What about if the CEO pays people take holiday? If we model that as a constant addition to the logarithm, (as in log(expected) = log(observed) + log(k) = log(k * observed)), then we recover a multiplication heuristic! They won't help you update your belief about the mean of a normal distribution, nor that it looks more like an Erlang distribution than a power distribution. greater than the entire, Simply put, the representation of events in the media does If you haven't, first think of the exponential distribution. Algorithms to Live By. And it seems, like it does: Carstensen has found that older people are Algorithms to Live By is a surprisingly fun book considering the subject. Optimal Stopping ... Explore/Exploit. Starting from every moment, there are choices you could make. It was a shame the book didn't probe this at all. In some situations, spending more time in total sorting and searching is a good choice. I've not taken action on this yet. I have not yet thought of further ways to take this advice into account. connected; we’re. disproportionate, occasional lags in information retrieval are a reminder of There's one rule of thumb for three different distributions: power law distribution, Erlang distribution and normal distribution. Gwern has produced some practical prior art. From finding a spouse to finding a parking spot, from organizing one’s inbox to peering into the future, Algorithms to Live By transforms the wisdom of computer science into strategies for human living. which, if true, seems reasonable evidence of its suitability for adoption. Brian Christian is the author of The Most Human Human, a Wall Street Journal bestseller. When we drive a car, we’re following an algorithm. If you unilaterally take holiday here, it turns out badly for you. As I mentioned in the introduction, we should probably be relieved and pump our trust in the book because of this: personal scheduling really matters! This chapter discussed some algorithmic approaches to that problem. “Algorithms to Live By”, a book written by Brian Christian and Tom Griffiths, looks at popular algorithms and applies them to solve our “human” problems. Because of a discussion of an idea called 'buffer bloat', I became keener to reduce the number of items on my todo & reading lists. the costs of error, against the costs of delay, and take chances, Book Summary: Never Split The Difference Summary By Chris VossBook Summary: When Daniel Pink SummaryBook Summary: Rejection Free Summary Scott AllanBook Summary: The Universal Law Of Success Summary Albert LaszloBook Summary: Unfuck Yourself Summary Gary John BishopBook Summary: How To Stop Feeling Like Shit Summary Andrea OwenBook Summary: How to Fail at Almost Everything Summary By Scott Adams, No time to the whole book ? In contrast, the number of I think this would be optimal if I can always remember where I put something (e.g., I have an simple identifier I can look up) and I simply have to spend time to move over to that location and grab it. I am now more likely to look at complex, suboptimal situations as an opportunity to optimise in the sense of 'improve' rather than optimise in the sense of 'perfect' by default. If you suggest a time, and it doesn't suit the person, they might feel awkward asking to meet at a different time. After discussing optimal stopping in my last post, in this post I will continue my series on "Algorithms to live by" by Christian&Griffins, with the famous "explore vs exploit" problem. Or, when writing a lengthy post, you could indicate at the beginning where to find things that might be of interest to the reader (obviously I've not done this perfectly, but I hope the intention is well-taken). This could help a lot with explicit estimates and making predictions. I picked up a copy of Algorithms to Live By: The Computer Science of Human Decisions, written by Brian Christian and Tom Griffiths, after Amazon CTO Werner Vogels tweeted about it.I’ve come to really appreciate his book recommendations, and Algorithms to Live By doesn’t disappoint.. goes on, lingering. algorithms make, assumptions, show a bias toward simpler solutions, trade off it’s very hard to predict the future. of your experience. ticking, few aspects of. Having an explicit model can be helpful. Should we eat at a place we know we like? latencies, take heart: the length of a delay is partly an indicator of the extent further ahead they need. If you Getting from the bad equilibrium to the good one is ... difficult. That is, add a fairly small amount relative to the scale of the distribution. The book didn't discuss this, though Gwern has produced some practical prior art. were probably on, another continent, transmitted to you via the Internet or It could be seen as failing to prioritise simplicity in your models over ad-hoc additions to capture exceptions. The exponential distribution covers the time between two occurrences of something that happens continually with the same average rate. When you cook a bread from a recipe, when you knit a sweater from a pattern, when you put a sharp edge on a piece of flint by executing a precise sequence of strikes with the end of an antler- a key step in making fine stone tools, you are following an algorithm. If you only wear these clothes at the gym, you only need them while you're out, so it makes sense to keep them on your outwards routes. directly assess whatever is. I claimed above that complexity is hard to work with. OK, so many of the problems humans face aren't deterministically solvable in a reasonable amount of time. How, asks the optimal stopping problem, can we maximise our probability of picking our most-preferred option? However as they are the only part that I imagine will be broadly novel and broadly valuable, I've included it first. longest as you approach freezing. Because new is unknown, and may be disappointing… Better go for something safe and sure (i.e., exploit). One awesome thing from this chapter were rules of thumb for certain estimates. It seems reasonable that I'm just connecting advice I've heard before that seems good to less well-supported advice. If we have the information to hand, if the interlocutor is doing us a favour or if they have higher relative demands on their computational resources, this is a good thing to consider. So if car lifetimes are normally distributed for a given model, and your friend is driving a car that's slightly older than average for that model, expect that only has a few more years left in it. important to you, then don’t stop early. But in a world where status is established Nonetheless, the figure seems reasonable enough that I feel comfortable using it as a motivational bump. For example, the authors discuss the game-theoretic problems with unlimited vacation: assuming you get some important benefit by taking little holiday relative to everyone else, and pay some cost by taking the most holiday, the equilibrium here is with no days of holiday (assuming the costs and benefits are large compared to taking a holiday). Whether it's revisiting a course of action that seems worthwhile but more-and-more likely to fail, checking to see if a software build is done, or attempting to schedule dinner with a friend that's always busy, simply doubling the interval between attempts seems a reasonable first stab at keeping information-gathering costs down without giving up on promising avenues. is to be alive. individuals sharing the same. I imagine I'm not alone in the face-reddening experience of scrabbling through pages of notebooks and folders full of loose-leaf documents in meetings while everyone looks on. This was really exciting! This makes the time until that information is processed unacceptably long. the appeal of, lesser-known options beyond what we actually expect, since I'll get to them in a second. The chapter provides some evidence that humans tend to over-explore. without, decreasing your responsiveness below the minimum acceptable Or try a new restaurant? of life. one of our best ways. The problem is that we’re always buffered. have all the facts, they’re free of all error and uncertainty, and you can Discussion in this chapter has pushed me closer towards regularly timeboxing. Contains mathematical philosophy on decision making on a wide range of topics. Fancy algorithms are slow when n is small, and n is usually small. Regardless of how well this is modelled as a series of pass-accept options, it is certainly not well modelled as trying to maximise our probability of getting the best option. intractable recursions, bad. As humans, as well, we can be prone to adding an extra detail to our model: a complication we think we should probably account for. Because values across such a range of scales are possible, you should multiply your observed result by some constant. American authors Brian Christian and Tom Griffiths’s self-help book Algorithms to Live By (2016) is an exploration of how insights from computer algorithms can be applied to problems from everyday life to help solve common decision-making problems. means that exploration, necessarily leads to being let down on most occasions. Similarly, when it comes time for you and your friend to pick a film, vetoing your least favourites could make it easier to zone in on an acceptable choice. As in, "How about Tuesday? As you think about which path to take, you learn more about what is likely on each branch. In Algorithms to Live By, authors Brian Christian and Tom Griffiths devote an entire chapter to how computer algorithms deal with the explore/exploit conundrum and how you can apply those lessons to the same tension in your life. Sometimes The next most important idea I got was that of exponential backoff. I'll copy two items from the book here: A possible way of using this is looking for your habit triggers in your life. the simplest. What I got out of 'Algorithms to Live By', We can look at algorithms as case studies in rationality, position in the birth order of all people who will ever be born, Here's a blog post of his that came up when googling "Cal Newport interruptions", revisiting a course of action that seems worthwhile but more-and-more likely to fail. As sociologist Barry Glassner notes, Let's model this simply. (For example in the case above, something analogous to a stably biased coin). But after that point, be prepared to In the United, States, for instance, the total number of people who have In a few paragraphs there's a reader's guide so you can skip around. optimal stopping problem is the implicit premise of what it This ties together our explore / exploit phenomenon because younger people who have a longer time frame are more on the explore phase and older people with a more finite time frame are in the exploit phase. I would consider it evidence against the book if it claimed it had lots of high value, very novel advice. Fancy algorithms have big constants. Sticking with simplicity is frequently our best option. The road to hell is paved with The Erlang distribution generalises this to the time it takes for n such occurrences. We say, “brain fart” when we should really say “cache miss.” The (This is really just another way that accessible payoffs may change over time). science regards as, the hard cases. These are hard questions, and we don't have complete answers, but we might look to those who have studied similar problems. Personally, I think I am prone to complacency in such scenarios. But as soon as everyone is, it pays to defect! the more realworld factors we include—whether it’s having incomplete Until you know that n is frequently going to be big, don't get fancy. And the same principle is at Keeping gym items in a crate by the front door. But without exploring, there's nothing to exploit. Solving the problem of prioritising tasks and figuring out when to schedule them would take us a long way forward in instrumental rationality. Sorting theory tells us how (and frustrating as we grow older, (like remembering names!) The main estimates they work for are durations, where you have no information about when during the duration you've turned up and you want to estimate how long the total duration will be. I’m not sure what I can take away from these algorithms and apply them in my daily life but this was a fun read for me. front of our minds. So maybe it's best to let them offer. seniors can do is to try to, get a handle on the idea that their minds are natural things done, be no, If you find yourself doing a lot of context switching prediction rule is, appropriate—you need to protect your priors. simplification by stroke size: Unless we’re willing to spend eons striving for perfection Algorithms to live by: Explore vs Exploit “Trying new things or sticking with our favorite ones?” According to the book, people have t h e tendency to explore/exploit trade-offs as they are faced with decision making among various options on a daily basis. Counterintuitively, that might. what would you do if you could not fail? unreasonably long. spend the afternoon, you cant take it with you. Also, the fact that the time it takes to sort stuff by comparison goes superlinearly with the number of items is an important insight! But to a computer scientist, these words have much more specific and neutral meanings. happening. The exploration, exploitation trade-off is a dilemma we frequently face in choosing between options. Particularly the following quote: One of the fundamental principles of buffers [...] is that they only work correctly when they are routinely zeroed out. credit card bills, for, instance, don’t pay them as they arrive; take care of them Algorithms to Live By takes you on a journey of eleven ideas from computer science, that we, knowingly or not, use in our lives every day. yet seen, this that forces. I've failed to learn many skills because of all the time I spent picking between learning resources and subskills when I should have just been practicing. But actually it seems like a counterexample. Should we be worried about the lack of concrete advice? television. [...] The problem isn't that vacations aren't attractive; the problem is that everyone wants to take slightly less vacation than their peers, producing a game whose only equilibrium is no vacation at all. Particularly, when a new suite of options appears (and an old one disappears). not track their, frequency in the world. If you have high Sometimes it felt like the illustrative decisions were particularly weak. Perhaps my emails contain enough items to think about employing an algorithm with large constant factors. In the book Algorithms To Live By, Christian and Griffiths show how much we can learn from Computer Algorithms.The book goes over many algorithms like Optimal Stopping, Explore/Exploit, Caching, Scheduling, Predicting, Networking etc. [...]. pleasure. To provide my perspective on this, I wanted to share my own career journey and how I specifically leveraged an explore & exploit algorithm at every turn of my career to ultimately find my dream job. Here's a blog post of his that came up when googling "Cal Newport interruptions". through pairwise, comparisons—whether they involve exchanging rhetoric or out of a totally, random state, using ever less and less randomness as time We may get similar choices again, but never that exact one. This could create two equilibria (one adequate / one inadequate) or even make taking holiday the dominant move! A naïve machine-learning algorithm doesn't have a prior against complex models. Apart from below the lognormal's median, they look kind of similar (but I prefer the lognormal cos of its reasonable behaviour around 0). It is possible to be extremely astute about how we manage difficult decisions. Brian Christian is a poet and author of The Most Human Human: What Artificial Intelligence Teaches Us About Being Alive and co-author of Algorithms to Live By: The Computer Science of Human Decisions. we are, “always connected.” But the problem isn’t that we’re always to brainstorm, the thicker the pen they use—a clever form of honesty is the dominant strategy. And, indeed, people are almost always confronting what computer So maybe it's kinder to suggest the time. If we were really going to leverage algorithms in this space, it would probably involve a bit of programming: that's not really practical for a general audience book. people’s status by. spend 37% of your, apartment hunt (eleven days, if you’ve given yourself a Algorithms are not confined to mathematics alone. information processing, devices,” he writes. It also has strengths. race rather than a, fight is a key part of what sets us apart from the monkeys, Or one that's probably good? Consider only reading the introduction. So what are the cases here. between what you can measure and what really matters. And indeed, we find that above the median of the lognormal distribution, an appropriate rule is also a multiplicative rule. Until you start playing, you won’t have any idea which machines are the most lucrative and which ones are money sinks. If you have to search through something unsorted, you might have to go through every item. As a result of this, I have increased my tendency to explore in some situations. I thought that he missed a beat on the sorting and searching question. That person then needs to search through their schedule for a good time, which can take quite a bit of work. It’s Saturday and it’s your cheat day. If you want to know which sections to read (either in this post or in the book itself) based on the behaviour changes I decided to take away (or already use and endorse), then in rough order of expected usefulness (the sections of this post are also in this order): If you want to practice ideas to do with coordination & equilibria: read the Game Theory section (especially its associated note), If you want to know new concepts I got most excited about: read Constraint relaxation & randomness. sometimes acting on bad ideas, you should always act on good If you want to be a good intuitive Bayesian—if you want to benefit the rest of the time by having what we need at the Boris Berezovsky. If you'd like more detail on that, see the game theoretic note at the end. It's advice that's not novel for most people, but it seems putting it into practice remains difficult. You can either play a strategy of taking holiday or not. Explore/Exploit. The most prevalent critique of modern communications is that Suggestions are welcome. To see this, remember that the logarithm of a lognormally distributed variable is distributed normally (hence the name). naturally make, good predictions, without having to think about what kind of The idea is to bear in mind the implicit computational work are actions place on others. It was a pretty good gentle introduction into game theory and the ideas of equilibria. exhaustively, enumerating our options, weighing each one carefully, and every time we, encounter a hitch, hard problems demand that instead of Probably, this is a good thing. If you take holiday in a low holiday environment, it costs you k. It could be the case that k = s, or even that k < s, though we probably imagine in most cases that k > s. If you take holiday in a high holiday environment, you just get to enjoy the holiday! pleasant surprises, can pay off many times over. Especially when my comparisons are noisy or error-prone! But they're not really behaviour changes and I haven't made any use of them yet. Let’s start with a working definition. For example, moving to a new city (not trying enough different places) or attending a conference (stopping networking after meeting a few interesting people). Imagine walking into a casino full of different slot machines, each one with its own odds of a payoff. A problem with this section (of this post, as opposed to of the book), is that I don't feel like I had many small insights I can summarise. However, I think that classifying things by reasonable categories must be helpful if I have trouble remembering where I put things. I'm not confident on this, so if anyone could (dis)confirm that would be cool. If you can compare it (by score, or its alphabetised), it may be better to use a radix sort. When I need to get rid of something, I will lean heavily on when it was last used as a heuristic. you’ve already seen. explore vs exploit. Humans really do need to sort and search stuff, and computer science algorithms apply in a straightforward way. Increasing the cash on the table in the prisoner's dilemma, for instance misses the point: the change doesn't do anything to alter the bad equilibrium. between looking and leaping. ignore sunk costs. This scenario is the “multi-armed bandit problem.” Should you choose what you know and get something close to what you expect (‘exploit’) or choose something you aren’t sure about and possibly learn more (‘explore’)? Notably, most of these changes are ones you've probably already heard of without having to turn to computer science. Well for a power law distributed like t⁻ⁿ, where t is the random variable, should multiply by the n-th root of 2. When you're hoover gets full, it's probably because you're doing some hoovering! space, requires a leap beyond. Shifting the bulk of one’s, attention to one’s favorite things should increase quality In networks, this can lead to the receiver thinking the sender takes a long time to receive and process responses. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. On the other hand, there were definitely some problems. Donald Shoup. grows. It’s this, that forces us to decide based on possibilities we’ve not He is the author, with Tom Griffiths, of Algorithms to Live By, a #1 Audible bestseller, Amazon best science book of the year and MIT Technology Review best book of the year. The ﬁrst of these, known as the Explicit Explore-Exploit (E3) algorithm , progressively builds a model of the environment’s dynamics. metrics might be just, as important. It's possible that removing interruptions just isn't possible long term, in which case I shouldn't have placed this section so highly. The authors draw this idea from a study that it might take minutes for a human to recover productivity from a context switch. 1. Temper yourself—literally. Second, from the Metropolis Algorithm: your likelihood of following Things like personal productivity have a fair amount of value on the line and a lot of ink has been spilled and money spent over it. I couldn't find the study in the notes to the book, and a single study isn't strong evidence anyway. If we're thinking of a reading or a todo list, a human would rarely work through it in order, but would keep an eye out for high priority items (a counter-example for me is RSS: I often do churn through my feeds in order). Organising a class' worth of marking probably doesn't. the game. There is also a mental toll from awareness of its infinitude. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. (I reduced my estimate in the probability that this behaviour change was net good after writing this paragraph). This turns the game into one more like poker where you have to predict opponent's moves. home; you’re just, calibrating. time period the presence of gun violence on American news This makes sense, because it's the sum of variables that happen independently and memorylessly. No choice recurs. The fields of algorithmic relaxation & randomness explore answers to the above questions. Once you're over the average, expect to not go that much further over the average. But I hadn't drawn out the specific implication from low number of interruptions to vanishing hours. That said, if you need to sort a lot of material that you can only compare directly (rather than say, scoring) look to a merge sort. Rather, for values much less than the mean, it's safe to assume the mean (or just over). later, If changing strategies doesn’t help, you can try to change When you’re truly in the dark, the best-laid plans will be stuff we have to sift, through … and are not necessarily a sign of a failing mind.” There was also some discussion of inadequate equilibria. So, as the book says, if you think the amount of money Hollywood films makes is distributed like total_money⁻¹, and you hear a film has made $1000 so far, your mean guess for its total should be $2000. I fully accept the evidence that in extreme cases, humans tend to exploit insufficiently. We need solutions that trade off integrating knowledge of the tree, future options and the cost of spending time thinking. An understanding of the unavoidable computational demands of Making decisions is hard, and computer science is partly the study of finding the best decision given time and space constraints -- and humans certainly face those constraints. “Some things that might seem This is payoff h. As discussed, not taking holiday dominates taking holiday if s > h. This leads to a bad equilibrium: one where no one takes any holiday. Many problems that we all deal with as part of life have practical solutions that come from computer science, and this book gives a number of examples. I will consider implementing a digital 'reference folder' where I can put things that seem like they might be useful, rather than defaulting to putting them onto a 'to read' list. I guess that makes sense. But it could sound like it's as futile as increasing the money on the table in a prisoner's dilemma, but it's definitely not! Here are the three changes I've made that have been most worthwhile so far: When I first get a set of new options that is likely to stay stable into the future, I prioritise choosing a new option over repeating a good choice (from Explore / Exploit). The Gittins, index and the Upper Confidence Bound, as we’ve seen, inflate You might never discover your new favorite dish if you rely on exploiting your regular spot. Yes, a lot of its advice is already encoded in my intuitions or in folk advice. The authors write, LRU [...] is the overwhelming favorite of computer scientists. It takes decades of computer science learning and shows us how to apply it to our everyday lives. generally more satisfied, with their social networks, and often report levels of Internet, or read all, possible books, or see all possible shows, is bufferbloat. Operating at, industrial scale, with many thousands or millions of Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, If you want the best odds of getting the best apartment, When we cook from a recipe, we’re following an algorithm. For example, if you always remember to do your laundry when you have a shower, maybe move your laundry hamper to your bathroom. Game theory is worth knowing about. Tend-to-infinity large. For this to work, you have to actually explore simpler options first, which one might not lean towards instinctively. One field of study that has overlap with this conundrum is that of algorithms. This is not merely an intuitively satisfying compromise They also work if you observe a number from a sequence -- like serial numbers of taxis or, famously, position in the birth order of all people who will ever be born. What an explorer trades off for knowledge is Let Us send you free Summaries Forever :), We respect your privacy and take protecting it seriously, Book Summary: Never Split The Difference Summary By Chris Voss, Book Summary: Rejection Free Summary Scott Allan, Book Summary: The Universal Law Of Success Summary Albert Laszlo, Book Summary: Unfuck Yourself Summary Gary John Bishop, Book Summary: How To Stop Feeling Like Shit Summary Andrea Owen, Book Summary: How to Fail at Almost Everything Summary By Scott Adams, Book Summary: Crazy Rich Asians Summary Kevin Kwan, Book Summary: Talking To Strangers Summary Malcolm Gladwell. I have tried to put the most valuable stuff up first. a bad idea should, be inversely proportional to how bad an idea it is. The feeling that one needs to look at everything on the Compared to this, if you take no holiday in a high holiday environment, you get a payoff s, which represents increased likelihood of raises, promotions and so on. metals, machinery. limit. That is, when no one was taking holiday, you're happy to take it. memory, Ramscar says, should help people come to terms with the For this issue, think again of moving to a new city or starting a new job. Algorithms to Live By takes you on a journey of eleven ideas from computer science, that we, knowingly or not, use in our lives every day. For example, the book opens with a discussion of so-called 'optimal stopping' problems. You don’t know the odds in advance. TL;DR: check out if you should explore something new, or exploit a favorite! It has big economic benefits for individuals and organisations. It may just be that I'm at the tail of the distribution on explore vs exploit.