TEDGlobal 2010
Tom Chatfield: 7 ways games reward the brain
Tom Chatfield：遊戲獎勵大腦的7種方式
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遊戲從越來越多的方面進入到我們的生活，我們花無數時間——以及真金白銀——去探索虛擬世界尋找想像中的寶藏。爲什麽？Tom Chatfield 向我們展示，遊戲被恰到好處地設計為提供一定的獎勵來吸引大腦，并使我們不斷索取更多。
Tom Chatfield - Gaming theorist
Tom Chatfield thinks about games -- what we want from them, what we get from them, and how we might use our hard-wired desire for a gamer's reward to change the way we learn. Full bio
Tom Chatfield thinks about games -- what we want from them, what we get from them, and how we might use our hard-wired desire for a gamer's reward to change the way we learn. Full bio
Double-click the English transcript below to play the video.
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）
ABOUT THE SPEAKER
Tom Chatfield - Gaming theoristTom Chatfield thinks about games -- what we want from them, what we get from them, and how we might use our hard-wired desire for a gamer's reward to change the way we learn.
Why you should listen
It can be difficult to wrap one's mind around the size and the reach of modern video- and online-game culture. But gaming is not only outstripping more-traditional media in revenue (it overtook music in 2008), it's become a powerful lens to re-examine our culture at large. Tom Chatfield, a longtime gamer, is the arts and books editor at the UK current-affairs magazine Prospect. In his book Fun Inc., he argues that games, with their immersive quests and deeply satisfying (and carefully designed) virtual rewards, are a great place to test new approaches to real-world systems that need a reboot.
More than a game journalist, Chatfield is a game theorist, looking at neurological research on how games engage our pleasure centers -- and then looking at a world where millions of videogame-veteran Generation Z'ers are entering the workforce and the voters' rolls. They're good with complex rule sets; they're used to forming ad hoc groups to reach a goal; and they love to tweak and mod existing systems. What if society harnessed that energy to redefine learning? Or voting? Understanding the psychology of the videogame reward schedule, Chatfield believes, is not only important for understanding the world of our children -- it's a stepping stone to improving our world right now.
Tom Chatfield | Speaker | TED.com