A simple algorithm to conceive of literary plots could be to slot them as belonging to one of these categories: Man vs. Nature, Man vs. Self, Man vs. A simple algorithm to conceive of literary plots could be to slot them as belonging to one of these categories: Man vs. Nature, Man vs. Self, Man vs. Man & Man vs. Society.
Brian & Tom enlists findings from computer science to guide us through these. Algorithms here are the shortcuts or even the intuitions that guide us through problems that are intractable at first glance. We, apparently, use them everyday. Brian & Tom are here to document this and to show how exactly we can make them more efficient, by exploring the idea of human algorithm design—searching for better solutions to the challenges people encounter every day. The central thesis is that it’s best to use shortcuts to improve your probability of success and remember that “perfection is the enemy of the good.” The book’s algorithms are intended to reduce time spent puzzling, conserve energy for the things that matter.
When it comes to the first two categories, computer science is shown to be a good guide to problems created by the fundamental structure of the world, and by our limited capacities for processing information. As with all the sciences before it, computer science and data science are pretty effective in dealing with these issues. And the computational approach seems to be a remarkably useful improvement in dealing with areas like self-control or complex everyday decisions.
In this part of the book, when we deal with Man vs. Nature & Man vs. Self, we mostly encounter well-defined problems and potential algorithms to deal with them.
We have a nice variety of approaches here: First, we are given a taste of the “Optimal stopping problems” which spring from the irreversibility and irrevocability of time - How do you decide when to stop searching, be it for a the perfect mate, the perfect employee, the perfect job or the perfect weekend movie? The answer seems to be simple: 37% - you stop once 37% of your options have been checked out. Much more useful than it sounds, this number is the output of an algorithm. Whether it’s an apartment, a parking space, or a spouse, the right moment to stop searching and start choosing falls under the umbrella of problems called “optimal stopping.” The general solution to optimal stopping problems reveals that you should spend 37 percent of your time gaining an impression of what’s out there and the rest of the time selecting anything better than the average of what you observed thus far. Need to rent an apartment in three weeks? Simply take one week to observe and two weeks to pounce on the next best thing. This means that you have a good sample of the options you have so you don’t jump to early decision and miss out on the good choices that were just around the corner, and at the same time, you don’t waste all your time only searching!
Then we are introduced to “the explore/exploit dilemma”, springing from time’s limited supply - should we revisit favourite restaurants and places and ensure a good time (exploit) or should we explore bravely out to new experiences and places (explore) in the hope that we might stumble on something incredible? If we don’t explore, we might miss out on a lot of YOLO stuff , but if we only explore and do not exploit the good stuff we have already discovered (a favourite dish, a cared-for home, spouse, close friends, etc.) then we might me missing out on even more. SO how do we figure out an optimal ration between Explore/Exploit? Turns out computer scientists have been working on finding this balance for more than fifty years. They even have a name for it: the explore/exploit tradeoff. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favourites. The answer is to think about the time you have left - the more time you have the more your strategy should shift. So the young should explore more and the elderly should exploit more and wherever you are in that continuum, you should ration the Es accordingly. YOLO, after all.
There are more: Relaxation and randomization emerge as vital and necessary strategies for dealing with the ineluctable complexity of challenges like trip planning and vaccinations, Sorting theory tells us how (and whether) to arrange our offices, Caching theory tells us how to fill our closets, Scheduling theory tells us how to fill the unforgiving minute well, etc.
Then comes the next two categories: Man vs. Man and Man vs. Society problems - these are, in effect, the problems that we pose and cause each other. Here the authors move away from computer science and enlists mathematics as well, specifically and predictably, game theory, to help us out. And the cross-pollination between game theory and computer science gives us algorithmic game theory for tackling issues like investing, bubble and even plain arguments. The solutions are much less rigorous here, with 1) the advice to “change the game” if the game threatens to go into less than optimal equilibriums and 2) an exhortation to be “computationally kind” to reduce the cognitive load of the participants, emerging as the main “algorithms to live by” when it comes to living in society.
So as always, the book would seem to be teaching us again that no matter how computationally adept we are, dealing with each other is something that just can’t be fitted into any algorithm, formula or thumb-rule. We gotta wing it....more
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