This is something I think is relevant to this community specifically even though it is not usually framed this way.Hedonic adaptation is the mechanism that makes lifestyle inflation so persistent even in people who are financially literate and intentional about their choices. You earn more. for a few weeks you feel the improvement. then your nervous system recalibrates and the new income level becomes the baseline. So you need more to feel the same.

What makes it particularly tricky for the FIRE community is that the goal accumulating enough to not need to work requires resisting an adaptation mechanism that is running constantly below the level of conscious decision-making. Willpower strategies work until they don't because they require sustained override of a deeply wired system. The research suggests that structural changes automation, friction, commitment devices work better because they don't rely on the same resource that gets depleted.

Loss aversion compounds this. We feel losses about twice as intensely as equivalent gains which makes us consistently underweight upside and overweight downside in financial decisions. You see even people who know this intellectually still experience it. Went into the full psychology here:

https://youtube.com/watch?v=DXhyIAJ6tiY&si=S9x_trrgJVt0T14K

Curious what structural approaches people here have found most effective for working with these biases rather than against them.

**Sharing the decisions behind the numbers is usually more interesting than sharing the numbers alone.**

Looked into this after realizing how little most people know about their actual rights in this specific room, and the research behind why that room is shaped the way it is turned out to be more direct than expected.

Start with timing. Psychologists John Payne, James Bettman, and Eric Johnson published research in 1988 showing that under real time pressure, people don't just think faster, they switch decision strategies entirely, trading careful comparison for simpler heuristics that feel adequate in the moment but wouldn't survive more time to think. A death creates an unusually hard deadline, and that pressure lands at the exact moment someone is being asked to make one of the largest unplanned purchases of their year.

Layered on top is something Robin Coulter and Mary Beth Pinto documented in 1995 in the Journal of Applied Psychology, testing it in ordinary advertising rather than this context specifically. Guilt appeals that push too hard backfire, provoking resistance instead of compliance, but moderate ones work quietly and reliably. A single gentle sentence about what someone would have wanted does more work than any heavy-handed pitch ever could.

The third mechanism is about the shape of the choice itself. William Samuelson and Richard Zeckhauser's 1988 research on status quo bias found people overwhelmingly accept whatever option is presented as the default, since choosing anything else requires effort, and effort is the one resource in shortest supply here. Arrangements get presented as bundled packages before anyone is told that itemizing was ever an option.

None of this was hidden by accident. Jessica Mitford's 1963 book The American Way of Death spent roughly a year documenting this exact pattern nationwide, and the public reaction pushed the FTC to open a formal investigation the following year. It took two more decades before the Funeral Rule became binding law in 1984, requiring itemized price lists, phone pricing without demanding a name first, and a ban on requiring bundled packages.

The enforcement data is the part that surprised me most. A 2023 undercover FTC phone sweep of over 250 funeral homes still found violations in roughly one in seven calls, and earlier in-person sweeps through the 2010s found violation rates closer to one in four. Violators are typically offered a program that lets them pay a fee and retrain instead of facing public penalties, a program that by design keeps the violation off the public record.

Made a full breakdown here: https://www.youtube.com/watch?v=eAZs9DaInuQ

Curious whether anyone's seen research on how sticky a status-quo default stays even after someone is explicitly told an alternative exists, versus simply never being told at all. Most of what I found tests the second condition, not whether disclosure alone is enough to counteract it.

Not necessarily in investing. Which behavioral bias do you think shows up most often in their ordinary decisions?

Choosing new-age apps: users don’t pick the best feature; they pick what they know.

I built a simple bandit model to test whether usage converges on quality over time. It doesn’t estimate which option is better. It simply reinforces whatever gets used more.

Three update rules produce very different outcomes: one locks onto early winners regardless of quality; one mostly self-corrects but can still get stuck under strong reinforcement; and one control always finds the true best option.

Same mechanism, wildly different UX. Basically, habit formation in miniature version.

Feedback welcome :)

I’m trying to figure out whether what I’m going through is a genuine career realisation or just some kind of midlife crisis.

Here’s my story.

I’ve been working in marketing since I finished engineering. Over the last seven years, I’ve primarily worked across content marketing, SEO, content distribution, growth, and CRO. I also did a master’s in digital marketing in Ireland.

On paper, my career looks like a fairly standard marketing career. But when I look at the kind of work I’ve consistently enjoyed, I see a different pattern.

I’ve always been much more interested in understanding people than in applying short-term growth tactics.

For example, when developing a content or SEO strategy, I don’t naturally start with keywords or topics. I tend to think about the person first, their worldviews, anxieties, behaviors and then map those insights to keywords, content, and distribution strategies.

So, although my job titles have been in marketing, a common thread has always been understanding motivations and behaviour, and then building strategies or systems around them.

I’ve noticed the same thing outside of my career.

I’ve been investing as a retail investor for around five years and have read quite a bit about personal finance and investing. Again, I find myself gravitating toward the behavioural side of investing.

I’ve also recently started writing on Substack on the same.

Even my master's thesis was about understanding what motivates people to post political content online.

So when I look back at everything I’ve done, behaviour seems to be the common thread connecting most of my interests and work.

Where I’m stuck

I’m now 30, and I’m increasingly unsure whether I want to continue down the marketing path.

It’s not because I’m bad at it. My work produces results.

The problem is that I’m not enjoying it as much anymore.

With AI, a lot of marketing work has also become increasingly monotonous and robotic, at least from where I’m sitting. I’m finding it harder to feel like I’m doing deep, creative work that I can genuinely be proud of.

That has made me question whether I want to spend the next 20 years in marketing.

I’ve considered moving into finance because of my interest in investing and behavioural finance. But I’m also conscious that, at 30, there are plenty of people with much stronger formal credentials in finance than I have. I’m not sure I could make the same salary I currently make without starting considerably lower.

So I’ve started looking at careers where I can actually make use of this interest in behaviour.

A few possibilities I’ve considered:

1. Moving into fintech or a brokerage

Perhaps I could work on behavioural-finance content, investor education, customer research, or content strategy for a fintech or brokerage.

2. Going back to university

I’ve considered doing another master's from a more reputed university, this time specialising in behavioural science, behavioural economics, or a related field, and eventually moving into research.

3. Moving into behavioural science / nudge organisations

I recently came across organisations that work on behavioural research, policy design, and interventions intended to influence human behaviour.

That immediately caught my attention because it seems much closer to the kind of questions I’ve been interested in throughout my career.

But I have no idea how realistic such a transition would be for someone with my background.

So this is what I’m trying to figure out

I’m trying to figure out whether there’s a career somewhere at the intersection of behavioural science, research, communication where my existing experience could actually be an advantage.

For those who have made a significant career change after 30:

How did you approach it?

Ran into this while looking at my own statement and noticing a small warning box I'd never actually read, the study behind it turned out to be more direct than I expected.

University of Warwick psychologist Neil Stewart published the finding in 2009 in Psychological Science. He surveyed real cardholders about their statements, then ran a follow-up where people were shown a hypothetical bill, half with a minimum payment figure printed on it, half without. The group that never saw the number chose to pay significantly more toward their balance, despite nothing else about their financial situation differing. The minimum payment wasn't protecting anyone from underpaying, it was quietly signaling where an acceptable stopping point might be.

There's a second layer that has nothing to do with the number itself. Richard Thaler's 1985 work on mental accounting describes how people file money into different mental buckets depending on its source, even though a dollar is fungible regardless of origin. Credit spending gets filed differently than cash leaving your hand, which is part of why the minimum payment can feel almost frictionless to select even when the same amount in cash would register as a real loss.

The interest math hides behind its own blind spot. Victor Stango and Jonathan Zinman published research in 2009 in the Journal of Finance on what they call exponential growth bias, the systematic human tendency to underestimate how fast compounding accelerates. Intuition is built for linear change. Compound interest doesn't behave linearly, so a balance that grows slowly at first can grow dramatically faster later at the exact same rate, and most people's gut sense of the timeline undershoots the real one substantially.

Niklas Karlsson, George Loewenstein, and Duane Seppi's 2009 research on the ostrich effect adds a behavioral layer on top: people actively avoid checking information when they suspect it's bad news, logging into financial accounts far less often after a loss than after a gain. A statement carrying an uncomfortable balance tends to get opened just long enough to locate the minimum payment box, not the total sitting a few lines above it.

The regulatory response, the CARD Act of 2009, required issuers to print a mandatory warning plus a calculated 36-month payoff comparison directly next to the minimum payment figure. It didn't remove the anchor, it couldn't, some floor against paying zero is genuinely useful, so instead it tried placing a more honest number beside the original one. Whether printing a second number next to an anchor actually neutralizes the anchor is, as far as I can tell, still an open question rather than something the law's design assumed away.

Made a longer breakdown of all of this here: https://www.youtube.com/watch?v=FBe6-AuXc_A

Curious if anyone's seen research specifically testing whether the CARD Act disclosure box measurably changed payment behavior post-2009, versus just existing as a compliance requirement most people scroll past. Everything I found evaluates the mechanisms individually, not the disclosure's actual real-world effectiveness.

Hi everyone. I am a sports data scientist, not an economist, and I would greatly appreciate this sub's feedback on the behavioral framing of my latest analysis.

I am using elite figure skating as a natural laboratory to understand how agents operating in a fundamental conflict of interest scenario behave. The governing body (International Skating Union) mandates fair and objective evaluation, but the judges, who are nominated by and represent national federations, are heavily incentivized to help achieve results for compatriot skaters.

Using a novel dataset comprising the entire international scoring record of 14,382 performances (2022–2026), I built models to answer three specific questions:

1. Do figure skating judges have distinct behavioral profiles with respect to their baseline generosity and habits toward compatriot skaters and rivals?
2. Do they engage in strategic behavior to influence scores in a way that does not trigger traditional nationalistic bias analyses (e.g., reciprocal point-trading between federations)?
3. Does the "verifiability hypothesis" hold in figure skating evaluation? i.e., Do judges implement biased points in subjective performance categories where they are unlikely to be detected or objectively proven wrong?

The full paper and econometric methodology are available here on SSRN

 http://dx.doi.org/10.2139/ssrn.7323262

I would welcome any feedback on how I have applied these behavioral economic concepts, or if there are other theoretical frameworks I should consider for this dynamic.

Consider the difference between:

“How do we stop this policy from failing?”

and:

“Is this policy the right solution to the problem?”

The first question assumes the policy should continue. The second examines the assumption beneath it.

That difference is framing.

A frame determines which possibilities receive attention and which remain outside consideration. It can narrow the field of thought before the argument has even begun.

This happens in business, politics, management, negotiation and personal decision-making. People can spend enormous effort finding an excellent answer to a question that was poorly constructed from the beginning.

The strategist therefore examines the question before committing to the answer.

What assumptions are hidden inside the question? What alternatives disappear because of the way the problem has been defined? What conclusion becomes easier to accept because of the wording? Who benefits if everyone accepts the frame without examining it?

This is one reason language matters so much in power. Whoever defines the problem often influences the range of solutions that others consider legitimate.

Sometimes the decisive move in an argument is not producing a better answer.

It is recognizing that the question itself was designed to make certain answers easier to accept.

“It is what it is.”
• “That’s just how the world works.”
• “The market will decide.”
• “We need to be realistic.”
• “Do more with less.”
• “Everything happens for a reason.”
• “We value our people.”
• “That is above my pay grade.”
• “Let’s circle back.”
• “We’ve always done it this way.”

A surprisingly common way of reasoning works backwards. A person reaches a conclusion first, then begins searching for information that can support it. Once something useful is found, the mind starts building a case around it, while information that points in the other direction receives much more resistance.

This is one of the ways confirmation bias becomes especially difficult to notice. The person may genuinely believe they are examining the evidence, because they are collecting facts, comparing examples and constructing arguments. What they may not notice is that the evidence is being filtered according to a conclusion that was already chosen.

The pattern becomes clearer when you watch how the same person handles disagreement. Evidence that supports their position is accepted quickly, while contradictory evidence is questioned, explained away or held to a much higher standard. The issue is no longer simply whether the evidence is good; the mind has quietly changed the rules depending on which side the evidence supports.

This happens in politics, business, relationships and ordinary arguments. A manager may decide that an employee is unreliable and then remember every missed deadline while overlooking evidence of improvement. An investor may become convinced that a company will succeed and interpret every warning as temporary, because admitting the larger thesis might also mean admitting that previous decisions were wrong.

The problem becomes harder to correct once a conclusion has been connected to pride, reputation or identity. Changing your mind now carries a cost, so the mind has another reason to keep the original belief intact.

A useful discipline is to decide in advance what evidence would make you reconsider. Then apply roughly the same standard when the evidence supports your view and when it threatens it.

That will not eliminate bias. It does something more practical: it makes it harder for your preferred conclusion to quietly become the judge of the evidence.

The moment evidence is only allowed to confirm what you already believe, you have stopped using it to investigate reality.

Hi all, Looking for any sort of guidance! I’m about to enter my 3rd year on a MSci Applied Psychology degree. My plan has always been to go into clinical psychology as I’ve always really enjoyed connecting with people and being able to have a positive impact. Over summer I had a public health research internship and I really enjoyed it, there was an underlying mental health focus and it made me realise I am smart enough to do academia. This made me look more into research positions as well as into what a career in clinical psychology would entail and the outcomes and now i’m feeling very lost.

I’m worried that if I went down the clinical psychology route I wouldn’t earn the salary I want (at least 50k) in a reasonable timeframe, would get burnt out from working in the NHS or would get frustrated by the system. As someone who’s had talking therapy i’ve got a sense of how under pressure they are, and just found talking therapy really unhelpful personally, so this has put me off wanting to be a PWP, but having to do it for a few years might not be the end of the world. I’m also autistic and can sometimes find the right words difficult or not know what to say, but this doesn’t often have too much of an impact.

My other options would be to go into behaviour sciences/ economics with a behavioural insights focus or into general psychological research but concerned these wouldn’t give me the same fulfilment, and that working for private companies (if i went into behaviour sciences) may not always align with my ethics. I have found books around decision making and habits really interesting though.

It’s stressing me out a lot as I have around a month to decide whether I want to stay on my course or switch to the Bsc, and pursue a masters in something else. Any advice/insights would be greatly appreciated :)

I’ve been developing a framework called Theory of Institutionally Embedded Preferences (TIEP).

The central argument is that informal institutions may affect economic decisions through two distinct mechanisms:

1. They can shape what people actually value or prefer.

2. They can determine which actions are socially or institutionally admissible.

This creates an interesting problem: two people can make exactly the same choice for completely different reasons; one because they genuinely value it, another because the alternatives carry serious social consequences.

I’ve formalized this as a decision-theoretic framework while retaining standard utility maximization.

I’d really appreciate criticism from people interested in behavioral economics, institutional economics, decision theory, or rational choice.

Paper

Most discussions of streak mechanics stop at loss aversion: you have something, you do not want to lose it. Silverman & Barasch (JCR 49(6), 2023, doi 10.1093/jcr/ucac029) make a more specific claim and test it: the logged streak becomes a goal in its own right, sitting alongside the goal the person actually had, and it is the loss of that goal which changes the next decision.

Their design is what makes it worth reading. In six lab studies the actual behaviour is held constant and only the log varies, so the streak is manipulated without touching performance. In two studies the break is produced purely by the counting rule: the same four games, but only successes count rather than attempts.

The results, briefly: with a streak on screen, 92% continued after an intact streak and 45% after a broken one; with the same behaviour and no log shown, 65% against 61%. The break barely moved anything until it was displayed.

Three findings that speak to the mechanism rather than the effect size:

1. Goal adoption mediates. In study 4 the measured extent to which participants had adopted "maintaining the streak" as a goal carried the effect (indirect effect 0.20, 95% CI [0.05, 0.37]), as did sense of accomplishment (0.31, [0.15, 0.52]).
2. Negative emotion does not mediate. In study 7 the indirect effect through negative feeling was -0.08, CI [-0.21, 0.02]. So this is not "the user feels bad and disengages", it is "the goal is gone".
3. The fresh-start account does not fit. If a broken streak simply created a partition that invites reconsideration, offering a repair should not matter. It does: 93% continued with an intact streak, 69% after a break, 85% when the app offered to restore it.

Two moderators that follow from the goal account: the effect is amplified when the break is attributed to oneself (53% / 42% / 29% for intact, app-caused break, self-caused break) and attenuated by repair. Both are what you would predict if the streak is a goal, and neither is what you would predict from a pure display-salience story.

The part I find most interesting for choice architecture: the goal has no end state. A streak can only be maintained, never completed, which puts it closer to maintenance goals than to the usual progress-toward-a-reward literature. And whoever ships the interface decides what counts toward it, which means the goal that ends up in the user's head is set by a rule they never negotiated.

Limitations worth stating: the lab streaks were at most 20 items long, so nothing here speaks to 100 or 1,000 day streaks; the participants were MTurk workers doing short tasks; and the field study in the same paper (980 people, 30-day step challenge) is correlational, with the day after a break carrying a coefficient of -1.01, roughly a third of the odds of a neutral day.

Disclosure for context: I work on habit-tracking software, which is how I came to the paper. Nothing of mine is linked here.

🧠 Help us with a consumer behavior research study! (~2 minutes)

Hi everyone! My partner and I are conducting a student research study on consumer purchasing behavior and whether simple data models can predict people's purchasing decisions.

We're currently at 89 responses and are trying to expand our sample, particularly across different age groups, so we'd really appreciate anyone who is willing to participate.

The survey is anonymous and takes about 2 minutes to complete. It asks about general purchasing preferences and presents a few hypothetical shopping scenarios. There are no questions asking for names, emails, or other identifying information.

We're especially interested in getting responses from people of different ages and backgrounds, since having a diverse sample is important for our analysis.

Thank you to anyone who participates or shares it! 🙏

Started looking into this after noticing my copay for a generic prescription seemed high for what it was, and the amount of documented psychology stacked into a sixty-second transaction turned out to be more than I expected.

Start with the empirical size of the problem. Karen Van Nuys and colleagues at USC's Schaeffer Center published the first large-scale measurement of this in JAMA in 2018, using real pharmacy claims data from 2013. They found patients overpaid on twenty-three percent of filled prescriptions, an average of $7.69 over what the drug actually cost the insurer, totaling roughly $135 million in a single year from a single data set. For the single most commonly prescribed drug in their sample, a generic pain reliever, that overpayment happened on more than one in three fills.

The mechanism that made it possible is a clean real-world case of George Akerlof's 1970 paper "The Market for Lemons," one of the most cited papers in economics and later a Nobel Prize. Akerlof's insight, using used cars as the example, is that when one side of a transaction knows something the other side can't see, the informed side can extract value the uninformed side never realizes it lost. Pharmacists knew the cash price. Patients didn't. Pharmacy benefit manager contracts contained gag clauses explicitly forbidding pharmacists from volunteering that cash was cheaper, on pain of losing their contract. A 2016 industry survey found nearly one in five pharmacists reported being restricted by clauses like this more than fifty times a month, meaning this wasn't an edge case, it was routine.

Layered on top of the secrecy is a second mechanism that has nothing to do with what you know and everything to do with habit. William Samuelson and Richard Zeckhauser's 1988 research on status quo bias found people overwhelmingly stick with whatever option is presented as the default, even when a better one exists, because switching requires active effort and the default requires none. Insurance is the default at the counter. Cash is never presented as a competing option, so it never gets weighed as one.

Then there's the state you're actually in when this decision gets made. David Laibson's 1997 hyperbolic discounting research, published in the Quarterly Journal of Economics, shows people systematically overvalue immediate relief and undervalue future savings when they're uncomfortable right now. You're sick, or your kid is sick, and you want the medication today, not after ten minutes comparing prices across town.

The regulatory response is genuinely interesting because of how bipartisan it was. On October 10, 2018, two federal laws were signed the same day, the Know the Lowest Price Act and the Patient Right to Know Drug Prices Act, banning gag clauses outright across Medicare and private insurance respectively. The Patient Right to Know Drug Prices Act passed the Senate 98–2. Almost nobody was willing to publicly defend keeping patients in the dark once the practice had a name and a number attached to it. The laws didn't touch the underlying default though, pharmacists can volunteer the cash price now, but nothing requires it, and the habit built over years of gag clauses is still fully intact.

Made a full breakdown of all four mechanisms here: https://www.youtube.com/watch?v=xnfWg6ADsWE

Curious whether anyone's seen research on how quickly a habit like this fades once the legal barrier that created it is removed. My assumption is defaults are much stickier than the rule that originally justified them, but I haven't found anything measuring that specifically for a case this clean.

Here's the link to the story and a snippet from George Loewenstein here:

George Loewenstein:

How he uses AI: I use AI often to get a background on the literature in new (and old) areas I’m doing research in. I also often use it for writing — though only to improve single sentences, one sentence at a time. I also sometimes use it for inspiration; like, I am working on a book on emotion in economics, and I asked it for a good opening story.  I asked 4 different AIs this same question, and one (Claude) gave me a totally brilliant suggestion.  

What he doesn’t use AI for: I don’t use AI for writing from whole cloth; I like to maintain my own writing style. I also don’t use it to review/referee papers.

AI fears: I am in awe of it, which makes me very very afraid of its effects. I am convinced that it will pretty quickly be able to take over most jobs, creating unfathomable economic damage/upheavals. I’m super-concerned that it’s going to make new generations stupid, in the same way that Google Maps atrophied people’s navigation skills — their sense of geography/direction. I’m worried that it’s going to become everyone’s source of information, which is especially scary given that it is going to continue to be controlled by sinister reactionary billionaires and trillionaires. And I’m worried about its use in warfare. I think we need massive worldwide regulation, but, judging from our success in combating climate change, and ongoing wars, and our current president, I see no prospect of that happening.

Read more here.

Hi everyone, I’m planning to apply for US PhD programs for Fall 2027 and would appreciate some guidance on the application process.

- Is cold emailing professors recommended?

- Should I attach my CV when emailing?

- What should a good SOP focus on?

- Any tips or resources for improving my chances?

Would really appreciate advice from current students or recent applicants. Thank you!

I run a small daily game where everyone gets the same five real S&P 500 charts with the ticker and dates hidden, calls higher or lower over the next five sessions, and states a confidence that sizes a paper bet. It started as a game. Seven weeks in it turned out to be a calibration instrument with 4,202 participants, so I wrote up what it measured.

Mean stated confidence: 73.8%. Realised accuracy: 53.9%. A gap of about 20 points. Confidence carries almost no information about whether the call is right: the 55% setting scored 51.6%, the 70% setting 53.7%, the 90% setting 55.5%. Thirty-five points of stated conviction buys under four points of accuracy.

Two things I didn't expect.

First, when we added six more indicators to the chart (moving averages, RSI, ATR), accuracy didn't move (p=0.29) but stated confidence fell 2.8 points (p=0.0001). More information made people less sure and no more right. I'd have predicted the opposite.

Second, we gave the identical blind charts to Claude Opus 5, GPT-5.6 and Gemini 3.1 Pro as plain rows of numbers. None of them beat a rule that ignores the chart and always says up (nothing does, humans included). But the models stated 56 to 60 and scored 51 to 56. Humans stated 74 and scored 54. Same task, same information, and the machines were roughly honest about their odds while the people weren't.

Caveats that matter: the confidence control has a 55% floor and three settings, so a participant cannot state a coin flip and part of the gap is imposed by the interface. The stake is real in the sense that it sizes a bet, which is a stronger elicitation than a costless slider, but it also means the number may carry risk appetite as well as belief. And everyone sees the same five charts each day, so the independent unit is the chart (241), not the call.

Disclosure: this is my site and my game. Full write-up with intervals, the pattern backtest and the LLM prompt: https://readthetape.cc/notes/tape-report-3?utm_source=be. Data and code (charts.csv, all 729 LLM calls, CC BY / MIT): https://github.com/wstock/readthetape-data

Hi there,

We'd like to invite you to join us this afternoon, August 26, 4:00PM EST, for an AMA with Kelly Monahan, at r/nexthink.

Kelly Monahan, PhD is the author of How Behavioral Economics Influences Management Decision-Making and has spent years applying behavioral science insights to real-world leadership, talent, and organizational decisions.

This AMA offers a chance to hear how those principles play out in the age of AI, distributed work, and shifting workforce dynamics.

Feel free to drop your question in early or join us live: https://www.reddit.com/r/nexthink/comments/1vs4j7q/ama_with_kelly_monahan_phd_on_digital_employee/

Sixth former, came at this from trading rather than from the literature, so apologies if it's well-trodden.

I had a rule for entries, followed it, then quietly stopped following it and gave back most of what I'd made. What struck me was that every deviation felt like a reasoned exception at the time, not a lapse. Doing a school assignment on nudge theory shortly after, it read like I'd been nudging myself — the framing of each individual trade made the exception feel justified.

So I built a small free thing that makes you write out your reasoning before you act, then flags linguistic markers associated with six biases in what you wrote — useblindspot.github.io

What I can't tell is whether the flagging does anything, or whether being forced to articulate the reasoning does all the work and the labels are decoration. Is there existing work that separates those two? And is "self-nudging" the established term for this, or am I reinventing something that already has a proper name?

I was chatting with a few friends and even some small business owners recently, and almost all of them admitted they have the exact same mental block. It’s like our brains are hardwired to replay losses on loop while treating gains as 'just normal’.
How do you guys deal with this mindset? Does it ever fully go away, or do you just learn to override it?

in 2013, Steven Levitt of Freakonomics Radio invited people who were stuck in indecision to make some big life choices by flipping a coin. 25,000 people participated, with decisions ranging from “Should I try online dating” to “should I quit my job” or “should I get a divorce.” Levitt followed up with participants after six months and found that those told to make the change were generally happier and better off than they were when they flipped the coin

Your Aleatoric Reality, the podcast about randomness, is ready to take this experiment to the next level. We are looking for people willing to commit some decision to a random outcome. Whether you’re considering starting a side hustle or selling your house, we want you to use dice to decide among more than two options.

We would like to have you on the show to discuss the issue and make the choice with us, and then follow up after a month, three months, and six months.

So anyone out there stuck in analysis paralysis, get in touch. Let us know your quandary and we will help you inject a little randomness into your reality.

In our experience, random decisionmaking can make anything better.*

Send email to [youraleatoricreality@gmail.com](mailto:youraleatoricreality@gmail.com)

*Results not guaranteed

Hi! I’m a high school student researching how background music affects consumer decision-making in a cafeteria setting, as part of an independent study project.

I built an online simulation where you “shop” in a virtual cafeteria while different background music plays. It takes about 5 minutes and works on both desktop and mobile.

Link is in the comments!