At Closet Factory we spend a lot of time helping people get their lives organized. Whether it’s living rooms or bedrooms, we’ve sorted them all out — especially closets. Wardrobe organization isn’t as easy as it sounds, and there are a hundred philosophies out there to follow. What if, however, the answer to a functional closet lies in your computer?
That’s exactly what the authors of “Algorithms to Live By: The Computer Science of Human Decisions” propose. According to Brian Christian and Tom Griffiths, the best system for organizing closets could be similar to the way a computer caches memory.
“Your closet presents much the same challenge that a computer faces when managing its memory: space is limited, and the goal is to save both money and time,” write Christian and Griffiths in Wired.
Cache? What’s that, you ask? It’s a portion of a computer’s system memory that is dedicated to storing information it believes will be used the most. Instead of having to access the hard drive every time a program or document is requested by the user, the computer can quickly pull up these queries through the cache — allowing for a much faster load time.
In essence, a computer’s cache is one of the ways it organizes itself. A hard drive is like a closet: it gets messy over time as information (clothes) is added or deleted. The way a computer’s memory cache works is intended to be reflective of the user’s most-needed items (favorite pieces of clothing). It is, however, limited by space, and when it’s full, it needs to decide what to remove, or “evict.” This is not much different than diving into a messy closet to reorganize and make space for new items of clothing.
There have been many theories on the best way to cache. According to László “Les” Bélády, author of a famous 1966 paper on caching algorithms, the best cache eviction policy is to “evict whichever item we’ll need again the longest from now.” Makes sense. Of course, without knowledge of the future, otherwise known as “clairvoyance,” this can be a bit tricky. How are you supposed to know what you’ll want to wear in a few weeks?
Some propose using a “First-In, First-Out” (FIFO) policy. For wardrobes, this basically means throw out the oldest stuff in the closet. There, is, however, a mathematically more efficient way: the “Least Recently Used” (LRU) method. This one translates to: “When was the last time I wore it?”
Although it may not seem like a big difference, LRU significantly outperforms FIFO. Using this policy in your closet should theoretically reduce your time spent looking for items, as well as present you with your most desired options (you shouldn’t have to hunt for your favorite dress or jeans every single time).
Christian and Griffiths offer tips on implementing LRU caching into your closet. It’s not necessary, they say, to throw out your old college t-shirt that you wear often. But if you’ve got plaid pants sitting around that you haven’t worn in a while, dump them.
Second, they advise you to “exploit geography.” What this means is store items close to where they’ll be used. Putting your workout gear behind your evening gowns isn’t just a hassle — it makes no sense (unless you attend galas more than you exercise). A doctor mentioned by Christian and Griffiths stated she stores extra vacuum cleaner bags behind the living room couch despite her kids thinking she’s wacky. Why? Because the vacuum is used mostly in the living room, and when a bag is full, another is quickly on hand, in the same room.
Lastly, they point out that just as computers have different cache levels, so should you.
“Where your belongings are concerned, your closet is one cache level, your basement another, and a self-storage locker a third,” writes Christian and Griffiths.
Of course, simply caching your closet and evicting unused clothing won’t solve all your organizational needs. It’s still important to implement other important tips such as color coordinating, utilizing vertical space, and properly storing different types of clothing.
In the end, however, remember that the challenges you face in the morning are apparently not that different than a computer booting up — perhaps there’s a thing or two to learn from our digital many-times-removed cousin.
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