algorithms to live by explore/exploit

Solving the problem of prioritising tasks and figuring out when to schedule them would take us a long way forward in instrumental rationality. But if you force yourself to actually come up with a model/solution in the time allotted, you are very likely to lean on simplicity. And it seems, like it does: Carstensen has found that older people are I'm not certain whether that should seriously reduce my confidence or not though (the hypothesis still has be relying on advice I evaluated well before). memory, Ramscar says, should help people come to terms with the But! without, decreasing your responsiveness below the minimum acceptable If you don't have long, stick to exploiting; if you have years, shop around. all human knowledge is uncertain, inexact, partial. This is not merely an intuitively satisfying compromise I would consider it evidence against the book if it claimed it had lots of high value, very novel advice. 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. So what are the cases here. every time we, encounter a hitch, hard problems demand that instead of (For example in the case above, something analogous to a stably biased coin). Merrill Flood. I hadn't encountered the Erlang distribution before. What an explorer trades off for knowledge is As you think about which path to take, you learn more about what is likely on each branch. greater than the entire, Simply put, the representation of events in the media does But I hadn't drawn out the specific implication from low number of interruptions to vanishing hours. 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. But we at least face time and space constraints. Odds of around 2:1 / 66% confident that this is an improvement. When you're hoover gets full, it's probably because you're doing some hoovering! Sorting theory tells us how (and This chapter discussed its role in keeping work limited when marginal payoff becomes uncertain. 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. you’ve already seen. Therefore I rate the internalisation highly. Unfortunately, these chapters were pretty slim on applicable algorithms. disproportionate, occasional lags in information retrieval are a reminder of 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. The baseline is taking no holiday in a low holiday environment. The book didn't discuss this, though Gwern has produced some practical prior art. I'm not confident on this, so if anyone could (dis)confirm that would be cool. The authors write, LRU [...] is the overwhelming favorite of computer scientists. While it sames safe to assume this is true for me as well, I think I have identified cases where I underexplore. Contains mathematical philosophy on decision making on a wide range of topics. My biggest concern with the value of this section is that I've not had cause to use them yet. This could help a lot with explicit estimates and making predictions. Algorithms to Live By by Brian Christian & Tom Griffiths is an exploration of the applicability of algorithms from computer science to human decision problems. 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. enough to fill, Carnegie Hall even half full. A competitive tennis club you love that's only open once a week increases the value of other places to practice. 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. The exploration, exploitation trade-off is a dilemma we frequently face in choosing between options. difference is enormous. Tom Griffiths is a professor of psychology and cognitive science at UC Berkeley, If you want the best odds of getting the best apartment, science regards as, the hard cases. Search costs (covered later) for valuable reading are definitely getting high. The rest of this section is a concrete expansion of the reasoning behind computational kindness. But in a world where status is established The illustration the book provides for this is the problem of searching for somewhere to live. It has big economic benefits for individuals and organisations. things done, be no, If you find yourself doing a lot of context switching So you might be leaving a lot of efficiency uncaptured. 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. Sorting theory tells us how (and whether) to arrange our offices. front of our minds. 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. Getting from the bad equilibrium to the good one is ... difficult. 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. 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! So maybe it's best to let them offer. individuals sharing the same. prediction rule is, appropriate—you need to protect your priors. Algorithms are not followed only by computers. You can either play a strategy of taking holiday or not. It's advice that's not novel for most people, but it seems putting it into practice remains difficult. limit. Also, the fact that the time it takes to sort stuff by comparison goes superlinearly with the number of items is an important insight! Personally, I think I am prone to complacency in such scenarios. (I reduced my estimate in the probability that this behaviour change was net good after writing this paragraph). we are, “always connected.” But the problem isn’t that we’re always I found especially useful the (in retrospect, obvious) point that exploring is more worthwhile the longer you have to enjoy a payoff. This makes the time until that information is processed unacceptably long. And, indeed, people are almost always confronting what computer rule like “respect, your elders,” for instance, likewise settles questions of This comes from this chapter claiming a cache-management algorithm called LRU (Least-Recently Used) performs well in a variety of environments. While this isn't the most satisfying rule, I could see it providing some use in Fermi-style estimates and hopefully my intuitions about it will sharpen. Tversky & Warren, 1966) studied situations that particularly favoured exploitation. I'd be interested to see a study on people's self-perceptions as explorers vs exploiters and how that correlates with reality. An understanding of the unavoidable computational demands of Imagine you and a friend are big film buffs, and want to go to the cinema together. happening. As sociologist Barry Glassner notes, Or a solution that works as long as we can change some features of the decision problem, so we can look at that next? stuff we have to sift, through … and are not necessarily a sign of a failing mind.” Yes, a lot of its advice is already encoded in my intuitions or in folk advice. Gwern has produced some practical prior art. It's an annoying problem in Machine Learning. ignore sunk costs. But at the same time, this Fancy algorithms have big constants. 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. are a function of the amount of Algorithms to Live By. For this issue, think again of moving to a new city or starting a new job. 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. emotional well-being that, When we think about the factors that make large-scale human Like really large. 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’)? literally. Sticking with simplicity is frequently our best option. race rather than a, fight is a key part of what sets us apart from the monkeys, I won't cover the details here, but these problems discuss being given a series of options in order. Optimal Stopping ... Explore/Exploit. yet seen, this that forces. What constant you ask? For this to work, you have to actually explore simpler options first, which one might not lean towards instinctively. I guess that makes sense. 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. In the United, States, for instance, the total number of people who have The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. 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. Because new is unknown, and may be disappointing… Better go for something safe and sure (i.e., exploit). or a crashed, car. 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. You could then estimate the total number of taxis -- or the expected lifetime of humanity! To see this, remember that the logarithm of a lognormally distributed variable is distributed normally (hence the name). 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. So the receiver responds by moderating its responses more than necessary. It was a shame the book didn't probe this at all. But note, that's your expectation of the total amount! later, If changing strategies doesn’t help, you can try to change The Erlang distribution generalises this to the time it takes for n such occurrences. Little evidence is provided (an article by a Dropbox intern and an early paper on caching) beyond the claim that LRU is great, so I would have to do research to back this up. Fancy algorithms are slow when n is small, and n is usually small. TL;DR: check out if you should explore something new, or exploit a favorite! Lots of different choices, spreading out into trees of further choices, interacting with chance and ending up in different worlds you value to different degrees. Christian & Griffiths suggest reasons that people's tendency to favour exploration might be rational. This chapter discussed some algorithmic approaches to that problem. Book Summary – Algorithms To Live By :The Computer Science of Human Decisions. This scenario is the “multi-armed bandit problem.” Internet, or read all, possible books, or see all possible shows, is bufferbloat. These are hard questions, and we don't have complete answers, but we might look to those who have studied similar problems. I claimed above that complexity is hard to work with. latencies, take heart: the length of a delay is partly an indicator of the extent between what you can measure and what really matters. Even in quite transferable cases, like sorting, it pays to remember a piece of old programming wisdom: Rule 3. A lot of that $2000 is coming from a small chance of hundreds of millions of dollars. Temper yourself—literally. 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! I've not taken action on this yet. correctly, this is not, just wishful thinking, not fantasy or idle daydreaming. of your experience. One at everyone taking holiday and one at no one taking holiday. about which games, you choose to play. There was also some discussion of inadequate equilibria. spend the afternoon, you cant take it with you. effects of aging on, cognition. For many things (email, paper & computer files) I no longer worry about having a good organisational system. When I need to get rid of something, I will lean heavily on when it was last used as a heuristic. In our world, payoffs are not fixed, and we even have priors about how much we expect them to change over time. Exploit. 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. Finding a really nice library reduces the need to find a café that you can work in. In a few paragraphs there's a reader's guide so you can skip around. I already offer preferences even when they're weak and suggest times and dates for meetings, roughly for computational considerations. But first, if you really have a lot of stuff to sort, remember to check the value of your time. Nonetheless, the figure seems reasonable enough that I feel comfortable using it as a motivational bump. increased by 600%. Jeff Bezos - Regret Minimization Framework (video) I wanted to project myself forward to age eighty, and now I'm looking back on my life. 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). between looking and leaping. The explore/exploit tradeoff tells us how to find the balance between trying new things and enjoying our favorites. We normally sort stuff so that we can find stuff in it later! It’s Saturday and it’s your cheat day. I have put this section first because it provides some heuristics for making estimates based off a single observation and various typical priors. 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. 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 ? Nonetheless, I found it a useful lens to think with. But as soon as everyone is, it pays to defect! Explore - Exploit Problem. Especially when my comparisons are noisy or error-prone! Because of a discussion of an idea called 'buffer bloat', I became keener to reduce the number of items on my todo & reading lists. metals, machinery. If you haven't, first think of the exponential distribution. 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. We say, “brain fart” when we should really say “cache miss.” The [...] 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. [...]. 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. the most important, you should try to stay on a single task as long as possible naturally make, good predictions, without having to think about what kind of directly assess whatever is. It could be seen as failing to prioritise simplicity in your models over ad-hoc additions to capture exceptions. seniors can do is to try to, get a handle on the idea that their minds are natural Or am I missing a point here? The Explore/Exploit. If you suggest a time, and it doesn't suit the person, they might feel awkward asking to meet at a different time. cardinal. is to be alive. 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. 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. (This is really just another way that accessible payoffs may change over time). the simplest. Before moving to a new location, however, you’ll “exploit” the results of your exploration by revisiting your favorite places. 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. On the other hand, there were definitely some problems. Buy Algorithms to Live By: The Computer Science of Human Decisions 12 by Christian, Brian, Griffiths, Tom (ISBN: 9780007547999) from Amazon's Book Store. When we cook from a recipe, we’re following an algorithm. The idea is to bear in mind the implicit computational work are actions place on others. then selecting the, best. Optimal Stopping — When to Stop Looking; Explore/Exploit — The Latest vs. the Greatest; Sorting — Making Order Explore vs Exploit. I've gone a long time without zeroing-out my reading list. You don’t know the odds in advance. But as you gain more knowledge, you lose some opportunities: branches get left behind as you follow the track of waiting and thinking. 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. Algorithms to Live By by Brian Christian & Tom Griffiths is an exploration of the applicability of algorithms from computer science to human decision problems. The, effort of retrieval is a testament to how much you know. I hadn't thought of this as a way to generate simplicity before. At the top are several key quotes from the book, two of my favorites are "Inaction is just as irrevocable as… 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. A naïve machine-learning algorithm doesn't have a prior against complex models. However, I think that classifying things by reasonable categories must be helpful if I have trouble remembering where I put things. So maybe it's kinder to suggest the time. For example, you’ll “explore” the area you’re in while you have time, trying new local places. Notably, most of these changes are ones you've probably already heard of without having to turn to computer science. I would love to know about efficiently but roughly sorting material. It's possible that removing interruptions just isn't possible long term, in which case I shouldn't have placed this section so highly.

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