Shame on you for clicking this post.
If you clicked on this blog post from one of my social media accounts, I apologize, but you really should know better. I did it to make a point.
Journalists today are like those drug dealers you see in old propaganda films from middle school.
“Pst… hey kid… look what I got. You’ll never believe how good this will make you feel.”
This tactic is called the curiosity gap. Those who use it work to exploit your fear of missing out (FOMO) and rob you of your precious time to collect clicks. Precious time that could be spent watching kitty cat videos like this one where you’ll never believe what this little furball does next (click here).
Truthfully, you can believe what happens next. For many of those links, you’re usually even able to guess what will happen before you click it. For example, “Runner falls on her face and you’ll never believe what she does next”. Umm… she gets up? <Click> … Yea she got up. Those motherfuckers.
It’s not just sites like Upworthy and Buzzfeed that employ these tactics but also “respectable” news organizations such as the Malaysian News Network otherwise known as CNN.
After being burned two or three times, I’ve learned to ignore links that use curiosity gap headlines. Using clear and direct headlines is something every kid learns in a high school journalism class. Whatever happened to those skills? Hey journalists… yes you. Wouldn’t you want honest clicks so you can see how readers behave without manipulation? I understand the name of the game is ad revenue; but a higher click through ratio from truly engaged readers is much more valuable than high bounce rates and nothing ad revenue (assuming pay-per-click and not pay-per-impression).
In closing, next time those MOFOs try to exploit your FOMO, just say NOMO and stop that PROMO. Thanks.
I’ve always been a fan of how math and science can be applied to romance (see related articles here). So when a friend linked me to Krulwich’s article “How To Marry The Right Girl: A Mathematical Solution” on NPR, I was immediately intrigued. To summarize, Krulwich describes a dilemma in which Johannes Kepler is troubled with choosing the right wife among eleven potential mates. For the best decision-making process, Krulwich introduces ‘The Marriage Problem’ solution as explained by Alex Bellos.
Here are the rules of the game. You have a finite number of choices. Each mate is evaluated sequentially one by one. Once you pass on a mate you cannot go back to that person. Once you’ve made an offer, the game ends. While this is a simple example of an optimal stop model, (i.e. it’s missing transaction costs for each evaluation), the results are interesting nonetheless.
The strategy that Krulwich highlights is relatively easy. Pass on the first 36.8% of potential mates. Next, when you meet a potential mate that’s better than ones in the first group, propose marriage. Note that you may run into a problem if the best potential mate was in the first group. Krulwich says if that happens you will at least get the second best pick (but that is untrue). Think about it, you will keep passing on people until you reach the nth potential mate in your queue. The nth mate may be riddled with character flaws or hideous (to you at least– everyone’s beautiful to someone, right?) Right. Anyhoo, what is true is you will indeed optimize your decision-making.
I decided to simulate the model for myself to determine the probability of outcomes. I ran the simulation for 1000 trials. The probability of landing at least an 8 hovers near 71.8% with a median score of 9.75 (see histogram above). Feel free to review or enhance my code here (myGithub).
A few caveats to my model (pardon any technical jargon): First, I used a random uniform distribution when in reality a gaussian curve is a better fit. Thus I imagine a more precise estimated median will be closer to an 8. Second, I used the strategy described in the NPR article and didn’t go into the nuances for a complex optimal stop. The model also assumes interests and ranked traits stay constant (obviously not ideal assumptions). Finally, realize that this model illustrates optimal choice and not optimal outcome as there is a bit of game theory involved when you figure the preferences of whom you’ve chosen.
How about other applications to this model? Should you accept the first offer on your home? B-School? Job? Figure out the average amount of offers for your specified timescale and you’re on your way to optimized decision-making.
Remember the movie Matrix where Trinity instantly learned how to fly a helicopter by downloading the program straight to her brain? While we’re not there yet (see perceptual learning via neurofeedback), it is possible to learn things in half the time by listening to lectures at double speed. I call it speed-watching.
If you’ve ever taken a class on Coursera, you’ve likely noticed the +/- buttons that allow you to do this. If you feel overwhelmed by 2x speeds, watch a lecture at 2x for one minute and then reduce to 1.75x. Sooner or later your brain will adjust to faster speeds and you’ll be blazing through lectures in no time. In fact, 2x will even start to feel slow at times.
I also ‘speed-watch’ my news, recipe videos, and topics on Khan Academy. The only time I don’t speed-watch is if I’m listening to music videos or learning a language. To access this speed feature in YouTube, you will need to enable HTML5. Do it here: https://www.youtube.com/html5.
Beyond saving time, speed-watching also keeps your mind engaged, thereby increasing comprehension. Lack of engagement is why many in classrooms let their minds wander, doodle, or catch up on email. Now that education can be customized to ‘your’ learning curve, what will education be like in the future?
If you can see the bigger picture, you recognize that we are at the verge of exciting times.
For those interested in accelerated learning, here’s a link that compiles a few resources to help your endeavor: http://nahyaninc.com/blog/accelerated-learning-level-1/
In January 2012, a few members from the Johns Hopkins Carey Business School c/o 2013 spent a month in Rwanda on international development consulting engagements as part of our Innovation for Humanity project.
by Robin Kabir, John Tai, and Daniel Tuan
According to the CDC, hospital acquired infections (HAI) in the US is a $35 billion problem infecting 1.7 million while killing roughly 99,000 people each year. In other words, HAI kills more people than breast cancer and prostate cancer combined. The main culprit leading to incidences of HAI is doctors and other healthcare providers forgetting or refusing to wash their hands. The reason for these failures may be explained using behavioral economic notions prospect theory, mental accounting, and decoupling effects that lead to hyperbolic discounting.
First, consider that each time healthcare providers wash their hands a transaction cost is incurred. In essence, the current system is set to disaggregate losses and aggregate gains (which is not a happy combination). As a result, this system is not optimized to produce the intended effect of compliance. In addition, all things equal, the marginal benefit of each subsequent hand wash can be minimal or even negative. A snapshot of the hand-washing utility model below shows how utility is negative after the first hand-washing using anti-microbial soap that kills 99.9% of germs against a microbe that replicates itself every 15 minutes. Note: Putting more weight to costs (losses) may produce a more accurate graph.
Hand-washing Utility Model
X-axis: Number of hand-washings
Infection control at hospitals is a serious matter. According to the World Health Organization (2002), infection control protocols call for several preventative measures such as hand-hygiene, use of surgical drapes, cold temperatures in operating rooms, minimizing operating times and length of hospital stay, and the use of anti-microbial prophylaxes. It isn’t too difficult to imagine that physicians may bundle all infection control protocol costs into one mental account. If so, this phenomenon may explain why physicians may abstain from hand washing. Instead of paying the immediate costs1 related to hand-washing efforts, physicians may be balancing costs in the infection control account by thinking other measures such as prescribing prophylaxes will cover the costs. In the cases described, decisions are being made piecemeal and are topical suggesting the use of a mental account.
Moreover, the effects of not washing your hands are not as immediate as, say, the effect of ‘forgetting’2 to use anesthesia. Thus, not washing hands becomes decoupled from the costs of future outbreaks. In turn, this effect reduces the salience of the outcome. Likewise, the perception of future benefits from washing hands is also minimized due to hyperbolic discounting.