I still think self-control is a very valuable trait – even if it’s not as predictive as once thought…
A team of psychologists have repeated the famous marshmallow experiment and found the original test to be flawed. It joins the ranks of many psychology experiments that cannot be repeated, which presents a considerable problem for its findings.
Source: The original marshmallow test was flawed, researchers now say
DIY toy taxidermy shop Build-A-Bear had decreed that Thursday, July 12, was Pay Your Age Day in shops across the U.S., Canada, and the U.K. The self-explanatory event lets bear lovers make a furry friend, stuff it with love, and pay a dollar amount that matched their current age—a payment model that vastly favored spoiled 1-year-old knee-biters over 50-year-olds who just needed something to love.
The 21 year old company was likely planning on leveraging nostalgia from grandparents and parents while building loyalty with a new generation of toy-lovers. Now was an especially great time for this campaign as Toys”R”Us has shut down hundreds of stores worldwide and there’s marketshare on the table to capture – especially for a focused, experiential brand like Build-A-Bear.
It was a good plan and a great marketing idea, save for one tiny little problem—the fans loved it too much. In fact, they loved it so much that they started lining up before Build-A-Bear Workshops opened in the U.S. The long lines made the company nervous about crowds and maybe bear riots, so they sent out a statement on social media saying it would limit the number of people who could take advantage of the deal due to safety concerns.
Customers were pissed – especially those who lined up around the block. Plus no one wants to hear you make excuses, blaming “safety concerns” and “local authorities.”
Sadly, all Build-A-Bear had to do was test this genius, goodwill generating campaign at a single store and then in increasingly larger markets to work out the kinks. To make matters worse, Build-A-Bear didn’t jump on the bad press and make anything of it.
Takeaway: Launch slowly – even if you have a great idea – so that any mistakes in your plan or missed assumptions don’t spiral out of control.
Earlier this week a woman was hit and killed by an autonomous car in Tempe, Arizona while she was crossing the road with her bike.
The accident is very sad and my heart goes out to the victim’s family and friends.
If you read my recent piece on the anatomy of disasters, you’ll recognize several of the common features here – although on a smaller scale.
The pedestrian was crossing a 5-lane, 45 MPH street in an area where drivers wouldn’t normally expect pedestrians. The autonomous car, operated by Uber, obviously failed to detect the pedestrian and stop in time. The “safety driver” wasn’t focused on the road or prepared to stop the vehicle.
And it didn’t help that it was very dark outside, this section of the road was unlit, and the pedestrian had no lighting or reflectors to make herself seen.
I’ve seen the video of the accident and it’s terrible. Unfortunately, I think that even an experienced driver would have hit the woman too.
In 2016, 37,461 people were killed in motor vehicle accidents. That’s over 100 people killed a day in the US. (National Highway Traffic Safety Administration)
Driving is an incredibly dangerous activity that we’ve made extremely safe through a lot of hard work over the past 5 decades. Currently the most accident-prone component of driving is us – humans.
We’re often slow, make poor choices, and drive when we’re tired, inebriated, and distracted. In theory, computers would make for much better drivers than humans.
If we are able to develop autonomous cars that are safer than human-driven cars, then I think we’ll be morally obligated to use them.
But that means that more people will die while we develop that capability. In the meantime, we need to have the courage, patience, and wisdom to get there – because it’ll be worth it when we do.
Elaine Herzberg, a 49 year old woman, was hit and killed by an autonomous car operated by Uber in Tempe, Arizona. She was crossing the road with her bike late Sunday night, March 18, 2018. I wrote about the accident in more detail here but I wanted to separate the video of the accident from my post so that people could choose whether they wanted to watch it or not.
Why don’t we learn from past experiences when it comes to planning new projects? Why aren’t even our best laid plans realistic?
Surely you’ve noticed this – whether it’s getting your taxes done, that big presentation for work, or planning your wedding.
Why do 80-90% of mega projects run over budget and over schedule?
Why has it taken – for example – nearly 100 years to expand the Second Avenue Subway in NYC? The original project was expected to cost 1.4 billion dollars (a 1929 estimate in 2017 dollars) and now with Phase 1 completed ($4.5 billion to build just 3 of the 16 proposed stations), Phase 2 is expected to cost $6 billion.
This phenomenon has been dubbed The Planning Fallacy – the topic of today’s Freakonomics podcast and the inspiration for this post.
Don’t have 45 minutes to listen? Keep reading.
Interested in why things fail? Read The Anatomy of a Disaster.
I build projects for a living – mostly product strategy and software for start-ups or innovation groups within larger companies. I plan and execute on projects everyday and I still struggle with the planning fallacy in other areas of my business (did I mention my corporate taxes are due in 7 days?).
But the secret sauce to my successes building products has always been to 1) have personal expertise in what’s being planned and built, 2) refine and go over the plans until your eye bleed looking for possible pitfalls, and 3) have a clear and easy-to-follow process to keep you focused on the right thing at the right time.
The Planning Fallacy – Poorly estimating the timeline, quality, and budget of a planned project while knowing that similar projects have taken longer, cost more, or had sub-par results.
The Optimism Bias – Focusing on the positives of a situation over the negatives.
Overconfidence – Thinking that we’ll perform better than we actually will.
Coordination Neglect – Failing to account for how difficult it is to coordinate efforts and combine all of the individual outputs into one complete system.
Procrastination – Choosing to do things that we enjoy in the short term instead of the things we think will make us better further down the road. In the episode, Katherine Milkman called procrastination a “self-control failure” – my new favorite phrase.
Reference Class Forecasting – Using past and similar projects as a benchmark for how your next project will perform.
Strategic Misrepresentation – Underestimating the costs and over representing the benefits of a project.
Algorithm Aversion – The big thing that Katy Milkman thinks is holding us back from using “data instead of human judgement to make forecasts” better.