Responding to the assertion that computers lack intuition, the philosopher and cognitive scientist Daniel Dennett once (counter-intuitively) argued that computers must have intuition. Ask a computer to calculate the square root of 54357.029. How did the computer get the answer? Lacking awareness, the computer doesn’t know. The answer wasn’t the result of deep thinking or concentration. It was intuition.
Humans, of course, also have intuition. We can make fast decisions without consciously thinking, but how do we arrive at the decision? The answer is often a mystery. Without accessing the “source code” of the brain, we might never know why our gut feelings tell us to choose a particular job applicant or buy a mutual fund.
I read Algorithms to Live By: The Computer Science of Human Decisions hoping to learn more about the source code of the brain. After all, the cover art (which authors often have little control over) depicts a tangled USB cord in the shape of a brain. Only a few chapters in, I realized that science journalist Brain Christian and cognitive scientist Tom Griffiths sought not to elucidate the hidden algorithms used by the brain, but rather to introduce engineered computer algorithms in the context of day-to-day life.
How long should you look at fast-selling apartments before committing on the spot? How can you best balance your time between checking email and getting chores done? How many factors should you consider when buying a mutual fund? Before reading this book, I might have told you there are no exact answers to these questions. Yet, in many cases, concepts from computer science and probability theory can be applied to these everyday situations to produce nearly exact solutions that often outperform human intuition. For instance, when apartment hunting, spend 37% of your time looking at different apartments to get a feel for what a good deal looks like, then take the first good deal you spot after calibrating.
Algorithms to Live By is filled with many such “life hacks” that teach fundamental computer science concepts like sorting and model fitting in a highly relatable manner, with an appendix of technical details for the mathematically inclined. My favorite chapter is dedicated to Bayes’ rule and the Copernican Principle, thinking tools that help computers and humans alike in choosing between different alternatives based on limited data. For instance, how can you best estimate the number of tramcars in a city given only a single tramcar? (Hint: double the serial number).
Judged on its own merits, Algorithms to Live By is a pleasant read that successfully offers computer algorithms to guide human intuition. Throughout the book, the authors also examine the decisions made by algorithm-naive individuals and compare such choices to those prescribed by the book’s algorithms. In some cases, the human brain seems to already be guided by similar algorithms. In other cases, computer algorithms greatly improve upon those of the brain.
While I enjoyed the book, I felt it might have been deepened by a more explicit consideration of the brain’s existing routines and algorithms, the ancient, evolutionary source code to which the authors offer modern, engineered alternatives. For instance, while the book praises the idiosyncratic organization followed by Amazon warehouses–storing diapers next to barbecue grills in a clever disregard for keeping like-with-like—the authors neglect to mention that some neuroscientists think that the brain follows a similar strategy of separating similar items to avoid accidental retrieval of an adjacent, similar item. The entorhinal cortex, a part of the brain involved in spatial navigation, features grid cells which encode an animal’s location in an abstract mathematical space where similar locations are, paradoxically, kept distant to avoid confusion. I’m not advocating a discussion of cognition that is only grounded in biology. After all, yours truly has written a notorious article on why reductionism isn’t always beneficial, necessary, or even possible. But if we’re asking what the human brain can learn from computers, an examination of biology also seems appropriate.
As a further example, the human brain has certain cognitive biases stemming from its neuronal architecture. As a stimulus becomes more intense, neurons increase their firing rates more slowly. Whether you’re aware of it not, two lightbulbs do not appear twice as bright as one (even though they actually are). Just as we might not notice if a bright light becomes brighter by a small percent, we might not notice if a large price becomes more expensive by a small percent. Consumers often have no problem tacking hundred dollar add-ons to a five-figure car, but will fight tooth and nail to save one dollar on a box of cereal. I can’t help but think that Christian and Griffith’s book would be made even better by exploring such “brain bugs.”
I recommend Algorithms to Live By to anyone interested in learning how engineered computer algorithms can improve their day-to-day thinking and decision making, or to anyone eager to learn about fundamental computer science concepts in a familiar context. If you’re reading the book to learn more about the source code of the brain–well, don’t judge a book by its cover … I eagerly await such a sequel.
Christian, Brian, and Tom Griffiths. Algorithms to Live By: The Computer Science of Human Decisions. Macmillan, 2016.
Dennett, Daniel. “Tools to Transform Our Thinking.” Royal Geographical Society. May 22, 2013. Lecture. Quote from lecture paraphrased at beginning of article: “It’s child’s play to make a computer that has intuition. You take a computer program that solves any problem you like. It might be long division, or weather prediction, or whatever. And you ask it a question, it gives an answer, and you say, ‘How did you work out that answer?’ And it says, ‘I don’t know, it just came to me.’ Intuition is when you’ve got a conviction and you haven’t the faintest idea how you got it.”
Sreenivasan, Sameet, and Ila Fiete. “Grid cells generate an analog error-correcting code for singularly precise neural computation.” Nature neuroscience 14.10 (2011): 1330-1337.
Dehaene, Stanislas, et al. “Log or linear? Distinct intuitions of the number scale in Western and Amazonian indigene cultures.” Science 320.5880 (2008): 1217-1220.
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