If there's one city in the world where it's hard to find a place to buy or rent, it's Sydney. And if you've tried to find a home here recently, you're familiar with the problem. Every time you walk into an open house, you get some information about what's out there and what's on the market, but every time you walk out, you're running the risk of the very best place passing you by. So how do you know when to switch from looking to being ready to make an offer?
Ako postoji i jedan grad na svijetu u kojem je teško kupiti ili iznajmiti stan, onda je to Sydney. Ako ste nedavno pokušali pronaći dom, znate o čemu pričam. Svaki put kad uđete u neku kuću, doznate neke informacije o tome što se događa, čega ima na tržištu. I svaki put kad iziđete van, suočavate se s rizikom da ćete propustiti najbolju ponudu. Pa kako da znate kada prestati gledati i dati ponudu za najam prostora?
This is such a cruel and familiar problem that it might come as a surprise that it has a simple solution. 37 percent.
To je tako okrutan i poznat problem pa će vam možda biti iznenađenje da mu je rješenje vrlo jednostavno. 37 posto.
(Laughter)
(Smijeh)
If you want to maximize the probability that you find the very best place, you should look at 37 percent of what's on the market, and then make an offer on the next place you see, which is better than anything that you've seen so far. Or if you're looking for a month, take 37 percent of that time -- 11 days, to set a standard -- and then you're ready to act.
Ako želite povećati vjerojatnost pronalaženja najboljeg mjesta, trebate obratiti pažnju na 37% onoga što se nudi, a zatim dati ponudu za sljedeći prostor koji ste našli, a koji je bolji od drugih koje ste vidjeli do tada. Ili ako tražite mjesec dana, uzmite 37% tog vremena -- 11 dana, da budemo precizni -- i spremni ste na djelovanje.
We know this because trying to find a place to live is an example of an optimal stopping problem. A class of problems that has been studied extensively by mathematicians and computer scientists.
Znamo za to, jer traženje prostora za život je primjer problema optimalnog odustajanja. To je vrsta problema koju intenzivno proučavaju matematičari i računalni znanstvenici.
I'm a computational cognitive scientist. I spend my time trying to understand how it is that human minds work, from our amazing successes to our dismal failures. To do that, I think about the computational structure of the problems that arise in everyday life, and compare the ideal solutions to those problems to the way that we actually behave. As a side effect, I get to see how applying a little bit of computer science can make human decision-making easier.
Ja sam računski kognitivni znanstvenik. Pokušavam razumjeti kako radi ljudski um u trenucima najvećih uspjeha i tužnih grešaka. Razmišljam o računskoj strukturi problema koji nastaju u svakodnevnom životu i uspoređujem načine na koji se ponašaju u stvarnom životu s njihovim idealnim rješenjima. Kao posljedicu mogu vidjeti kako primjena malo računalne znanosti može ljudske odluke učiniti lakšima.
I have a personal motivation for this. Growing up in Perth as an overly cerebral kid ...
Za to imam osobnu motivaciju. Odrastajući u Perthu, kao dijete koje je previše razmišljalo --
(Laughter)
(Smijeh)
I would always try and act in the way that I thought was rational, reasoning through every decision, trying to figure out the very best action to take. But this is an approach that doesn't scale up when you start to run into the sorts of problems that arise in adult life. At one point, I even tried to break up with my girlfriend because trying to take into account her preferences as well as my own and then find perfect solutions --
pokušao sam se ponašati na način za koji sam mislio da je racionalan, prosuđujući svaku odluku, pokušavajući shvatiti koja je najbolja aktivnost koju trebam poduzeti. To je pristup koji se ne može primijeniti na velikim problemima, s onima na koje nailazimo u životu odraslih. U jednom sam trenutku pokušao prekinuti s mojom djevojkom jer su me pokušaji da zadovoljim i njene i svoje sklonosti na najbolji mogući način --
(Laughter)
(Smijeh)
was just leaving me exhausted.
potpuno iscrpljivali.
(Laughter)
(Smijeh)
She pointed out that I was taking the wrong approach to solving this problem -- and she later became my wife.
Ona je naglašavala da sam krenuo pogrešnim pristupom u rješavanje problema -- kasnije smo se oženili.
(Laughter)
(Smijeh)
(Applause)
(Pljesak)
Whether it's as basic as trying to decide what restaurant to go to or as important as trying to decide who to spend the rest of your life with, human lives are filled with computational problems that are just too hard to solve by applying sheer effort. For those problems, it's worth consulting the experts: computer scientists.
Bez obzira je li banalno poput izbora restorana ili važno poput izbora s kim ćete provesti ostatak života, ljudski životi su ispunjeni računskim problemima koje je teško riješiti primjenom malog napora. Za takve probleme vrijedi savjetovati se sa stručnjacima: računalnim znanstvenicima.
(Laughter)
(Smijeh)
When you're looking for life advice, computer scientists probably aren't the first people you think to talk to. Living life like a computer -- stereotypically deterministic, exhaustive and exact -- doesn't sound like a lot of fun. But thinking about the computer science of human decisions reveals that in fact, we've got this backwards. When applied to the sorts of difficult problems that arise in human lives, the way that computers actually solve those problems looks a lot more like the way that people really act.
Ako tražite savjet o životu, računalni znanstvenici vjerojatno nisu prvi izbor za razgovor. Žive život poput računala -- stereotipno određen, iscrpljujuć i precizan -- što ne zvuči baš zabavno. No razmišljati o računalnoj znanosti ljudskih odluka otkriva da su, u stvari, stvari obrnute. Kad se primjenjuje na zahtjevne probleme koji se pojavljuju u ljudskim životima, način na koji računala zapravo rješavaju te probleme više liči na način na koji se ljudi zaista ponašaju.
Take the example of trying to decide what restaurant to go to. This is a problem that has a particular computational structure. You've got a set of options, you're going to choose one of those options, and you're going to face exactly the same decision tomorrow. In that situation, you run up against what computer scientists call the "explore-exploit trade-off." You have to make a decision about whether you're going to try something new -- exploring, gathering some information that you might be able to use in the future -- or whether you're going to go to a place that you already know is pretty good -- exploiting the information that you've already gathered so far. The explore/exploit trade-off shows up any time you have to choose between trying something new and going with something that you already know is pretty good, whether it's listening to music or trying to decide who you're going to spend time with. It's also the problem that technology companies face when they're trying to do something like decide what ad to show on a web page. Should they show a new ad and learn something about it, or should they show you an ad that they already know there's a good chance you're going to click on?
Uzmite, na primjer, pokušaj odabira restorana u koji ćete ići. To je problem koji ima osobitu računsku strukturu. Imate nekoliko mogućnosti, vi ćete izabrati jednu od njih, a sutra ćete se ponovno susresti s istom odlukom. U takvoj situaciji susrećete se s onim što računalni znanstvenici nazivaju "balans traženja i korištenja". Morate donijeti odluku o tome hoćete li isprobati nešto novo -- tražiti, prikupljati neke informacije koje vam mogu biti od koristi u budućnosti -- ili ćete ići na neko mjesto koje već znate da je dobro -- koristeći informacije koje ste prikupili ranije. Balans traženja i korištenja se pojavljuje svaki put kad trebamo izabrati između istraživanja nečeg novog i korištenja nečeg za što već znamo da je prilično dobro, bez obzira radi li se o slušanju glazbe ili pokušaju da odredimo s kim ćemo provesti vrijeme. To je problem s kojim se susreću tehnološka poduzeća, kad pokušavaju odlučiti koji oglas se treba pojaviti na internet stranici. Trebaju li pokazati novi oglas i naučiti nešto iz toga, ili trebaju pokazati oglas za koji već znaju da ima dobre mogućnosti da se na njega klikne.
Over the last 60 years, computer scientists have made a lot of progress understanding the explore/exploit trade-off, and their results offer some surprising insights. When you're trying to decide what restaurant to go to, the first question you should ask yourself is how much longer you're going to be in town. If you're just going to be there for a short time, then you should exploit. There's no point gathering information. Just go to a place you already know is good. But if you're going to be there for a longer time, explore. Try something new, because the information you get is something that can improve your choices in the future. The value of information increases the more opportunities you're going to have to use it.
Tijekom prošlih 60 godina računalni znanstvenici su značajno napredovali u razumijevanju balansa traženja i korištenja, a njihovi rezultati nude neke iznenađujuće uvide. Kad nastojite odlučiti u koji ćete restoran ići, najprije se trebate zapitati koliko ćete dugo još biti u tom gradu. Ako ćete biti samo još kratko vrijeme, trebali biste se služiti korištenjem. Nema smisla skupljati informacije. Samo otiđite u restoran za koji već znate da je dobar. Ako ćete biti tamo duže vrijeme, istražujte. Iskušajte nešto novo, jer su informacije koje ćete prikupiti nešto što će unaprijediti donošenje odluka u budućnosti. Vrijednost informacija se povećava s povećanjem broja prilika u kojima ćete ih moći koristiti.
This principle can give us insight into the structure of a human life as well. Babies don't have a reputation for being particularly rational. They're always trying new things, and you know, trying to stick them in their mouths. But in fact, this is exactly what they should be doing. They're in the explore phase of their lives, and some of those things could turn out to be delicious. At the other end of the spectrum, the old guy who always goes to the same restaurant and always eats the same thing isn't boring -- he's optimal.
Ovo načelo nam također daje uvid u strukturu ljudskog života. Male bebe nisu baš poznate po tome da su posebno racionalne. Stalno iskušavaju nove stvari i, znate, pokušavaju ih ugurati u usta. No, to je, zapravo, upravo ono što bi i trebale raditi. One su u istraživačkoj fazi svojih života. Neke od tih stvari se mogu pokazati slasnima. Na drugom kraju spektra, starac koji uvijek ide u isti restoran i uvijek naručuje isto jelo se ne dosađuje -- on optimizira.
(Laughter)
(Smijeh)
He's exploiting the knowledge that he's earned through a lifetime's experience. More generally, knowing about the explore/exploit trade-off can make it a little easier for you to sort of relax and go easier on yourself when you're trying to make a decision. You don't have to go to the best restaurant every night. Take a chance, try something new, explore. You might learn something. And the information that you gain is going to be worth more than one pretty good dinner.
On koristi znanje koje je skupio tijekom života. Općenito govoreći, znanje o balansu traženja i korištenja vam može olakšati, opustiti vas, da vam bude lakše prilikom donošenja odluka. Ne morate ići u najbolji restoran svaku večer. Riskirajte, probajte nešto novo, istražujte. Možda ćete nešto naučiti. Informacija koju prikupite će biti puno važnija nego jedna jako dobra večera.
Computer science can also help to make it easier on us in other places at home and in the office. If you've ever had to tidy up your wardrobe, you've run into a particularly agonizing decision: you have to decide what things you're going to keep and what things you're going to give away. Martha Stewart turns out to have thought very hard about this --
Računalna znanost nam također može pomoći i olakšati stvari, bilo kod kuće ili u uredu. Ako ste ikad trebali pospremiti ormare, naletjeli ste na posebno očajan zahtjev: morali ste odlučiti koju ćete odjeću zadržati, a koju ćete dati nekome. Martha Stewart je jako puno razmišljala o tome --
(Laughter)
(Smijeh)
and she has some good advice. She says, "Ask yourself four questions: How long have I had it? Does it still function? Is it a duplicate of something that I already own? And when was the last time I wore it or used it?" But there's another group of experts who perhaps thought even harder about this problem, and they would say one of these questions is more important than the others. Those experts? The people who design the memory systems of computers. Most computers have two kinds of memory systems: a fast memory system, like a set of memory chips that has limited capacity, because those chips are expensive, and a slow memory system, which is much larger. In order for the computer to operate as efficiently as possible, you want to make sure that the pieces of information you want to access are in the fast memory system, so that you can get to them quickly. Each time you access a piece of information, it's loaded into the fast memory and the computer has to decide which item it has to remove from that memory, because it has limited capacity.
i ima prilično dobrih savjeta. Kaže: "Pitajte se četiri pitanja: Koliko dugo to već imam? Može li se koristiti? Je li slično nečemu što također već imam? Kad sam zadnji put to obukla?" No, postoji i druga grupa stručnjaka koji su se još više udubili u ovaj problem, i oni bi rekli da je jedno od tih pitanja puno važnije od ostalih. Ovi stručnjaci? Ljudi koji su osmislili sustave računalnog pamćenja. Većina računala ima dva sustava pamćenja: brzi sustav pamćenja, poput kompleta memorijskih čipova ograničenog kapaciteta jer su ti čipovi skupi, i spori sustav pamćenja koji je puno veći. Kako bi računalo radilo što djelotvornije, želite biti sigurni da su dijelovi informacija kojima želite pristupiti u brzom sustavu pamćenja, kako biste brzo došli do njih. Svaki put kad posegnete za dijelom informacije, ona se učitava u brzu memoriju, a računalo treba odlučiti koji dio mora maknuti iz te memorije jer je ona ograničenog kapaciteta.
Over the years, computer scientists have tried a few different strategies for deciding what to remove from the fast memory. They've tried things like choosing something at random or applying what's called the "first-in, first-out principle," which means removing the item which has been in the memory for the longest. But the strategy that's most effective focuses on the items which have been least recently used. This says if you're going to decide to remove something from memory, you should take out the thing which was last accessed the furthest in the past. And there's a certain kind of logic to this. If it's been a long time since you last accessed that piece of information, it's probably going to be a long time before you're going to need to access it again. Your wardrobe is just like the computer's memory. You have limited capacity, and you need to try and get in there the things that you're most likely to need so that you can get to them as quickly as possible. Recognizing that, maybe it's worth applying the least recently used principle to organizing your wardrobe as well. So if we go back to Martha's four questions, the computer scientists would say that of these, the last one is the most important.
Tijekom godina računalni znanstvenici su pokušali drugačije strategije za odlučivanje o tome što maknuti iz brze memorije. Pokušali su ideje poput slučajnog odabira ili primjenu načela koje se naziva "prvi unutra, prvi van", koji zahtijeva da se makne onaj dio koji je najduže bio u memoriji. No, najdjelotvornija je strategija koja se usredotočila na dijelove koji nisu bili korišteni u bliskoj prošlosti. Ona kaže da, ako želite nešto maknuti iz pamćenja, onda to trebate napraviti s onim dijelom koji je korišten daleko u prošlosti. Postoji određena logika za to. Prošlo je dosta vremena otkad ste zadnji put pristupili tom dijelu informacije pa je vjerojatno da će proći dugo vremena prije nego ćete ga ponovno trebati. Vaš ormar je baš poput računalne memorije. Imate ograničeni kapacitet i trebate pokušati u njega ugurati one stvari koje ćete najvjerojatnije trebati kako biste do njih došli što je prije moguće. Svjesni toga, možda vrijedi primjeniti načelo nedavnog korištenja i za organizaciju ormara. Ako se vratimo na četiri pitanja koja je postavila Martha, računalni znanstvenici bi rekli da je od njih posljednje pitanje najvažnije.
This idea of organizing things so that the things you are most likely to need are most accessible can also be applied in your office. The Japanese economist Yukio Noguchi actually invented a filing system that has exactly this property. He started with a cardboard box, and he put his documents into the box from the left-hand side. Each time he'd add a document, he'd move what was in there along and he'd add that document to the left-hand side of the box. And each time he accessed a document, he'd take it out, consult it and put it back in on the left-hand side. As a result, the documents would be ordered from left to right by how recently they had been used. And he found he could quickly find what he was looking for by starting at the left-hand side of the box and working his way to the right.
Ta ideja o organizaciji stvari tako da su stvari koje ćete najvjerojatnije trebati najdostupnije, može se primijeniti i u vašem uredu. Japanski ekonomist Yukio Noguchi je izmislio sustav arhiviranja koji ima upravo to svojstvo. Započeo je s kartonskom kutijom u koju je stavio dokumente s lijeve strane na desnu. Svaki put kad je dodao dokument, pomaknuo bi već posložene dokumente i dodao taj dokument u kutiju s lijeve strane. I svaki put kad bi trebao neki dokument, izvukao bi ga van, proučio ga i vratio u kutiju s lijeve strane. Posljedica je da su dokumenti poslagani slijeva na desno prema tome koliko nedavno su korišteni. Shvatio je da može brzo pronaći ono što traži ako krene tražiti s lijeve strane kutije i nastavi prema desnoj strani.
Before you dash home and implement this filing system --
Prije nego otrčite kući kako biste primijenili ovaj sustav,
(Laughter)
(Smijeh)
it's worth recognizing that you probably already have.
važno je napomenuti da ste to već vjerojatno napravili.
(Laughter)
(Smijeh)
That pile of papers on your desk ... typically maligned as messy and disorganized, a pile of papers is, in fact, perfectly organized --
Ta hrpa papira na vašem stolu -- obično oklevetana kao neuredna i neorganizirana, je zapravo savršeno organizirana --
(Laughter)
(Smijeh)
as long as you, when you take a paper out, put it back on the top of the pile, then those papers are going to be ordered from top to bottom by how recently they were used, and you can probably quickly find what you're looking for by starting at the top of the pile.
sve dok, nakon što papir koji ste iz nje izvadili, vratite na vrh hrpe pa će ti papiri biti poredani od vrha prema dnu prema načelu nedavnog korištenja, a vi, vjerojatno, možete brzo pronaći ono što tražite ako krenete tražiti s vrha.
Organizing your wardrobe or your desk are probably not the most pressing problems in your life. Sometimes the problems we have to solve are simply very, very hard. But even in those cases, computer science can offer some strategies and perhaps some solace. The best algorithms are about doing what makes the most sense in the least amount of time. When computers face hard problems, they deal with them by making them into simpler problems -- by making use of randomness, by removing constraints or by allowing approximations. Solving those simpler problems can give you insight into the harder problems, and sometimes produces pretty good solutions in their own right.
Organizirati ormar, odnosno vaš stol, vjerojatno nisu najvažniji problemi u vašem životu. Ponekad su problemi koje trebamo riješiti vrlo, vrlo teški. Ali čak i u takvim slučajevima, računalna znanost može ponuditi neke strategije i možda malo utjehe. Najbolji algoritmi su oni koji imaju najviše smisla u najmanje vremena. Kad računala naiđu na problem, nose se s njim tako da ga rastave na manje probleme -- koristeći slučajan odabir, uklanjanjem ograničenja ili dopuštajući približne vrijednosti. Rješavanje tih jednostavnijih problema vam može dati uvid u rješavanje onih težih i ponekad dovode do prilično dobrih rješenja u zadanim okvirima.
Knowing all of this has helped me to relax when I have to make decisions. You could take the 37 percent rule for finding a home as an example. There's no way that you can consider all of the options, so you have to take a chance. And even if you follow the optimal strategy, you're not guaranteed a perfect outcome. If you follow the 37 percent rule, the probability that you find the very best place is -- funnily enough ...
Znajući to, bilo mi je lakše opustiti se kod donošenja odluka. Možete, na primjer, uzeti pravilo 37% za pronalaženje doma. Nema načina da uzmete u obzir sve mogućnosti pa morate riskirati. Čak i korištenje optimalne strategije vam ne jamči savršen ishod. Ako se služite pravilom 37%, vjerojatnost da ćete pronaći najbolje mjesto je -- zabavno, zar ne? --
(Laughter)
(Smijeh)
37 percent. You fail most of the time. But that's the best that you can do.
37 posto. Griješite većinu vremena. No, to je najbolje što možete učiniti.
Ultimately, computer science can help to make us more forgiving of our own limitations. You can't control outcomes, just processes. And as long as you've used the best process, you've done the best that you can. Sometimes those best processes involve taking a chance -- not considering all of your options, or being willing to settle for a pretty good solution. These aren't the concessions that we make when we can't be rational -- they're what being rational means.
Konačno, računalna znanost nam može pomoći da postanemo blaži prema našim vlastitim ograničenjima. Ne možete kontrolirati ishode, samo procese. I dok god koristite najbolje procese, učinili ste najbolje što možete. Ponekad najbolji procesi zahtijevaju i nešto rizika -- jer nisu uzete u obzir sve mogućnosti, ili volja da se odlučite za prilično dobro rješenje. Nisu to ustupci koje radimo kada ne možemo biti racionalni -- oni, zapravo, predstavljaju racionalnost.
Thank you.
Hvala vam.
(Applause)
(Pljesak)