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Summary Algorithms to Live By PDF | Computer Science & Human Decision Guide

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These study notes and test bank for Algorithms to Live By: The Computer Science of Human Decisions provide a comprehensive overview of how computer science principles can be applied to everyday decision-making. Topics include algorithmic problem solving, optimal stopping, scheduling, game theory, sorting, caching, and exploring trade-offs in human behavior. Each concept is explained with examples and practical scenarios, demonstrating how algorithms can guide optimal choices in real life. Designed for students, self-learners, and enthusiasts of computational thinking, these notes simplify complex ideas and reinforce understanding. Structured for easy revision, exam preparation, and practical application, the material helps learners connect theoretical computer science concepts with human behavior, improves decision-making skills, and enhances logical reasoning. Ideal for courses in computer science, behavioral analysis, or applied algorithms.

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Table of Contents

About the Authors

Copyright Page

, Introduction
Algorithms to Live By




Imagine you’re searching for an apartment in San Francisco—arguably the most
harrowing American city in which to do so. The booming tech sector and tight
zoning laws limiting new construction have conspired to make the city just as
expensive as New York, and by many accounts more competitive. New listings
go up and come down within minutes, open houses are mobbed, and often the keys
end up in the hands of whoever can physically foist a deposit check on the landlord
first.
Such a savage market leaves little room for the kind of fact-finding and
deliberation that is theoretically supposed to characterize the doings of the rational
consumer. Unlike, say, a mall patron or an online shopper, who can compare options
before making a decision, the would-be San Franciscan has to decide instantly either
way: you can take the apartment you are currently looking at, forsaking all others,
or you can walk away, never to return.
Let’s assume for a moment, for the sake of simplicity, that you care only
about maximizing your chance of getting the very best apartment available. Your
goal is reducing the twin, Scylla-and-Charybdis regrets of the “one that got
away” and the “stone left unturned” to the absolute minimum. You run into a
dilemma right off the bat: How are you to know that an apartment is indeed the best
unless you have a baseline to judge it by? And how are you to establish that baseline
unless you look at (and lose) a number of apartments? The more information you
gather, the better you’ll know the right opportunity when you see it—but the more
likely you are to have already passed it by.
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