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This is the 22nd installment of 774: Weekly lessons from history about science, technology, and the miscellaneous.
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Dirty Jobs
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In 2004, MTV and Paramount Pictures released the comedy Napoleon Dynamite. The movie follows the strange home dynamics, love interests, and bullying experienced by the film’s namesake. Early in the movie Napoleon befriends Pedro, an exchange student from Juarez, Mexico, who just transferred to his high school. Napoleon’s friendship gives Pedro the confidence to run for student government, leading to one of the worst campaign speeches of all time:
I don't have much to say, but I think it would be good to have some Holy Santos brought to the high school, to guard the hallway and to bring us good luck. El Santo Niño de Atocha is a good one. My Aunt Concha has seen him. And we have a great FFA schedule lined up, and I'd like to see more of that. If you vote for me, all of your wildest dreams will come true. Thank you.
As bad as Pedro’s speech is, it hits notes similar to many real world campaign speeches: making promises of additions to an institution which they claim will improve it. According to Pedro, the addition of Holy Santos to the hallways and more FFA events will improve life at his school. According to researchers at The University of Virginia, the tendency to create additive solutions applies to far more than high school politics. Whether it’s stimulus packages, walls, or El Santo Niño de Atocha, those vying for your vote in an election tend to promise new things they will add to a situation (as opposed to the removal of old things) to try and rectify it.
The researchers,1 whose findings were published in Nature, posit that humans attempting to solve problems default to searching for solutions that are additive instead of subtractive. The researchers best demonstrated this default using LEGOs.
The researchers presented a LEGO structure to participants that was set up as it is in the image above. You’ll notice that there is one, 2x2 brick underneath one corner of the roof. The researchers approached students and asked them to successfully complete the following task: given unlimited time and LEGO bricks, make changes to the structure that will support the weight of one masonry brick. In exchange for the successful completion of this task, the student would get $1, but would lose $0.10 for each additional brick added. 86% percent of participants solved the puzzle in the same way: add a brick to the opposite corner from the existing brick, providing an equal distribution of weight between the two pillars, saving the LEGO figurine below. That solution would return $0.90 for successful completion. However, there is another solution, which is just as easy to complete, returns $1, and only 12% of participants thought to do: remove the lone brick and have the roof rest on the base. This solution removes a brick, thus costing nothing. When unprompted, most participants elected to add instead of subtract to the structure in order to improve it.
The researchers conducted a handful of these experiments demonstrating a human proclivity for solutions that bend towards being additive versus subtractive. The first question that might arise after learning that you default to additive solutions is to ask “is that a problem?” The answer is “not necessarily.” There are times when additive solutions are the only good ones. For example, another experiment that was part of the research in question provided the following image to participants:
The image above was accompanied by a prompt asking for potential changes to the miniature golf course. The overwhelming majority of responses were additive (“add more sand traps or a windmill”) rather than subtractive (“get rid of the sand trap and the bumper”). This outcome should be expected because we all know the purpose of a miniature golf course is to be entertaining and challenging, not just a straightforward put. To subtract from this course would make it uninteresting, and making a miniature golf course interesting should be the end goal of any mini-golf architect.
So there are scenarios where additive solutions are preferable, and ones where subtractive solutions are better. How can one decipher when he or she is actually enacting the best solution or just defaulting to their default for additive solutions?
One option would be to assign an imaginary cost to additive measures and incentivize subtracting. Sometimes this happens naturally. Are you coding a certain function that keeps returning an error, leading you to add more code in an attempt to solve it? That additional code takes time to write, and time is money. There is a natural cost to additive solutions, but the LEGO experiment demonstrates that even a direct monetary cost to additive solutions doesn’t necessarily mean one will search for a cheaper solution, even if it would be just as easy to enact. A method for ensuring you choose the best solution is to form two potential solutions, one subtractive and one additive. Restrict oneself to formulating a subtractive solution without considering any additive measures. Once a subtractive solution has been gamed, compare it to the additive solution that your brain will naturally want to implement. Weighing these two options should reveal whether an additive or subtractive solution is optimal. Consider the LEGO experiment again: while most participants defaulted to adding bricks to the structure, few would conclude that their choice was better than simply removing one brick and keeping their $1, they just didn’t initially consider it. If given the opportunity to contrast an additive and subtractive solution, the logical person would conclude the subtractive choice is better in that instance, they simply need the opportunity to compare the subtractive.