The document discusses various algorithms and decision-making strategies based on the concepts from the book by Brian Christian and Tom Griffiths. Key topics include optimal stopping (the 37% rule), exploit/explore strategies, scheduling techniques, and Bayesian predictions. It emphasizes practical applications of these algorithms in everyday choices, such as dating, parking, and task management.

1. Algorithms to Live By
The Computer Science of Human Decisions

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How Can CS Help Us In Our Lives?
● Based on the namesake book by Brian
Christian and Tom Griffiths

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Optimal Stopping (The 37% Rule)
● The “Secretary Problem”
– Have a list of candidates for a position. Want to choose the best. Do not have
enough resources to interview everyone. When to stop interviewing?
– Formulated in 1960, published in Scientific American
– Take the best applicant after interviewing 37% (1/e) applicants!
● “Lover’s Leap”: at what age to stop dating and start building a
family?
– Have a time range (say, from 18 to 40). The 37% rule: when you are 26 y/o
(37% of 18--40), marry one of those dates!

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Optimal Stopping (cont.)
● When to park?
– Move along an infinitely long street with parking meters towards your
destination
– Get N blocks close to the destination, then take the first available spot
● N is the function of occupancy rate: 1 for 50%, 7 for 90%, 69 for
99%, 693 for 99.9% (this is ridiculous!)
● When to sell?
● When to quit a risky business?

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Exploit/Explore
● Stick to something old (“exploit”) or try something new (“explore”)?
– “Explore” = gather information [e.g., about restaurants], “exploit” = use
information [e.g., re-visit a known restaurant]
● Possible solution: “Win-Stay, Lose-Shift” (1952)
– As long as exploitation pays off, keep exploiting.
– When it does not, try something else.
– Better than random, but not perfect (“shift” in a rush?)

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Exploit/Explore (cont.)
● Better solution: Gittins Index, a combined measure of wins and losses
– Can be tabulated. Consider two restaurants:
● GI1(2 wins, 2 losses)=0.6010
● GI2(0 wins, 0 losses)=0.7029
– Always follow the strategy with the highest Gittins Index:
● Switch to the second restaurant!
● Yet another alternative solution: minimize regret!
– Regret = satisfaction vs. expectation. Can be quantified.

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Caching
● Libraries are an example of memory hierarchy:
– display space at the front (“primary memory”)
– stacks (“secondary memory”)
– offsite storage (“tertiary memory”)
● Where to put returned books?
– To the front! (“Most Recently Used,” if temporal locality holds)
● What do libraries do?
– Put the most recent acquisitions to the front! (FIFO)
– Turn the library inside out!

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Scheduling
● Gantt charts for your task list (1910s)
● How to execute your task list if you have deadlines?
– Earliest Due Date strategy minimizes maximum lateness
● The optimal strategy
– Modification, Moore’s Algorithm: “throw away the biggest item”
● If you cannot do everything by the due date, minimize the number
of unfinished tasks

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Scheduling (cont.)
● Have no deadlines? Minimize the collective outsider’s delay!
– Shortest Processing Time: always do the quickest task. Get things done!
● What if the tasks have priorities/weights?
– Divide each task’s weight by its processing time to get density
– Execute in the order of decreasing densities
● Paying debts
– Want to minimize the payoff? Funnel the money in the debt with the highest
interest rate.
– Want to minimize the number of debts? Pay the smallest debt.

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Scheduling (cont.)
● Preemption
– Execute tasks in small blocks. Reduces fatigue.
– The price of “context switch” from one task to another (attention switch).
● Metawork! (Rather than “real work.”)
● Thrashing
– Preoccupied with metawork?
– Solution: the art of saying “no.”

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Bayesian Predictions
● To make a prediction, we need to know prior probabilities (“given
that ...”)
● Copernican Principle: absent any prior knowledge about a process,
we are likely to be in the middle of it.
– It will take the process the same time terminate as it took to begin.
– “The [near] future is the same as the [near] past.”
● Will a 99-y/o live another 99 years?

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Bayesian Predictions (cont.)
● Power law data (scale free; no average; “rich get
richer”; preferential attachment)
– Multiplicative rule: predict by multiplying by some
constant (x2 for Copernican principle, x1.4 for lifetime
grosses of movies)
● Normal data
– Average rule (predict the average if below the average; add
small constant if above the average)
– A 99-y/o will live to 99+Y years
● Erlang data (distribution of phone call durations)
– Additive rule (predict some constant additional duration)
– “Just five more minutes!”

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Randomness
● Problem: organize a 10-city world vacation with the cheapest air
transportation
– 10! = 3,628,800 variants
● Greedy approach: always fly to the next cheapest destination
– Does not produce globally optimal solution
● Hill climbing: construct a random itinerary, randomly swap pairs of
cities if a swap decreases the cost
– May end up in a local maximum: good locally, bad globally

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Randomness (cont.)
● Hill climbing with jitter: climb the hill but also randomly accept
results that make the outcome worse
● Hill climbing with restarts: climb the hill many times starting from
different initial random itineraries; continue until no improvement
● Simulated annealing: climb the hill but randomly accept results that
make the outcome worse with the decreasing probability of
acceptance
– Starts as hill climbing with jitter
– Becomes the regular hill climbing at the end
– Best results!