cover the part where people don't cancel even after they've mentally clocked the waste. Money already spent is gone regardless of what happens next, but it doesn't feel that way, continuing to pay gets framed as "not wasting" the earlier spend instead of a second, separate loss.

And Amos Tversky and Daniel Kahneman's anchoring research (1974) shows up in the pricing tiers themselves, three-tier menus where the priciest option sits just slightly above the middle one, making the expensive plan look like the generous choice regardless of whether you'll use the extras.

Planet Fitness is a useful real-world stress test of all of this at once. As of their most recent earnings the company has more than 20 million members on roughly $10–15/month plans, a price that only works if most members don't show up often. It's not a flaw in their model, it's reportedly the model.

The pattern got documented enough that the FTC finalized a "click-to-cancel" rule in 2024 requiring cancellation to be as easy as sign-up. A federal court vacated it on procedural grounds in 2025, and the FTC has since opened a new rulemaking process. States didn't wait either, California has had its own automatic renewal law on the books for years covering exactly this kind of contract.

Made a full breakdown of all five mechanisms here: https://www.youtube.com/watch?v=tOskQyuqi70

Curious whether anyone here has seen research treating the sunk cost effect and status quo bias as interacting rather than independent, my instinct is the sunk cost framing is doing a lot of the work in making the status quo feel actively justified rather than just default, but I haven't found a study that isolates that specifically.

Spent the week digging into the psychology of the F&I office, the back room at car dealerships where financing and add-on products get sold, and the mechanisms stack up more than I expected.

The four-square worksheet is the entry point, a single sheet split into trade-in value, purchase price, down payment, and monthly payment. A former dealership salesman interviewed by Consumer Reports described how salespeople shift numbers between the boxes while buyers fixate on the smallest one, the monthly payment, since it feels most connected to their actual budget. Total price moves quietly while attention stays locked on one number.

Many versions of the worksheet also include a line near the top asking buyers to initial that they'll purchase the car today if the offer is acceptable, before any real numbers are even discussed. Jonathan Freedman and Scott Fraser's 1966 foot-in-the-door study found that securing a small early commitment measurably increases the odds someone agrees to a much larger one shortly after. The initials aren't binding, but they function as an early psychological commitment.

The financial stakes are bigger than most buyers realize. Industry data from the Haig Report put average finance and insurance profit at $2,534 per vehicle in Q3 2025, while separate benchmarking data showed front-end vehicle profit had shrunk to around $400 by year end, meaning close to nine of every ten profit dollars on a typical deal now come from the finance office, not the car.

The regulatory story is what got me though. The FTC finalized the CARS Rule in December 2023 specifically to address these tactics, projected to save consumers $3.4 billion annually. Dealer trade groups sued, and the Fifth Circuit struck the entire rule down in January 2025 on procedural grounds before it meaningfully took effect. Separately, the newer hotel/ticketing Junk Fees Rule explicitly excludes car dealerships, since they were supposed to be covered by CARS instead. As of now there's no dedicated federal rule governing this at all.

Made a full breakdown here: https://www.youtube.com/watch?v=QRT4wMLrzPw

Anyone know of other cases where a federal consumer protection rule was fully created, then struck down before implementation rather than just delayed or watered down? Seems like a different pattern than the usual regulatory story.

Anchoring used to need a wheel of fortune and a made-up number (Tversky & Kahneman's classic experiment). Now it just needs an AI-generated first draft that nobody admits to using.

Picture the meeting: someone presents a tidy three-part framework as "here's what I've been thinking," everyone nods, feedback is positive. Except the framing, the assumptions, the argument structure were all decided by a model before the human touched the keyboard. What the human actually did was edit - and under-adjust from the anchor, same as we all reliably do.

The interesting part isn't that this happens. It's what it does to credit. The senior person "refining" a draft used to be doing real framing work; the junior person with the fastest prompting fingers can now produce the anchor everyone else adjusts around. Nobody in the room would call that a power transfer - it gets filed under "efficient collaboration." And credit for original thinking still flows to whoever typed the final version, even though the actual frame-setting happened upstream and invisibly.

I wrote a longer piece pulling this apart (self-assessments, performance reviews, and the "who actually wrote this" problem) if anyone wants to dig in further: https://medium.com/@nudgeintended/the-anchor-nobody-named-423b4931522d

Curious whether others here have seen this play out - the anchor shifting from "whoever's senior" to "whoever prompts fastest."