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THINK BIG!


第 三 章 THINK BIG 一

THINK BIG!


ど ッ グ シ ン ク THINK BIG! ウ ォ ー ル 街 か ら ワ シ ン ト ン D. C. へ 寺 澤 芳 男 角 川 文 庫 98

THINK BIG!


″ 異 様 な 〃 日 本 の 習 慣 彼 我 の 差 「 官 と 民 」 第 三 章 THINK BIG 一 秘 書 ダ イ ア ン ・ シ ン プ ソ ン 博 士 THINK BIG 一 や ま と な で し こ ガ イ ハ レ 「 大 和 撫 子 」 イ ン グ リ ッ シ ュ あ れ こ れ 第 四 章 祖 国 ニ ッ ポ ン 老 後 を ア メ リ カ で ー ー だ が 、 し か し 日 本 と ア メ リ カ の ″ 不 倫 格 差 〃 東 洋 人 の 忍 耐 花 嫁 に み る 新 夫 婦 像 「 待 っ ー 我 慢 九 五 メ く . 五 . . 五 . 六 七 ノ 、 - ヒ - ヒ ・ プ く ー 五 プ も 区 日 / 、

The Best Software Writing I. Selected and Introduced by Joel Spolsky


100 THE BEST SOFTWARE WRITING I The FinaI Frontier After software, the most important tool to a hacker is probably his office. Big companies think the function Of Office space is tO express rank. But hackers use their offices for more than that: they use their office as a place t0 think in. And if you're a technology company, their thoughts are your product. SO making hackers work in a noisy, distract- ing envlronment is like having a paint factory where the air is full Of SOOt. The cartoon strip Dilbert has a 10t to say about cubicles, and with good reason. AII the hackers I know despise them. The mere prospect 0f being interrupted is enough tO prevent hackers from working on hard problems. If you want t0 get real work done in an office with cubicles, you have tWO options: 、 at home, or come early or late or on a weekend, When no one else iS there. Don't compames realize thiS iS a S1gn that something is broken? An Office envlronment is supposed tO be something you work in, not something you work despite. Companies like CiSCO are proud that everyone there has a cubicle, even the CEO. But they're not so advanced as they think; obviously they still view Office space as a badge Of rank. NOte t00 that Cisco is famous for doing very little product development in house. They get new tech- nology by buying the startups that created it—where presumably the hackers did have somewhere quiet tO work. One big company that understands what hackers need is Microsoft. I once saw a recruiting ad for Microsoft with a big picture Of a door. Work for us, the premise was, and we'll give you a place to work where you can actually get work done. And you know, Microsoft is remarkable among big companies in that they are able tO develop software in house. Not well, perhaps, but well enough. If companies want hackers t0 be productive, they should look at what they do at home. At home, hackers can arrange things themselves so they can get the most done. And when they work at home, hackers don't work in n01SY, open spaces; they work in rooms with doors. They work in cozy, neighborhoody places with people around and somewhere to walk when they need to mull something over, instead of in glass boxes set ⅲ acres 0f parking 10ts. They have a sofa they can take a nap on

The Great Adventures of Dirty Pair


ladies are here at the invitation of the Foundation. 。 、 4r. Merutonan, ” replied Bayleaf stiffly. "lt's true the Gravas Foundation virtually controls Dangle. Even so, I don't think it would be wise to make enemies Of the Central Police. ” "S ay what you like , " retorted Merutonan brusque- ly. "On my legitimate authority, I intend to take these two ladies with me. ” "You do, do you!" exclaimed BayIeaf, crumbling in the face Of Merutonan s resolve. "Then dO as you please!" Bayleaf reluctantly left the lobby, his twenty un- derlings in tow. "NOW then, ” said Merutonan, turning t0 face us. He still spoke crisply from the previous argument, and his splendid bald dome gleamed with sweat. "l'm terribly sorry to have been so late. We'll go straight tO Gravas Heavy lndustries headquarters. We were promptly bundled into a big black air- car limouslne waiting outside the spaceport terml- nal. The limousine lifted 0 任 , and moments later we were barreling down the highway, due south.

The selfish gene


召 な ″ ん ツ ツ ん 工 い 153 genes tO the new individual, and they alSO contribute equal amounts Of fOOd reserves. Sperms and eggs t00 contribute equal numbers Of genes, but eggs contribute far more in the way Of fOOd reserves: indeed sperms make no contribution at all, and are simply concerned with transporting their genes as fast as possible tO an egg. At the moment Of conception, therefore, the father has lnvested less than his fair share (). e. 50 per cent) Of resources ln the offspring. Since each sperm IS SO tiny, a male can afford tO make many millions 0f them every day. This means he is poten- tially able t0 beget a very large number of children in a very short period of time, using different females. This is only possible because each new embryo is endowed with adequate f00d by the mother in each case. This therefore places a limit on the number of children a female can have, but the number of children a male can have is virtually unlimited. Female exploitation begins here. Parker and others showed how this asymmetry might have evolved from an originally isogamous state 0f affairs. ln the days when all sex cells were interchangeable and of roughly the same size, there would have been some which just happened t0 be slightly bigger than others. ln some respects a big isogamete would have an advantage over an average—sized one, because it would get its embryo 0ff t0 a good start by givmg it a large initial 応 od supply. There might therefore have been an evolutionary trend towards larger gametes. But there was a catch. The evolu— t10n Of isogametes which were larger than was strictly necessary would have opened the door to selfish exploitation. lndividuals whO produced 〃 / ん r than average gametes could cash in, provided they could ensure that their small gametes fused with extra-big ones. This could be achieved by making the small ones more mobile, and able t0 seek out large ones actively. The advan- tage t0 an individual 0f producing small, rapidly movmg gametes would be that he could afford to make a larger number 0f gametes, and therefore could potentially have more children. Natural selection favoured the production Of sex cells which were small, and which actively sought out big ones t0 fuse with. SO we can think Of tWO divergent sexual 'strategles' evolving. There was the large-investment or 、 honest' strategy. This automatically opened the way for a small-investment exploitative or 、 sneaky' strategy. Once the divergence between the tWO strategies had

The Best Software Writing I. Selected and Introduced by Joel Spolsky


MARY POPPENDIECK 161 Dysfunction # 2 : The Perception ofUnfairness There is no greater demotivator than a reward system that is perceived tO be unfair. lt doesn't matter whether the system is fair or not. If there is a perception Of unfairness, then those whO think that they have been treated unfairly will rapidly lose their motivation. people perceive unfairness when they mlSS out on rewards they think they should have shared. What if the vice president had given Sue a big reward but not rewarded the team? Even if Sue had acknowledged the hard work of her team members, they would probably have felt that she was profiting at their expense. You can be sure that Sue would have had a difficult time generating enthusiasm for work on the next release, even if the evaluation issues had not surfaced. Here's another scenano: what would have happened if Sue's team had been asked out to dinner with the VP and each member had been given a good-sized bonus? The next day the operations people wh0 worked late nights and weekends t0 help get the product out on time would have found out and felt cheated. The developers who took over maintenance tasks SO their colleagues could work full time on the prod- uct also would have felt slighted. Other teams might have felt that they could have been equally successful, except that they got assigned t0 the wrong product. Dysfunction # 3 : The Perception oflmpossibility sue's team met its deadline by following the Scrum practice 0f releasing a high-quality product containing only the highest-priority functionality. But let's try a different scenario: let's assume that the team was gwen a non-negotiable list 0f features that had t0 be done bY a non-negotiable deadline, and let's further speculate that the team was 100 percent

The selfish gene


ん ど ど 〃 ど 川 ac ん / 〃 ど 60 water-hole. Which is the best gambling strategy depends on all sorts 0f complex things, not least the hunting habit of the predators, which itself is evolved to be maximally efficient from their point 0f view. Some form of weighing up of the odds has to be done. But Of course we do not have to think of the animals as making the calculations consciously. All we have to believe is that those individuals whose genes build brains in such a way that they tend to gamble correctly are as a direct result more likely to survrve, and therefore tO propagate those same genes. We can carry the metaphor of gambling a little further. A gambler must think Of three maln quantities, stake, Odds, and prize. If the prize IS very large, a gambler is prepared to risk a big stake. A gambler who risks his all on a single throw stands to gain a great deal. He alSO stands tO lose a great deal, but on average high-stake gamblers are no better and no worse Off than other players wh0 play for 10W wmnings with low stakes. An analogous comparison is that between speculative and safe investors on the stock market. ln some ways the stock market is a better analogy than a caslno, because caslnos are deliberately rigged in the bank's favour (which means, strictly, that high-stake players will on average end up poorer than low-stake players; and low stake players poorer than those wh0 d0 not gamble at all. But this is for a reason not germane tO our discussion). lgnoring this, bOth high- stake play and low-stake play seem reasonable. Are there animal gamblers who play for high stakes, and others with a more con- servative game? ln Chapter 9 we shall see that it is often possible t0 picture males as high-stake high-risk gamblers, and females as safe investors, especially in polygamous specles in which males compete for females. NaturaIists wh0 read this book may be able to think of species which can be described as high-stake high-risk players, and Other specles which play a more conservative game. I now return tO the more general theme Of hOW genes make predic- tions' about the future. One way for genes to solve the problem of making predictions ln rather unpredictable envlronments IS tO build in a capacity for learning. Here the program may take the form of the following instructlons tO the survival machine: 'Here IS a list Of things defined as rewarding: sweet taste in the mouth, orgasm, mild temperature, smiling child. And here is a list 0f nasty things:

The Best Software Writing I. Selected and Introduced by Joel Spolsky


PAUL GRAHAM 97 That's not a new idea. Fred Brooks wrote about it in 1974 3 and the study he quoted was published in 1968. But I think he underestimated the varlation between programmers. He wrote about productivity in lines Of code: the best programmers can solve a given problem in a tenth of the time. But what if the problem isn't given ~ ln programming, as in many fields, the hard part isn't solving problems, but deciding what problems tO SOlve. lmagrnation is hard tO measure, but in practice it dominates the kind Of productivity that's measured in lines Of COde. Productivity varies in any field, but there are few in which it varies so much. The variation between programmers IS SO great that it becomes a difference in kind. I don't think this is something intrinsrc tO program- ming, though. ln every field, technology magnifies differences in productivity. I think what's happening in programmmg is just that we have a 10t of technologicalleverage. But in every field the lever is getting longer, SO the variatlon we see IS something that more and more fields Will see as tlme goes on. And the success Of compames, and countnes, will depend increasingly on how they deal with it. If variation in productivity increases with technology, then the contribution Of the most productive individuals will not only be dispro- portionately large but will actually grow with time. When you reach the point where 90 % 0f a group's output is created by 1 % 0f its members, you lose big if something (whether Viking raids, or central planning) drags their productivity down tO the average. If we want tO get the most out Of them, we need tO understand these especially productive people. What motivates them? What d0 they need to do their jobs? How do you recognize them? How do you get them to C01 れ e and work for you? And then Of course there's the question, hO 、 dO you become one ~ Morethan Money I know a handful of super-hackers, so I sat down and thought about what they have in common. Their defining quality is probably that they 3. . in his book The M ァ 舫 Ma 〃 Mo れ 舫 . ー Ed.

THINK BIG!


ビ ッ グ THINK BIG ! て ら さ わ 当 お 寺 澤 方 男 ク 角 川 文 庫 8098 平 成 三 年 三 月 十 日 初 版 発 行 発 行 者 ー ー 角 川 春 樹 発 行 所 ー ー ー 株 式 会 社 角 川 書 店 東 京 都 千 代 田 区 富 士 見 二 ー 十 三 ー 三 編 集 部 ( 〇 三 ) 三 八 一 七 ー 八 四 五 一 電 話 営 業 部 ( 〇 三 ) 三 八 一 七 ー 八 五 二 一 〒 一 〇 二 振 替 東 京 ③ 一 九 五 二 〇 八 印 刷 所 ー ー ー 暁 印 刷 製 本 所 ー ー 本 間 製 本 装 幀 者 ー ー ー 杉 浦 康 平 落 丁 ・ 乱 丁 本 は お 取 替 え い た し ま す 。 定 価 は カ ・ ハ ー に 明 記 し て あ り ま す 。 Printed in Japan ISBN4 ー 04 ー 174603 ー 5 C0195 て 2 ー 3