The title of this post is inspired by Scott Alexander’s Never Tell Me The Odds (Ratio). The goal of this post is to explain the meanings of (commonly-heard) metrics that indicate the “odds” of something (either directly or indirectly).
Just because these terms are commonly-heard does not mean they are commonly-understood. The odds are that most people don’t understand the numbers related to the odds – and misinterpret how big the odds really are.
Let’s take an example, borrowed from Scott Alexander:
Suppose you run a drug trial. In your control group of 1000 patients, 300 get better on their own. In your experimental group of 1000 patients (where you give them the drug), 600 get better.
The relative risk of recovery from the drug = probability of recovering from the drug in the experimental group ÷ probability of recovering on one’s own in the control group = (600 / 1000) ÷ (300 / 1000) = 60% ÷ 30% = 2.0.
The odds from recovering from the drug in the experimental group = probability of recovering ÷ probability of not recovering = 600 ÷ (1000 – 600) = 3/2. Likewise, the odds from recovering on your own in the control group = 300 ÷ (1000 – 300) = 3/7.
The odds ratio = odds of recovering from the drug ÷ odds of recovering on one’s own = (3/2) ÷ (3/7) = 3.5.
The Cohen’s d effect size takes the difference in the average of two groups (x1 – x2) and divides it by the standard deviation (s):
To recap, for the example above, we got the following results:
Relative risk (drug vs. self-recovery) = 2.0
Odds ratio (drug vs. self-recovery) = 3.5
Cohen’s d effect size = 0.6
The numbers lie on a wide range from 0.6 to 3.5 – and depends on which one is reported, and in what fashion, it could bias up (or down) the reader’s perception on how effective the drug is (vs. self-recovery). As Scott Alexander puts it:
The moral of the story is that (to me) odds ratios sound bigger than they really are, and effect sizes sound smaller, so you should be really careful comparing two studies that report their results differently.
My ratings of the book Likelihood to recommend: 3.5/5 Educational value: 4/5 Engaging plot: 3/5 Clear & concise writing: 3/5 Suitable for: anyone interested in how to host better gatherings, be it a birthday party, a family dinner, or a business meeting
Me: “I am reading a book called The Art of Gathering – it’s about tips on how to be a better host of gatherings.”
Response: “I like how you are reading about gatherings when we can’t have gatherings during social distancing. :)” Fair point – this may not be a good time to host a gathering, nevertheless it doesn’t hurt to think about how to become a better host. The learnings from the book will become especially handy when we resume normal social activities (and fingers crossed the situation would improve soon).
Before digging into the key takeaways, general comments on the book – I gave this book 3.5 stars out of 5:
WhatI like is the insights on gatherings – the book is less about what to do at gatherings (though there is a fair share of that) and more about how to think about gatherings(a mindset shift). This is not the typical logistical advice you would expect (e.g., how to arrange seats or dinner recipes). Instead, Priya Parker tells us how to re-imagine our roles as a host and the meanings of a gathering. This book reads like a combo of instructional manual + philosophy – that’s worth a 4 stars on educational value.
What I don’t enjoy as much is the narration style – some examples shared in the book feels a bit too wordy and could be slimmed down. For this reason, I find myself flipping through some chapters where I feel I have captured the main points, yet the examples shared are too detailed for my taste. Hence only a 3-star rating on plot & style.
And now to key takeaways from the book:
1/ Figuring out the real reason that matters is halfway towards a successful gathering. Importantly, a category is NOT a purpose, e.g., the purpose of a birthday party is NOT “to celebrate my birthday.“ A better but bland purpose would be “to mark the year,” and even better purposes could be along the lines of “to surround myself with the people who bring out the best in me,” “to set some goals for the year ahead with people who will help me stay accountable,” “to take a personal risk/do something that scares me.”
2/ Gathering that please everyone are rarely exciting – great gatherings are not afraid of alienating, which is not the same asbeing alienating. It is about taking a stand with a purpose of the gathering that stands out; it is about saying “no” to someone who want to join the gang; it is about enforcing rules to honor the purpose of the gathering and not succumbing to so-called etiquette.
“(Some purposes) fail at the test for a meaningful reason for coming together: Does it stick its neck out a little bit? Does it take a stand? Is it willing to unsettle some of the guests (or maybe the host)? Does it refuse to be everything to everyone?“
“A good gathering purpose should also be disputable. If you say the purpose of your wedding is to celebrate love, you may bring a smile to people’s faces, but you aren’t really committing to anything, because who would dispute that purpose? … A disputable purpose, on the other hand, begins to be a decision filter. If you commit to a purpose of your wedding as a ceremonial repayment of your parents … that is disputable, and it will immediately help you make choices. That one remaining seat will go to your parents’ long-lost friend, not your estranged college buddy.“
3/ A good host is never a chill host who sits back and lets guests organize themselves. I love how Priya Parker puts it: “Gathering well isn’t a chill activity. If you want chill, visit the Arctic.” Or in the words of Isaiah Berlin: “Freedom for the wolves has often meant death to the sheep.“
“The chill approach to hosting is all too often about hosts attempting to wriggle out of the burden of hosting. In gatherings, once your guests have chosen to come into your kingdom, they want to be governed – gently, respectfully, and well. When you fail to govern, you may be elevating how you want them to perceive you over how you want the gathering to go for them. Often, chill is you caring about you masquerading (instead of) you caring about them.”
“Behind the ethic of chill hosting lies a simple fallacy: Hosts assume that leaving guests alone means that the guests will be left alone, when in fact they will be left to one another. Many hosts I work with seem to imagine that by refusing to exert any power in their gathering, they create a power-free gathering. What they fail to realize is that this pulling-back, far from purging a gathering of power, creates a vacuum that others can fill. These others are likely to exercise power in a manner inconsistent with your gathering’s prupose, and exercise it over people who signed up to be at your – the hosts’s – mercy, but definitely didn’t sign up to be at the mercy of your drunk uncle.”
4/ Hosting a gathering is not a democratic activity, so don’t be afraid of being the boss if you are the host. Be assertive in introducing your guests to each other a lot. Be assertive in seating guests next to people who are from different walks of life yet still complementary. Be assertive in setting your own rules, e.g., break up two friends who are talking with themselves in the corner and encourage them to mingle with everyone else.
5/ A gathering starts when your guests first hear about it, and don’t waste the time from then until the date of the gathering to prime your guests for the event. Priya Parker calls this “pregame window” a chance to shape the guests’ journey into the gathering – it is about priming the guests to get them in the right mood & mindset before the event, so that they could exhibit the behavior you would like.
“The pregame should sow in guests any special behaviors you want to blossom right at the outset. If you are planning a corporate brainstorming session and you’re going to be counting on your employees’ creativity, think about how you might prime them to be bold and imaginative from the beginning. Perhaps by sending them an article on unleashing your wildest ideas a few days beforehand. If, for example, you are planning a session on mentorship in your firm, and you need people to show up with their guards down, send an email out ahead of time that includes real, heartfelt testimonials from three senior leaders sharing personal, specific examples of the transformative power that a mentor had on them.“
“In my own work with organizations, I almost always send out a digital ‘workbook’ to participants to fill out and return to me ahead of the gathering. I design each workbook afresh depending on the purpose of the gathering and what I hope to get guests to think about in advance. The workbooks consist of six to ten questions for participants to answer…The workbooks aren’t so different from a college application in that sense … they also help the person think through the things they value before they arrive. I then design the day based on what I see in their answers. I also weave quotes from their workbooks into my opening remarks at the convening.“
6/ Quit starting or ending with logistics, such as where you should go next. It is extremely anti-climatic.
“I’m speaking, in short, of every gathering whose opening moments are governed by the thought: ‘Let’s first get some business out of the way.'”
“Just as you don’t open a gathering with logistics, you should never end a gathering with logistics, and that include sthank-yous. I am not suggesting that you cannot thank people. I simply mean that you shouldn’t thakn them as the last thing you do when gathering. Here’s a simple solution: do it as the second-to-last thing.”
“Goldman is a much-beloved teacher and singer-songwriter…To close (his classes) he strums the first note of the final song, his version of the last call, triggering the expectation of a closing in the kids, and then he pauses and makes announcements while still holding the note: Please turn in your check to me if you haven’t already. No classes next week. Someone left their jacket. He technically does these logistics between the first and second note of the final song. Once he’s finished with the logistics, he resumes the goodbye song. It’s subtle but quietly brilliant.”
7/ A soft close tactic, if done well, gives some guests the freedom to leave if they wish while lets other guests who want to stay feel welcome to linger around. Priya Parker shares a tip of inviting guests to the living room for a nightcap as a soft close for her house gatherings.
“The trouble for the host is that, for every person who is tired or checking out, there are presumably others who look as if they could keep going for hours. One of the most interesting – and divisive – dilemmas in hosting is what to do in this situation.”
“Once I can see the conversation petering out after dessert (at a home gathering), I pause, thank everyone for a beautiful evening, then suggest we move to the living room to have a nightcap. I give the guests who are tired the opportunity to leave, but both my husband and I emphasize that we’d rather everyone stay.”
“That invitation to the living room is a soft close; in a sense, it’s the equivalent of the last call. You can ask for the check, so to speak, or you can order another round. Those who are tired can leave without appearing rude, and those who want to stay can stay. The party, relocated and trimmed, resumes.”
And to heed my own advice, I should close this post with a thoughtful closing – at least somewhat thoughtful. I would like to share with you what Priya Parker wrote in the introduction of the book: there are no pre-requisites to being a good gatherer. No, you don’t have to be talkative, you don’t need to have a fancy venue, and you don’t need to hide a dozen jokes in your sleeves to entertain your guests. The magic recipe is some deliberate thought into why you are gathering, which identities of you the gathering is enforcing, and what spirit you are bringing into the gathering – it is likely to go well (or better than you imagined) if you have “the curiosity, willingness, and generosity of spirit to try.“
For those who are short on time and want to get straight to the “talking points”, Roger has summarized the key takeaways in Appendix – Lessons to Lead By. I quote some of my favorites below:
“To tell great stories, you need great talent.”
“I talk a lot about ‘the relentless pursuit of perfection’…It’s not about perfectionism at all costs. It’s about creating an environment in which people refuse to accept mediocrity. It’s about pushing back against the urge to say that ‘good enough’ is good enough.”
“Don’t start negatively, and don’t start small. People will often focus on little details as a way of masking a lack of any clear, coherent, big thoughts. If you start petty, you seem petty.”
“Don’t let ambition get ahead of opportunity. By fixating on a future job or project, you become impatient with where you are. You don’t tend enough to the responsibilities you do have, and so ambition can become counterproductive.”
“If something doesn’t feel right to you, it won’t be right for you.”
“When hiring, try to surround yourself with people who are good in addition to being good at what they do. Genuine decency – an instinct for fairness and openness and mutual respect – is a rarer commodity in business than it should be.“
Some other takeaways from the book:
1/ Great leaders value ability over experience. This is not to say that experience is not important, but to highlight that if it comes down to placing your bet on one of the two, you should “bet on brains”.
“Tom and Dan were the perfect bosses in this regard. They would talk about valuing ability more than experience, and they believed in putting people in roles that required more of them than they knew they had in them. It wasn’t that experience wasn’t important, but they ‘bet on brains.'”
2/ A dysfunctional leadership between senior management hurts the morale of the entire company, making the staff confused, afraid, or both. It rarely ends up well.
“When the two people at the top of a company have a dysfunctional relationship, there’s no way that the rest of the company beneath them can be functional. It’s like having two parents who fight all the time.”
3/ Respecting people’s time is underrated – how you deal with time is one of the things that immediately solidifies your reputation (or breaks it). People remember the seemingly small things.
“Once, he took a call, in my office, from President Clinton, talking with him for forty-five minutes while I sat outside. A call from Tom Cruise interrupted another meeting.”
“Meeting after meeting was either canceled, rescheduled, or abbreviated, and soon every top executive at Disney was whispering behind his back about what a disaster he was. Managing your own time and respecting others’ time is one of the most vital things to do as a manager.”
4/ Micromanagement not only frustrates your employees, but could make you look petty and narrow-minded as a leader.
“Michael was proud of his micromanagement, but in expressing his pride, and reminding people of the details he was focused on, he could be perceived as being petty and small-minded. I once watched him give an interview in the lobby of a hotel and say to the reporter, ‘You see those lamps over there? I chose them.’ It’s a bad look for a CEO.”
5/ Don’t forget people who have helped you, and don’t step on them to get your own way. I respect how Iger tried to not look better at the expense of Michael, Disney’s CEO before him, who had a bad reputation and was blamed for Disney’s troubles.
“I respected Michael and was grateful for the opportunities he’d given me. I’d also been COO of the company for five years, and it would have been hypocritical, transparently so, to lay all of the blame on someone else. Mostly, though, it just wouldn’t have been right to make myself look better at Michael’s expense. I vowed to myself not to do that.”
6/ A big question to ask yourself is: who do you want to be remembered as? What is a defining feature of your identity? For George Lucas, his identity and values are largely defined by the Star Wars series – and it is touching to see how much that one thing matters to him and gives his life meaning.
“He (George Lucas) said something else that I kept in mind in every subsequent onversation we had: ‘Whe nI die, the first line of my obituary is going to read ‘Star Wars creator George Lucas…’ It was so much a part of who he was, which ofcouse I knew, but having him look into my eyes and say it like that underscored the most important factor in these conversations.”
7/ Doing what’s right as a CEO doesn’t necessarily mean doing what’s financially right. Doing what’s right means literally what it says: doing what’s right. Kudos to Iger’s decision to terminate a high-profile employee after her inappropriate Tweet.
“‘We have to do what’s right. Not what’s politically correct, and not what’s commercially correct. Just what’s right. If any of our employees tweeted what she tweeted, they’d be immediately terminated.’ I told them (the management) to feel free to push back or tell me I was crazy (to fire her), but no one did.”
“It was an easy decision (to let her go), really. I never asked what the financial repercurssions would be, and didn’t care. In moments like that, you have to look past whatever the commercial losses are and be guided, again, by the simple rule that there’s nothing more important than the quality and integrity of your people and your product. Everything depends on upholding that principle.”
In general, I find Iger’s tone to be matter-of-the-fact without much self-promotion (of himself or the company). I appreciate how he points out what he sees as strengths and weaknesses of people whom he has worked with, including his former managers or mentors.
There are some things that I think would be good to include in the book:
A/ The one business decision that Iger made, which I was not sure about, was passing the opportunity to acquire Twitter. Iger said it did not feel right, and he was worried about the (potential) liability to manage and / or moderate an open platform where anyone could post anything. It would be interesting to see what Disney would have made out of Twitter – at least I would have liked Iger to share more about what he and Disney’s Board & management initially planned to do with Twitter.
B/ I would have wanted Iger to talk more about what he felt were missed opportunities or mistakes on his own part. I felt the book largely focused on what he did right – and while he narrated these stories in a fairly neutral way (and I believe he does deserve credit where it is due), I would have liked to see his candid self-assessment on what he did wrong.
C/ One thing that the book didn’t touch upon too much is how to manage an amusement park the scale of Disney. Iger mentioned he learnt many things from his predecessors on the various aspects of design & management. It would be really cool to know what are the details that Disney management pays attention to.
That being said, the book overall has not disappointed, and could be finished in half a day. Do consider giving it a try.
My ratings of the book Likelihood to recommend: 5/5 Educational value: 5/5 Engaging plot: 5/5 Clear & concise writing: 5/5 Suitable for: everyone
Humble Pi is a witty & funny book that could let anyone (re)discover their love for mathematics! Overall, Matt Parker’s book is an appetizing combo of mathematics and comedy – if you want to learn mathematics while having tons of fun, this is one of the best books to start with, regardless of your background or fluency in maths.
Beyond making maths digestibly fun (and funnily digestible), another highlight of the book is how to think about thinking. In other words, the philosophy of thinking – such as how to be rational and how to prevent errors.
I particularly enjoyed the “Swiss cheese” model in thinking about errors: think about each error like a hole in a slice of cheese. And horrible sh*t (disaster) happens when somehow the holes are lined up together and the error falls through slices of cheeses, and lands in the pot of catastrophe. More often than not, a catastrophic consequence is the accumulation of a few errors – seemingly minor errors if we look at them alone – but when added together could bring explosive effects. What this means is instead of focusing too much on achieving 0 errors (which is desirable yet almost always impossible), what is more practical is to focus on improving error-detection that spots an error early – patch the first hole in the first slice of cheese, so that it does not trickle down into the remaining slices.
I would also highly recommend checking out Matt Parker’s YouTube videos: his talks at Google and the Numberphile channel, which features bite-sized videos by various mathematicians on everyday-maths and has 3M+ subscribers to date (April 12th, 2020).
Below I quote some parts of the book that I personally find insightful:
1/ We are used to going from theory to application, though sometimes the reverse happens: the application comes first, and then we discover the underlying theory afterwards. We should not let the joy of discovering the application over-shadow the need to fully understand the theory behind – otherwise, using the tool without really understanding its risks could hit us in the foot.
“There is a common theme in human progress. We make things beyond what we understand, as we always have done. Steam engines worked before we had a theory of thermodynamics; vaccines were developed before we knew how the immune system works; aircraft continue to fly to this day, despite the many gaps in our understanding of aerodynamics. When theory lags behind application, there will always be mathematical surprises lying in wait. The important thing is that we learn from these inevitable mistakes and don’t repeat them.“
2/ Don’t underestimate how little attention the public & institutions could pay to math – and what is most frustrating is not the mistakes themselves (which could be absurdly hilarious), but the lack of respect for mathematical facts or a pursuit of truth.
Matt Parker wrote to the UK government after he discovered that the geometric shape of the football was wrongly painted on signs in the UK (unlike the white hexagons, the black shapes on the ball’s surface should be pentagons instead of hexagons). However, the official response from the UK Department for Transport was: “Changing the design to show accurate geometry is not appropriate in this context.” Matt Parker clearly did not think too highly of the response he got:
“They (the Department of Transport) rejected my request. With a rather dismissive response! They claimed that (1) the correct geometry would be so subtle that it would ‘not be taken in by most drivers’ and (2) it would be so distracting to drivers that it would ‘increase the risk of an incident.’ And I felt that they hadn’t even read the petition properly. Despite my asking for only new signs to be changed, they ended their reply with: ‘Additionally, the public funding required to change every football sign nationally would place an unreasonable financial burden on local authorities.’ So the signs remain incorrect. But at least now I have a framed letter from the UK government saying that they don’t think accurate math is important and they don’t believe street signs should have to follow the laws of geometry.“
3/ While (most rational) people agree that 1 + 1 = 2, people don’t always agree on how the same number should be interpreted. A number ceases to be objective when subjective narratives are at play, hence we should not let our guard down and think an argument is “logical” just because numbers are used.
“It seems that, if the Trump administration couldn’t change the ACA (Affordable Care Act) itself, it was going to try to change how it was interpreted. It’s like trying to adhere to the conditions of a court order by changing your dog’s name to Probation officer.”
“[T]he Trump administration wanted to allow insurance companies to charge their older customers up to 3.49 times as much as younger people, using the argument that 3.49 rounds down to 3. […] They might as well have crossed out thirteen of the twenty-seven constitutional amendments and claimed nothing had changed, provided you rounded to the nearest whole constitution.”
“If there are enough numbers being rounded a tiny amount, even though each individual rounding may be too small to notice, there can be a sizeable cumulative result. The term ‘salami slicing’ is used to refer to a system by which something is gradually removed one tiny unnoticeable piece at a time. Each slice taken off a salami sausage can be so thin that the salami does not look any different, so, repeated enough times, a decent chunk of sausage can be subtly sequestered.”
4/ Precision and accuracy on two concepts with nuanced differences, and it is important to not mix the two. Precision is “the level of details given“, while accuracy is “how true something is“.
5/ Be ware of the word: average. Whenever you hear someone talk about averages, emind yourself of this commentary on the census from the Australian Bureau of Statistics: “While the description of the average Australian may sound quite typical, the fact that no one meets all these criteria shows that the notion of the ‘average’ masks considerable (and growing) diversity in Australia.” I would also add that the notion of the “average” masks how the average person is likely to overrate the concept of averages.
“After the 2011 census, the Australian Bureau of Statistics published who the average Australian was: a thirty-seven year old woman who, among other things, ‘lives with her husband and two children…in a house with three bedrooms and two cars in a suburb of one of Australia’s capital cities.’ And then they discovered that she does not exist. They scoured all the records and no one person matched all the criteria to be truly average.“
6/ Correlation does not mean causation. Just because two things have a high chance of happening at the same time does not mean one caused another. For example, I don’t think the number of math PhDs has any causal relationships with how much cheese people eat.
“For the record, in the US the number of people awarded math PhDs also has an above 90 percent correlation over ten years or more with: uranium stored at nuclear-power plants, money spent on pets, total revenue generated by skiing facilities, and per capita consumption of cheese.“
7/ Finally, this is one of my favorite quotes of the book on what mathematics is: “Mathematicians aren’t people who find math easy; they’re people who enjoy how hard it is.“
I hope this book will rekindle your love for mathematics – or help you find it if you have never fallen in love with it in the first place.
Donald E. Knuth, professor of computer science at Stanford University, popularized this phrase used in the programming community: “Premature optimization is the root of all evil.” Little did he know, however, this statement about “all evil” opened a Pandora’s Box – fierce / passionate / headbanging / crazy debates all the way from optimization to the meaning of engineering.
“Programmers waste enormous amounts of time thinking about, or worrying about, the speed of noncritical parts of their programs, and these attempts at efficiency actually have a strong negative impact when debugging and maintenance are considered.”
“We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil.“
“Yet we should not pass up our opportunities in that critical 3%. A good programmer will not be lulled into complacency by such reasoning, he will be wise to look carefully at the critical code; but only after that code has been identified.”
Warning: you are about to peek inside the Pandora’s Box…which may lead to either an insightful soul-searching journey or a mental hurricane or somewhere in between.
Still with me? Then let’s dive in! 🙂
Premature Optimization vs. Technical Debt
A (somewhat) relevant concept to premature optimization is technical debt. Although most in the software engineering world would agree on the definitions of either term, folks are less aligned when it comes to how these two terms relate to each other – are they synonyms or opposites?
Technical debt refers to the “cost of additional rework caused by choosing an easy solution now instead of using a better approach that would take longer.(Wikipedia)” In layman terms, technical debt means if you are lazy now, you will have to make up for it later. Just like if you stock up dirty laundry, you will have to clean them sooner or later. And sooner is better than later better than never – that’s what people really mean when they remind you to “avoid technical debt“.
“Technical debt” as a phrase is looked upon favorably by programmers who believe chivalry isn’t dead. For them, “please avoid technical debt” is a civil alternative to “stop being lazy and get the $%@!#$$# up and do something.” So you could say “technical debt” existed in peace and had its supporters until it was put next to the “premature optimization,” and things get interesting.
This post asks the interesting question of whether premature optimization is “the opposite concept of technical debt”? What’s more interesting than the question itself are the comments that followed – highly recommend a read.
Some believe that “premature optimization” is generally a worse offense than “technical debt”, because at least technical debt saves you time now (although you need to pay back later), and the argument is that technical debt wastes less time than premature optimization on a net basis:
“There is no optimization included in this concept (of premature optimization). Optimization is doing something to improve value delivery. Eliminating waste is one form of optimization. This premature “optimization” introduces waste now (time is spent while not adding value). And if that isn’t bad enough, it introduces future waste as well.“
“To me it (premature optimization) seems even worse than technical debt. Both (premature optimization and technical debt) result in future waste, but with technical debt you at least don’t waste a whole lot of time now.”
However, is it really true that premature optimization only wastes time and creates no benefit at all? Randall Hyde argues that premature optimization is not as bad as it sounds – on the contrary, programmers could gain experience and the code as a whole does not suffer a lot:
“One thing nice about optimization is that if you optimize a section of code that doesn’t need it, you’ve not done much damage to the application. Other than possible maintenance issues, all you’ve really lost is some time optimizing code that doesn’t need it. Though it might seem that you’ve lost some valuable time unnecessarily optimizing code, don’t forget that you have gained valuable experience so you are less likely to make that same mistake in a future project.”
To put it simply, Hyde considers premature optimization to be a “tuition” paid for how-to-code-better. If we go with Hyde’s argument, then the logical implication would be that technical debt is worse than premature optimization – the former teaches you nothing (other than that being lazy in the moment has its consequences down the road – which is something you have chosen to conveniently forget the moment you decide to go lazy and let the technical debt accumulate).
Some say premature optimization and technical debt, instead of being opposite concepts, overlap in meaning:
“You suggest premature optimization as an opposite, but I would say that premature optimization is technical debt. At least in a software context, optimization usually comes at the expense of readability and maintainability of the underlying code. If you didn’t need the optimization to support the use of the system under design, all you accomplished is making the code more difficult to maintain. This difficulty in maintenance is likely to cause new features to take longer to design, develop, test, and deploy, which is a key indicator of technical debt.”
To rephrase, Owens’ comment above argues that premature optimization creates problems that need to be remedied later, and I agree with him on that. What I disagree with, however, is that premature optimization creates “technical debt.” If we use the definition from Wikipedia above, technical debt refers specifically to problems caused by being lazy now (going for an easy solution or not doing anything), instead of being inappropriately / unwisely diligent (i.e., premature optimization). Owens has broadened the definition of “technical debt” in his comment to refer to code with any kind of problems – regardless of whether the cause was laziness (technical debt) or wrongly-guided diligence (premature optimization). And the preceding sentence is a nice way to summarize where I stand on this:
I believe both “premature optimization” and “technical debt” create problematic code that need to be fixed later – the key difference is in the root cause of the problem. Premature optimization is caused by misguided diligence, which creates very low ROI at its best or 0% ROI (100% wasted efforts) at its worst; technical debt is caused by mere laziness. While technical debt reinforces the old lesson that one should not be lazy, premature optimization shows that too much of diligence could be a bad thing.
Writing great code does not mean writing perfect code at every single step, and not every single line is worth investing the same amount of time & energy. Premature optimization is the result of incorrectly optimizing your time – which is of limited supply – and is the cause of failed maximization of the quality of your code output.
That was a mouthful yet just the start on the interesting debates surrounding premature optimization. We then slide further down the slippery slope to talk about the slippery slope itself.
The Premature Slippery Slope and the “Swiss Cheese” Model
Slippery slope means “a relatively small first step leads to a chain of related events culminating in some significant effect.(Wikipedia)” It has been more than four decades since Donald Knuth first popularized “premature optimization” in his 1974 paper – and four decades is a time long enough for his statement to fall down a premature slippery slope. 🙂
Some programmers say they want to avoid “premature optimization” as an excuse for being lazy or thoughtless. In this post, Joe Duffy expresses frustration when programmers use Knuth’s statement “to defend all sorts of choices, ranging from poor architectures, to gratuitous memory allocations, to inappropriate choices of data structures and algorithms…in other words, laziness.” It sounds like “premature optimization is the root of all evil” has been slipped down the slope to “optimization is the root of all evil” to “optimization is evil”.
Check out this humorous yet witty take by Randall Hyde on the various manifestations of the “slippery slope” gone too far: “The Fallacy of Premature Optimization”. My favorite part are the sarcastic observations he makes of programmers – some are a bit exaggerating and obviously don’t apply to every programmer, yet they are food for thought and I find myself guilty of slipping into some errors in non-programming fields:
“Observation #3: Software engineers use the Pareto Principle (also known as the “80/20 rule”) to delay concern about software performance, mistakenly believing that performance problems will be easy to solve at the end of the software development cycle. This belief ignores the fact that the 20 percent of the code that takes 80 percent of the execution time is probably spread throughout the source code and is not easy to surgically modify. Further, the Pareto Principle doesn’t apply that well if the code is not well-written to begin with (i.e., a few bad algorithms, or implementations of those algorithms, in a few locations can completely skew the performance of the system).”
“Observation #4: Many software engineers have come to believe that by the time their application ships CPU performance will have increased to cover any coding sloppiness on their part. While this was true during the 1990s, the phenomenal increases in CPU performance seen during that decade have not been matched during the current decade.”
“Observation #6: Software engineers have been led to believe that their time is more valuable than CPU time; therefore, wasting CPU cycles in order to reduce development time is always a win. They’ve forgotten, however, that the application users’ time is more valuable than their time.“
The central point that Hyde is trying to get across is that when some programmers claim to be “minimizing premature optimization”, what they are actually doing is “minimizing the time spent on thoughtful design”and, as a consequence, is a betrayal of the engineering ethos to maximize performance. There is no excuse for not investing the time to think through the systematic performance of the system as a whole – this is what expected of any good software developer, per Charles Cook (unfortunately, the link to Cook’s blog article is no longer valid):
“Its usually not worth spending a lot of time micro-optimizing code before it’s obvious where the performance bottlenecks are. But, conversely, when designing software at a system level, performance issues should always be considered from the beginning. A good software developer will do this automatically, having developed a feel for where performance issues will cause problems. An inexperienced developer will not bother, misguidedly believing that a bit of fine tuning at a later stage will fix any problems.”
“Now, it’s time for the simple yet clever rule: Never give up your performance accidentally. That sums it up for me, really. I have used other axioms in the past — rules such as making sure you measure, making sure you understand your application and how it interacts with your system, and making sure you’re giving your customers a “good deal.” Those are all still good notions, but it all comes down to this: Most factors will tend to inexorably erode your performance, and only the greatest vigilance will keep those forces under control.“
“If you fail to be diligent, you can expect all manner of accidents to reduce your system’s performance to mediocre at best, and more likely to something downright unusable. If you fail to use discipline, you can expect to spend hours or days tuning aspects of your system that don’t really need tuning, and you will finally conclude that all such efforts are ‘premature optimizations’ and are indeed ‘the root of all evil.’ You must avoid both of these extremes, and instead walk the straight and narrow between them.“
Rico’s principle of “never give up your performance” – whether accidentally or consciously – is applicable to all walks of life, not just programming. It is particularly important when we are dealing with complex systems:
“What are good values for performance work? Well, to start with you need to know a basic truth. Software is in many ways like other complex systems: There’s a tendency toward increasing entropy. It isn’t anyone’s fault; it’s just the statistical reality. There are just so many more messed-up states that the system could be in than there are good states that you’re bound to head for one of the messed-up ones. Making sure that doesn’t happen is what great engineering is all about.“
There you go: great engineering is about great performance indeed, but great engineering is not about guaranteeing a perfect performance – in fact, that is downright impossible. Great engineering is about preventing, or minimizing, the chance of resulting in performance that is so messed up that you bring about catastrophic consequences. Great engineering is not about delivering a perfect show 100% of the time – it is about making sure that a messed up sh*t-show happens 0% (or close to 0%) of the time. Therefore, a truly great engineer will steer away from wasteful “premature optimization”, while never forgetting or giving up on the goal of performance optimization. In fact, avoiding premature optimization itself is a tactic to optimize performance by investing time where it matters the most for the output.
“[The] Swiss cheese model of disasters, which looks at the whole system, instead of focusing on individual people. The Swiss cheese model looks at how ‘defenses, barriers, and safeguards may be penetrated by an accident trajectory.’ This accident trajectory imagines accidents as similar to a barrage of stones being thrown at a system: only the ones that make it all the way through result in a disaster. Within the system are multiple layers, each with its own defenses and safeguards to slow mistakes. But each layer has holes. They are like slices of Swiss cheese.”
” I love this view of accident management, because if acknowledges that people will inevitably make mistakes a certain percentage of the time. The pragmatic approach is to acknowledge this and build a system robust enough to filter mistakes out before they become disasters. When a disaster occurs, it is a system-wide failure, and it may not be fair to find a single human to take the blame.“
The Swiss cheese model is very easy to visualize: imagine putting slices of Swiss cheese on top of each other, each slice with holes on them representing problems. Imagine catastrophic events only happen if the holes on each slice happens to line up, and an error could pass through them in a straight line. As Matt Parker points out, when a bunch of mistakes “conveniently” line up and result in a gigantic mistake, it is usually indicative of some systematic issues. This is not to say that individuals or specific actions are not at fault – but one should not focus on the tree and forget about the forest, i.e., the system as a whole. There is often lots to be done on a systematic level, e.g., improved processes or better tools.
Two final remarks:
(1) I am not a programmer and I don’t code myself, so yes, I am commenting on an area of trade that I have little experience of. That being said, just as you don’t have to be a professional mathematician to apply mathematical thinking in your daily life, I believe you don’t have to be a full-time software engineer to appreciate computational thinking. At the end of the day, although concepts like “premature optimization” and “technical debt” originated in the context of software, they could be applied to and maintain relevance in all walks of life;
(2) I highly recommend Matt Parker’s highly entertaining & educational book on mathematics: Humble Pi: When Math Goes Wrong in the Real World. If you love mathematics, there is no reason not to read it. If you hate mathematics, the biggest reason to read it is it will make you fall in love with math. Mathematics is a truly beautiful language and way of thinking.
Read-Me-First: Much is being posted about the coronavirus on a daily, or even hourly, basis – sometimes a bit too much with fake news / data / pictures coupled with conspiracy theories, accusations of racism, and doomsday predictions. This blog post – live updated from time to time – aims to filter out the signal amidst the noise: data & opinions on the COVID-19 that (a) I think are worth knowing & reflecting about, and (b) are inevitably colored with my own biases & POV. Do your own research, form your own (informed) opinions, and stay safe!
Table of Contents (updated April 17, 2020)
[Set the Stage] Other than masks, stop up some humor too
[Science] Getting familiar with COVID-19 symptoms (vs. cold, flu, allergy)
[Science] Understanding how fast the virus spreads and incubates
[Protective Measures] Response of Individuals: Stock-Up vs. Laissez-Faire
[Protective Measures] Response of Governments: Lock-Down vs. Herd Immunity
[Thinking Smart] What a conspiracy theory teaches us about critical thinking
[Thinking Smart] Veterans merely make better guesses – nobody knows for sure
[Thinking Smart] “Aha” moments from working from home
[Thinking Smart] Defining information
[Thinking Smart] What went wrong with media coverage? A failure, but not of prediction
[Set the Stage] Other than masks, stock up some humor too
If you have not yet heard about the “coronavirus disease 2019” (COVID-19) – which is aptly named with a “19” suffix because we were obviously certain it would spread into 2020 and achieve monopoly over this year’s headlines (joking) – you must be living in a cave.
Rest assured, even if that were the case, I would not mock you. On the contrary, I would envy you, because living in a cave like Robinson Crusoe these days is probably one of the safest ways to protect yourself from the coronavirus. 🙂 Moreover, if you were able to get Wi-Fi connection in your cave, you could post on social media with glorious hashtags like #not-lonely-when-am-alone, #perfect-social-distancing, #responsible-self-quarantine etc.,
Let’s not forget to keep some happy smiley faces up even when COVID-19 was called a pandemic by the WHO and the stock market + oil market + crypto market + [insert your past-favorite / now-most-hated market] are trapped by NOVGRA-20, a shorthand for “novel gravitational force 2020”. Can the Einstein-of-our-times come up with a new theory of relativity to explain what the h*** is going on?
Since searching for the next Einstein-of-our-times is too challenging, I opted for an easier option – searching on Google about what is interesting to know about the COVID-19. Here is your curated feed on “uncommon sense” about the coronavirus: not-your-typical headlines, yet probably worthy of attention.
[Science] Getting familiar with COVID-19 symptoms (vs. cold, flu, allergy)
To start with, let us first familiarize ourselves with what the virus does. As Peter Attia, MD with training in immunology, said in a podcast, the coronavirus mainly attacks the type II pneumocyte cell that makes surfactin. Surfactin lets the air sacs of the lungs to overcome the tension on the surface and hence open successfully. In other words, without sufficient surfactin, individuals could suffer from respiratory collapse. Dr. Attia recommends all infected persons with difficulty breathing to seek medical attention ASAP – regardless of their age.
COVID-19 could be tricky to diagnose because of overlapping symptoms with the cold, the flu and allergies. This article from Business Insider (March 2020) gives a good comparison of the symptoms across the 4 diseases.The key point is the three most common symptoms of COVID-19 are: fever + dry cough + shortness of breath.
The good news is: if you are sneezing and have a runny nose, it is very unlikely that you have COVID-19 – the flu or allergies are probably to blame.
The important footnote is: while nausea and diarrhea are rare for COVID-19, these symptoms could still be “early cues of infection (of COVID-19)” and thus should not be taken lightheartedly.
[Science] Understanding how fast the virus spreads and incubates
When it comes to studying the spread of the virus, a key concept to know is the viral coefficient, denoted by “R”. “R” stands for the number of people that each infected person goes on to infect. You may have also heard about R0, which stands for the viral coefficient in a community with no natural immunity against the virus and takes no special protective measures. Getting a fair estimate of R (and R0) could help us assess how viral the virus is and how effective interventions are:
“In the long term, the only way that this pandemic can actually end is for the R value of the virus to plunge below 1, consistently, in every part of the world, for a prolonged period of time.”
“[T]here is still not an academic consensus on the basic replication number of the Wuhan coronavirus. Models range from finding an Ro of 1.4 after assuming a latent period of 14 days, to finding one of 4.0 after assuming only 4 days.“
“There were 181 confirmed cases with identifiable exposure and symptom onset windows to estimate the incubation period of COVID-19. The median incubation period was estimated to be 5.1 days (95% CI, 4.5 to 5.8 days), and 97.5% of those who develop symptoms will do so within 11.5 days (CI, 8.2 to 15.6 days) of infection. These estimates imply that, under conservative assumptions, 101 out of every 10,000 cases (99th percentile, 482) will develop symptoms after 14 days of active monitoring or quarantine.”
The Johns Hopkins research suggests that 14 days is a reasonable length for quarantine – cases that have longer incubation periods are possible yet unlikely outliers:
“Based on our analysis of publicly available data, the current recommendation of 14 days for active monitoring or quarantine is reasonable, although with that period, some cases would be missed over the long-term.”
Despite progress in understanding the viral coefficient (R) and the length of the incubation period, we are still not sure about when someone is contagious – in particular whether a person is contagious during the incubation period. The website of the US Center for Disease Control (accessed on March 16, 2020) reads: “[D]etection of viral RNA does not necessarily mean that infectious virus is present…it is not yet known what role asymptomatic infection plays in transmission. Similarly, the role of pre-symptomatic transmission (infection detection during the incubation period prior to illness onset) is unknown.“
[Protective Measures] Response of Individuals: Stock-Up vs. Laissez-Faire
On the question of how to respond to the COVID-19 outbreak, the responses fall on two-ends of the spectrum (for individuals): go full force or do (almost) nothing. We see a juxtaposition of two contrasting camps: (1) Camp-Stock-Up rushing to supermarkets and stocking up on years of toilet paper vs. (2) Camp-Laissez-Faire wandering the streets without masks – assuming they have or are able to get masks – either a/ thinking optimistically that the COVID-19 is not that dangerous and everyone is making a fuss or b/ thinking pessimistically that all prevention measures are useless because they would get infected sooner or later.
Where should we pick our stance between the two extremes? Below is a stance that I find to be reasonable, which thinks about social distancing the way we think about car safety: “not as a single binary decision to go Full Turtle and shelter in place, but as a collection oflittle risk-reducing behaviors that add up to a big win“:
“To really get your mind around how this works, think about all the little things you do to manage risk when driving a car: wear a seat-belt, use a turn signal, drive the speed limit, don’t drink or text and drive, have your brakes checked regularly, etc. Each of these things helps a little, and when done together they all add up to a dramatically safer driving experience — both for you and those you share the road with — than if you didn’t do any of them at all.”
Another key point this author points out is “every new day is riskier than the previous one” – at least in the short term – as the number of infections increases and we are not yet fully equipped with dealing with the disease. What this entails is it makes sense for each individual to progressively level-up their self-protection every single day, at least until (a) we see reliable signs that the spread of the virus has been contained and / or (b) we have developed a solid cure and / or vaccine.
Most of us are probably working from home, but for those who are working in the office or in public places, consider this piece of advice:
“Take on progressively more social and reputational risk in order to reduce your physical risk: e.g., If you’re working a retail counter tomorrow and an obviously ill customer approaches you, discretely excuse yourself for the restroom at the risk of having that person try to get you fired. You might want to start using sick days next week. Get bold and creative with how to distance yourself in-the-moment, and be more willing to offend people as this progresses.”
“Be more willing to offend people.” If you are working in the office and a colleague is coughing, ask him / her to work from home or see a doctor. Do not be afraid to offend your colleague, because it is a responsible thing to do for both you and your colleague and everyone else in the office. Plus, if you were asking in a nice way and explain your rationale, most people in your colleague’s shoes should be able to understand.
[Protective Measures] Response of Governments: Lock-Down vs. Herd Immunity
The response of governments around the world could be broadly put into 2 types:
Camp Eradicate: represented by China, this group takes a resolute stance including city-wide lock-downs and quarantine at the cost of disrupting economic activities;
Camp Herd Immunity: represented by the UK (which has since then modified its stance to be more hard-line) is to focus on “flattening the curve,” i.e., focus on protecting the more vulnerable people. Instead of trying to eradicate the virus, this camp would try to slow down the spread of the disease a bit so as to “flatten the curve,” i.e., a slower spread of the disease could prevent over-burdening the healthcare system.
Scott Adams asks an interesting question about whether these two camps could co-exist in harmony. As long as Camp #2 Herd Immunity exists, does this mean Camp #1 Eradicate cannot possibly exist or sustain its success?
The UK’s proposal of “herd immunity” has been under criticism:
Some argue that “herd immunity” is a by-product of preventive measures, and should not be mistaken as an end in itself:
“[T]alk of ‘herd immunity as the aim’ is totally wide of the mark. Having large numbers infected isn’t the aim here, even if it may be the outcome. A lot of modellers around the world are working flat out to find best way to minimise impact on population and healthcare. A side effect may end up being herd immunity, but this is merely a consequence of a very tough option – albeit one that may help prevent another outbreak.”
[Thinking Smart] What a conspiracy theory teaches us about critical thinking
A Reddit post from February 2020 went viral with the title: “Quadratic Coronavirus Epidemic Growth Model seems like the best fit” – it posits that the total case numbers reported by China fits “uncannily” well with a quadratic curve (15 days’ of data, R-squared value of .9995). Given none of the current epidemiological models supports a quadratic growth curve, the Reddit post makes a not-so-subtle hint that the Chinese numbers may be fabricated to fit a quadratic curve.
And the situation quickly gets dramatic, and like all (good) dramas do, the situation quickly gets messy with people pointing their fingers at the Chinese government and / or the WHO for allegedly making up and / or covering up the number of total cases in China.
Before anyone gets excited thus far, let us take a look at both sides of the debate. Ben Hunt from Epsilon Theory – one of my frequently-read and highly-recommended blogs on “the narratives that drive markets, investing, voting and elections” – sides with the Reddit skeptic:
“All epidemics – before they are brought under control – take the form of a green line, an exponential function of some sort. It is impossible for them to take the form of a blue line, a quadratic or even cubic function of some sort. This is what the R-0 metric of basic reproduction rate means, and if – as the WHO has been telling us from the outset – the nCov2019 R-0 is >2, then the propagation rate must be described by a pretty steep exponential curve. As the kids would say, it’s just math.”
“[T]o be clear, at some point the original exponential spread of a disease becomes ‘sub-exponential’ as containment and treatment measures kick in. But I’ll say this … it’s pretty suspicious that a quadratic expression fits the reported data so very, very closely. In fact, I simply can’t imagine any real-world exponentially-propagating virus combined with real-world containment and treatment regimes that would fit a simple quadratic expression so beautifully.”
Add a few more days of data to the original data-set (of 15 days) and what we get “is far from being a perfect quadratic”;
“If you look at the data from outside China, which is definitely not being faked by China, and fit a quadratic to cumulative case numbers, you’ll get a similarly eye-catching R-squared value of .992.”
Fitting data into a quadratic function is easier than it may sound: “Any data whatsoever with n points can be fit perfectly, with absolutely no error, using a polynomial of degree n-1.”
The author goes on to say we should pay attention to the fact that “modern statistical software can fit many types of models to the same data,” and therefore we should be extra-cautious with what conclusions we draw – especially when the data has a small sample size:
“[A]s our Redditor friend acknowledges, he tried many models before choosing the one with the most eye-catching R-squared value.”
“And the curve of a growing epidemic has some properties that inherently can make it kind of similar to a quadratic. It will be monotonically upward, and growing at an increasing rate. This means the regression calculation’s job is made easier by this crude similarity, and allows those eye-catching R-squared numbers. The R-squared value is calculated using the square of the differences between the model and reality, so it punishes a few large deviations more harshly than many small ones. That is, the joint information of the two curves being high is really just the observation that in general the curves look pretty similar, not a clinching judgment that the curve was faked using a model.”
The author concludes with this stance: “We’re not saying the data is reliable, just that it’s not faked,” citing “even if every single authority in the world were the most competent they could possibly be and were reporting everything they knew with complete candor, the data would still not be accurate, because many cases are latent with no symptoms, and even among symptomatic cases, most are not known to public health authorities.” In short, it is impossible to have “accurate” (and timely) data when it comes the total number of cases – just as it is impossible to have “perfect” testing that covers every single case in real time.
The purpose of me sharing the above is not to tell you which side you should pick – to be honest, I think the real question here is not who to side with, but how to analyze data (& inferences, opinions) critically. To help us remember how easy it is to misinterpret data – whether intentionally or by accident – I would like to show you this graph where a quadratic curve and an exponential curve look very similar within a small range of data:
Here is an explanation of the graph above:
“We generated two curves, one exponential and one quadratic, that both start at 100 on day 1 and end at 1440 or so on day 29. We then fit a quadratic to the exponential, and vice versa. These data really are synthetic and perfect, and we’re fitting the wrong model to each one. But in both cases, the fit is close and the R-squared value is .97 when we fit the exponential to the quadratic, and .994 when we fit the quadratic to the exponential.“
“You can see that both fitted models start to fail at the end, as the exponential data grows faster than the quadratic model will allow, and vice versa.”
It may be a good time to remind everyone of Cowen’s first law from Tyler Cowen, professor of economics: “There is something wrong with everything (by which I mean there are few decisive or knockdown articles or arguments, and furthermore until you have found the major flaws in an argument, you do not understand it).” I would say that is a good attitude to adopt when we read anything, what do you say? And that is a trick question – because if you agree with me, then it implies you think there is nothing wrong with my statement, but that is self-defeating of Cowen’s First Law; if you disagree with me, then it implies you think there is something wrong, which is an example that fits Cowen’s First Law.
Okay – I am just having fun with logic games. 🙂 The point is: do your own research, do your own research on the pro vs. against, and do your own research from every possible angle. Everyone could be wrong. Everyone must be wrong in some way – the only difference is whether you spot where they are wrong or not.
[Thinking Smart] Veterans merely make better guesses – nobody knows for sure
Howard Marks is the co-founder of Oaktree Capital Management, one of the largest investors in distressed securities. He publishes memos on his views on the market, investing, current affairs and other topics. In his latest memo “Nobody Knows II”, which I think is worth a 10-minute read from start to end, Howard shared his take on the coronavirus and the recent market downturn.
Howard breaks down information about the virus into 3 types:
“As Harvard epidemiologist Marc Lipsitch said on a podcast on the subject, there are (a) facts, (b) informed extrapolations [inferences] from analogies to other viruses and (c) opinion or speculation. The scientists are trying to make informed inferences. Thus far, I don’t think there’s enough data regarding the coronavirus to enable them to turn those inferences into facts. And anything a non-scientist says is highly likely to be a guess.“
In Howard’s previous memo called “You Bet” (January, 2020), he shared some quotes by Annie Duke, a PhD dropout who later became what Howard calls “the best-known female professional poker player” with over $4 million winnings from tournaments:
“[W]orld-class poker players taught me to understand what a bet really is: a decision about an uncertain future…[T]here are exactly two things that determine how our lives turn out: the quality of our decisions and luck. Learning to recognize the difference between the two is what thinking in bets is all about.”
“[W]inning and losing are only loose signals of decision quality. You can win lucky hands and lose unlucky ones…What makes a decision great is not that it has a great outcome. A great decision is the result of a good process, and that process must include an attempt to accurately represent our own state of knowledge. That state of knowledge, in turn, is some variation of ‘I’m not sure.’…What good poker players and good decision-makers have in common is their comfort with the world being an uncertain and unpredictable place…instead of focusing on being sure, they try to figure out how unsure they are, making their best guess at the chances that different outcomes will occur.”
“[W]e can make the best possible decisions and still not get the result we want. Improving decision quality is about increasing our chances of good outcomes, not guaranteeing them.“
Nowadays with (almost) everyone being called (or calling themselves) an “expert” and giving their (solicited and unsolicited) opinions on the Internet, let’s take a step back to ask ourselves what it means to be an expert:
“An expert in any field will have an advantage over a rookie. But neither the veteran nor the rookie can be sure what the next flip will look like. The veteran will just have a better guess.“
I applaud this tweet of Francois Balloux, a computational / system biologist working on infectious diseases. In sharing his opinion of the virus, he candidly admits: “Predictions from any model are only as good as the data that parametrised it. There are two major unknowns at this stage. (1) We don’t know to what extent covid-19 transmission will be seasonal. (2) We don’t know if covid-19 infection induces long-lasting immunity.” I recommend reading his full Twitter thread here:
We need more consciously-responsible experts as such – experts who are candid in sharing their opinions and in admitting that they could be wrong and they could never be perfectly right. Nobody ever knows for sure. I’d like to share this quote on humility:
“Humility not in the idea that you could be wrong, but given how little of the world you’ve experienced you are likely wrong, especially in knowing how other people think and make decisions.”
[Thinking Smart] “Aha” moments from working from home
This Tweet on technical difficulties people run into when they are working from home is a vivid illustration of the point: it is time to rethink work and work-tech.
The thing with a business continuity plan is it rarely gets the credit when business continues as usual. To the contrary, it is only missed (or blamed) when the business cannot continue as usual.
Re-imagining work extends to re-imagining the office building – this Tweet predicts voice or gesture controlled activation could become more prevalent.Imagine your office building lift becomes a mini Siri, Alexa or Google Assistant. Try saying: “Hey Lift, take me to the 19th floor.”
Other than conversations on work-tech, this “hot” Tweet takes it to the level of class consciousness:
[Thinking Smart] “Aha” moments from working from home
With the surge of cases worldwide comes a surge in “information” about the coronavirus – though the information we see vary greatly in quality. I strongly recommend Defining Information from the Stratechery blog that shares insights on how to think about information:
“Given that over 90% of the PCs in the world ran Windows, writing a virus for Windows offered a far higher return on investment for hackers that were primarily looking to make money. Notably, though, if your motivation was something other than money — status, say — you attacked the Mac.”
“I suspect we see the same sort of dynamic with information on social media in particular; there is very little motivation to create misinformation about topics that very few people are talking about, while there is a lot of motivation — money, mischief, partisan advantage, panic — to create misinformation about very popular topics. In other words, the utility of social media as a news source is inversely correlated to how many people are interested in a given topic.“
In simple terms, as more people start talking about a topic, the average quality of the information you get drops. This is not surprising for two reasons: (a) you are more likely to hear higher number of repetitions of popular opinions and narratives; (b) there is a higher incentive for people to create or spread misinformation on a hot topic.
The Stratechery blog goes on to propose some helpful heuristics on how to deal with different types of information:
“For emergent information, like the coronavirus in February, you need a high degree of sensitivity and a high tolerance for uncertainty.”
“For facts, like the coronavirus right now, yo uneed a much lower degree of sensitivity and a much lower tolerance of uncertainty: either something is verifiably known or it isn’t.”
[Thinking Smart] What went wrong with media coverage? A failure, but not of prediction
Slate Star Codex is one of my favorite blogs by far. Scott Alexander’s post A FAILURE, BUT NOT OF PREDICTION is an insightful take on what went wrong with the media coverage on the coronavirus. A key concept that Scott discusses is that of probalistic reasoning:
“A surprising number of these people had signed up for cryonics – the thing where they freeze your brain after you die, in case the future invents a way to resurrect frozen brains. Lots of people mocked us for this – ‘if you’re so good at probabilistic reasoning, how can you believe something so implausible?’ I was curious about this myself, so I put some questions on one of the surveys.”
“The results were pretty strange. Frequent users of the forum (many of whom had pre-paid for brain freezing) said they estimated there was a 12% chance the process would work and they’d get resurrected. A control group with no interest in cryonics estimated a 15% chance. The people who were doing it were no more optimistic than the people who weren’t. What gives?”
“I think they were actually good at probabilistic reasoning. The control group said ‘15%? That’s less than 50%, which means cryonics probably won’t work, which means I shouldn’t sign up for it.’ The frequent user group said ‘A 12% chance of eternal life for the cost of a freezer? Sounds like a good deal!'”
Scott summarized it well when he said: “Making decisions is about more than just having certain beliefs. It’s also about how you act on them.“
He shared a diagram showing two types of people: Goofus and Gallant. Goofus requires “incontrovertible evidence” before believing something is true, i.e., false until proven true. On the contrary, Gallant embraces uncertainty and does not look at things in an all-or-nothing fashion: he reasons in probability.
Scott argued that people behaved like Goofus when the coronavirus first started to spread:
“I think people acted like Goofus again.” “People were presented with a new idea: a global pandemic might arise and change everything. They waited for proof. The proof didn’t arise, at least at first. I remember hearing people say thing like ‘there’s no reason for panic, there are currently only ten cases in the US’. This should sould like ‘there’s no reason to panic, the asteroid heading for Earth is still several weeks away’.The only way I can make sense of it is through a mindset where you are not allowed to entertain an idea until you have proof of it. Nobody had incontrovertible evidence that coronavirus was going to be a disaster, so until someone does, you default to the null hypothesis that it won’t be.“
“Gallant wouldn’t have waited for proof. He would have checked prediction markets and asked top experts for probabilistic judgments. If he heard numbers like 10 or 20 percent, he would have done a cost-benefit analysis and found that putting some tough measures into place, like quarantine and social distancing, would be worthwhile if they had a 10 or 20 percent chance of averting catastrophe.“
Goofus-Gallant reasoning could also be applied to the debate about whether face masks are effective:
“Goofus started with the position that masks, being a new idea, needed incontrovertible proof. When the few studies that appeared weren’t incontrovertible enough, he concluded that people shouldn’t wear masks.”
“Gallant would have recognized the uncertainty – based on the studies we can’t be 100% sure masks definitely work for this particular condition – and done a cost-benefit analysis. Common sensically, it seems like masks probably should work. The existing evidence for masks is highly suggestive, even if it’s not utter proof. Maybe 80% chance they work, something like that? If you can buy an 80% chance of stopping a deadly pandemic for the cost of having to wear some silly cloth over your face, probably that’s a good deal. Even though regular medicine has good reasons for being as conservative as it is, during a crisis you have to be able to think on your feet.”
You take a Uber ride. You get off the car, open your Uber app, and leaves the driver a 4.0 / 5.0 rating. As you walk away from the car, the driver pulls out her app, and gives you a 5.0 / 5.0 rating. This is an example of reciprocal ratings, i.e., where both parties get to rate each other.
There are many examples of reciprocal ratings, especially in areas where the experience is co-created and / or shared by both parties (albeit could be in different ways). For example, on Airbnb, the guest(s) get to rate the host(s) and vice versa. In debate competitions, the adjudicators score the speakers and the speakers often get a chance to rate the adjudicators in return based on the justification of their decisions and the quality of their feedback.
The question I’d like to discuss in this post is: do reciprocal ratings bring net benefits or net harm?
The Case For Reciprocal Ratings: Fairness & Incentives to Perform
Starting from principles, it seems fair to let both sides rate each other, especially if both sides share responsibility in an experience and / or are impacted by the other side’s actions. For example, the holistic Uber ride experience is affected by both the driver’s performance (e.g., cleanliness of vehicle) and the user’s behavior (e.g., arriving on time).
I really like the concept of The Wittgenstein’s Ruler, which Nassim Nicholas Taleb (the author of “Black Swan”) talked about in a Tweet:
It is worth repeating: “When you use a ruler to measure the table, you are also using the table to measure the ruler.” Sometimes, the best measurement of how good a ruler is is not an external judge, but the tables that are measured by itself.
If we look at practical consequences, reciprocal ratings may help both parties become more accountable for their behavior and / or decisions. In the example of a debate competition, for example, letting debaters rate adjudicators in return incentives the adjudicators to: (a) be more responsible in reaching a decision, and (b) be more detailed & elaborate in explaining their decision. Just as an adjudicator’s feedback could help debaters improve, so does a debater’s feedback let an adjudicator learn how to better judge a debate. Taking a step back, this type of benefit is not unique to reciprocal ratings, but to ratings in general: when people know that their performance is being measured (and that measurement is linked to some carrots or sticks), they are more likely to put in more effort. It is all about incentives. Economics 101.
You could say that reciprocal ratings make the interests of both parties more intertwined with each other – because debaters have a chance to rate adjudicators, it is now in both the debater and the adjudicators’ best interests to let debaters receive well thought-through feedback after a debate round. Reciprocal ratings put everyone “in the same boat” in a way.
The Case Against Reciprocal Ratings: Inflated Ratings?
But sometimes you could go from two parties being close to each other to two parties being too close to each other. Making the interests of both parties interrelated provides incentives to cooperate, as well as incentives to cheat. For example, if debater feedback for adjudicators were submitted using their real names in a debate competition, then one could argue some debaters may inflate their score for an adjudicator for fear of retaliation by that adjudicator (assuming the debaters have a significant chance of running into the same adjudicator in a future round).
There are real-world examples where people are asked to leave comments under their real name – Airbnb guest reviews, for example, are published under the guests’ real names & profile pictures. This helps to increase the perceived legitimacy and authenticity of the reviews in the eyes of interested people checking out the property’s profile page.
Assuming that (a) hosts get to rate guests in return and (b) hosts get to see the guests’ ratings & comments, then one possible scenario may happen: a guest inflates the rating for his / her host out of fear that if he / she gives the host the low rating, the host would retaliate with a low (or even lower) rating in return. The problems is symmetrical, as in one could argue that the host also has an incentive to inflate his / her ratings of the guest for the exact same reasons.
Assuming the ratings are indeed inflated, would that break the whole rating system?
Before we dive into this question, let us first look at the bigger picture: why do ratings matter in the first place? How are ratings used by a platform like Airbnb? It is worth pointing out that what matters more is the relative ranking rather than the absolute score. It is the differential rather than the absolute value that holds the key. For example, Airbnb uses relative ranking of host property to decide the ranking of search results for properties that match a user’s search criteria; similarly, Uber uses driver ratings to prioritize ride assignment.
With that established, let’s come back to the inflated ratings problem. For simplicity, let us study one side of the problem, i.e., let us assume Airbnb guests inflate their ratings of their hosts. What happens then? (Note the other side of the problem, i.e., hosts inflating their ratings of the guests, should follow a similar thought process as below.)
Let’s break down the problem into two possible scenarios:
[Scenario A] Rating inflation is a generic problem, i.e., the majority of guests inflate their ratings of hosts, or what the defenders of fairness would call “the whole system is rigged”.
There are two sub-scenarios:
(A1) If the majority of guests inflate their ratings by a similar absolute amount, e.g., +1 star higher. => Verdict: In this sub-scenario, rating inflation does not impact the effectiveness of the search ranking algorithm. This is because if the score of every host gets bumped up by +1 star, then their ranking does not change, i.e., a potential guest searching for a property would still see a list of hosts ranked in the same order;
(A2) If the majority of guests inflate their ratings to a certain level, e.g., if everyone gives their hosts 4 stars (regardless of whether they think they only deserve 2 stars or 3 stars), then things get a bit tricky. You could say in this case, the really stellar hosts will still get their 5-star ratings and rise to the top of the competition – they would still be prioritized by Airbnb’s search ranking algorithm. However, in this case, one could no longer differentiate between the mediocre hosts (e.g., those who deserve 3 stars) from the really bad ones (e.g., those who deserve 2 stars), as their scores are all inflated to 4 stars across the board. => Verdict: In this sub-scenario, rating inflation does make the ranking algorithm less effective – it is still able to break down hosts into groups based on their ratings (stellar hosts vs. other hosts), and prioritize the groups in search results. However, the grouping becomes less granular. One could argue the practical results may not be too bad – as the super-stellar hosts that get 5 stars would still come up at the top of search results for hosts. If we assume that the top search results are also the most-clicked-on results by potential guests, then it is likely that the final choice of the guests are not distorted that much. This kind of reasoning reminds me of the Pareto principle (80/20 rule) – applied in this context, 20% of your search results (the top ones) may generate 80% of your revenue. If this holds, then as long as the top search results are not distorted, then the search ranking algorithm has served its purpose.
[Scenario B] Rating inflation is an isolated problem, i.e., only a very small % of the guests inflate their ratings of the hosts. The majority of the guests rate their hosts honestly.
The answer here is quite straightforward: this would have very limited impact on the search ranking results. Perhaps a small number of hosts would get their ratings bumped up a bit, but the majority of the hosts are ranked fairly. By definition of an “isolated problem” above, this is not a problem that causes massive headaches for the average user – and hence not worth losing your sleep over.
The Verdict on Reciprocal Ratings
Having reciprocal ratings is probably a good idea – based on the very limited analysis thus far. Caveats: 1) I have (very lazily) only considered inflated ratings as a down side to reciprocal ratings, though there could be many more, and 2) the designs of the ratings could affect the incentives of players – for example, is one side asked to rate another side first? Are the ratings published in real time? Are the ratings published anonymously? Etc.,
All in all, I find reciprocal ratings design – and ratings in general – to be a fascinating real-world game-theory topic. The next time I take a Uber ride and rate a driver, I’ll certainly “think twice” before giving that 5 stars.
Foreword: This is a flash fiction about dating & relationships. All characters and stories are fictional.
Harvey pushed open the doors of the bar and said to the receptionist without turning his head: “Reservation under Rachel M.”
“Yes sir. This way please.”
The receptionist led Harvey to a table with sofa seats right next to the bartender’s. He casually scanned the room – there was a lady with wavy dark hair in a pantsuit drinking alone, one hand holding the glass and the other hand mindlessly tapping the table. Harvey saw her briefcase had the letters Kimberly & Partners marked on it – it is the name of a law firm in the office building right across the street.
She is likely a frequent visitor of this bar, Harvey thought to himself, and made a mental note to get her number some time.
His thoughts were interrupted by a “thud” sound coming from the table. He turned around to see a black notebook land on the marble surface, followed by a “cling” sound of a pen landing next to it. He looked up in amusement as the owner of the stationery took off her suit jacket, put it on the empty sofa seat alongside her laptop bag, and sat directly opposite him.
“Good evening, Mr. Weinstein.” Rachel said without a smile.
Harvey chuckled at the sarcastic reference of Harvey Weinstein – film producer and convicted serial sex offender. “Good evening, babe. Mr. Weinstein here was planning to treat you to an unforgettable night…if you look like your profile pics.”
“Scarlett Johansson sends her regrets for a last-minute schedule clash. She has sent me in her place and hopefully I would meet your high standards.”
Harvey burst out laughing while shaking his head. “Rachel Mckingsley – the girl who bites with her tongue. Painful yet pleasing. How I have missed your spice.”
Rachel smiled. “Harvey Hamilton – the guy who flirts with his little toes. Annoying yet never knows to back off. How I have not missed your shamelessness.”
Harvey signaled for the waiter to order.
“Virgin mojito.” Rachel said.
Harvey raised his eyebrows. “Very fitting drink for our upcoming conversation on Tinder. So tell me, what’s up in life? How come you are working on a reality show about dating now? I was like FML when you told me this on the phone – what happened to the Rachel who is passionate about documentaries & live debates?”
Rachel let out a sigh. “I’ve been working on the The Weekend Chat since I joined NetFox TV 3 years ago – and I love the autonomy I have in running the show, the professionalism of my team, and the depth of analysis we are able to do and present. But the viewing statistics have been dropping – and dropping hard – Alex is having a hard time convincing the management to keep the show. One of the conditions of the show’s continuance is that everyone is 50/50 staffed – so I am working on The Weekend Chat and launching our new reality show on dating at the same time. I don’t have a very good idea yet on the format of the show – there are so many matchmaking or dating shows out there, and I am yet to find THE idea that could ‘wow’ people.”
“A reality show about dating?” Harvey laughed. “Sure, I could use some advice or probably offer some as the King of Dating.”
“Who has had all kinds of fantastic experiences that blow your mind away. So shoot Mr. Charming – tell me all about your fantastic Tinder journey. What’s your count for Tinder dates now? 157?”
“Sounds about right. You wanna be the 158th date?” Harvey added a wink.
“Why not? I am open-minded to being the 158th if you are able to get me just one referral from one of your past 157.” Rachel blinked and gave Harvey the told-you-don’t-mess-with-me stare.
“Wow girl, I won’t toy with that murderous look of yours.” Harvey shrugged. “Elle a les yeux revolver. Elle a le regard qui tue. Elle a tiré la première…” Harvey started singing the French pop song Elle A Les Yeux Revolver (She Has Eyes Like Revolvers):
Elle a les yeux revolver Elle a le regard qui tue Elle a tiré la première M’a touché, c’est foutu * * * She has eyes like revolvers She has a look that kills She has fired first That has hit me, and it is all finished
Rachel couldn’t resist cracking up with laughter. She shook her head in disbelief as Harvey still sang off tune – even though this did not discourage him from joining the university choir, where he met Rachel.
Harvey was the “life of the party” at college, and has a reputation among their social circle of being the typical Butterfly – a “serial dater” as in one who hops between one “short-term date” to the other, usually a few weeks long and almost never more than two months. Rachel remembers the last time hearing Harvey say he has a girlfriend was when they were back in college.
“Okay, let’s get down to ‘business.'” Rachel uncapped her pen and started writing in her notebook. “I remember you mentioned you have been using Tinder for more than 2 years. Tell me more about what the Tinder experience is like for you?”
“Amazon.” Harvey said.
“Excuse me?” Rachel took a sip of the virgin mojito that just arrived.
“Swiping on Tinder is similar to shopping on Amazon.” Harvey clarified. “For me at least.”
“I have heard that analogy before, comparing online dating to online shopping.”
“Bingo!” Harvey snapped his fingers. “You know my style, Rachel – I am not looking for anything ‘stable’ or however you call it. At this stage of my life, I just wanna look for some fun. Dating for me is the icing on the cake – it is sweet and pleasant, but not something that I’d lose sleep over.”
“Am I right to say that for you, swiping profiles on Tinder is similar to browsing restaurants on a food delivery app?”
“That’s not a bad way to put it.” Harvey nodded. “In a way, yes. And don’t give me the ‘you are toying around with woman’ kind of line. I know it’s typical for people – especially women – to point their fingers at me and call me a playboy. But hey, you know what, when they tell you ‘all’s fair in love and war,’ they mean nothing‘s fair in love and war. There’s no such thing as a universal rule for dating – who says that I must enter the game with the ‘pure’ intention of looking for something committed? It’s a free market economy Rachel – and people freely choose what kind of dating they want. Going for casual dating is as legit as looking for commitment.”
Rachel took some notes and sipped some more mojito. “If you don’t misrepresent your intentions and are open about what you’re looking for, then sure why not? I’m not judging you for your dating model. I’m trying to understand what dating means for you.”
“Whatever.” Harvey shrugged. “You and I are both people who don’t hold back their thoughts, and I’ll be straightforward with you. I don’t care if people call me a playboy – or is there a new term called f***boy nowadays? As in guys who get a fat share of ‘Netflix & chills’? I’d say that’s just a jealous reaction from guys who have pathetically few matches and aren’t able to catch the hot women out there – who are all, unsurprisingly, falling for hot dudes like me.”
“I’ll give you 3 seconds to feel good about yourself. Now let’s come back: You don’t mind being called a playboy, or you take pride in being called a playboy?” Rachel paused writing and looked up at Harvey.
Harvey took a sip of his tequila. “Man, you’ve got some tough questions.”
“That’s because man, you’ve got some juicy answers.” Rachel smiled and raised her cup. “Plus, correction: ‘boss lady, you’ve got some tough questions.’”
Harvey pursed his lips for a while. “I think you are onto something. If I am completely honest, it does feel good to tell people things like you’ve dated XYZ number of girls this month. And it doesn’t hurt when some of them are Victoria’s Secret model material. Makes me come off as a lady’s man – which I am by the way.”
“Have you shared pictures of your dates with friends or family?”
“I know what you mean.” Harvey winked. “Yep, I confess I like to show off pictures of extremely hot dates to some pals and that’s my ego at play.”
“You see dating as a competition in a sense, don’t you?”
“Who says it isn’t? Dating – or mating – is a competition. Guys do compare who’s walking next to the hottest girl, and I bet you ladies size up each other’s boyfriends too. Come on, we are visual animals. Whether we realize it or not, we are comparing who’s more attractive than whom all the time. We all have an animal’s brain, Rachel. Not much better than the monkeys in the wild who fight to mate. It’s competition in the free market dear. By the way, have you noticed one thing?”
“Noticed what?” Rachel looked puzzled.
“Your pupils totally dilated just now when you looked at the bartender. He’s quite a handsome guy right?”
Rachel gave a shrug. “Or my pupils dilated because I couldn’t handle the strong liquor in my virgin mojito.”
“Hahahaha!” Harvey burst out laughing. “Good one Rachel! I’m glad all those years of serious investigative journalism haven’t taken away your humor. I’m starting to look forward to that dating show of yours – might be something really fun and funny.”
“And I believe it would be fun and funny to invite you to the show if I were not afraid of being accused as an accomplice in the conspiracy to break hearts around the world.”
“So what dating show ideas do you have in mind? Bounce them off me.” Harvey asked.
“One idea that’s being discussed is conflict resolution. An idea pitched is bringing in couples – married or not – who have problems and help them get over the issues.”
Harvey frowned. “Couple problems? You mean like people who can’t decide who throws out the trash or can’t figure out whether their other half is cheating?”
“Sounds like you’ve had your lucky share of problems. Tell me: what are some common problems in your past 157 Tinder dates? How come none of the amazing hot ladies you dated have met your critical eye and become your official girlfriend?”
“That’s a good question.” Harvey nodded. “I know I’ve got this double-reputation as a playboy and a picky dude because I date a lot and I’ve never ‘settled down’ with someone. To be fair, most of the ladies I’ve dated would make great girlfriends – there may be areas where we don’t gel, like I’m a night owl and she’s a morning bird – but then again there’s no such thing as the perfect girl. I’d be dating my clone – which would be boring.”
“If you accept that nobody is perfect and everybody has flaws, why didn’t you develop a more serious relationship with one of your dates?”
“I guess you could call this the ‘easy way out’ type of mindset. Think about it Rachel: there are hundreds and thousands of Tinder profiles right at my fingertips. When you’ve got a conflict with your date partner, it is usually much easier to swipe and find another date than to talk with your current partner and try to work things out. Yeah – Tinder is an easy retreat. Why not take the easy route?”
“The best one is always the next one?” Rachel rephrased. “Does that sound like a good description of what you’re thinking?”
“You could say that.” Harvey said thoughtfully. “It’s like how you ladies view clothes. The new arrivals are always prettier than the old wardrobe. Same logic.”
“What’s on your mind?” Harvey asked as he saw Rachel fell silent.
“I remembered reading about the definition of commitment in a book.” Rachel replied. “It says part of being committed to someone means you put all your eggs in one basket – you never wonder whether the grass is greener on the other side; you never ask yourself whether there could be someone out there that could be better than your partner; you never look back and second guess your decision. What you said reminds me of this. Being committed to someone – by this definition at least – is hard, and it is even harder with Tinder. I get what you are saying. Nowadays it is more difficult to not wonder whether there’s someone out there who is a better match. This is the allure of Tinder: the promise of options, even though the next option is never guaranteed to be better than the current one.”
“That’s some deep s***.” Harvey said. “Way too deep for a drunkard like me to handle.”
“Then I’d say it’s time to put some food in your belly to neutralize the alcohol.” Rachel waved at a waiter and asked for the menu.
The waiter returned with two sets of menus – a booklet of regular food items, and a separate list of a few seasonal specials. Rachel looked at the two menus and thought of something.
“Harvey, I’d suggest we forget about the thick regular menu and choose only from this short list of seasonal specials.”
“Oh?” Harvey raised his eyebrows. “Did you have a bad experience with any of the items on the main menu?”
“Nope,” Rachel shook her head. “But a story about jam tells us that we’d probably be happier with our choices if we pick from a shorter list. When shoppers are asked to choose from a larger number of jam varieties, they take longer to make their decision and feels worse about their decision afterwards. So I’d say we start with a smaller sample and go from there.”
“Why am I sensing a reference to Tinder here?”
“There is.” Rachel nodded. “Have you ever wondered whether having more choices on Tinder is making you less satisfied with your choice? One reason could be, as you said, that you are more likely to wonder whether the other choices out there are better. Just like if you pick one jam bottle out of 1,000 options, you’d lose more sleep over whether the remaining 999 taste better, and probably fidget less if you picked one out of two jam flavors.”
Harvey chuckled. “Oh my Rachel, you could make your own show being the relationship therapist. You sound like you’ve been giving couples therapy for thirty something years!”
“I’ll take that as a compliment.” Rachel said as she stood up. “Excuse me for a toilet break. Tell you something interesting about a super dating app idea when I’m back.”
“Super dating app? I’m all in for it!”
“Wait and see.” Rachel smiled. This conversation on dating has turned out to be far more interesting than she expected.
Context: “Dare to be happy” were the words gifted to me by a V.I.P. in my life. Our conversation on happiness reminds me of recent shows I’ve watched, from Billions(Showtime) to Sex Education(Netflix) to Devil Wears Prada(Fox), hence this post on happiness was born. May we all kick start 2020 with happy vibes! 🙂
Let’s Dance with Ben Kim
For those who follow the US TV show Billions, I highly recommend checking out Ben Kim’s (hilarious and stunning) elevator dance scene (a.k.a. “public self-initiated humiliation”) in Season 3 Episode 10. Here is a clip:
For those who raise an eyebrow and go: “What is Billions?” I’d recommend giving the Billions show a shot – probably a good match for those who are looking for a smoothie blending together entrepreneurial vibes from Silicon Valley, juicy backstabbing from House of Cards, and legal heat from The Good Wife.
Back to the Ben Kim dance scene – I love it! Not to mention the clip on its own is funny, but also bear in mind that this is a very out-of-character move for Ben Kim. He is the type of person who wants to duck down rather than stand out, who prefers to sit downstairs with regular staff rather than sit upstairs in the C-suite, who aims to survive rather than thrive. His self-remark at his annual compensation review meeting with Axe is a vivid reflection of his personality:
I should not throw out the first number (of bonus that I would want to get), because I have a tendency to undervalue myself.
Ben Kim to Bobby Axelrod, Billions Season 3 (see clip here)
Ben Kim is the “good old guy” who feels happy at getting a new title while keeping the old salary. This pretty much sums up the trait that makes him stand out – and ironically, it is precisely the desire of him to not stand out.
You may pause here and ask: if Ben Kim is such a shy person who has trouble standing up for himself, where on earth did he garner the courage to dance (and strip his shirt off) in a lift with his big boss and complete strangers?
Answer: per the advice of Wendy Rhoades, the “spiritual animal” of Axe Capital, to step out of his comfort zone and have a voice of his own. (Though Wendy did try and failed to warn Ben Kim not to ruin the elevator ride with Axe and the fund’s potential investors.)
The elevator dance scene was a turning point for Ben Kim – afterwards, when Axe confronted him with a sharp: “What the hell was that?” Ben Kim, unlike his usual tongue-tied self when dealing with higher authorities, found the courage to spit out an investment idea he has held under his belt for a long time:
After spitting the investment idea out and receiving Axe’s pat on the shoulder, Ben Kim breathes a sigh of relief and is finally happy. He is happy because he has allowed himself to be happy by allowing himself to say what he wants to say – and this is no small feat for Ben Kim: a short while back, he had trouble peeing in the toilet after his half-fleshed out idea was challenged.
For Ben Kim, the question to ask is not: “Do you want to be happy?” A better question to ask is: “Do you allow yourself to be happy?” In other words: Do you dare to be happy?
Do You Dare to be Happy?
We tend to think of happiness as a wish beyond our control, when it could be and can be an option of our choice. We tend to think of happiness as an elusive goal to seize around us, when it could be and can be an inner state right within us. To borrow the words of the Bible to fit this context: “Ask and it will be given to you; seek and you will find; knock and the door will be opened to you.”
The question is not: can we be happy? The question is: why can’t we be happy?
The question is not: why doesn’t this (thing or person) make us happy? The question is: why don’t we allow ourselves to be happy?
Hence the ask is not about wanting to be happy, but about daring to be happy: Do we dare to dance in the elevator like Ben Kim? Do we dare to be crazy in the eyes of others and crazily happy in the eyes of ourselves? Do we dare to strip free of our shirt (metaphorically) alongside the weight of caring too much about how others look at us?
Ironically, in a sense the ask is about whether we truly want to be happy – because if we truly, desperately, seriously want to be happy with all our heart, then we would dare to be happy. Then we would overcome each and every single fear. Then we would say “go to hell” to any doubt, any worry, any fear. Then we would care about and only care about our happiness, because we want it so much.
If we truly want something badly enough, we would not hesitate to go for it. The “dare” would hardly be a hard choice – it would be natural step we take without hesitation. Ben Kim wanted to prove to Wendy – and ultimately to himself – that he could have an independent voice that he is daring to dance half-naked in the elevator.
It’s Not Crazy to be A Little Crazy
I’m a big fan of the song “Crazy” by Alanis M. as featured in the movie Devil Wears Prada. Quoting the lyrics:
But we’re never gonna survive, unless We get a little crazy No we’re never gonna survive, unless We are a little crazy – – – In a sky full of people, Only some want to fly, Isn’t that crazy?
“In a sky full of people, only some want to fly. Isn’t that crazy?” I love this sentence – what is crazy is not that some people want to fly, but that so few people want to. What is crazy is not that some people day-dream, but that so few people do.
Where is the fun in life if we never get crazy? If we never experience something in life that we did not already predict? If we never dare to be happy and go against the inertia of “life as yesterday”?
Last but not least, I share the MTV of “Crazy” with you – may (a healthy dose of) crazy vibes bring us happy vibes! Cheers to a happy 2020 where we dare to be happy, dare to be crazy, and dare to be free! 😀
I went to the woods because I wanted to live deliberately […] I wanted to live deep and suck out all the marrow of life.
Henry David Thoreau (quoted in the movie “Dead Poets Society”)
carpe diem quam minimum credula postero * * * Seize the Day Trust Tomorrow as Little as You May
“Carpe diem. Seize the day, boys. Make your lives extraordinary.” Such was the advice Mr. John Keating gave his students in the movie Dead Poets Society. Along with this, he passed along an answer to the meaning of life: “That you are here – that life exists, and identity; that the powerful play (of life) goes on and you may contribute a verse.”
But how do we seize the day? What is happiness, the one thing that we seem to be dreaming so much of and capturing so little of?
Carpe Diem = Reject Living Conditionally
“We don’t want to be unconditionally happy. I’m ready to be happy provided I have this and that and the other thing. But this is really to say to our friend or to our God or to anyone, ‘You are my happiness. If I don’t get you, I refuse to be happy.‘ – Awareness: The Perils and Opportunities of Reality
Happiness, for most people on most days, rarely comes with “no strings attached.” Happiness is the product of an “if…then…” clause, which is typically phrased in one of two ways:
If I have [X], then I will be happy.
If I do not have [X], then I cannot be happy.
I think the above is more accurately stated as:
If I have [X], then I will be happy for a limited time only (until I see a better alternative to [X] called [Y]).
If I do not have [X], then I choose to be unhappy.
In his eye-opening book Awareness: The Perils and Opportunities of Reality, Anthony de Mello shares an FAQ he gets: “Nobody loves me; how, then, can I be happy?” Anthony replies with this witty question: “You mean you never have any moments when you forget you’re not loved and you let go and are happy?”
“Until everyone started getting transistors, they were perfectly happy without one. That’s the way it is with you. Until somebody told you you wouldn’t be happy unless you were loved, you were perfectly happy. You can become happy not being loved, not being desired by or attractive to someone. You become happy by contact with reality. That’s what brings happiness, a moment-by-moment contact with reality.” – Awareness: The Perils and Opportunities of Reality
In the words of Naval Ravikant: “That’s the fundamental delusion – that there is something out there that will make you happy forever.” Once we drop this illusion and come into contact with reality, that is when we are better positioned to Seize the Day.
Carpe Diem = Embrace Living Deliberately
A common rejection to carpe diem is that we should be “rational being” and not be driven by “irrational whims.”
John Keating’s quote in Dead Poets Society in some ways answers this concern: “There’s a time for daring and there’s a time for caution, and a wise man understands which is called for.” Rather than being the slave of our desires & wants, we should be their Captain.
Such is living deliberately – choosing what preferences to satisfy with a deliberate purpose to stay true to ourselves, and to stay honorable to our values. In the words of Ayn Rand: “Happiness is that state of consciousness which proceeds from the achievement of one’s values.” Living deliberately means being able and willing to choose actions that not only satisfy our pleasure, but also match our values.
To all friends and readers – Carpe Diem. Make Life Extraordinary. Let us all remember to better seize the day as the footsteps of a brand new year draws near. May we all be better present for 2020 ahead.