1장
When Big Data Meets Your Big Decisions
What if everything you thought you knew about success, happiness, and life's biggest decisions was wrong? Seth Stephens-Davidowitz's "Don't Trust Your Gut" challenges our most fundamental assumptions about how to navigate life's crucial choices. As a New York Times bestselling author and former Google data scientist with degrees from Stanford and Harvard, Stephens-Davidowitz has established himself as a master of extracting profound insights from massive datasets. This book has become required reading in Silicon Valley and on Wall Street, where data-driven decision-making is revolutionizing industries. Even celebrities like Mindy Kaling have praised its counterintuitive wisdom, calling it "the book I wish I'd read before making every major life decision." The book's genius lies in its ability to transform complex data analysis into actionable insights about relationships, parenting, career choices, and happiness-revealing that our instincts often lead us astray in ways we never imagined.
2장
The Surprising Science of Finding Love
When it comes to finding a romantic partner, we might be looking for all the wrong things. Canadian scientist Samantha Joel conducted groundbreaking research combining 43 studies with 11,196 couples, creating an unprecedented dataset on relationship satisfaction. Using sophisticated machine learning techniques, Joel analyzed hundreds of variables-from physical appearance to sexual preferences to personality traits-seeking reliable predictors of relationship happiness.
The results were shocking. Despite the enormous dataset and sophisticated AI analysis, relationships remain largely unpredictable. Demographics, preferences, and values showed surprisingly little power in predicting relationship satisfaction. While AI can now defeat chess champions and detect health issues through odors, it remains "just as clueless as the rest of us" at determining romantic compatibility.
This unpredictability has counterintuitive implications for dating strategies. The research reveals a crucial insight: while good romantic partners are difficult to predict with data, desired romantic partners are easy to predict-suggesting many people are dating all wrong.
The rise of online dating has provided unprecedented data on romantic preferences. While people claim to value "dependable character" most in surveys, actual behavior tells a different story. Physical attractiveness explains about 30% of female daters' success and 18% of male daters' success, making it the single most important predictor of dating interest.
Height dramatically impacts men's desirability-men between 6'3" and 6'4" receive 65% more messages than those between 5'7" and 5'8". This height advantage translates to significant economic value-a 5'6" man would need to earn approximately $175,000 more annually to be considered as desirable as a similar 6-foot man.
Dating data also reveals significant racial discrimination patterns. Black women face particular challenges, receiving fewer responses from men of almost all racial groups, while white men enjoy the highest response rates overall. When controlling for factors like income, the bias becomes even more pronounced-an Asian man would need to earn $247,000 more annually to be as attractive to the average white woman as a white man with similar characteristics.
Income predictably affects dating desirability, particularly for men. When a man's earnings increase from $35K to $200K, women are 8.9% more likely to contact him. For women with the same income increase, men are only 3.9% more likely to reach out. However, occupation can sometimes matter more than salary-a $60K firefighter is generally more attractive to heterosexual women than a $200K hospitality worker.
Research shows that certain first names significantly impact dating success, with some names being twice as likely to get responses as others. Contrary to the popular belief that "opposites attract," data from millions of eHarmony matches demonstrates that similarity strongly predicts attraction. People are even 11.3% more likely to match with someone sharing their initials.
While online daters predictably pursue certain qualities, research suggests these preferences may be misguided. Joel's extensive study found surprisingly weak connections between specific partner traits and relationship satisfaction. The data reveals that a person's own pre-existing happiness and mental health are approximately four times more predictive of their relationship satisfaction than all their partner's traits combined.
Analysis of 11,000+ long-term couples reveals the "Irrelevant Eight" traits that barely predict relationship happiness: race/ethnicity, religious affiliation, height, occupation, physical attractiveness, previous marital status, sexual tastes, and similarity to oneself. Strikingly, these traits almost perfectly match what's most valued in the dating market. This creates what could be called the "First Law of Love": people fiercely compete for mates with qualities that don't increase chances of romantic happiness.
The dating market resembles pre-Moneyball baseball, where people overvalue superficial qualities while underrating traits that truly contribute to relationship success. By focusing on undervalued groups in the dating market (short men, tall women, Asian men, Black women, men in less prestigious careers, less conventionally attractive people), you face less competition for potentially great partners.
Research identified several qualities that actually predict romantic happiness: satisfaction with life (being generally content), secure attachment style (ability to trust and be intimate), conscientiousness (being disciplined and reliable), and growth mindset (believing one can improve through effort). These findings contradict online dating's emphasis on appearance-despite people's desperate pursuit of attractive partners, couples with partners possessing these character traits report greater happiness.
3장
The One Parenting Decision That Actually Matters
Parenting involves countless difficult decisions-an estimated 1,750 in a baby's first year alone. While Google offers abundant parenting advice, it tends to be either painfully obvious ("Be a Good Role Model") or frustratingly contradictory (some experts recommend timeouts while others condemn them).
How much do parents actually matter? Science suggests less than most people think. Bruce Sacerdote's study of Korean adoptees randomly placed with American families revealed that nurture has surprisingly modest effects-about 2.5 times smaller than nature's influence. Parents have only small effects on life expectancy, health, education, religiosity and adult income, though they do moderately influence religious affiliation, teen behavior choices, and how children feel about them.
Studies show that many hotly debated parenting techniques have minimal impact: breastfeeding shows no significant long-term effects in randomized trials; TV exposure doesn't affect test scores long-term; chess and other cognitively demanding games don't make children smarter; and bilingual education has only small effects on cognitive performance. As economist Bryan Caplan advised parents based on decades of social science research: "Lighten Up."
However, one parenting decision stands out as critically important: neighborhood choice. Economist Raj Chetty's groundbreaking research using IRS data on the entire population of American taxpayers tracked where people lived throughout childhood and their adult earnings.
To establish causation rather than mere correlation, Chetty studied siblings who moved as children. When families relocated, younger siblings who spent more years in better neighborhoods consistently showed better outcomes than their older siblings. This methodology controlled for family genetics and parenting style, isolating the neighborhood effect.
The research revealed that "SuperMetros" like Seattle, Minneapolis, and Salt Lake City can increase a child's income by about 12% compared to average places. Even more significantly, specific neighborhoods within cities showed dramatic differences in outcomes.
When comparing Sacerdote's adoption studies (showing parents influence income by about 26%) with Chetty's neighborhood research, a striking conclusion emerges: approximately 25% of a parent's overall effect comes solely from neighborhood choice. Among thousands of parenting decisions, where you raise your children matters most.
After identifying which neighborhoods helped children thrive, Chetty analyzed what characteristics these successful neighborhoods shared. Contrary to what many might expect, the three strongest predictors weren't about schools, jobs, or urban density-they were about the adults living there:
1. Percentage of residents who are college graduates
2. Percentage of two-parent households
3. Percentage of people who return their census forms
These factors all reflect the quality of adult role models in a community. The correlation suggests that the adults children are exposed to may have an outsized impact on their development-more influential than school quality or economic conditions.
In their study "Who Becomes an Inventor in America?", Chetty's team discovered that children who grew up in neighborhoods with many adult inventors were significantly more likely to become inventors themselves. This effect was highly specific-children exposed to medical device inventors tended to invent medical devices. Most strikingly, girls' chances of becoming inventors increased substantially when they grew up near female inventors, but exposure to male inventors had virtually no effect on them.
The research on parenting leads to two seemingly contradictory recommendations. First, parents can relax about most decisions they agonize over-the data shows these choices have limited impact. For most parenting decisions, "trust your gut" is reasonable advice, not because intuition is magical but because these decisions simply aren't that consequential.
However, one area deserves careful attention: the adults you expose your children to. Parents should deliberately connect their children with inspiring role models who embody qualities they'd like their children to develop.
4장
The Genetic Lottery: Which Sports Give You a Fighting Chance
For those without natural athletic gifts, certain sports offer much better odds of success than others. ScholarshipStats.com reveals fascinating patterns in college athletic scholarship opportunities. For male athletes, gymnastics (20:1) and fencing (22:1) offer the best scholarship odds, while volleyball (177:1) and wrestling (176:1) offer the worst. For female athletes, rowing (2:1) and equestrian (3:1) offer remarkably favorable odds, while bowling (94:1) has the worst. Women generally have better scholarship odds across all sports compared to men.
Behavioral geneticists use twins to solve the nature-versus-nurture puzzle by comparing identical twins (sharing 100% of genes) with fraternal twins (sharing 50% of genes but the same upbringing). This approach reveals how much different traits and abilities are influenced by genetics.
Sports with high genetic dependence show a high prevalence of identical twins at elite levels. Basketball, where height is crucial, has had ten pairs of twin brothers in the NBA, with at least nine being identical twins. This suggests an identical twin of an NBA player has over 50% chance of making the NBA himself-compared to the average American male's 1 in 33,000 chance. The data suggests basketball ability is approximately 75% determined by genetics.
While basketball success is highly genetic, other major American sports show less genetic influence. In baseball, an identical twin of a pro baseball player has roughly 14% chance of making the majors. Football shows similar patterns. The analysis estimates that genetic contribution to baseball and football skill is around 25%-less than half as important as in basketball.
The data reveals dramatic differences in genetic influence across sports. Olympic diving, equestrian sports, and weightlifting show 0% identical twin correlation, suggesting these sports might offer better opportunities for those without special genetic gifts. Bruce Springsteen's daughter Jessica proves this point-while Bruce may have been "Born to Run" (track being highly genetic), Jessica became obsessed with riding horses at age four and eventually won an Olympic silver medal in equestrian.
5장
The Secret Path to Wealth Most People Miss
Do you want to hear a boring story? Kevin Pierce is a wholesale beer distributor who runs Beeraro, started by his grandfather in 1935. His daily routine involves spreadsheets, supplier meetings, and managing deliveries-all wrapped up by 5 PM daily. Despite the "insanely boring" nature of his work, Kevin has made millions over the years in what economists have identified as one of the industries with the highest chance of reaching the top 0.1% of earners.
Until recently, our understanding of wealth in America was limited by reliance on self-reporting and media stories that favor sensational wealth narratives. Now, academics working with anonymized IRS data have been able to study the entire universe of wealthy Americans, revealing patterns previously hidden from view.
The tax data reveals that the majority of wealthy Americans own businesses rather than earning salaries. Only about 20% of the top 0.1% earn most of their money from wages, while 84% receive at least some income from business ownership. For every Jamie Dimon (employee) earning top-tier wealth through salary, there are three Kevin Pierces (owners) making their fortunes through business profits.
This ownership advantage is dramatically illustrated by former NFL player Jerry Richardson, who caught just 15 passes in his brief career but built a $2 billion fortune by owning 500+ Hardee's franchises-about 50 times more wealth than Jerry Rice, arguably the greatest receiver ever, who earned $42.4 million over his legendary 20-year career.
While business ownership is the predominant path to wealth, the specific field matters enormously. Data from the U.S. Bureau of Labor Statistics shows that "sexy" businesses-those children might dream of starting-tend to fail quickly. Record stores last just 2.5 years on average, while amusement arcades, toy stores, bookstores, clothing stores, and beauty supply stores all have median lifespans under 4 years.
When analyzing which businesses create wealth, it's not enough to count how many rich people exist in a field-you must consider the percentage of owners who get rich. While restaurants have 4,471 rich owners, they represent just 2% of all restaurant owners. Meanwhile, auto dealerships have 5,236 rich owners representing a remarkable 20.1% of all dealerships-making your odds ten times better than restaurants.
After analyzing hundreds of fields, only seven met both criteria of having at least 1,500 owners in the top 0.1% AND at least 10% of businesses creating wealthy owners. This "Big Six" (combining real estate categories) includes: real estate (43.2%), investing (18.5%), auto dealerships (20.8%), independent creatives (12.5%), market research (10.6%), and middlemen/wholesalers (10%).
Most businesses fail to create wealthy owners due to the zero-profit condition. When a business makes large profits, competitors enter and undercut prices until profits approach zero. This process continues until no one makes enough profit to incentivize new entrants or further price cuts.
To consistently maintain profits, business owners must avoid competitors undercutting their prices. The "Big Six" businesses that create many millionaires all provide ways to avoid this ruthless price competition-through legal protection (auto dealerships, beer distribution), scale advantages (investing, market research), or brand loyalty (independent creatives).
Not all industries that avoid price competition create wealthy owners. Some become dominated by one or two enormous firms, making it impossible for smaller players to compete. The Big Six businesses avoid this fate through natural factors preventing domination-real estate markets remain localized, investment firms specialize in particular strategies, auto dealerships enjoy legal protection, and artists maintain unique appeal to their specific fans.
To become rich, you must answer "yes" to three critical questions:
1. Do I own a business?
2. Does the business have a path to avoid ruthless price competition?
3. Does the business have a path to avoid being dominated by a global behemoth?
6장
The Patient Path to Success: Slow and Steady Wins the Race
Every aspiring entrepreneur should look to Tony Fadell as a model. Unlike the stereotypical young tech founder, Fadell created Nest Labs in his early forties after building a stellar career as an employee. His story challenges conventional wisdom about entrepreneurship and represents a common path among successful founders.
When we think of successful founders, names like Steve Jobs (21), Bill Gates (19), and Mark Zuckerberg (19) typically come to mind-all young when they started their empires. This isn't coincidental; media coverage heavily favors young entrepreneurs. Business magazines' "Entrepreneurs to Watch" sections feature founders with a median age of just 27. This creates a distorted perception.
The venture capital world has embraced this narrative. Vinod Khosla claims "people under 35 make change happen," while Y Combinator's Paul Graham grows skeptical of founders over 32. But comprehensive data tells a different story. Studying 2.7 million American entrepreneurs, researchers found the average founder is 41.9 years old-over a decade older than those featured in media. Success rates actually increase with age, with 60-year-old founders three times likelier to create valuable businesses than 30-year-olds. Even in tech, the average successful founder is 42.3 years old.
Could being an outsider to an industry be an entrepreneurial advantage? Y Combinator founder Paul Graham's provocative essay "The Power of the Marginal" suggests that "great new things often come from the margins." But comprehensive tax data contradicts this romantic notion. Researchers examined wage histories of every American startup founder and found the conventionally successful massively outperform others as entrepreneurs. Founders are roughly twice as likely to build extremely successful companies if they previously worked in the same field.
These findings aren't actually surprising. Of course entrepreneurs succeed more after spending years rising to the top of their field. Yet these intuitive findings contradict narratives that have captured public imagination.
These are what Stephens-Davidowitz calls "counter-counterintuitive ideas." First, there's common sense (older, experienced people make better entrepreneurs). Then exceptional stories emerge that contradict this (like 19-year-old Zuckerberg). Because these exceptions are surprising, they make great stories. Media amplifies them until the counterintuitive becomes conventional wisdom. Finally, comprehensive data brings us back to the original intuitive truth.
Data cuts through media noise and unrepresentative examples to reveal the true formula for entrepreneurial success: spend years mastering a field, prove your worth as a top employee, then strike out on your own. This formula isn't exciting, but it's effective.
7장
Hacking Luck: Creating More Opportunities for Success
In October 2007, unemployed roommates Brian Chesky and Joe Gebbia rented air mattresses in their San Francisco apartment during a design conference to help pay bills. Seeing potential, they launched airbedandbreakfast.com with friend Nathan Blecharczyk, but the business initially went nowhere. Despite setbacks, they persisted through scrappy moves like selling Obama and McCain-themed cereal during the 2008 Democratic convention. Their pivotal moment came when Barry Manilow's drummer requested to rent his entire apartment while on tour. This sparked their "aha" realization: drop air mattresses and breakfast, focus on whole-apartment rentals. Rebranded as Airbnb, they finally gained traction.
Many successful people attribute their achievements to luck. Nobel Prize-winning economist Paul Krugman claims he "was very lucky to be in the right place at the right time," while actors John Travolta and Anthony Hopkins similarly credit luck for their success.
But data suggests luck may play a smaller role than we think. Jim Collins and Morten Hansen's research on "10X companies" (businesses that outperformed peers by at least 10x) revealed fascinating patterns. Each 10X company experienced about seven lucky breaks. However, when comparing these 10X companies to less successful competitors, Collins and Hansen found no statistical difference in the number of lucky breaks. The 10X companies averaged 7 lucky breaks; the comparison companies averaged 8. The difference wasn't in having more luck, but in capitalizing on the luck they received.
The art world provides fascinating insights into how luck operates. Albert-Laszlo Barabasi distinguishes between fields where performance is easily measured (like sports) versus those where quality assessment is subjective (like art). In sports, the best are clearly identifiable-Michael Jordan was demonstrably basketball's best, Michael Phelps swam faster than everyone else. But in art, quality judgments are far more ambiguous, as demonstrated when renowned violinist Joshua Bell went unrecognized busking in a metro station.
The data reveals a stark contrast between successful and unsuccessful artists' exhibition strategies. While unsuccessful Category 1 artists typically present repeatedly at the same local galleries in their home country, successful Category 2 artists like German painter David Ostrowski aggressively pursue opportunities across multiple countries and venues.
Bruce Springsteen exemplifies this principle perfectly. At age 21, he recognized that staying in the Jersey Shore would limit his chances of discovery, so he gathered his band and declared they must "venture into parts unknown" to be seen and heard. This led to a cross-country journey for a New Year's Eve gig in California, driving non-stop in a station wagon. For years afterward, Springsteen lived the Category 2 artist's life-traveling nationwide for any gig, meeting musicians, and occasionally auditioning for record producers who rejected him.
Professor Dean Simonton discovered a fascinating pattern: artists who produce more work tend to have more masterpieces. Shakespeare wrote 37 plays in two decades, Beethoven composed over 600 pieces, Bob Dylan wrote more than 500 songs, and Pablo Picasso released an astonishing 1,800 paintings and 12,000 drawings.
This correlation exists for several reasons. Talented artists may naturally find it easier to produce volume. Early success may provide resources to create more work. But most importantly, prolific artists have more chances to get lucky. Artists often can't predict which works will become masterpieces. Beethoven disliked at least eight pieces that became masterworks. Woody Allen begged United Artists not to release "Manhattan," offering to make another film for free to avoid embarrassment.
Dating follows the same mathematical principles as artistic success. While attractive people receive more responses online, the actual response rates for "out of your league" attempts are surprisingly high. When the least attractive men message the most attractive women, they still get responses about 14% of the time. For women messaging more attractive men, it's even better-around 29%.
These numbers reveal a profound dating strategy: ask out many people. A man in the bottom attractiveness decile who messages 10 women in the top decile has an 80% chance of getting at least one response. With 30 attempts, that rises to 99%. Women's odds are even better when initiating contact with desirable men.
8장
The Surprising Science of What Makes Us Happy
Most people are terrible at predicting what will make them happy. Daniel Gilbert's groundbreaking research shows how consistently wrong we are about the impact of major life events on our happiness. In one experiment, assistant professors pursuing tenure predicted their happiness would depend significantly on whether they received it. However, when researchers surveyed professors who had already gone through the tenure process, they found no significant happiness difference between those who received tenure and those denied it.
We struggle to accurately remember our experiences of pleasure and pain due to cognitive biases. A study involving colonoscopy patients revealed this phenomenon: Patient A experienced 8 minutes of pain (levels 0-8) while Patient B endured over 20 minutes at similar pain levels. Though Patient B objectively suffered more, they recalled experiencing less pain afterward. This disconnect occurs because of two key biases: duration neglect (failing to account for how long an experience lasted) and the peak-end rule (giving undue weight to the highest intensity moments and how experiences conclude).
Researchers George MacKerron and Susana Mourato revolutionized happiness research with their smartphone app Mappiness, which pinged users throughout the day asking what they were doing, who they were with, and their happiness level (1-100). This approach generated over 3 million happiness measurements from more than 60,000 people. Using sophisticated statistical techniques that compared the same person at similar times doing different activities, the researchers could estimate the causal impact of various activities on happiness, not merely correlations.
The most happiness-producing activities include intimate relations (+14.20), theater/dance/concert (+9.29), sports/exercise (+8.12), gardening (+7.83), singing/performing (+6.95), and exhibitions/museums (+6.77). The Mappiness data continues with pet care (+3.63), listening to music (+3.56), puzzles/games (+3.07), shopping (+2.74), gambling (+2.62), watching TV (+2.55), computer games (+2.39), eating (+2.38), cooking (+2.14), drinking tea/coffee (+1.83), reading (+1.47), podcasts (+1.41), grooming (+1.18), relaxing (+1.08), smoking (+0.69), internet browsing (+0.59), and social media (+0.56).
Activities that actually decrease happiness include housework (-0.65), commuting (-1.47), meetings (-1.5), admin tasks (-2.45), waiting in line (-3.51), caring for adults (-4.3), working (-5.43), and being sick in bed (-20.4). The data reveals we systematically overestimate passive activities (relaxing, watching TV, gaming) and underestimate active ones (museums, exercise, gardening) when predicting happiness.
Spencer Greenberg and the author studied whether people could accurately predict which activities make us happiest. They discovered people generally got the extremes right but misjudged many activities in between. The most underrated happiness-producing activities (things that make us happier than we expect) include museums/exhibitions, sports/exercise, drinking alcohol, gardening, and shopping. The most overrated activities (things less happiness-inducing than we believe) include relaxing, computer games, watching TV, eating/snacking, and internet browsing. The pattern is clear: we overestimate passive activities and underestimate those requiring energy and initiative.
9장
Avoiding the Happiness Traps of Modern Life
The chapter opens with the popular saying "Everything is amazing, and nobody is happy." The author examines this paradox through data, noting that while not literally true, it contains truth. Psychiatrist Scott Alexander's observations reveal that roughly half of Americans face severe problems at any given time-from chronic pain (20%) to depression (7%) and alcoholism (7%). Despite GDP doubling over fifty years and free digital services worth thousands annually to users, happiness levels remain unchanged-in 1972, 30% of Americans reported being "very happy," nearly identical to today's figures.
Work ranks as the second most miserable activity in the Happiness Activity Chart, beaten only by being sick in bed. Despite what people might say at social gatherings about loving their jobs, Mappiness data reveals a grimmer reality: when anonymously reporting their feelings while working, people rate work as more miserable than doing chores, caring for elderly people, or waiting in line.
Most adults can't quit working, but there are ways to make work less miserable. Research by MacKerron and Bryson identified three key factors: listening to music (+3.94 happiness points), working from home (+3.59 points), and most significantly, working with friends (+6.25 points). Someone working from home with friends while listening to music could be as happy as someone playing sports-one of the happiest activities.
Friends are crucial for happiness across all dimensions of life. Research by MacKerron and Mourato compared people doing the same activities but with different companions. The findings show that people gain over four happiness points when with romantic partners or friends compared to being alone. However, being with other types of people-colleagues, clients, or distant acquaintances-provides minimal happiness gains or even makes people less happy than solitude.
Social media makes us miserable for multiple reasons. The Happiness People Chart shows we're happier with close friends and romantic partners than with weak ties-which dominate social media interactions. Additionally, social media scores as the lowest happiness-producing leisure activity on the Happiness Activity Chart. A randomized controlled experiment by NYU and Stanford researchers provided conclusive evidence: people paid to stop using Facebook for four weeks spent 60 minutes less on social media daily, redirecting that time to friends and family. Their happiness increased significantly-about 25-40% of the well-being gain from individual therapy.
Despite the author's passionate love for sports, research reveals that sports fandom creates a net happiness loss. When teams win, fans gain 3.9 happiness points, but losses cost 7.8 points. The math suggests fans need teams winning at least 66.7% of games for net positive happiness. Even switching to better teams doesn't solve the problem. MacKerron and Dolton found that our brains adjust to team quality-when supporting a team expected to win, victories bring only 3.1 points of pleasure while losses cost 10 points.
According to Mappiness data analyzed by MacKerron and Baumberg Geiger, alcohol makes people about four points happier when consuming it during the same activity, with generally no negative mood impact the following morning (just slightly increased tiredness). Interestingly, people typically drink when already doing enjoyable activities like socializing, attempting to make great experiences epic. However, the data reveals alcohol provides the biggest happiness boost during otherwise unpleasant activities like commuting, waiting in line, or grooming.
MacKerron and Mourato's research reveals that being in natural settings significantly impacts happiness. Their data shows the most scenic places add 2.8 happiness points compared to less scenic areas. The "Happiness Weather Chart" demonstrates that warm days (24C/75.2F or higher) provide the largest weather-related happiness boost at 5.13 points, far outweighing the negative effects of rain or cold.
The author reflects on how Big Data reveals truths about life that often contradict our assumptions. The happiness research by MacKerron, Mourato and others shows that happiness isn't complicated-it comes from straightforward activities like spending time with friends and being in nature. Yet modern society pushes us toward activities that data shows make us unhappy: overworking at jobs we dislike, obsessing over social media, and staying indoors.
The author distills all the happiness research into one humorous "data-driven answer to life": "be with your love, on an 80-degree and sunny day, overlooking a beautiful body of water, having sex." This conclusion emphasizes that the path to happiness often involves simple pleasures rather than complex solutions.
