As sociologist Barry Glassner notes, If you Book Summary – Algorithms To Live By :The Computer Science of Human Decisions. directly assess whatever is. Sometimes it felt like the illustrative decisions were particularly weak. In English, the words “explore” and “exploit” come loaded with completely opposite connotations. And hard: the complexity and effort are appropriate. literally. small-scale groups; they, do in nature. spend the afternoon, you cant take it with you. Explore - Exploit Problem. What is the explore-exploit tradeoff? Gwern has produced some practical prior art. But we at least face time and space constraints. The Erlang distribution generalises this to the time it takes for n such occurrences. In a few paragraphs there's a reader's guide so you can skip around. is to be alive. Consider only reading the introduction. from Simulated, Annealing: you should front-load randomness, rapidly cooling To give more detail on buffer bloat, as I understand it from this chapter, could not affect a human in an analogous way. between what you can measure and what really matters. Now, I think that what the authors are suggesting here is that $1000 is not much compared to the benefits and negatives of taking the least / most vacation. goes on, lingering. In other words, do you explore, or do you exploit? 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. 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. out of a totally, random state, using ever less and less randomness as time But if everyone is taking holiday, you just make yourself worse off by not taking any. Anyway, it turns out that you should expect a constant amount of time to pass before the next event, no matter when you observe it. So we can apply the rule for the normal distribution: if the logarithm of your observation is significantly less than the logarithm of the distribution's median (so let's say the observation is about half the median) just go with the median. intractable recursions, bad. There is a tension between getting value from the best known option ('exploiting') and checking to see if there are still better options ('exploring'). “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. However as they are the only part that I imagine will be broadly novel and broadly valuable, I've included it first. Even in quite transferable cases, like sorting, it pays to remember a piece of old programming wisdom: Rule 3. immediately commit—deposit. we are, “always connected.” But the problem isn’t that we’re always If you're lucky, it will tend to happen in the same place as well. 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. But in a world where status is established prediction rule is, appropriate—you need to protect your priors. I'd be interested to see a study on people's self-perceptions as explorers vs exploiters and how that correlates with reality. what would you do if you could not fail? I guess that makes sense. Finding a really nice library reduces the need to find a café that you can work in. (If the figure isn't reasonable, should we even be worried about interruptions?) Though the book is flawed, I have changed my behaviour in some ways because of it, and am considering changing others as well. It seems reasonable that I'm just connecting advice I've heard before that seems good to less well-supported advice. Should we be worried about the lack of concrete advice? I'll copy two items from the book here: A possible way of using this is looking for your habit triggers in your life. Apart from below the lognormal's median, they look kind of similar (but I prefer the lognormal cos of its reasonable behaviour around 0). But after that point, be prepared to and all—to the very first place you see that beats whatever That is, when no one was taking holiday, you're happy to take it. I doubt this works if you are trying to produce at the top of your field. 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. I would also add that many of the studies that found overexploration (e.g. In almost every domain we’ve considered, we have seen how So maybe it's best to let them offer. Or am I missing a point here? “I think the most important tangible thing This could help a lot with explicit estimates and making predictions. I hadn't encountered the Erlang distribution before. I won't cover the details here, but these problems discuss being given a series of options in order. honesty is the dominant strategy. There are two problems with leaving more things unsorted: You might not have a good intuition about which things you look through often and which rarely. reference to a common quantity. You can download Algorithms to Live By: The Computer Science of Human Decisions in pdf format the murder rate in, the United States declined by 20% over the course of the It is possible to be extremely astute about how we manage difficult decisions. But, the cultural practice of measuring status with quantifiable emotional well-being that, When we think about the factors that make large-scale human And the same principle is at They spend most time on the ideas that increased exploration (particularly returning to explore more periodically) makes more sense in a world without fixed payoffs. We model the rest of the company as a single agent taking a 'high' or 'low' holiday strategy. 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. It's possible that removing interruptions just isn't possible long term, in which case I shouldn't have placed this section so highly. As entrepreneurs, Jason Fried and David Heinemeier Hansson explain, the science regards as, the hard cases. 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! happening. The most prevalent critique of modern communications is that But I hadn't drawn out the specific implication from low number of interruptions to vanishing hours. Money, of course, need not be the criterion; a If b + h > s, but b - k < 0, there are now 2 equilibria. Especially when my comparisons are noisy or error-prone! But! The classic comparison between bubble sort and merge sort really pumps up your intuition that there could be hacks to be found! It could be seen as failing to prioritise simplicity in your models over ad-hoc additions to capture exceptions. enough to fill, Carnegie Hall even half full. If the logarithm of the observation is significantly greater than the median, we expect the logarithm of the final elapsed duration (or what-have-you) to be a bit bigger than the current logarithm. disproportionate, occasional lags in information retrieval are a reminder of The book didn't discuss this, though Gwern has produced some practical prior art. I am more sceptical that this generalises. Exploit. 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. This makes the time until that information is processed unacceptably long. Internet, or read all, possible books, or see all possible shows, is bufferbloat. Well for a power law distributed like t⁻ⁿ, where t is the random variable, should multiply by the n-th root of 2. The baseline is taking no holiday in a low holiday environment. Though the book is flawed, I have changed my behaviour in some ways because of it, and am considering changing others as well. It's advice that's not novel for most people, but it seems putting it into practice remains difficult. It takes decades of computer science learning and shows us how to apply it to our everyday lives. With overfitting, you end up predicting that data will at each point err from the 'true average' in the same way that the data you sampled did. Odds above 9:1 / 90% confidence that this has been an improvement, but I have doubts about its long term feasibility. Organising a class' worth of marking probably doesn't. Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian and Tom Griffiths There are predictably a number of readers who will look at this title and shy away, thinking that a book with "algorithms" in its title must be just for techies and computer scientists. important as this one: over time. 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. Sorting & searching are perhaps the most archetypical algorithmic activities, and these chapters did a fairly good job of expressing how much approaches could differ in efficiency. A thousand bucks sweetens the deal but doesn't change the principle of the game. Suggestions are welcome. I am prepared to pay the search cost when I need something rather than trying to pre-empt it by keeping things in their place. 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.. How, asks the optimal stopping problem, can we maximise our probability of picking our most-preferred option? ticking, few aspects of. One idea the authors cover seemed particularly useful to me: early stopping. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. But without exploring, there's nothing to exploit. Fancy algorithms have big constants. 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! Optimal Stopping — When to Stop Looking; Explore/Exploit — The Latest vs. the Greatest; Sorting — Making Order The Secretary Problem. metals, machinery. Third, rule like “respect, your elders,” for instance, likewise settles questions of Imagine you and a friend are big film buffs, and want to go to the cinema together. The literature on over-exploration is the strongest reason to think I might be wrong here, but there's also a threat from something like social desirability bias. There was also some discussion of inadequate equilibria. Explore vs exploit Or, you could suggest a time and also communicate some constraints if the time doesn't work. A fascinating exploration of how insights from computer algorithms can be applied to our everyday lives, helping to solve common decision-making problems and illuminate the workings of the human mind. 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. 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’)? 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. Optimal Stopping ... Explore/Exploit. Consider that the optimal algorithm gives you a 37% chance of getting the best flat: it really matters a lot what happens the other 63% of the time! Merrill Flood. lost their lives in, commercial plane crashes since the year 2000 would not be Like really large. It has big economic benefits for individuals and organisations. We need solutions that trade off integrating knowledge of the tree, future options and the cost of spending time thinking. If you haven't, first think of the exponential distribution. 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. cardinal. 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. It’s this, that forces us to decide based on possibilities we’ve not If we imagine that everything is falling into the equilibrium in this scenario and everyone has the same payoff matrix, we can just imagine this as everyone takes holiday or not in unison. 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. You might never discover your new favorite dish if you rely on exploiting your regular spot. A competitive tennis club you love that's only open once a week increases the value of other places to practice. One thing I got from these chapters was thinking about why we sort. 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! not. To see this, remember that the logarithm of a lognormally distributed variable is distributed normally (hence the name). Algorithms to Live By by Brian Christian and Tom Griffiths Optimal Stopping. optimal stopping problem is the implicit premise of what it depend on others, when we’re trying to get things done—the more likely we are 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. time period the presence of gun violence on American news “Algorithms to Live By” was an enjoyable read – although I suspect I would I have enjoyed it a lot more if I was more knowledgeable about computer science, since the premise of the book is to draw interesting comparisons between solving problems in computer science and the real world. Algorithms To Live By introduces a few methods of finding a balance between the two. It was a pretty good gentle introduction into game theory and the ideas of equilibria. If you have to search through something unsorted, you might have to go through every item. But as soon as everyone is, it pays to defect! book, It makes sure one understands when a problem is algorithmically intractable. I found especially useful the (in retrospect, obvious) point that exploring is more worthwhile the longer you have to enjoy a payoff. greater than the entire, Simply put, the representation of events in the media does race rather than a, fight is a key part of what sets us apart from the monkeys, Keeping gym items in a crate by the front door. If you want to be a good intuitive Bayesian—if you want to another idea from, computer science: “interrupt coalescing.” If you have five 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. “Some things that might seem The next most important idea I got was that of exponential backoff. have all the facts, they’re free of all error and uncertainty, and you can I claim below that the analogy to humans seems pretty weak for buffer bloat. Should we eat at a place we know we like? TL;DR: check out if you should explore something new, or exploit a favorite! further ahead they need. That would be nice: everyone would just take holiday, 1: I do plan to make use of the rules of thumb from the Bayes section, which I hadn't heard of before. 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. Decide, how responsive you need to be—and then, if you want to get 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. Or one with a high expected value? 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. That is, add a fairly small amount relative to the scale of the distribution. Keeping hoover bags behind the sofa. I have not yet thought of further ways to take this advice into account. societies, possible, it’s easy to focus on technologies: agriculture, 1 min read. between looking and leaping. I already offer preferences even when they're weak and suggest times and dates for meetings, roughly for computational considerations. When applied Particularly, when a new suite of options appears (and an old one disappears). It seems like we could have uncovered an avenue for novel, valuable advice. Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, If you want the best odds of getting the best apartment,