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譯者: yuanyuan liang
審譯者: Shelley Krishna Tsang
00:15
I love video games.
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我熱愛電子遊戲。
00:18
I'm also slightly in awe of them.
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我還有點小小地敬畏它們。
00:21
I'm in awe of their power
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我敬畏它們在
00:23
in terms of imagination, in terms of technology,
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想像力,技術
00:25
in terms of concept.
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和概念方面的力量。
00:27
But I think, above all,
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但是,最重要的,
00:29
I'm in awe at their power
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我敬畏它們能夠
00:31
to motivate, to compel us,
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促使我們,強迫我們,
00:34
to transfix us,
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讓我們目瞪口呆,
00:36
like really nothing else we've ever invented
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這是人類其它發明
00:39
has quite done before.
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所不能企及的。
00:41
And I think that we can learn some pretty amazing things
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而且我認為我們能從中瞭解到很多驚人的事實,
00:44
by looking at how we do this.
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就是看看我們是如何玩電子遊戲的。
00:46
And in particular, I think we can learn things
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特別是可以瞭解到
00:48
about learning.
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關於人的認知。
00:51
Now the video games industry
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目前電子遊戲產業
00:53
is far and away the fastest growing
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發展之快遠遠超越了
00:55
of all modern media.
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其他現代媒體。
00:57
From about 10 billion in 1990,
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從1990年的一百億
00:59
it's worth 50 billion dollars globally today,
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到今天的全球產值五百億。
01:02
and it shows no sign of slowing down.
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而且完全沒有放緩的跡象。
01:05
In four years' time,
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預計在未來的四年,
01:07
it's estimated it'll be worth over 80 billion dollars.
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將超過八百億美圓。
01:10
That's about three times the recorded music industry.
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這是唱片業的三倍。
01:13
This is pretty stunning,
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相當驚人的數字,
01:15
but I don't think it's the most telling statistic of all.
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但我認為這還不是最說明問題的數據。
01:18
The thing that really amazes me
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真正讓我驚訝的是
01:20
is that, today,
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現在
01:22
people spend about
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人們可以
01:24
eight billion real dollars a year
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一年花實實在在的八百億
01:27
buying virtual items
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購買虛擬的iTunes
01:29
that only exist
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只存在於
01:31
inside video games.
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電子遊戲裡。
01:34
This is a screenshot from the virtual game world, Entropia Universe.
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這是一個虛擬的遊戲世界《Entropia Universe》的遊戲截屏。
01:37
Earlier this year,
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就在前不久,
01:39
a virtual asteroid in it
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這個遊戲中的一個虛擬的小行星
01:41
sold for 330,000 real dollars.
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竟以三十三萬美圓的價格售出。
01:45
And this
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而這個
01:47
is a Titan class ship
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是一艘泰坦級的宇宙飛船
01:50
in the space game, EVE Online.
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來自EVE Online 這個太空遊戲。
01:52
And this virtual object
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而這艘虛擬的飛船
01:54
takes 200 real people
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需要200個真人
01:56
about 56 days of real time to build,
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花費56天建造出來,
01:59
plus countless thousands of hours
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還要加上不知幾千小時的
02:02
of effort before that.
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前期工作。
02:04
And yet, many of these get built.
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類似這樣被造出的還有很多。
02:07
At the other end of the scale,
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而另一方面,
02:09
the game Farmville that you may well have heard of,
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Farmville這個遊戲,可能你們已經聽說了,
02:12
has 70 million players
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有七千萬個玩家
02:14
around the world
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遍佈全世界,
02:16
and most of these players
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而且這些玩家中的大多數
02:18
are playing it almost every day.
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幾乎每天都在玩。
02:20
This may all sound
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可能這聽上去
02:22
really quite alarming to some people,
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會令一些人相當警惕,
02:24
an index of something worrying
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覺得是社會上那些令人焦慮
02:26
or wrong in society.
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或不正確的現象。
02:28
But we're here for the good news,
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但是我們來這是聽好消息的,
02:30
and the good news is
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好消息就是
02:32
that I think we can explore
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我認為我們能夠研究一下
02:34
why this very real human effort,
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爲什麽這種真實的人類勞動,
02:37
this very intense generation of value, is occurring.
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這麼巨大的價值的創造會得以出現。
02:41
And by answering that question,
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通過回答這個問題,
02:43
I think we can take something
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我覺得我們可以從中得到
02:45
extremely powerful away.
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極其強大的信息。
02:47
And I think the most interesting way
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我認為最有趣的
02:49
to think about how all this is going on
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思考這些問題的角度
02:51
is in terms of rewards.
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就是獎賞。
02:53
And specifically, it's in terms
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更具體來說,
02:56
of the very intense emotional rewards
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就是非常密集的情感獎賞,
02:58
that playing games offers to people
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通過玩遊戲提供給人們,
03:00
both individually
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既是個人的,
03:02
and collectively.
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也有集體的。
03:04
Now if we look at what's going on in someone's head
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如果我們觀察一下某人的大腦,
03:06
when they are being engaged,
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當他們忙碌時是怎樣運作的,
03:08
two quite different processes are occurring.
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兩個相當不同的進程同時發生著。
03:11
On the one hand, there's the wanting processes.
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一方面是想要的進程。
03:14
This is a bit like ambition and drive -- I'm going to do that. I'm going to work hard.
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有些類似進取心和動機——我要做那件事。我要努力工作。
03:17
On the other hand, there's the liking processes,
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而另一方面是喜歡的進程。
03:19
fun and affection
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樂趣和喜愛
03:21
and delight
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以及快樂——
03:23
and an enormous flying beast with an orc on the back.
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這是一個巨型飛行獸,上頭騎著一個獸人。
03:25
It's a really great image. It's pretty cool.
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這幅圖很棒,很酷。
03:27
It's from the game World of Warcraft with more than 10 million players globally,
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它來自魔獸世界,全球的玩家超過一千萬,
03:30
one of whom is me, another of whom is my wife.
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其中一個就是我,另外一個就是我老婆。
03:33
And this kind of a world,
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在這種世界裡
03:35
this vast flying beast you can ride around,
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你可以騎著這種巨型的飛行獸到處閒逛,
03:37
shows why games are so very good
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而這正顯示出爲什麽遊戲是多麼善於
03:39
at doing both the wanting and the liking.
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讓人同時做要做和喜歡做的事。
03:42
Because it's very powerful. It's pretty awesome.
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因為這很強大,相當厲害。
03:44
It gives you great powers.
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它給予你強大的力量。
03:46
Your ambition is satisfied, but it's very beautiful.
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你的野心得到滿足,但又非常美麗。
03:49
It's a very great pleasure to fly around.
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飛來飛去帶來絕大的快感。
03:52
And so these combine to form
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所有這些組合起來形成
03:54
a very intense emotional engagement.
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非常巨大的情感投入。
03:56
But this isn't the really interesting stuff.
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但這還不是真正有趣的部份。
03:59
The really interesting stuff about virtuality
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虛擬世界真正有趣的地方在於
04:01
is what you can measure with it.
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你從中可以量度的東西。
04:03
Because what you can measure in virtuality
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因為你在虛擬世界中能度量的東西
04:06
is everything.
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就是最重要的東西。
04:08
Every single thing that every single person
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每一個人在遊戲中做的每一件事
04:10
who's ever played in a game has ever done can be measured.
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都可被度量。
04:13
The biggest games in the world today
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今天世界上最大型的遊戲
04:15
are measuring more than one billion points of data
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正在量度玩家的上十億的數據
04:19
about their players, about what everybody does --
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具體到每個人做的事——
04:21
far more detail than you'd ever get from any website.
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其細緻程度超過任何其他網站。
04:24
And this allows something very special
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而這就使得一些非常特別的東西可以
04:27
to happen in games.
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存在於遊戲中。
04:29
It's something called the reward schedule.
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這就是獎賞機制。
04:32
And by this, I mean looking
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通過這個機制,
04:34
at what millions upon millions of people have done
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觀察成百萬上千萬的人是怎麼玩的,
04:36
and carefully calibrating the rate,
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然後仔細校準比率,
04:38
the nature, the type, the intensity of rewards in games
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屬性,類型,以及遊戲中獎賞的強度
04:41
to keep them engaged
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令人持續投入
04:43
over staggering amounts of time and effort.
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數量驚人的時間和努力。
04:46
Now, to try and explain this
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現在為了試圖用一些實際的概念
04:48
in sort of real terms,
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來闡釋這個機制,
04:51
I want to talk about a kind of task
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我要討論一種任務
04:53
that might fall to you in so many games.
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就是你在很多遊戲中會遇到的那種任務。
04:55
Go and get a certain amount of a certain little game-y item.
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去找到一定數量的某種遊戲小道具。
04:58
Let's say, for the sake of argument,
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比如說,
05:00
my mission is to get 15 pies
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我的任務是得到15個餡餅,
05:03
and I can get 15 pies
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然後為了這15個餡餅
05:06
by killing these cute, little monsters.
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我要殺死這些可愛的小怪物。
05:08
Simple game quest.
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很簡單的遊戲任務。
05:10
Now you can think about this, if you like,
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現在如果你喜歡可以把這個想像為
05:12
as a problem about boxes.
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一個關於盒子的問題。
05:14
I've got to keep opening boxes.
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我需要不斷打開盒子。
05:16
I don't know what's inside them until I open them.
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我不知道裡頭有什麽,直到我打開它們。
05:19
And I go around opening box after box until I've got 15 pies.
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然後我四處去打開一個又一個盒子,直到得到15個餡餅。
05:22
Now, if you take a game like Warcraft,
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現在如果你在玩的是魔獸世界這樣的遊戲,
05:24
you can think about it, if you like,
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如果你願意可以把它想像為
05:26
as a great box-opening effort.
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一個繁重的開盒子的勞動。
05:29
The game's just trying to get people to open about a million boxes,
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遊戲想讓人去打開大約一百萬個盒子,
05:32
getting better and better stuff in them.
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從裡頭找到越來越好的東西。
05:34
This sounds immensely boring
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聽上去是極度枯燥,
05:37
but games are able
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但遊戲卻能夠
05:39
to make this process
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使得這個過程
05:41
incredibly compelling.
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極其吸引人。
05:43
And the way they do this
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而它們所使用的方法
05:45
is through a combination of probability and data.
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就是把概率和數據結合起來。
05:48
Let's think about probability.
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讓我們來想想概率問題。
05:50
If we want to engage someone
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如果我們想讓人去
05:52
in the process of opening boxes to try and find pies,
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打開盒子尋找餡餅,
05:55
we want to make sure it's neither too easy,
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我們想確保它不要太容易,
05:57
nor too difficult, to find a pie.
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也不能太困難。
05:59
So what do you do? Well, you look at a million people --
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那該怎麼辦?那麼你觀察一百萬個人——
06:01
no, 100 million people, 100 million box openers --
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不,一億個人,一億個開盒子的人——
06:04
and you work out, if you make the pie rate
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然後來計算一下,如果你設定餡餅出現的比率
06:07
about 25 percent --
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大約為25%——
06:09
that's neither too frustrating, nor too easy.
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這樣不會太令人挫敗,也不會太容易;
06:12
It keeps people engaged.
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這樣就能讓人投入進去——
06:14
But of course, that's not all you do -- there's 15 pies.
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當然,這還不是全部——這只是15個餡餅。
06:17
Now, I could make a game called Piecraft,
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現在,我可以做一個遊戲叫做餡餅世界,
06:19
where all you had to do was get a million pies
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你在這裡要做的就是找到一百萬個餡餅,
06:21
or a thousand pies.
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或一千個。
06:23
That would be very boring.
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這個遊戲會很無聊。
06:25
Fifteen is a pretty optimal number.
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15是一個最優化的數字。
06:27
You find that -- you know, between five and 20
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你要尋找的,——你知道,在5到20之間,
06:29
is about the right number for keeping people going.
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這是讓人願意玩下去的一個恰到好處的數量。
06:31
But we don't just have pies in the boxes.
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但我們在盒子里找到的不只是餡餅。
06:33
There's 100 percent up here.
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這點我敢百分百肯定。
06:35
And what we do is make sure that every time a box is opened,
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我們所做的就是要確保每次盒子一打開,
06:38
there's something in it, some little reward
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裡頭總有點什麽,一些小小的獎勵,
06:40
that keeps people progressing and engaged.
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就是這些東西令人投入地玩下去。
06:42
In most adventure games,
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在大部份的冒險遊戲裡,
06:44
it's a little bit in-game currency, a little bit experience.
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這獎賞會是一點遊戲幣,一點經驗值,
06:47
But we don't just do that either.
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但我們也不是僅僅為了這個才玩。
06:49
We also say there's going to be loads of other items
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可以說裡頭還有一些其他道具
06:51
of varying qualities and levels of excitement.
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帶著不同的內容和不同級別的興奮感。
06:53
There's going to be a 10 percent chance you get a pretty good item.
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大約有十分之一的機會你可能得到一個相當好的道具。
06:56
There's going to be a 0.1 percent chance
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而有大概千分之一的機會
06:58
you get an absolutely awesome item.
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會得到一件絕對厲害的道具。
07:01
And each of these rewards is carefully calibrated to the item.
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而所有這些獎賞都小心地與道具調整在一起。
07:04
And also, we say,
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而且,我們還會說,
07:06
"Well, how many monsters? Should I have the entire world full of a billion monsters?"
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“好,放多少鬼怪呢?我是不是應該讓整個世界充滿十億個鬼怪?”
07:09
No, we want one or two monsters on the screen at any one time.
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不,我們只想讓一到兩隻鬼怪同時出現在屏幕上。
07:12
So I'm drawn on. It's not too easy, not too difficult.
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於是我就被吸引住了。這不太容易,也不太難。
07:15
So all this is very powerful.
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加在一起就很強大了。
07:17
But we're in virtuality. These aren't real boxes.
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但是我們是在虛擬世界;這些都不是真的盒子。
07:20
So we can do
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所以我們還可以做一些
07:22
some rather amazing things.
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更加令人驚奇的事。
07:24
We notice, looking at all these people opening boxes,
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在觀察所有這些人打開盒子時,我們注意到,
07:28
that when people get to about 13 out of 15 pies,
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當人們拿到15個餡餅中的13個時,
07:31
their perception shifts, they start to get a bit bored, a bit testy.
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他們的注意力發生轉移,他們開始覺得有點無聊,開始急躁。
07:34
They're not rational about probability.
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他們并沒有理性理解概率。
07:36
They think this game is unfair.
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他們認為這個遊戲不公平。
07:38
It's not giving me my last two pies. I'm going to give up.
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它沒給我最後兩個餡餅。我快要放棄了。
07:40
If they're real boxes, there's not much we can do,
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如果要找的是真正的盒子,那到這裡我們就無能為力了,
07:42
but in a game we can just say, "Right, well.
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但是在遊戲裡,我們只需說,“好吧,這樣。”
07:44
When you get to 13 pies, you've got 75 percent chance of getting a pie now."
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當你拿到13個餡餅時,現在你拿到餡餅的機會提高到75%。
07:48
Keep you engaged. Look at what people do --
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這樣就會令你繼續玩下去。觀察人們如何玩遊戲——
07:50
adjust the world to match their expectation.
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調整這個世界符合他們的期待。
07:52
Our games don't always do this.
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而我們的遊戲并不總是如此。
07:54
And one thing they certainly do at the moment
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目前有一件事它們肯定會做的就是
07:56
is if you got a 0.1 percent awesome item,
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如果你拿到那個千分之一機會才能得到的道具,
07:59
they make very sure another one doesn't appear for a certain length of time
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它們會確保另一個這樣的道具在相當長一段時間內不會出現
08:02
to keep the value, to keep it special.
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以此令其保值,讓它特殊。
08:04
And the point is really
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而關鍵就在於
08:06
that we evolved to be satisfied by the world
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我們適應了以某種特定的方式
08:08
in particular ways.
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從周圍的世界獲得滿足感。
08:10
Over tens and hundreds of thousands of years,
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通過幾百萬年,
08:13
we evolved to find certain things stimulating,
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我們演化成尋找某種刺激性的事物,
08:15
and as very intelligent, civilized beings,
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並且作為非常智能和文明化的生物,
08:17
we're enormously stimulated by problem solving and learning.
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我們通過解決問題和學習知識獲得巨大的刺激。
08:20
But now, we can reverse engineer that
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但是現在,我們能反向設計這一行為
08:22
and build worlds
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構造出遊戲世界
08:24
that expressly tick our evolutionary boxes.
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很明顯地突出我們的演化特徵。
08:27
So what does all this mean in practice?
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那麼所有這些在實踐中有什麽意義?
08:29
Well, I've come up
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我總結出
08:31
with seven things
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七個要點
08:33
that, I think, show
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我認為表明了
08:35
how you can take these lessons from games
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你如何從遊戲中有所學習
08:37
and use them outside of games.
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并將它們應用到遊戲以外。
08:40
The first one is very simple:
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第一點很簡單:
08:42
experience bars measuring progress --
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用經驗值條量度進程——
08:44
something that's been talked about brilliantly
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有人已經很出色地討論過這個問題
08:46
by people like Jesse Schell earlier this year.
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如今年年初時的Jesse Schell 。
08:49
It's already been done at the University of Indiana in the States, among other places.
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在美國的印第安那大學和其他一些地方已經這樣去做了。
08:52
It's the simple idea that instead of grading people incrementally
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很簡單的道理就是,不用增量的方式給人打分,
08:55
in little bits and pieces,
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不要去算計那些點點滴滴,
08:57
you give them one profile character avatar
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你給他們一個角色化身
08:59
which is constantly progressing
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這個化身會持續地發展
09:01
in tiny, tiny, tiny little increments which they feel are their own.
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一點一點地,以非常微弱的量發展,他們會感同身受。
09:04
And everything comes towards that,
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然後一切都朝向那個目標前進,
09:06
and they watch it creeping up, and they own that as it goes along.
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他們會看著它不斷增長,然後隨著它的發展他們對之認同。
09:09
Second, multiple long and short-term aims --
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第二,多進程的長短期目標——
09:11
5,000 pies, boring,
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五千個餡餅,太煩了,
09:13
15 pies, interesting.
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十五個,有意思。
09:15
So, you give people
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因此你要給人們
09:17
lots and lots of different tasks.
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很多很多不同的任務。
09:19
You say, it's about
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你要說,這是
09:21
doing 10 of these questions,
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解決10個這樣的問題,
09:23
but another task
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而另一個任務
09:25
is turning up to 20 classes on time,
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是在規定時間內升20級,
09:27
but another task is collaborating with other people,
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但再另外一個任務是和別人合作,
09:30
another task is showing you're working five times,
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再另一個任務是展示你的工作五次,
09:33
another task is hitting this particular target.
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再一個任務是擊中這個特定的標靶。
09:35
You break things down into these calibrated slices
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你把任務拆分成這些經過調校的小塊,
09:38
that people can choose and do in parallel
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人們可以挑選,以及並行處理
09:40
to keep them engaged
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以令他們保持投入
09:42
and that you can use to point them
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并將它們和
09:44
towards individually beneficial activities.
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個人的獲利行為掛鉤。
09:48
Third, you reward effort.
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第三,獎賞努力工作。
09:50
It's your 100 percent factor. Games are brilliant at this.
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這是你的萬靈丹。遊戲在這點上極其擅長。
09:53
Every time you do something, you get credit; you get a credit for trying.
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每次你做點什麽事時,你都得到分數,從嘗試中得分。
09:56
You don't punish failure. You reward every little bit of effort --
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你不會懲罰失敗;你會獎勵每一點微小的努力——
09:59
a little bit of gold, a little bit of credit. You've done 20 questions -- tick.
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一小塊金子,一小點分數——你已經做完了20個問題了——完成。
10:02
It all feeds in as minute reinforcement.
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這些都是通過小小的鼓勵實現的。
10:05
Fourth, feedback.
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第四,反饋。
10:07
This is absolutely crucial,
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這絕對是個關鍵,
10:09
and virtuality is dazzling at delivering this.
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而虛擬世界為實現這一點做的讓人眼花繚亂。
10:11
If you look at some of the most intractable problems in the world today
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如果你看那些當今世界上最難解決的一些問題,
10:14
that we've been hearing amazing things about,
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關於這些問題我們已經聽到很多驚人的東西,
10:16
it's very, very hard for people to learn
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人們很難有所長進
10:19
if they cannot link consequences to actions.
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如果他們無法將結果與行為聯繫起來。
10:22
Pollution, global warming, these things --
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污染,全球暖化,這些問題,
10:24
the consequences are distant in time and space.
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其後果從時間空間上看都還很遙遠。
10:26
It's very hard to learn, to feel a lesson.
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結果就很難學到,感受到其中的教訓。
10:28
But if you can model things for people,
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但如果你可以給人們一些這類事情的模型,
10:30
if you can give things to people that they can manipulate
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如果你可以給一些東西他們可以操控
10:32
and play with and where the feedback comes,
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玩耍并從中獲得反饋,
10:34
then they can learn a lesson, they can see,
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那麼他們就能從中有所學習,他們就能看到,
10:36
they can move on, they can understand.
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他們就能進步,能理解。
10:39
And fifth,
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第五,
10:41
the element of uncertainty.
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不確定性因素。
10:43
Now this is the neurological goldmine,
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目前這是神經科學的寶庫,
10:46
if you like,
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你可以這麼說,
10:48
because a known reward
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因為一個已知的獎勵
10:50
excites people,
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會讓人們興奮,
10:52
but what really gets them going
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但真正驅動他們的
10:54
is the uncertain reward,
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是不確定的獎勵,
10:56
the reward pitched at the right level of uncertainty,
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帶著適當程度的不確定性的獎勵,
10:58
that they didn't quite know whether they were going to get it or not.
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也就是說人們不太知道是否能得到。
11:01
The 25 percent. This lights the brain up.
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四分之一的概率。這就能使大腦興奮。
11:04
And if you think about
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如果你想
11:06
using this in testing,
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把這點用於測試,
11:08
in just introducing control elements of randomness
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就只需引入隨機性的控制因素
11:10
in all forms of testing and training,
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放在各種形式的測試和訓練中,
11:12
you can transform the levels of people's engagement
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你能夠改變人們的投入程度
11:14
by tapping into this very powerful
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通過引入這種非常強大的
11:16
evolutionary mechanism.
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演化機制。
11:18
When we don't quite predict something perfectly,
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當我們無法相當完美地預測某事時,
11:20
we get really excited about it.
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對它就會特別興奮。
11:22
We just want to go back and find out more.
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我們就想回去發現更多。
11:24
As you probably know, the neurotransmitter
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你可能知道,神經遞質
11:26
associated with learning is called dopamine.
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伴隨學習產生的神經遞質叫做多巴胺。
11:28
It's associated with reward-seeking behavior.
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它出現在尋找獎勵的行為中。
11:31
And something very exciting is just beginning to happen
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一些激動人心的工作正在
11:34
in places like the University of Bristol in the U.K.,
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展開,如英國的布裡斯托爾大學,
11:37
where we are beginning to be able to model mathematically
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在那裡我們開始能夠用數學的方式
11:40
dopamine levels in the brain.
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建構大腦中多巴胺水平的模型。
11:42
And what this means is we can predict learning,
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這意味著我們可以預測學習,
11:44
we can predict enhanced engagement,
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我們可以預測加強的行為,
11:47
these windows, these windows of time,
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這些機會期,這些時間的機會期,
11:49
in which the learning is taking place at an enhanced level.
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其中所發生的學習行為處在一個加強的水平。
11:52
And two things really flow from this.
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從中產生兩個結果。
11:54
The first has to do with memory,
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第一與記憶有關,
11:56
that we can find these moments.
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就是我們可以找到這些瞬間。
11:58
When someone is more likely to remember,
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當某人想記住什麽時,
12:00
we can give them a nugget in a window.
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我們可以給他們提供機會期這一寶貴資源。
12:02
And the second thing is confidence,
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第二就是信心,
12:04
that we can see how game-playing and reward structures
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我們能看到遊戲的操作和獎賞結構是如何
12:06
make people braver, make them more willing to take risks,
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令人更勇敢,令人更樂於冒險,
12:09
more willing to take on difficulty,
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更願意面對困難
12:11
harder to discourage.
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更不容易灰心。
12:13
This can all seem very sinister.
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這些可以是些不好的跡象。
12:15
But you know, sort of "our brains have been manipulated; we're all addicts."
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但是你知道,有人會說“我們的大腦都被控制了,我們都是癮君子。”
12:17
The word "addiction" is thrown around.
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“上癮”這個詞到處可見。
12:19
There are real concerns there.
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這的確是個問題。
12:21
But the biggest neurological turn-on for people
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但是對人來說,最大的神經刺激
12:23
is other people.
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來自他人。
12:25
This is what really excites us.
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這才是真正令我們興奮的。
12:28
In reward terms, it's not money;
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就獎賞來說,并不是金錢,
12:30
it's not being given cash -- that's nice --
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並不是得到現金——當然那也不錯——
12:33
it's doing stuff with our peers,
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而是和同伴一起做事,
12:35
watching us, collaborating with us.
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注視我們,和我們合作。
12:37
And I want to tell you a quick story about 1999 --
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我想很快地講一個小故事,1999年
12:39
a video game called EverQuest.
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有個電子遊戲叫做《無盡任務》。
12:41
And in this video game,
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在這個遊戲裡,
12:43
there were two really big dragons, and you had to team up to kill them --
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有兩頭巨大的龍,你必須組隊才能殺掉它們——
12:46
42 people, up to 42 to kill these big dragons.
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42個人——必須要42個人才能殺掉巨龍。
12:49
That's a problem
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這是個問題,
12:51
because they dropped two or three decent items.
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因為這些龍會丟出兩三個重要的道具。
12:54
So players addressed this problem
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於是玩家處理這個問題的方法是
12:57
by spontaneously coming up with a system
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自發地建立起一套體系
12:59
to motivate each other,
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來激勵每個玩家,
13:01
fairly and transparently.
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公平地,透明地。
13:03
What happened was, they paid each other a virtual currency
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結果,他們付給每個玩家虛擬貨幣
13:06
they called "dragon kill points."
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他們稱之為殺龍點數。
13:09
And every time you turned up to go on a mission,
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每次出發去完成一個任務
13:11
you got paid in dragon kill points.
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都會得到一些殺龍點數。
13:13
They tracked these on a separate website.
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他們用另一個獨立的網站記錄這些點數。
13:15
So they tracked their own private currency,
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這樣就可以記錄自己的貨幣,
13:17
and then players could bid afterwards
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之後玩家就可以用來競拍
13:19
for cool items they wanted --
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他們想要的厲害道具——
13:21
all organized by the players themselves.
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這些都是玩家自己組織起來的。
13:23
Now the staggering system, not just that this worked in EverQuest,
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目前這個令人難以置信的系統不僅出現在《無限任務》
13:26
but that today, a decade on,
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而是今天,十年以後,
13:28
every single video game in the world with this kind of task
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世界上的每一款有這類任務的電子遊戲
13:31
uses a version of this system --
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都在使用某個版本的這個系統——
13:33
tens of millions of people.
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上千萬的人。
13:35
And the success rate
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而成功率
13:37
is at close to 100 percent.
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接近百分之百。
13:39
This is a player-developed,
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這是一個玩家開發的,
13:41
self-enforcing, voluntary currency,
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自動實施的,自願的貨幣,
13:44
and it's incredibly sophisticated
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這就是玩家複雜到令人無法相信的
13:46
player behavior.
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玩家行為。
13:50
And I just want to end by suggesting
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最後我想建議
13:52
a few ways in which these principles
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一些方法使這些原則
13:54
could fan out into the world.
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可以擴散到全世界。
13:56
Let's start with business.
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首先是商業。
13:58
I mean, we're beginning to see some of the big problems
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我認為我們將會看到一些非常巨大的問題
14:00
around something like business are
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出現在諸如商業裏面,
14:02
recycling and energy conservation.
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循環利用和節約能源。
14:04
We're beginning to see the emergence of wonderful technologies
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我們將會看到一些很奇妙的技術出現
14:06
like real-time energy meters.
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如實時的能量計。
14:08
And I just look at this, and I think, yes,
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看著這些,我會想,對啊,
14:10
we could take that so much further
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我們可以更充分地使用這些技術
14:13
by allowing people to set targets
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讓人們設定目標
14:15
by setting calibrated targets,
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通過設定標準化的目標,
14:17
by using elements of uncertainty,
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通過使用不確定性因素,
14:20
by using these multiple targets,
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通過多任務進程,
14:22
by using a grand, underlying reward and incentive system,
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通過使用一個巨大的,潛在的獎賞和激勵機制,
14:25
by setting people up
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來激發人們
14:27
to collaborate in terms of groups, in terms of streets
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以團體和街區的形式合作,
14:29
to collaborate and compete,
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既合作又競爭,
14:31
to use these very sophisticated
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利用這些非常複雜的
14:33
group and motivational mechanics we see.
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組織和激勵機制。
14:35
In terms of education,
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在教育方面,
14:37
perhaps most obviously of all,
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可能是最顯著的,
14:39
we can transform how we engage people.
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我們能改變吸引人注意的方式。
14:42
We can offer people the grand continuity
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我們可以提供給人們愉快的連續的
14:44
of experience and personal investment.
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經驗和個人的發展。
14:47
We can break things down
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我們可以把事務拆分為
14:49
into highly calibrated small tasks.
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高度調整過的小任務。
14:51
We can use calculated randomness.
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我們可以利用計算過的隨機性。
14:53
We can reward effort consistently
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我們可以持續地獎勵努力
14:55
as everything fields together.
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調動所有方面。
14:58
And we can use the kind of group behaviors
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我們還能利用這種團隊行為
15:00
that we see evolving when people are at play together,
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也就是當人們一起玩遊戲時看到的演化,
15:03
these really quite unprecedentedly complex
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這些真是前所未有的複雜的
15:06
cooperative mechanisms.
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協作機制。
15:08
Government, well, one thing that comes to mind
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我想到的另一個就是政府,
15:10
is the U.S. government, among others,
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尤其是美國政府
15:13
is literally starting to pay people
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已經真的開始付錢給民眾
15:15
to lose weight.
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去減肥。
15:17
So we're seeing financial reward being used
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所以我們所說的就是利用經濟獎賞
15:19
to tackle the great issue of obesity.
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去解決肥胖這個大問題。
15:21
But again, those rewards
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但是同樣,這些獎勵
15:23
could be calibrated so precisely
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可以被精確地分配
15:26
if we were able to use the vast expertise
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如果我們能夠使用遊戲系統的大量專業技術
15:29
of gaming systems to just jack up that appeal,
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去提升吸引力,
15:32
to take the data, to take the observations,
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去採集數據,觀察,
15:34
of millions of human hours
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上百萬的人小時
15:36
and plow that feedback
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并將這些反饋用回到
15:38
into increasing engagement.
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提升人的參與度。
15:40
And in the end, it's this word, "engagement,"
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最後,就是這個詞,參與度,
15:43
that I want to leave you with.
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我想留給大家。
15:45
It's about how individual engagement
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就是如何使個人的參與
15:47
can be transformed
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可以發生轉化,
15:49
by the psychological and the neurological lessons
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通過心理學和神經學方面的經驗
15:52
we can learn from watching people that are playing games.
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就是我們從觀察人玩遊戲獲得的經驗。
15:55
But it's also about collective engagement
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但是還有集體的參與度
15:58
and about the unprecedented laboratory
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以及前所未有的實驗
16:01
for observing what makes people tick
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觀察是什麽使人行動
16:03
and work and play and engage
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工作,遊戲和投入
16:05
on a grand scale in games.
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大量精力到遊戲中。
16:08
And if we can look at these things and learn from them
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如果我們觀察這些并從中有所學習
16:11
and see how to turn them outwards,
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并看到如何將它們應用到遊戲以外,
16:13
then I really think we have something quite revolutionary on our hands.
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那麼我真的認為我們正在做的是具有革新意義的事情。
16:16
Thank you very much.
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非常感謝。
16:18
(Applause)
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(觀眾掌聲)
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