Adjustable Space Shuttle Components - Packing for Mars - Mary Roach - 99 Percent Invisible

Adjustable Space Shuttle Components

Imagine driving your car without an adjustable seat. Imagine if every component of your vehicle was designed for an “average” sized person. Your seat probably wouldn’t fit you right, your legs may not reach the pedals well, or your head might be bumping up against the roof of the car. Standardized sizes that can’t be adjusted and that are based on an average for each person end up failing to actually fit anyone.
But super adjustable seats are not always a great thing either. In her book Packing for Mars, Mary Roach writes about the costs and engineering challenges that adjustable components on space stations present. “As things stand,” she writes, “NASA has to spend millions of dollars and man-hours making seats lavishly adjustable. And the more adjustable the seat, generally speaking, the weaker and heavier it is.”
When quoting NASA Crew Survivability Expert Dustin Gohmert, Roach includes, “The Russians have a much narrower range of crew sizes,” which means that they don’t have to adjust their seats, space suits, and various technology to the same extent as NASA which recruits astronauts with more varied bodies. Roach continues, “This wasn’t always the case. Apollo astronauts had to be between 5’5″ and 5’10”.” Today, however, we don’t want to limit someone’s opportunity to contribute their talents to space exploration and missions, even if they are a tad short or a bit taller than typical. We want the best people on our missions, and that means engineering expensive adjustable components with multiple potential fail points.
Adjustability is important in almost anything we design. Human bodies all come in different shapes and sizes and One-Size-Fits-All garments, seats, and utensils can normally do a good job for most, but not all of our bodies. Making the world more adjustable is definitely a slower and more expensive process, but it generally leads to better inclusion and better results for everyone. This isn’t necessarily the case for the space program, where designing ever more flexibility into the components of the system can mean more failure points and risk for everyone involved. Space travel is full of trade offs, and the trade offs can be expensive, time consuming, and even pose safety risks. Roach explores these tradeoffs in her book and looks at the ways we have calculated these tradeoffs throughout our history to show how much society has changed in terms of inclusion, thinking about designing for the average versus individual flexibility, and what it means to be human in spaces our bodies didn’t evolve to fit.
Imagine driving your car without an adjustable seat. Imagine if every component of your vehicle was designed for an “average” sized person. Your seat probably wouldn’t fit you right, your legs may not reach the pedals well, or your head might be bumping up against the roof of the car. Standardized sizes that can’t be adjusted and that are based on an average for each person end up failing to actually fit anyone.
But super adjustable seats are not always a great thing either. In her book Packing for Mars, Mary Roach writes about the costs and engineering challenges that adjustable components on space stations present. “As things stand,” she writes, “NASA has to spend millions of dollars and man-hours making seats lavishly adjustable. And the more adjustable the seat, generally speaking, the weaker and heavier it is.”
When quoting NASA Crew Survivability Expert Dustin Gohmert, Roach includes, “The Russians have a much narrower range of crew sizes,” which means that they don’t have to adjust their seats, space suits, and various technology to the same extent as NASA which recruits astronauts with more varied bodies. Roach continues, “This wasn’t always the case. Apollo astronauts had to be between 5’5″ and 5’10”.” Today, however, we don’t want to limit someone’s opportunity to contribute their talents to space exploration and missions, even if they are a tad short or a bit taller than typical. We want the best people on our missions, and that means engineering expensive adjustable components with multiple potential fail points.
Adjustability is important in almost anything we design. Human bodies all come in different shapes and sizes and One-Size-Fits-All garments, seats, and utensils can normally do a good job for most, but not all of our bodies. Making the world more adjustable is definitely a slower and more expensive process, but it generally leads to better inclusion and better results for everyone. This isn’t necessarily the case for the space program, where designing ever more flexibility into the components of the system can mean more failure points and risk for everyone involved. Space travel is full of trade offs, and the trade offs can be expensive, time consuming, and even pose safety risks. Roach explores these tradeoffs in her book and looks at the ways we have calculated these tradeoffs throughout our history to show how much society has changed in terms of inclusion, thinking about designing for the average versus individual flexibility, and what it means to be human in spaces our bodies didn’t evolve to fit.
The Peak-End Rule - Joe Abittan

The Peak-End Rule

Our experiencing self and our remembering self are not the same person. Daniel Kahneman shows this in his book Thinking Fast and Slow by gathering survey information from people during unpleasant events and then asking them to recall their subjective experience of the event later. The experiencing self and the remembering self rate the experiences differently.

 

We can see this in our own lives. During the day you may have had a frustrating project to work on, but when you lay down at night and reflect on the day, you might not remember the project being as bad as it felt in the moment. Alternatively, you might sit around all day binging a TV series and really enjoy a lazy relaxing day. However, you might remember the day much differently when you look back at it, no longer appreciating the experience but regretting it.

 

With our brain experiencing and remembering events differently, we are set up for some strange cognitive biases when we reflect on past events and think about how we should behave in the future. The Peak-End Rule is one bias that factors into how we remember events and can influence our future choices.

 

You might expect to rate a poor experience based on how bad the worst moment of the experience was. Say you had to go to a child’s gymnastics routine that you were really dreading. A certain part of the routine may have been all but unbearable to you, but if at the end you found a $20 bill on your way back to the car. Your judgement of the event is going to be influenced by your good luck. Rather than basing your judgement of the show purely on that dreadful routine, or on an average of the whole evening, you are going to find a spot somewhere between the worst moment and the happy moment when you found $20. Its not an average of the whole time, and its not really indicative of your actual experience. A random factor at the end shifted your perspective.

 

In his book Kahneman writes about the Peak-End Rule as “The global retrospective rating predicted by the average of the level of pain reported at the worst moment of the experience and at its end.” This definition from Kahneman comes after describing a study with participants sticking their hands in icy cold water and subjectively judging the experience later.

 

The peak-end rule is not limited to painful and unpleasant experiences. Instead of a miserable experience, you could have a truly wonderful experience that ends up being remembered somewhat poorly by a momentary blip at the end. Picture a concert that is great, but flops at the end with the speaker system failing. You won’t reflect back on the entirety of the experience as positively as you should simply because a single song at the end was ruined.

 

What we should remember from this is that endings matter a lot. Don’t end your meeting with the bad news, end it with the good news so that people walk out on a positive note. The ending of an experience weighs much more heavily than everything in the middle. The points that matter are the peak (either the best or worst part) and the ending. A great ending can buoy a poor experience while a bad ending can tank a great experience. For company meetings, job interviews, or performances, make sure you bring the ending to a high point to lift the overall level of the subjective experience.
Regression to the Mean

Praise, Punishment, & Regression to the Mean

Regression to the mean is seriously underrated. In sports, stock market funds, and biological trends like generational height differences, regression to the mean is a powerful, yet misunderstood phenomenon. A rookie athlete may have a standout first year, only to perform less spectacularly the following year. An index fund may outperform all others one year, only to see other funds catch up the next year. And a tall man may have a son who is shorter. In each instance, regression to the mean is at play, but since we underrate it, we assume there is some causal factor causing our athlete to play worse (it went to his head!), causing our fund to earn less (they didn’t rebalance the portfolio correctly!), and causing our son to be shorter (his father must have married a short woman).

 

In Thinking Fast and Slow Daniel Kahneman looks at the consequences that arise when we fail to understand regression to the mean and attempt to create causal connections between events when we shouldn’t. Kahneman describes an experiment he conducted with Air Force cadets, asking them to flip a coin backwards over their head and try to hit a spot on the floor. Those who had a good first shot typically did worse on their second shot. Those who did poor on their first shot, usually did better the next time. There wasn’t any skill involved, the outcome was mostly just luck and random chance, so if someone was close one time, you might expect their next shot to be a little further out, just by random chance. This is regression to the mean in an easy to understand example.

 

But what happens when we don’t recognize regression to the mean in a random and simplified experiment? Kahneman used the cadets to demonstrate how random performance deviations from the mean during flight maneuvers translates into praise or punishments for the cadets. Those who performed well were often praised, only to regress to  the mean on their next flight and perform worse. Those who performed poorly also regressed to the mean, but in an upward direction, improving on the next flight. Those whose initial performance was poor received punishment (perhaps just a verbal reprimand) between their initial poor effort and follow-up improvement (regression).  Kahneman describes the take-away from the experiment this way:

 

“The feedback to which life exposes us is perverse. Because we tend to be nice to other people when they please us and nasty when they do not, we are statistically punished for being nice and rewarded for being nasty.”

 

Praise a cadet who performed well, and they will then perform worse. Criticize a cadet who performed poorly, and they will do better. Our minds overfit patterns and start to see a causal link between praise and subsequent poor performance and castigation and subsequent improvement. All that is really happening is that we are misunderstanding regression to the mean, and creating a causal model where we should not.

 

If we better understood regression to the mean, we wouldn’t be so shocked when a standout rookie sports star appears to have a sophomore slump. We wouldn’t jump on the bandwagon when an index fund had an exceptional year, and we wouldn’t be surprised by phenotypical regression to the mean from one generation to the next. Our brains are phenomenal pattern recognizing machines, but sometimes they see the wrong pattern, and sometimes that gives us perverse incentives for how we behave and interact with each other. The solution is to step back from individual cases and try to look at an average over time. By gathering more data and looking for longer lasting trends we can better identify regression to the mean versus real trends in performance over time.