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5 Ways We Trick Ourselves Into Bad Financial Habits

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Being smart with money is easier said than done. Despite our good intentions, we overspend, bust our budgets, or derail our debt payoff goals—and we only have ourselves to blame. Here are five habits and biases that affect our financial decision-making. 


“Most people believe they are better at many things than they actually are, from driving to investing,” Certified Financial Planner Benjamin Sullivan tells mental_floss. And when it comes to budgeting, we tend to be overconfident with our willpower. You vow to cut your restaurant spending to zero, assuming you’ll be able to successfully fight off your sushi cravings throughout the month. You know you can go a month without buying another pair of shoes online—if you could only block all of the ads for sales... This overconfidence can backfire when you eventually give in and wreck your budget.

In a study published in the journal Psychological Science [PDF], researchers put a series of volunteers to the test to find out how strong their impulse-control actually was. In one test, they looked at the "empathy gap." This is the tendency to underestimate our impulses (such as hunger), because while we can remember the circumstances and strength of impulsive states, we can’t remember how they actually feel. (For instance, you can remember that you were hungry because you skipped breakfast, but you cannot recall that growling sensation in your stomach.) So when we’re not experiencing a craving, it’s easy to overestimate our willpower.

In another test, the researchers convinced some heavy smokers that they had a strong control over their cigarette cravings, while members of another group were told that they had very little self-control over their cravings. They were then all given a test to win money that involved a cigarette—such as, holding an unlit cigarette in their mouths without smoking it in order to win €8. The subjects who were told they had high control had a significantly higher failure rate than those in the group that were told they had low control, largely because, as the paper says, “many of these smokers exposed themselves to more temptation than they could handle” because they felt that they had self control.

“Whether it's picking stocks or frequent trading, overconfidence leaves investors focusing on games they can’t win,” Sullivan says. “Instead, investors would be better served by focusing on what they can control—their own behavior, including their overall asset allocation, and their spending and saving habits.”


“In investing, our bias toward the familiar is why many people invest most of their money in areas they feel they know best rather than in a properly diversified portfolio,” Sullivan says. “The known feels safe; the unknown feels risky.” 

This behavior is also known as status quo bias [PDF]. We prefer choices that feel familiar and don’t disrupt our lives very much. Fear of risk is one thing, but sometimes we simply fear what’s not comfortable. If you’re used to living above your means, for example, it can be tough to change your spending habits and cut back on certain areas—it’s uncomfortable and unfamiliar territory.

Similarly, the bandwagon effect can impair our judgment, too. Instead of making decisions that are good for our own unique situations, we simply do what’s considered popular or socially acceptable. For example, your friends have nothing saved for retirement, so you figure there’s no harm in postponing your own retirement savings. (This is false; you should start saving today!)


Sullivan brings up another interesting habit: anchoring. Anchoring is our tendency to use a given figure as a point of reference for our decisions. For example, you’re at a restaurant and you see a $25 entree on the menu; this seems overpriced at first glance, but now the $15 entree seems cheap in comparison.

“This tendency to fixate on a point of reference may seem like an easy mistake to spot, but in practice, it can be hard to dislodge a perception that is anchored this way.”

Research from the Institute of Psychology at the University of Würzburg found just how effective the anchoring effect can be. Researchers approached mechanics with a used car that needed repairs, asking the mechanics to name the value of the car—but only after the researchers themselves gave an opinion as to the value. Half of the researchers posited the car had a low value (DM 2800) and half suggested it had a higher value (DM 5000). When the researchers gave a high anchor, mechanics valued the car DM 1000 more.

Advertisers use this tactic quite often (on restaurant menus, for example) but it can also come into play with negotiating. Let’s say you’re interviewing for a job and expect the compensation to be in the $40,000 to $50,000 range. Your potential employer throws out a figure that’s much lower: $25,000. Suddenly, your own expectation seems ridiculously high, so you're more willing to make a bigger sacrifice with your counteroffer.


“Not only do we tend to cling to what we know and anchor to historical prices that are clear in our minds, but we generally avoid facing the truth of a financial loss,” Sullivan says.

Our aversion to loss results in the sunk cost trap, the pressure to follow through on a decision because you've already put a lot of time and effort into it. In practice, this might come up if you’re shopping for something specific, like a pair of jeans, and you can’t find the pair you want, so you impulsively buy something else at the store to justify the time and effort you’ve already spent (I couldn't find any jeans, but at least I have new sunglasses!).

“In Economics 101, students learn about sunk costs—costs that have already been incurred,” Sullivan explains. “Students also learn that they should typically ignore such costs in decisions about future actions, since no action can recover them.”

For starters, buying a new pair of sunglasses won’t make up for the time you’ve lost searching for your jeans.


You’ve just impulsively purchased a laptop you can’t afford, destroying your budget in the process. Maybe you have a bit of buyer’s remorse, but you justify the purchase by telling yourself you’ll use it all day, every day; it’s been a while since you’ve had a new computer; it was a solid, smart purchase.

This is post-purchase rationalization in action, also known as buyer’s Stockholm Syndrome: We tend to look for information that supports a choice we’ve already made. In other words, we justify a purchase to avoid dealing with the remorse of that purchase. It could be anything from a small splurge to a bad investment; either way, post-purchase rationalization keeps us from looking at our financial decisions objectively.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
May 21, 2017
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Nick Briggs/Comic Relief
What Happened to Jamie and Aurelia From Love Actually?
May 26, 2017
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Nick Briggs/Comic Relief

Fans of the romantic-comedy Love Actually recently got a bonus reunion in the form of Red Nose Day Actually, a short charity special that gave audiences a peek at where their favorite characters ended up almost 15 years later.

One of the most improbable pairings from the original film was between Jamie (Colin Firth) and Aurelia (Lúcia Moniz), who fell in love despite almost no shared vocabulary. Jamie is English, and Aurelia is Portuguese, and they know just enough of each other’s native tongues for Jamie to propose and Aurelia to accept.

A decade and a half on, they have both improved their knowledge of each other’s languages—if not perfectly, in Jamie’s case. But apparently, their love is much stronger than his grasp on Portuguese grammar, because they’ve got three bilingual kids and another on the way. (And still enjoy having important romantic moments in the car.)

In 2015, Love Actually script editor Emma Freud revealed via Twitter what happened between Karen and Harry (Emma Thompson and Alan Rickman, who passed away last year). Most of the other couples get happy endings in the short—even if Hugh Grant's character hasn't gotten any better at dancing.

[h/t TV Guide]